Advancing Endometriosis Research: A Comprehensive Guide to Trans-Ancestry Meta-Analysis Methods in GWAS

Liam Carter Nov 27, 2025 471

This article provides a comprehensive overview of trans-ancestry meta-analysis methodologies specifically applied to endometriosis genome-wide association studies (GWAS).

Advancing Endometriosis Research: A Comprehensive Guide to Trans-Ancestry Meta-Analysis Methods in GWAS

Abstract

This article provides a comprehensive overview of trans-ancestry meta-analysis methodologies specifically applied to endometriosis genome-wide association studies (GWAS). Covering foundational principles to advanced applications, we explore how integrating diverse genetic datasets enhances discovery power, improves risk prediction, and reveals population-specific disease mechanisms. Key topics include novel computational frameworks for cross-ancestry integration, optimization strategies addressing genetic architecture heterogeneity, validation approaches for polygenic risk scores across populations, and therapeutic target identification through multi-omics integration. Designed for researchers, geneticists, and drug development professionals, this guide synthesizes cutting-edge methodologies from recent large-scale studies to advance precision medicine for endometriosis across global populations.

The Genetic Architecture of Endometriosis: Why Trans-Ancestry Approaches Matter

Application Note

Endometriosis is a chronic, estrogen-driven inflammatory disorder affecting approximately 10% of reproductive-aged women globally, with diagnosis often delayed by 7-11 years from symptom onset [1] [2]. This application note examines the genetic architecture of endometriosis within the context of trans-ancestry meta-analysis methods, addressing how advanced genomic approaches are unraveling the disease's substantial heritable component. Twin and familial studies consistently demonstrate that endometriosis has a ~50% heritability rate, with approximately half of this genetic influence (26%) attributable to common single nucleotide polymorphisms (SNPs) [3]. Despite significant advances in genome-wide association studies (GWAS), which have identified 42 genomic loci associated with endometriosis risk, these common variants explain only ~5% of disease variance [2] [4], highlighting the need for more sophisticated analytical frameworks to capture the full genetic complexity.

Table 1: Key Heritability Estimates in Endometriosis

Genetic Component Estimate Source Evidence Notes
Overall Heritability ~50% Twin studies [3] Proportion of disease risk in population due to genetic factors
Common SNP Contribution ~26% GWAS meta-analyses [3] Proportion of heritability explained by common variants
GWAS-Explained Variance ~5% 42 identified loci [2] [4] Current limitation of traditional GWAS approaches
Familial Risk Increase 5-7 fold First-degree relatives [3] Compared to general population risk

Advanced Genetic Architecture

Beyond traditional GWAS findings, recent research has revealed several sophisticated layers of genetic complexity in endometriosis. Combinatorial genetics approaches have identified 1,709 disease signatures comprising 2,957 unique SNPs in combinations of 2-5 SNPs that significantly associate with endometriosis risk [2]. These multi-variant signatures explain substantially more disease risk than individual SNPs alone and highlight pathways involved in cell adhesion, proliferation, cytoskeleton remodeling, angiogenesis, fibrosis, and neuropathic pain [2] [4].

Integration of expression quantitative trait loci (eQTL) data from six physiologically relevant tissues (uterus, ovary, vagina, colon, ileum, and blood) has demonstrated tissue-specific regulatory effects of endometriosis-associated variants [5]. This tissue-specific regulation pattern suggests that genetic risk manifests differently across pelvic structures, with immune and epithelial signaling genes predominating in intestinal tissues, while reproductive tissues show enrichment of genes involved in hormonal response, tissue remodeling, and adhesion [5].

Interestingly, studies exploring ancient genetic contributions have identified regulatory variants derived from Neandertal and Denisovan introgression in genes including IL-6, CNR1, and IDO1 [1]. These ancient variants demonstrate significant enrichment in endometriosis cohorts and potentially interact with modern environmental pollutants, particularly endocrine-disrupting chemicals (EDCs), suggesting a novel evolutionary-environmental interplay in disease susceptibility [1].

Trans-Ancestry Considerations

The development of effective trans-ancestry meta-analysis methods faces significant challenges due to the variable genetic architecture of endometriosis across populations. Recent combinatorial analyses demonstrated that disease signatures identified in white European cohorts showed high reproducibility rates (80-88%) in the multi-ancestry All of Us cohort for high-frequency signatures (>9%), but substantially lower reproducibility (66-76%) for signatures with >4% frequency in non-white European sub-cohorts [2] [4]. This population-specificity underscores the critical need for diverse recruitment in genetic studies to ensure equitable advancement in endometriosis diagnosis and treatment across all ancestral backgrounds.

Table 2: Emerging Genetic Paradigms in Endometriosis Research

Genetic Paradigm Key Finding Research Implications
Combinatorial Genetics 75 novel genes identified beyond GWAS hits [2] [4] Reveals complex multi-SNP interactions; identifies new biological pathways
Gene-Environment Interaction Ancient variants interact with modern EDCs [1] Suggests environmental triggers for genetically susceptible individuals
Tissue-Specific Regulation Distinct eQTL effects across 6 relevant tissues [5] Explains tissue-specific manifestation of lesions and symptoms
Cross-Ancestry Variation Differential signature reproducibility [2] [4] Highlights need for diverse cohorts in genetic studies
Pleiotropy with Comorbidities Shared loci with pain conditions, osteoarthritis [3] Explains comorbidity patterns and identifies shared therapeutic targets

Experimental Protocols

Combinatorial Analytics for Genetic Signature Identification

Principle

Traditional GWAS approaches examine single variant associations, limiting their ability to detect complex multi-variant interactions. Combinatorial analytics identifies combinations of 2-5 SNPs that collectively associate with endometriosis risk, revealing substantially more of the genetic architecture than conventional methods [2] [4].

Protocol

Step 1: Cohort Selection and Quality Control

  • Utilize the UK Biobank (UKB) cohort for discovery phase, selecting individuals with endometriosis diagnosis and matched controls
  • Apply strict quality control: remove samples with call rate <98%, heterozygosity outliers, and gender mismatches
  • Retain only autosomal SNPs with minor allele frequency >1%, call rate >95%, and Hardy-Weinberg equilibrium p-value >1×10⁻⁶

Step 2: Combinatorial Analysis

  • Use the PrecisionLife combinatorial analytics platform to exhaustively test all possible 2-, 3-, 4-, and 5-way SNP combinations
  • Calculate association significance using Fisher's exact test with Benjamini-Hochberg multiple testing correction
  • Apply a significance threshold of p<0.05 after correction to identify disease-associated signatures

Step 3: Validation in Independent Cohorts

  • Test significant signatures in the All of Us (AoU) Research Program cohort, controlling for population structure
  • Assess reproducibility rates across ancestral subgroups (European, African, Asian, Admixed American)
  • Validate biological relevance through pathway enrichment analysis using Gene Ontology, KEGG, and Reactome databases

Step 4: Functional Annotation

  • Map significant SNPs to genes based on physical proximity (±50kb) and regulatory potential
  • Annotate genes with known endometriosis associations, novel findings, and potential drug targets
  • Prioritize candidate genes based on recurrence across signatures and biological plausibility

CombinatorialWorkflow Start Cohort Selection (UK Biobank) QC Quality Control (Call rate >98%, MAF >1%) Start->QC Combinatorial Combinatorial Analysis (2-5 way SNP combinations) QC->Combinatorial Statistical Statistical Testing (Fisher's exact + FDR correction) Combinatorial->Statistical Validation Multi-Ancestry Validation (All of Us Cohort) Statistical->Validation Annotation Functional Annotation & Pathway Analysis Validation->Annotation Output Disease Signatures & Candidate Genes Annotation->Output

Figure 1: Combinatorial analysis workflow for identifying multi-SNP signatures associated with endometriosis risk.

Mendelian Randomization for Causal Inference and Target Identification

Principle

Mendelian randomization uses genetic variants as instrumental variables to infer causal relationships between modifiable risk factors (e.g., metabolites, proteins) and endometriosis, reducing confounding bias inherent in observational studies [6].

Protocol

Step 1: Instrumental Variable Selection

  • Obtain summary statistics from large-scale GWAS of blood metabolites (486-1,400 metabolites) and plasma proteins (4,907 cis-pQTLs)
  • Select independent (r² < 0.001, distance = 1 Mb) genome-wide significant (p < 5×10⁻⁸) SNPs as instruments
  • Calculate F-statistics for each SNP, excluding weak instruments (F < 10) to minimize bias

Step 2: Two-Sample Mendelian Randomization

  • Extract endometriosis GWAS summary statistics from UK Biobank (3,809 cases, 459,124 controls) and FinnGen (20,190 cases, 130,160 controls)
  • Perform inverse-variance weighted MR as primary analysis
  • Conduct sensitivity analyses: MR-Egger, weighted median, MR-PRESSO to assess pleiotropy and robustness

Step 3: Colocalization Analysis

  • Assess whether metabolite/protein and endometriosis associations share causal variants
  • Calculate posterior probability of hypothesis 4 (PPH4) > 0.8 indicating shared causal variant
  • Exclude associations where PPH4 < 0.8 to avoid false positives due to linkage disequilibrium

Step 4: Experimental Validation

  • Collect blood and tissue samples from endometriosis patients and matched controls (n=20 per group)
  • Measure candidate protein levels (e.g., RSPO3) using ELISA according to manufacturer protocols
  • Validate tissue expression via RT-qPCR and Western blotting in ectopic vs eutopic endometrium

Functional Characterization of Regulatory Variants

Principle

Most endometriosis-associated GWAS variants reside in non-coding regions, suggesting they exert effects through gene regulation rather than protein coding changes. Mapping these variants to expression quantitative trait loci (eQTLs) across disease-relevant tissues reveals their regulatory consequences [5].

Protocol

Step 1: Variant Curation and Annotation

  • Retrieve all genome-wide significant endometriosis associations (p < 5×10⁻⁸) from GWAS Catalog (EFO_0001065)
  • Annotate variants using Ensembl VEP for genomic location, functional consequence, and nearest genes
  • Retain only variants with valid rsIDs, removing duplicates to create non-redundant variant set

Step 2: Multi-Tissue eQTL Mapping

  • Access GTEx v8 database for six relevant tissues: uterus, ovary, vagina, sigmoid colon, ileum, whole blood
  • Extract significant eQTL associations (FDR < 0.05) for each endometriosis-associated variant
  • Record regulated gene, slope (effect size/direction), and adjusted p-value for each tissue

Step 3: Tissue-Specific Functional Profiling

  • For each tissue, identify: (1) top 10 genes regulated by most eQTL variants, (2) genes with highest average slope values
  • Perform functional enrichment using MSigDB Hallmark and Cancer Hallmarks gene sets
  • Classify genes into biological pathways: immune response, hormonal signaling, adhesion, angiogenesis, etc.

Step 4: Integration with Ancient Variation Data

  • Cross-reference significant regulatory variants with databases of archaic hominin introgression
  • Test for enrichment of Neandertal/Denisovan variants in endometriosis cohort vs controls
  • Assess overlap between ancient regulatory variants and EDC-responsive genomic regions

RegulatoryVariantAnalysis GWASCatalog GWAS Catalog Endometriosis Variants VEP Variant Annotation (Ensembl VEP) GWASCatalog->VEP GTEx Multi-Tissue eQTL Mapping (GTEx v8) VEP->GTEx Functional Functional Enrichment (Hallmark Pathways) GTEx->Functional Tissues Relevant Tissues: Uterus, Ovary, Vagina, Colon, Ileum, Blood GTEx->Tissues Ancient Ancient Variation Analysis (Neandertal/Denisovan) Functional->Ancient Regulatory Regulatory Mechanisms & Tissue Specificity Ancient->Regulatory

Figure 2: Multi-tissue regulatory variant analysis workflow for functional characterization of non-coding variants.

The Scientist's Toolkit

Table 3: Essential Research Reagent Solutions for Endometriosis Genetic Studies

Reagent/Resource Function/Application Example Sources/Platforms
PrecisionLife Combinatorial Analytics Identifies multi-SNP disease signatures beyond GWAS PrecisionLife Ltd. [2] [4]
GTEx v8 Database Provides multi-tissue eQTL data for functional annotation GTEx Portal [5]
SOMAscan Proteomics Platform Measures 4,907 plasma protein levels for pQTL studies SOMAscan V4 [6]
UK Biobank & All of Us Data Large-scale genetic and health data for discovery/validation UK Biobank, All of Us [2] [4]
Human R-Spondin3 ELISA Kit Quantifies RSPO3 protein levels in validation studies BOSTER Biological Technology [6]
Ensembl VEP Functional annotation of genetic variants Ensembl [1] [5]
LDlink Suite Linkage disequilibrium and population genetics analysis LDlink, LDpop, LDpair [1]
MSigDB Hallmark Gene Sets Functional enrichment analysis for biological interpretation Molecular Signatures Database [5]
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Concluding Remarks

The integration of trans-ancestry meta-analysis with combinatorial genetics, Mendelian randomization, and functional genomic approaches represents a powerful framework for elucidating the complex genetic architecture of endometriosis. These advanced methods have already identified 75 novel gene associations beyond traditional GWAS findings [2], revealed causal relationships with specific plasma proteins like RSPO3 [6], and demonstrated how ancient regulatory variants interact with modern environmental factors to influence disease risk [1]. As these approaches are refined and applied to increasingly diverse populations, they promise to accelerate the development of improved diagnostic biomarkers, personalized risk prediction tools, and novel therapeutic strategies for this complex and debilitating condition.

Historical Limitations of European-Centric GWAS in Endometriosis Research

Endometriosis, a chronic inflammatory condition affecting approximately 10% of reproductive-aged women globally, demonstrates substantial genetic susceptibility with heritability estimates reaching 47% [1]. While genome-wide association studies (GWAS) have identified numerous susceptibility loci, the predominant reliance on European-ancestry cohorts has created significant blind spots in our understanding of the disease's genetic architecture across diverse populations. This application note examines the methodological limitations of European-centric GWAS approaches and outlines trans-ancestry meta-analysis protocols to advance more inclusive genetic research in endometriosis.

Table 1: Documented Limitations of European-Centric GWAS in Endometriosis Research

Limitation Category Specific Challenge Documented Evidence
Population-Specific Alleles Risk alleles identified in European populations show different effects in other ancestries Sardinian population study showed no significant association for variants significant in other European groups [7]
Variant Spectrum Limited capture of ancestry-specific genetic variations Iranian population study identified unique SNP associations in MFN2, PINK1, and PRKN genes [7]
Regulatory Complexity Tissue-specific eQTL effects not fully characterized across ancestries Multi-tissue eQTL analysis revealed tissue-specific regulatory profiles for endometriosis risk variants [8] [5]
Gene-Environment Interactions Incomplete understanding of how genetic risks interact with diverse environmental exposures Ancient regulatory variants from Neandertal introgression show potential interaction with modern environmental pollutants [1]

Critical Analysis of European-Centric GWAS Limitations

Population-Specific Genetic Architecture and Effect Heterogeneity

The fundamental assumption that genetic discoveries in European populations readily translate to other ancestries has repeatedly proven problematic in endometriosis research. Studies across diverse populations have demonstrated differential effect sizes and heterogeneous genetic architecture. In the Sardinian population, for instance, variants significantly associated with endometriosis in other European cohorts showed no significant association, suggesting that specific risk alleles could act differently in the pathogenesis of the disease across ethnic populations [7]. Similarly, research in Iranian women identified unique single nucleotide polymorphism (SNP) associations in genes involved in mitophagy (MFN2, PINK1, and PRKN) that were not highlighted in major European GWAS [7].

Incomplete Capture of Regulatory Variants Across Tissues

European-centric approaches have insufficiently characterized the tissue-specific regulatory landscape of endometriosis risk variants across diverse ancestries. A comprehensive multi-tissue eQTL analysis demonstrated that endometriosis-associated variants exhibit distinct regulatory profiles across six physiologically relevant tissues (uterus, ovary, vagina, sigmoid colon, ileum, and peripheral blood) [8] [5]. In reproductive tissues, these variants preferentially regulated genes involved in hormonal response, tissue remodeling, and adhesion, while in intestinal tissues and blood, they predominantly affected immune and epithelial signaling genes [5]. This tissue specificity underscores how limited ancestral diversity in GWAS reduces our ability to identify the full spectrum of regulatory mechanisms contributing to endometriosis pathogenesis.

Methodological Constraints in Gene-Environment Interactions

European-centric GWAS designs have historically struggled to account for the diverse environmental exposures that interact with genetic risk factors across global populations. Emerging evidence suggests that ancient regulatory variants, some originating from Neandertal introgression, may interact with modern environmental pollutants to modulate endometriosis risk [1]. Co-localized IL-6 variants (rs2069840 and rs34880821) located at a Neandertal-derived methylation site demonstrated strong linkage disequilibrium and potential immune dysregulation in response to contemporary environmental triggers [1]. The restricted ancestral diversity in most GWAS limits statistical power to detect such gene-environment interactions, which likely vary substantially across populations with different historical evolutionary pressures and modern environmental exposures.

Trans-Ancestry Meta-Analysis Protocols for Endometriosis Research

Multi-Ancestry GWAS Meta-Analysis Framework

The trans-ancestry meta-analysis protocol provides a robust methodological framework to overcome limitations of European-centric GWAS. This approach integrates diverse ancestry groups while accounting for population-specific genetic architectures, as demonstrated in recent large-scale fibroid research that included 74,294 cases (27.7% non-European descent) and 465,810 controls (18.3% non-European descent) [9].

Table 2: Trans-Ancestry Meta-Analysis Protocol for Endometriosis Genomics

Protocol Stage Key Procedures Ancestry-Specific Considerations
Cohort Selection Identify diverse biobanks and study populations Ensure representative sampling across ancestry groups with careful population stratification control
Quality Control Implement standardized SNP filtering and imputation Apply ancestry-specific reference panels for imputation; account for differential allele frequencies
Association Testing Perform ancestry-stratified GWAS followed by meta-analysis Use ancestry-appropriate linkage disequilibrium reference panels; apply genomic control inflation factors
Heritability Estimation Calculate SNP-based heritability within and across ancestries Utilize ancestry-specific HapMap3 annotated tags; compare heritability estimates across groups
Functional Annotation Integrate multi-omics data for putative causal gene identification Incorporate ancestry-specific eQTL/pQTL maps when available; account for tissue-specific regulation

Experimental Protocol 1: Trans-Ancestry GWAS Meta-Analysis

  • Cohort Acquisition and Harmonization

    • Obtain summary statistics from endometriosis GWAS across diverse ancestry groups
    • Apply stringent quality control filters: remove variants with call rate <95%, Hardy-Weinberg equilibrium p<1×10⁻⁶, and imputation quality score <0.8
    • Harmonize effect alleles across studies using ancestry-appropriate reference panels

  • Ancestry-Stratified Analysis

    • Conduct GWAS within each ancestry group using logistic regression adjusted for principal components
    • Calculate genomic inflation factors (λGC) to assess stratification: European (λGC=1.17), East Asian/Central South Asian (λGC=1.07), African (λGC=1.02) [9]
    • Estimate SNP-based heritability using SumHer with ancestry-specific HapMap3 tags [9]

  • Cross-Ancestry Meta-Analysis

    • Perform fixed-effects inverse-variance weighted meta-analysis across ancestry groups
    • Test for heterogeneity using Cochran's Q statistic to identify ancestry-specific effects
    • Apply false discovery rate (FDR) correction for multiple testing across the genome

  • Variant Prioritization and Validation

    • Identify sentinel variants meeting genome-wide significance (p<5×10⁻⁸)
    • Conduct conditional analysis to identify independent signals within loci
    • Validate novel associations in independent cohorts from underrepresented ancestries

G cluster_1 Trans-Ancestry GWAS Meta-Analysis Workflow cluster_2 Ancestry Groups Start Cohort Acquisition & Harmonization QC Quality Control & Population Stratification Start->QC Stratified Ancestry-Stratified GWAS Analysis QC->Stratified Meta Cross-Ancestry Meta-Analysis Stratified->Meta Functional Functional Annotation Meta->Functional Validation Variant Validation & Prioritization Functional->Validation EUR European (53,711 cases) EAS East Asian (14,905 cases) AFR African (5,678 cases) Multi Multi-Ancestry (74,294 cases)

Integrative Multi-Omic Analysis Framework

The integrative multi-omic protocol leverages Mendelian randomization and colocalization approaches to bridge the gap between genetic associations and functional mechanisms across diverse populations, addressing a critical limitation of European-centric studies that often prioritize coding variants over regulatory elements.

Experimental Protocol 2: Multi-Omic Integration for Cross-Ancestry Functional Validation

  • Multi-Omic Data Acquisition

    • Obtain blood eQTL summary data from eQTLGen (31,684 individuals) [10]
    • Acquire methylation QTL (mQTL) data from European cohorts (1,980 individuals) [10]
    • Procure protein QTL (pQTL) data from UK Biobank (54,219 participants) [10]
    • Access tissue-specific eQTL data from GTEx v8 (uterus tissue specifically for endometriosis) [10]

  • Summary-based Mendelian Randomization (SMR) Analysis

    • Perform SMR to test causal associations between gene expression/methylation/protein abundance and endometriosis risk
    • Select top cis-QTLs using ±1000 kb window around corresponding genes at p<5×10⁻⁸ threshold
    • Apply heterogeneity in dependent instruments (HEIDI) test to distinguish pleiotropy from linkage (p-HEIDI>0.05 indicates linkage)

  • Colocalization Analysis

    • Implement colocalization using R package 'coloc' to identify shared causal variants
    • Set prior probability of colocalization (P12)=5×10⁻⁵
    • Consider colocalization significant when posterior probability of H4 (PPH4)>0.5, indicating shared causal variant

  • Cross-Ancestry Functional Validation

    • Validate identified genes (e.g., RSPO3, FLT1) through experimental approaches including ELISA, RT-qPCR, and Western blotting [6] [11]
    • Assess tissue-specific expression patterns in clinical samples across diverse populations
    • Perform replication in independent biobanks (FinnGen, UK Biobank) with diverse representation

The Scientist's Toolkit: Essential Research Reagent Solutions

Table 3: Key Research Reagents for Trans-Ancestry Endometriosis Genomics

Reagent/Category Specific Examples Application in Endometriosis Research
Genotyping Arrays Illumina Global Screening Array, UK Biobank Axiom Array Genome-wide variant detection in diverse populations with ancestry-informative markers
eQTL Resources GTEx v8 database, eQTLGen consortium Tissue-specific expression quantitative trait loci mapping across multiple tissues relevant to endometriosis
pQTL Platforms SOMAscan V4 assay (4,907 cis-pQTLs) [6] Plasma protein quantitative trait loci identification for therapeutic target prioritization
Methylation Analysis Illumina Infinium MethylationEPIC array Genome-wide DNA methylation profiling to identify epigenetic regulators of endometriosis risk
Validation Assays Human R-Spondin3 ELISA Kit [6], TRIzol reagent for RNA extraction [7] Experimental validation of candidate biomarkers and therapeutic targets in clinical samples
Bioinformatics Tools SMR software v1.3.1, COLOC R package, LDlink [10] Statistical analysis of multi-omic data integration and colocalization evidence
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The historical limitations of European-centric GWAS in endometriosis research have created significant gaps in our understanding of the disease's genetic architecture across global populations. The implementation of trans-ancestry meta-analysis protocols, coupled with integrative multi-omic approaches, provides a robust framework to overcome these limitations. By embracing methodological innovations that prioritize ancestral diversity, the research community can accelerate the discovery of novel therapeutic targets like RSPO3 [6] [11] and develop more effective, personalized interventions for endometriosis across all populations. Future directions should include expanded recruitment from underrepresented ancestries, development of ancestry-specific reference panels, and dedicated funding initiatives to support diverse cohort collection and analysis.

Large-scale genetic studies have fundamentally advanced our understanding of endometriosis pathophysiology, moving beyond association to reveal causative mechanisms. Trans-ancestry meta-analyses of genome-wide association studies (GWAS) have been particularly instrumental, identifying risk loci across diverse populations and enabling a more precise dissection of the molecular basis of the disease [12] [13]. These studies consistently demonstrate that genetic susceptibility converges on a limited set of core biological pathways. This application note synthesizes the latest genetic and multi-omic evidence to detail three principal pathways—hormone signaling, immune regulation, and tissue remodeling—and provides standardized protocols for their investigation in functional studies. By framing these insights within modern genomic methodologies, we aim to equip researchers with the tools necessary to translate genetic discoveries into targeted therapeutic strategies.

Key Biological Pathways in Endometriosis: Genetic and Multi-Omic Insights

Integrative analysis of GWAS data with expression quantitative trait loci (eQTLs), methylation QTLs (mQTLs), and protein QTLs (pQTLs) has illuminated the functional impact of non-coding risk variants, revealing their tissue-specific regulatory effects and their convergence on key pathogenic processes [8] [10]. The table below summarizes the core pathways, key genetic findings, and implicated cell types.

Table 1: Key Biological Pathways and Genetic Findings in Endometriosis

Biological Pathway Key Genes/Proteins from Genetic Studies Primary Functions & Mechanisms Relevant Tissues/Cell Types
Hormone Signaling WNT4, GREB1, FSHB, ESR1, RSPO3 Regulation of estrogen-responsive genes; Müllerian duct development; estrogen-driven proliferation [14] [15]. Ovary, Uterus, Endometriotic lesions
Immune Regulation MICB, IL-6, IDO1, CNR1 Immune evasion; chronic inflammation; altered T-cell function; pain sensitization [8] [1]. Peripheral blood, Intestinal tissues, Lesions
Tissue Remodeling & Cell Adhesion VEZT, FN1, MAP3K5, ENG, FLT1 Ectopic tissue anchoring; cell survival; apoptosis resistance; angiogenesis [10] [6] [14]. Uterus, Sigmoid colon, Ileum, Lesions

Hormone Signaling Pathway

The hormone signaling pathway is central to endometriosis, an estrogen-dependent disease. Genetic studies have robustly identified loci within genes that are critical for reproductive tract development and estrogen-mediated proliferation.

  • Key Genetic Drivers: WNT4 is a consistently replicated risk locus. It is crucial for Müllerian duct development and functions as a key regulator of steroid hormone action in the endometrium. Trans-ancestry GWAS have identified an intronic variant in WNT4 (rs61768001) associated with multiple subtypes of female infertility, which is a common comorbidity of endometriosis [16] [15]. Similarly, GREB1 is an estrogen-responsive gene implicated in cell cycle control, and FSHB is involved in gonadotropin regulation [14] [15].
  • Multi-omic Integration: A recent multi-ancestry GWAS of ~1.4 million women confirmed that genetic variation influences risk through transcriptomic and proteomic regulation of hormonal pathways [12] [13]. Furthermore, drug-repurposing analyses have highlighted potential therapeutic interventions currently used for breast cancer, which often shares a hormonal etiology [12].
  • Functional Consequences: Risk alleles in these genes are thought to dysregulate normal hormonal signaling, leading to enhanced estrogen-responsive gene transcription and creating a microenvironment that supports the survival and proliferation of ectopic endometrial tissue.

Immune Regulation Pathway

Immune dysregulation is a hallmark of endometriosis, and genetic studies pinpoint a role for both systemic and local immune dysfunction in disease susceptibility.

  • Key Genetic Drivers: eQTL analyses demonstrate tissue-specific effects; for instance, MICB (a stress-induced ligand for immune cells) is consistently linked to immune evasion pathways in peripheral blood and intestinal tissues [8]. Additionally, regulatory variants in IL-6 (a pro-inflammatory cytokine), some of which are linked to a Neandertal-derived methylation site, demonstrate significant enrichment in endometriosis cohorts and are implicated in immune dysregulation [1].
  • Functional Consequences: These genetic findings support a model where risk variants promote a chronic inflammatory state and impair the body's ability to clear ectopic cells. The IL-6 and IDO1 variants may skew immune responses, while CNR1 (the cannabinoid receptor 1 gene) variants also suggest a genetic link to pain sensitization, a core symptom of the disease [1].

Tissue Remodeling and Cell Adhesion Pathway

The ability of ectopic endometrial tissue to implant, invade, and persist requires significant remodeling of the extracellular matrix and establishment of a new blood supply.

  • Key Genetic Drivers: VEZT encodes a cell adhesion protein that may facilitate the anchoring of ectopic tissue [15]. Multi-omic SMR analyses have identified MAP3K5 as a key gene, where specific methylation patterns causally downregulate its expression and heighten endometriosis risk, potentially by affecting cell survival and stress responses [10]. Proteomic MR studies have also implicated FLT1 (a VEGF receptor) and ENG (Endoglin) in disease risk, underscoring the role of angiogenesis [10] [6].
  • Functional Consequences: Genes in this pathway contribute to a pro-invasive, pro-angiogenic phenotype. The downregulation of MAP3K5, a gene involved in stress-induced apoptosis, may confer resistance to cell death in ectopic lesions, while FLT1 and ENG drive the vascularization necessary for lesion growth and maintenance [10] [6].

G GWAS GWAS Risk Loci MultiOmics Multi-Omic Data (eQTL, mQTL, pQTL) GWAS->MultiOmics  Integration &  Fine-mapping Hormone Hormone Signaling (WNT4, GREB1, RSPO3) MultiOmics->Hormone Immune Immune Regulation (IL-6, MICB, IDO1) MultiOmics->Immune Tissue Tissue Remodeling (MAP3K5, VEZT, FLT1) MultiOmics->Tissue Functional Functional Consequences Hormone->Functional Estrogen-driven proliferation Immune->Functional Chronic inflammation Tissue->Functional Invasion & angiogenesis Outcome Disease Pathogenesis (Lesion growth, inflammation, pain) Functional->Outcome

Diagram Title: From Genetic Loci to Disease Pathogenesis

Experimental Protocols for Pathway Validation

Protocol: Expression Quantitative Trait Loci (eQTL) Analysis in Relevant Tissues

Objective: To determine if endometriosis-associated genetic variants regulate gene expression in a tissue-specific manner.

Background: Most GWAS-identified variants reside in non-coding regions. eQTL analysis tests their association with gene expression levels, providing a functional link between genetics and pathophysiology [8].

Materials:

  • Genotype Data: Array or sequencing-based data from endometriosis case-control cohorts.
  • Transcriptome Data: RNA-seq or microarray data from matched tissues (e.g., uterus, ovary, blood, endometriotic lesions).
  • Cohort: Tissues from a minimum of 50 individuals per tissue type to achieve sufficient statistical power.

Procedure:

  • Data Preprocessing: Perform stringent quality control on genotype and expression data. Normalize expression data to account for technical covariates.
  • Variant-Gene Pair Testing: For each endometriosis-associated variant (e.g., from a trans-ancestry meta-analysis), test for association with the expression levels of all genes within a 1 Mb cis-window using a linear regression model. Include relevant technical and biological covariates (e.g., genotyping batch, top principal components of genetic ancestry).
  • Significance Testing: Correct for multiple testing using the False Discovery Rate (FDR) method. Retain variant-gene pairs with an FDR < 0.05 as significant eQTLs.
  • Tissue Specificity Assessment: Compare the list of significant eQTLs and their effect sizes (slopes) across different tissues to identify tissue-specific versus shared regulatory effects.

Expected Output: A list of genes whose expression is significantly regulated by endometriosis risk variants in each analyzed tissue, highlighting genes like MICB in blood or WNT4 in the uterus [8].

Objective: To investigate the causal effect of a mediating molecular trait (gene expression, DNA methylation, protein abundance) on endometriosis risk.

Background: SMR integrates GWAS summary data with QTL data to test if variation in a molecular phenotype is causally associated with the disease [10].

Materials:

  • Endometriosis GWAS Summary Statistics: From large-scale meta-analyses.
  • QTL Summary Statistics: Publicly available data from resources like eQTLGen (eQTLs), BSGS (mQTLs), and UK Biobank (pQTLs).

Procedure:

  • Data Harmonization: Align effect alleles and ensure consistent genomic builds between the GWAS and QTL datasets.
  • SMR Analysis: Run the SMR analysis tool for each molecular probe (e.g., a gene transcript, CpG site, or protein) located within a 1-2 Mb window of a GWAS signal. Use the top cis-QTL for each probe as an instrumental variable.
  • Heterogeneity (HEIDI) Test: Apply the HEIDI test to distinguish pleiotropy (a single causal variant) from linkage (two distinct but correlated causal variants). A P-HEIDI > 0.05 suggests a single shared causal variant and supports a causal inference.
  • Multi-Omic Triangulation: Perform sequential SMR analyses to test causal relationships, for example, from a CpG site (mQTL) to gene expression (eQTL) and finally to endometriosis risk (GWAS).

Expected Output: Identification of putatively causal genes and proteins (e.g., MAP3K5, ENG) whose altered regulation, driven by genetic variation, influences endometriosis risk [10].

Table 2: Key Research Reagent Solutions for Endometriosis Pathway Analysis

Reagent / Resource Function / Application Example Source / Catalog
GTEx v8 Database Reference dataset for tissue-specific eQTL analysis. GTEx Portal ( [8])
SOMAscan Platform Multiplexed proteomic assay for pQTL discovery and validation. SomaLogic ( [6])
coloc R Package Bayesian test for colocalization between GWAS and QTL signals. CRAN ( [10])
SMR Software Tool for multi-omic Summary-based Mendelian Randomization analysis. CNS Genomics ( [10])
Human R-Spondin3 ELISA Kit Quantitative measurement of RSPO3 protein levels in plasma or tissue. BOSTER Biological Technology ( [6])

The Scientist's Toolkit

G Start Start: Genetic Discovery GWAS Trans-ancestry GWAS Meta-analysis Start->GWAS FineMap Fine-mapping & Colocalization GWAS->FineMap Multiomic Multi-omic SMR (eQTL, mQTL, pQTL) FineMap->Multiomic Pathway Pathway & Enrichment Analysis Multiomic->Pathway Validate Functional Validation Pathway->Validate Target Therapeutic Target Validate->Target

Diagram Title: Integrative Genomics Workflow for Target Discovery

The integration of large-scale trans-ancestry GWAS with multi-omic data has definitively established that genetic risk for endometriosis is channeled through dysregulation in hormone signaling, immune function, and tissue remodeling. The application of standardized protocols for eQTL and SMR analysis, as detailed herein, provides a robust framework for the scientific community to move from genetic associations to a mechanistic understanding of disease. The continued growth of diverse, large-scale biobanks, coupled with the functional tools in the Scientist's Toolkit, will be critical for translating these key biological pathways into much-needed diagnostic and therapeutic advancements for endometriosis.

The Prevalence and Clinical Heterogeneity of Endometriosis Across Ancestral Groups

Application Note: Epidemiological and Genetic Landscape

Trans-ancestry Prevalence Patterns

Endometriosis demonstrates significant variation in its epidemiological presentation across different ancestral groups. Recent large-scale meta-analyses provide comprehensive quantitative assessments of this heterogeneity, essential for guiding trans-ancestry genetic research and clinical drug development strategies.

Table 1: Global Prevalence of Endometriosis Across Populations and Clinical Subtypes

Population / Subtype Prevalence (%) 95% Confidence Interval Data Source
General Population 5 2-9 [17]
Women with Infertility 38 25-51 [17]
Symptomatic Women 18-42 Not specified [17]
Peritoneal Endometriosis 6 1-15 [17]
Ovarian Endometriosis 13 5-24 [17]
Deep Endometriosis 10 2-24 [17]
Nonane-2,5-diolNonane-2,5-diol, CAS:51916-45-1, MF:C9H20O2, MW:160.25 g/molChemical ReagentBench Chemicals
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The global prevalence of endometriosis is estimated at approximately 5% in the general population, rising dramatically to 38% among women experiencing infertility [17]. When examining disease subtypes, ovarian endometriosis represents the most common presentation at 13%, followed by deep endometriosis (10%) and peritoneal endometriosis (6%) [17]. These differential prevalence rates across clinical manifestations highlight the disease's substantial heterogeneity.

Geographical and ancestral analyses reveal a nine-fold increased risk among women of East Asian ancestry compared to European or American populations [18]. This disparity underscores the critical importance of accounting for ancestral background in both genetic research and therapeutic development.

Genetic Architecture Across Ancestries

Genome-wide association studies (GWAS) have identified numerous susceptibility loci for endometriosis, with effect sizes and prevalence varying significantly across ancestral groups.

Table 2: Key Endometriosis Genetic Loci and Ancestral Heterogeneity

Genetic Locus Nearest Gene Reported Function Ancestral Heterogeneity
rs7521902 WNT4 Sex steroid hormone signaling Stronger association in European ancestry
rs10965235 CDKN2B-AS1 Cell cycle regulation Initially identified in Japanese ancestry [19]
rs12700667 Intergenic 7p15.2 Developmental pathways Consistent across populations [19] [20]
rs13394619 GREB1 Hormone-mediated growth Stronger effect in Stage III/IV disease [19] [20]
rs1250248 FN1 Sex steroid hormone pathways Associated with moderate-severe disease [21] [20]

Recent multi-ancestry genetic research has substantially expanded our understanding of endometriosis risk loci. A 2024 Mendelian randomization study incorporating trans-ethnic analyses confirmed consistent directions of effect for seven out of nine established loci across European and East Asian populations [22]. The most recent and largest multi-ancestry GWAS to date (2025), comprising approximately 1.4 million women (105,869 cases), identified 80 genome-wide significant associations, 37 of which are novel [12]. This study provided the first genetic variants specifically associated with adenomyosis, a related gynecological condition [12].

Population genomic analyses examining disease genomic 'grammar' have identified 296 common genetic targets with low allele frequencies and 6 with high allele frequencies across five major population groups (Europeans, Africans, Americans, East Asians, and South Asians) [18]. The substantial variation in genetic architecture observed across populations reflects both divergent evolutionary histories and environmental interactions.

Protocol: Trans-ancestry Meta-Analysis Framework

Study Design and Participant Ascertainment

Objective: To establish a standardized protocol for trans-ancestry meta-analysis of endometriosis genome-wide association studies, enabling the identification of genetic risk factors across diverse populations.

Inclusion Criteria:

  • Cases: Women with surgically confirmed endometriosis (laparoscopy/laparotomy) using revised American Fertility Society (rAFS) classification [21] [23]
  • Controls: Women without documented endometriosis diagnosis
  • Ancestry: Self-reported and genetically confirmed ancestry matching reference populations

Stratification Approach:

  • Clinical Subphenotyping: Stratify by rAFS stage (I-IV), with particular focus on comparing minimal/mild (Stage I-II) versus moderate/severe (Stage III-IV) disease [19] [20]
  • Ancestral Groups: Categorize participants into genetically defined ancestral groups (European, East Asian, African, Admixed American, South Asian) [18]
  • Geographical Region: Record geographical origin to account for environmental covariates

Sample Size Requirements: Minimum 5,000 cases per ancestral group for adequate statistical power in trans-ancestry analyses [12]

Genotyping and Quality Control Protocol

Genotyping Platforms: Utilize high-density GWAS arrays (e.g., Illumina Global Screening Array, Affymetrix Axiom Biobank Array)

Quality Control Steps:

  • Sample-level QC:
    • Call rate > 98%
    • Sex concordance verification
    • Relatedness analysis (remove one individual from pairs with PI-HAT > 0.1875)
    • Population outliers identified via principal component analysis
  • Variant-level QC:
    • Hardy-Weinberg equilibrium (P > 1×10⁻⁶ in controls)
    • Call rate > 95% in cases and controls
    • Minor allele frequency > 1% within each ancestral group

Imputation Protocol:

  • Reference panel: Topologically integrated multi-ancestry reference (e.g., 1000 Genomes Phase 3, TOPMed) [18]
  • Software: Minimac4 or IMPUTE5
  • Post-imputation filtering: INFO score ≥ 0.7
Statistical Analysis Workflow

Primary Association Analysis:

  • Perform logistic regression assuming additive genetic model
  • Covariates: Age, principal components (ancestry proxies)
  • Software: REGENIE, SAIGE, or PLINK2

Meta-Analysis Approach:

  • Ancestry-specific analysis: Conduct GWAS within each ancestral group
  • Trans-ancestry meta-analysis: Combine results using inverse-variance weighted fixed-effects or sample-size based methods
  • Heterogeneity assessment: Calculate Cochran's Q and I² statistics to identify ancestry-specific effects

Conditional Analysis:

  • Stepwise approach to identify independent signals within associated regions
  • Use GCTA-COJO or approximate conditional analysis in METAL

Functional Annotation:

  • Integrate with epigenomic data (ENCODE, Roadmap Epigenomics)
  • Perform colocalization with eQTL/pQTL data (GTEx, eQTLGen)
  • Implement fine-mapping (SuSiE, FINEMAP) to identify causal variants

G start Study Design qc Genotyping & Quality Control start->qc imp Imputation qc->imp anc_gwas Ancestry-Specific GWAS imp->anc_gwas meta Trans-ancestry Meta-analysis anc_gwas->meta het Heterogeneity Assessment meta->het het->anc_gwas Heterogeneous Effects down Downstream Analysis het->down Homogeneous Effects

Trans-ancestry GWAS workflow illustrating the iterative process for identifying heterogeneous genetic effects across populations.

Pathway Analysis and Biological Mechanisms

Hormone Signaling Pathways

Genetic studies consistently implicate genes involved in sex steroid hormone biosynthesis and signaling in endometriosis pathogenesis. Key pathways identified through trans-ancestry analyses include:

Estrogen Receptor Signaling:

  • ESR1 locus: Contains multiple independent association signals [21]
  • GREB1: Rapid estrogen-regulated gene involved in endometrial growth [19] [20]
  • FSHB: Follicle-stimulating hormone subunit, regulates ovarian steroidogenesis [21]

Progesterone Resistance Pathways:

  • WNT4: Critical for female reproductive tract development and hormone response [19] [20]
  • ID4: Implicated in endometrial proliferation and differentiation [20]

G GeneticRisk Genetic Risk Variants Hormonal Hormonal Dysregulation GeneticRisk->Hormonal Immune Immune Dysfunction GeneticRisk->Immune Angio Aberrant Angiogenesis GeneticRisk->Angio Pain Pain Perception Pathways GeneticRisk->Pain Clinical Clinical Heterogeneity Hormonal->Clinical Estrogen Progesterone Immune->Clinical Inflammation Cytokines Angio->Clinical Lesion Vascularization Pain->Clinical Nociceptive Sensitization

Endometriosis pathogenesis network showing how genetic risk variants influence multiple biological pathways contributing to clinical heterogeneity.

Lipid Metabolism Connections

Emerging evidence from Mendelian randomization studies indicates causal relationships between lipid metabolism and endometriosis risk:

  • Triglycerides: Genetically predicted triglyceride levels show causal effects on endometriosis risk [22]
  • Lipid-modifying targets: LPL, PPARA, ANGPTL3, and APOC3 identified as potential therapeutic targets [22]
  • Trans-ethnic consistency: Causal effects observed across European and East Asian populations [22]

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Research Reagents for Endometriosis Trans-ancestry Studies

Reagent Category Specific Examples Research Application Protocol Considerations
Genotyping Arrays Illumina Global Screening Array, Affymetrix Axiom Biobank Array Genome-wide variant detection Ancestry-specific content optimization [18]
Imputation Panels 1000 Genomes Phase 3, TOPMed, HRC Genotype gap filling Multi-ancestry reference panels improve imputation accuracy [18]
Functional Validation CRISPR/Cas9 systems, organoid culture models Mechanism investigation Patient-derived organoids from diverse ancestries [12]
Bioinformatics Tools METAL, REGENIE, GCTA, PLINK Statistical analysis Trans-ancestry meta-analysis software [19] [20]
Pathway Analysis GARFIELD, FUMA, DEPICT Functional annotation Integration with multi-omic databases [12]
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Protocol: Functional Validation of Candidate Loci

In Silico Functional Annotation Pipeline

Step 1: Chromatin State Mapping

  • Utilize endometrium-specific epigenomic data (ENCODE, Roadmap Epigenomics)
  • Identify candidate causal variants in regulatory elements (enhancers, promoters)

Step 2: Colocalization Analysis

  • Integrate with endometrium/e endometriotic lesion eQTL data (GTEx, eQTLGen)
  • Bayesian colocalization (COLOC) to identify shared causal variants

Step 3: High-Throughput Functional Screens

  • Massively parallel reporter assays (MPRA) for enhancer activity
  • CRISPR screens in endometrial cell models
Experimental Validation in Disease-Relevant Models

Cell Culture Models:

  • Primary endometrial stromal cells from diverse donors
  • Immortalized endometrial epithelial cells
  • Patient-derived endometriosis organoids

Functional Assays:

  • Luciferase reporter assays for regulatory variant testing
  • CRISPR-based genome editing (KO, KI, base editing)
  • RNA-seq and ATAC-seq on edited cells
  • Hormone response assays (estradiol, progesterone)

This integrated framework for trans-ancestry analysis of endometriosis provides a comprehensive approach to elucidate the genetic architecture and biological mechanisms underlying this complex gynecological disorder, with direct implications for targeted therapeutic development across diverse populations.

Recent advances in trans-ancestry genomic research have dramatically accelerated the discovery of genetic loci associated with endometriosis. This application note details how multi-ancestry genome-wide association studies (GWAS) have expanded the catalog of significant endometriosis loci from approximately 45 to over 80 through the inclusion of diverse cohorts. We present quantitative evidence from a landmark study of ~1.4 million women, including 105,869 cases, which identified 80 genome-wide significant associations—37 of which are novel. This document provides detailed methodologies for implementing trans-ancestry meta-analysis approaches, including specific protocols for statistical analysis, functional annotation, and therapeutic target discovery. The presented framework demonstrates how genetic studies encompassing diverse ancestral backgrounds enhance discovery power, improve fine-mapping resolution, and facilitate the translation of genetic findings into pathogenic mechanisms and potential therapeutic interventions.

Endometriosis is a common, estrogen-dependent inflammatory condition affecting approximately 10% of women of reproductive age, characterized by the presence of endometrial-like tissue outside the uterine cavity [8]. The disease presents with chronic pelvic pain, dysmenorrhea, and infertility, with significant impacts on quality of life. The heritability of endometriosis is estimated at around 52%, highlighting the substantial role of genetic factors in disease pathogenesis [19].

Early GWAS efforts in endometriosis identified approximately 45 significant genetic loci, primarily in populations of European and Japanese ancestry [19]. However, these studies were limited by their predominantly single-ancestry focus, which constrained discovery power and fine-mapping resolution. The recent application of trans-ancestry meta-analysis approaches has dramatically expanded our understanding of the genetic architecture of endometriosis, increasing the number of genome-wide significant loci to over 80 [12] [13].

This application note documents the methodologies and protocols that enabled this expansion, focusing specifically on the integration of diverse cohorts in endometriosis genetics research. We present comprehensive data from a recent multi-ancestry GWAS of ~1.4 million women, experimental protocols for trans-ancestry meta-analysis, and visualization of key signaling pathways implicated by the discovered loci.

Quantitative Data Synthesis

Evolution of Endometriosis Loci Discovery

Table 1: Chronological Expansion of Significant Endometriosis Loci

Study Period Sample Size Cases Significant Loci Key Genetic Findings Population Focus
Pre-2023 Meta-analyses ~44,000 ~11,500 ~45 Associations near WNT4, VEZT, GREB1 Primarily European and Japanese
2023 Nature Genetics 762,601 60,674 42 (49 signals) Shared pathways with pain conditions European and East Asian
2025 Multi-ancestry GWAS ~1.4 million 105,869 80 (37 novel) First adenomyosis loci; immune and tissue remodeling pathways Multi-ancestry

Cohort Diversity in Recent Large-Scale Studies

Table 2: Cohort Characteristics in Landmark Endometriosis GWAS

Ancestral Group 2023 Study (N=762,601) 2025 Study (N=~1.4M) Notable Population-Specific Findings
European Primary focus Expanded inclusion Strongest associations with stage III/IV disease
East Asian Included Included Consistent effect directions with European associations
African Limited representation Increased inclusion Improved fine-mapping resolution
Other Ancestries Limited Expanded Novel loci discovery in admixed populations

The 2025 multi-ancestry GWAS represents the largest genetic study of endometriosis to date, achieving a 78% increase in significant loci compared to pre-2023 findings [12] [13]. This expansion was facilitated by a 318% increase in sample size and deliberate inclusion of diverse ancestral groups, enabling the discovery of 37 novel loci and the first five genetic variants associated with adenomyosis [13].

Experimental Protocols

Trans-ancestry Meta-analysis Workflow

G cluster_0 Input Cohorts GWAS1 Individual GWAS Cohort 1 QC Quality Control (- SNP call rate - Sample contamination - Ancestry verification) GWAS1->QC GWAS2 Individual GWAS Cohort 2 GWAS2->QC GWAS3 Individual GWAS Cohort N GWAS3->QC AncestryAxes Ancestry Axis Calculation (Multi-dimensional scaling of genetic variation) QC->AncestryAxes MRMEGA MR-MEGA Analysis (Trans-ethnic meta-regression) AncestryAxes->MRMEGA NovelLoci Novel Loci Discovery (37 new associations) MRMEGA->NovelLoci Finemapping Improved Fine-mapping (50+ causal loci) MRMEGA->Finemapping

Detailed Methodological Protocols

Protocol 3.2.1: Implementation of Trans-ancestry Meta-analysis Using MR-MEGA

Purpose: To detect and fine-map complex trait association signals while accounting for heterogeneity in allelic effects correlated with ancestry.

Materials:

  • GWAS summary statistics from diverse populations
  • MR-MEGA software (http://www.geenivaramu.ee/en/tools/mr-mega)
  • Genetic reference panels (1000 Genomes Project Phase 3)
  • High-performance computing resources

Procedure:

  • Calculate genetic distance matrix: Compute mean pairwise allele frequency differences between all study populations using 13,189 autosomal variants with MAF >5% separated by at least 1 Mb.
  • Derive ancestry axes: Perform multi-dimensional scaling of the distance matrix to generate three axes of genetic variation that separate ancestry groups.
  • Prepare input files: Format GWAS summary statistics to include effect sizes (beta or OR), standard errors, and allele frequencies for each study.
  • Execute meta-regression: Run MR-MEGA with the following command:

    where filelist.txt contains paths to all GWAS summary statistics files.
  • Partition heterogeneity: The model will automatically separate heterogeneity into:
    • Ancestry-correlated heterogeneity (captured by the ancestry axes)
    • Residual heterogeneity (due to other factors)
  • Interpret results: Genome-wide significant associations are identified at P < 5 × 10⁻⁸. Variants with significant ancestry-correlated heterogeneity (P < 0.05) indicate population-specific effects.

Validation: Compare power and fine-mapping resolution against fixed-effects and random-effects meta-analysis using simulated datasets [24].

Protocol 3.2.2: Multi-omics Integration for Functional Validation

Purpose: To characterize the functional consequences of identified variants through transcriptomic, epigenetic, and proteomic data integration.

Materials:

  • GTEx v8 database (https://gtexportal.org/home/)
  • ENCODE annotation data
  • eQTL mapping tools (TensorQTL, FastQTL)
  • Colocalization methods (coloc, eCAVIAR)

Procedure:

  • Identify regulatory effects: Cross-reference endometriosis-associated variants with tissue-specific eQTL data from GTEx v8 for six relevant tissues: uterus, ovary, vagina, sigmoid colon, ileum, and peripheral blood [8].
  • Filter significant eQTLs: Retain only eQTLs with false discovery rate (FDR) < 0.05 and note the slope value (direction and magnitude of effect on gene expression).
  • Perform colocalization analysis: Test for shared causal variants between endometriosis GWAS signals and eQTL signals using Bayesian colocalization (e.g., coloc R package).
  • Annotate functional pathways: Input eQTL-regulated genes into MSigDB Hallmark gene sets and Cancer Hallmarks platform to identify enriched biological pathways.
  • Integrate epigenetic data: Overlap endometriosis-associated variants with chromatin accessibility (ATAC-seq) and histone modification (ChIP-seq) data from relevant cell types.

Validation: Prioritize candidate genes based on (1) number of associated eQTL variants and (2) magnitude of regulatory effect (slope value) across multiple tissues [8].

Signaling Pathways and Biological Mechanisms

G cluster_0 Multi-omics Integration GeneticRisk Genetic Risk Variants (80 significant loci) Transcriptomic Transcriptomic Regulation (eQTL effects in multiple tissues) GeneticRisk->Transcriptomic Epigenetic Epigenetic Modulation (DNA methylation changes) GeneticRisk->Epigenetic Proteomic Proteomic Alterations (Protein level changes) GeneticRisk->Proteomic Immune Immune Dysregulation (Altered immune cell function) Transcriptomic->Immune TissueRemodeling Tissue Remodeling (Abnormal growth/invasion) Transcriptomic->TissueRemodeling CellDifferentiation Altered Cell Differentiation (Impaired cellular identity) Epigenetic->CellDifferentiation Pain Pain Pathways (NGF, GDAP1, BSN) Proteomic->Pain Inflammation Chronic Inflammation (Cytokine signaling) Proteomic->Inflammation Immune->Pain TissueRemodeling->Inflammation CellDifferentiation->TissueRemodeling

The biological pathways illuminated by the expanded genetic discoveries reveal a complex interplay of mechanisms in endometriosis pathogenesis. Multi-omics integration demonstrates that genetic variation influences disease risk through transcriptomic, epigenetic, and proteomic regulation across multiple tissues, converging on pathways involved in immune regulation, tissue remodeling, and cell differentiation [12] [13]. Specific genes identified in these pathways include:

  • Immune Regulation: MICB, CLDN23 - involved in immune evasion and epithelial barrier function [8]
  • Tissue Remodeling: GATA4 - transcription factor regulating cellular growth and differentiation
  • Pain Pathways: NGF (nerve growth factor), GDAP1, BSN - associated with pain perception and maintenance [25]

Notably, a substantial subset of regulated genes was not associated with any known pathway, indicating potential novel regulatory mechanisms in endometriosis pathogenesis [8].

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Research Materials for Endometriosis Genetic Studies

Reagent/Resource Specific Example Function/Application Key Features
GWAS Arrays Illumina Global Screening Array Genotyping of common variants ~650,000 markers with imputation to millions of variants
Reference Panels 1000 Genomes Phase 3 Imputation and ancestry determination 2,504 individuals from 26 populations
eQTL Databases GTEx v8 Tissue-specific expression quantitative trait loci 54 tissues from 948 donors
Meta-analysis Software MR-MEGA Trans-ethnic meta-regression Accounts for ancestry-correlated heterogeneity
Functional Annotation Ensembl VEP Variant effect prediction Genomic context and functional consequences
Pathway Analysis MSigDB Hallmark Biological pathway enrichment 50 well-defined biological states
Colocalization Tools coloc R package Bayesian colocalization of GWAS and molecular QTLs Determines shared causal variants
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Therapeutic Implications and Drug Repurposing

The expansion of endometriosis loci has enabled drug-repurposing analyses that highlight potential therapeutic interventions currently used for breast cancer and preterm birth prevention [12] [13]. These analyses leverage the integration of genetic findings with drug target databases to identify existing medications that might be effective for endometriosis treatment based on shared pathogenic mechanisms.

Genetic correlations between endometriosis and other pain conditions, including migraine, back pain, and multisite chronic pain (MCP), suggest that targeted investigations of shared mechanisms could aid the development of new treatments and facilitate early symptomatic intervention [25]. The polygenic risk for endometriosis has been shown to interact with abdominal pain, anxiety, migraine, and nausea, providing insights for managing the complex symptomatology of the condition [12].

The strategic inclusion of diverse cohorts in endometriosis genetic research has substantially expanded our understanding of the genetic architecture of this complex condition. The application of trans-ancestry meta-analysis methods has enabled the discovery of 37 novel loci in addition to the previously known 45 associations, providing a more comprehensive picture of the biological pathways involved in disease pathogenesis.

The protocols and methodologies detailed in this application note provide a roadmap for researchers seeking to implement similar approaches for other complex traits. The continued expansion of diverse biobanks and advancements in trans-ancestry analytical methods will further accelerate gene discovery, fine-mapping precision, and the translation of genetic findings into clinically actionable insights for endometriosis diagnosis and treatment.

Implementing Trans-Ancestry Frameworks: From SNP to Pathway Integration

Endometriosis is a heritable, hormone-dependent gynecological disorder affecting 6-10% of women of reproductive age, with an estimated common SNP-based heritability of 0.26 [21]. Trans-ancestry meta-analysis has emerged as a powerful approach for elucidating the genetic architecture of endometriosis, enabling the identification of susceptibility loci across diverse populations. Genome-wide association studies (GWAS) have successfully identified multiple genetic loci associated with endometriosis risk, with recent large-scale meta-analyses identifying five novel loci in genes involved in sex steroid hormone pathways (FN1, CCDC170, ESR1, SYNE1, and FSHB) [21]. However, the analysis of complex genetic data from diverse ancestral backgrounds presents significant methodological challenges, requiring advanced statistical frameworks to maximize discovery while ensuring equitable performance across populations.

The integration of polygenic risk scores (PRS), Bayesian modeling, and pathway-based approaches represents a paradigm shift in endometriosis genetics. These methods address critical limitations of conventional GWAS by incorporating SNPs with modest effect sizes, accounting for linkage disequilibrium differences across populations, and enabling multi-locus testing. This article outlines core analytical frameworks specifically applied to endometriosis research, providing detailed protocols for implementing PRS-CSx, Bayesian graphical models, and Adaptive Rank Truncated Product methods in trans-ancestry meta-analysis contexts.

Polygenic Risk Score Methods for Cross-Population Analysis

Theoretical Foundation and Endometriosis Applications

Polygenic Risk Score (PRS) analysis predicts an individual's genetic risk for targeted traits by aggregating the effects of numerous genetic variants across the genome. Unlike conventional GWAS that focuses on statistically significant markers, PRS incorporates single nucleotide polymorphisms (SNPs) with low effect sizes that collectively contribute to disease heritability [26]. This approach is particularly valuable for endometriosis research, where the condition exhibits a complex polygenic architecture with contributions from many genetic variants of small effect.

The clinical application of PRS in endometriosis faces the significant challenge of reduced predictive power in non-European populations due to insufficient GWAS data and differences in genetic architecture [26]. A 2022 study investigating the applicability of PRS in endometriosis clinical presentation found inverse associations between PRS and spread of endometriosis, involvement of the gastrointestinal tract, and hormone treatment, though the specificity and sensitivity were low [27]. The authors concluded that specific PRS should be developed to predict clinical presentations in patients with endometriosis, highlighting the need for more sophisticated cross-population methods.

PRS-CSx Framework and Implementation

PRS-CSx is a Bayesian regression framework that addresses cross-population PRS applications by using a continuous shrinkage (CS) prior on SNP effect sizes and leveraging multi-ancestry reference panels [26]. This method improves the accuracy of PRS application across multi-ethnic populations through a posterior inference algorithm that accounts for genetic architecture differences between populations.

Table 1: Key PRS Methods and Their Applications in Endometriosis Research

Method Approach Computational Platform Key Features Endometriosis Application
PRS-CSx Bayesian shrinkage with continuous prior Python Multi-ancestry inference, improves cross-population portability Trans-ancestry risk prediction for diverse cohorts
LDpred Bayesian shrinkage prior Python/R Uses prior on effect sizes and LD information Disease risk prediction in European populations
PRSice Clumping + thresholding (C+T) R, C++ User-friendly, automated PRS analysis Clinical presentation association studies
BayesR Hierarchical Bayesian mixture model Fortran Simultaneous variant discovery and variance estimation Modeling polygenic architecture

Experimental Protocol: Implementing PRS-CSx for Endometriosis

Required Input Data:

  • GWAS summary statistics from trans-ancestry meta-analysis of endometriosis
  • Reference panels matching ancestral backgrounds (e.g., 1000 Genomes, HRC)
  • Target genotype data with matching ancestral composition

Step-by-Step Procedure:

  • Data Preparation and Quality Control

    • Convert summary statistics to standard format (CHR, SNP, A1, A2, EFFECT, P)
    • Apply genomic control to correct for inflation
    • Filter SNPs with imputation quality score < 0.6 and MAF < 0.01

  • LD Reference Panel Processing

    • Download and process population-specific LD reference panels
    • For endometriosis analysis, include diverse reference panels (European, East Asian, African)
    • Ensure matching build and allele coding across all datasets

  • PRS-CSx Execution

    • Run PRS-CSx with multi-ancestry setting: python PRScsx.py --sumstats1 EUR.txt --sumstats2 EAS.txt --ref1 EUR_ref --ref2 EAS_ref --out endometriosis_prs
    • Set global shrinkage parameter φ or enable auto-estimation
    • Specify MCMC parameters (burn-in: 10,000, iterations: 20,000)

  • Score Calculation and Validation

    • Generate PRS in target samples: plink --score endometriosis_prs.txt
    • Evaluate predictive performance using Area Under Curve (AUC) statistics
    • Assess calibration across ancestral groups

G start GWAS Summary Statistics (Multiple Ancestries) qc Quality Control & Standardization start->qc ld LD Reference Panel Processing qc->ld prscsx PRS-CSx Bayesian Regression ld->prscsx score PRS Calculation in Target Samples prscsx->score eval Cross-Ancestry Performance Validation score->eval

Figure 1: PRS-CSx Workflow for Trans-ancestry Endometriosis Risk Prediction

Bayesian Modeling Approaches for Multi-SNP Analysis

Bayesian Graphical Models for GWAS

Bayesian graphical models provide a powerful framework for multi-SNP analysis of GWAS data, addressing limitations of standard single-marker approaches. These methods enable simultaneous assessment of multiple SNPs that can be linked or unlinked and can interact or not, providing a more comprehensive understanding of genetic architecture [28]. For endometriosis research, this approach is particularly valuable given the complex, polygenic nature of the disease.

The fundamental advantage of Bayesian methods lies in their ability to model complex dependency structures among genetic variants while accounting for population structure and multiple testing through posterior probabilities [28]. Unlike single-SNP GWAS that test each marker independently, Bayesian graphical models evaluate the joint effect of multiple SNPs, potentially identifying combinations of variants that collectively influence endometriosis risk.

Bayesian Alphabet for Genomic Prediction

The "Bayesian Alphabet" encompasses a family of methods for genomic prediction and GWAS, each employing different prior distributions for marker effects [29]. Key methods include:

  • Bayes-A: Each marker has a normal prior with its own variance
  • Bayes-B: Bayesian variable selection model with a prespecified proportion of markers having zero effects
  • Bayes-C: Non-zero effects sampled from a single normal distribution
  • Bayes-R: Mixture of normal distributions as prior for marker effects

These methods have been shown to map quantitative trait loci (QTL) more precisely than standard single-SNP GWAS, with applications demonstrating higher accuracy for QTL detection in complex traits [29]. For endometriosis, which involves multiple genetic variants of small to moderate effects, Bayesian methods offer enhanced power to detect genuine associations.

Table 2: Bayesian Methods for Genomic Analysis in Endometriosis Research

Method Prior Distribution Key Features Implementation Endometriosis Relevance
Bayes-A Normal with marker-specific variance Accommodates large effects BGLR, Gensel Captures effect size heterogeneity
Bayes-B Mixture with point mass at zero Variable selection capability JWAS, BGLR Identifies causal SNPs among thousands
Bayes-C Single normal for non-zero effects Intermediate complexity Gensel, BGLR Balanced approach for polygenic traits
Bayes-R Mixture of normals Models effect size distribution BayesR software Optimal for highly polygenic architecture

Experimental Protocol: Bayesian Graphical Model for Endometriosis GWAS

Required Input Data:

  • Genotype data for endometriosis cases and controls
  • Phenotypic information with detailed sub-phenotype stratification (e.g., rAFS stage)
  • Prior biological knowledge from functional annotations

Step-by-Step Procedure:

  • Data Preprocessing

    • Encode SNP data as discrete variables (0,1,2)
    • Stratify endometriosis cases by disease stage (rAFS I-IV)
    • Incorporate functional annotations as informative priors

  • Model Specification

    • Define graphical model structure based on biological knowledge
    • Set prior probabilities for edge inclusion
    • Specify hyperparameters for marker effect distributions

  • Stochastic Search Execution

    • Implement Mode Oriented Stochastic Search (MOSS) algorithm
    • Run Markov Chain Monte Carlo (MCMC) sampling with 100,000 iterations
    • Use burn-in period of 10,000 iterations

  • Posterior Inference

    • Calculate posterior probabilities for SNP associations
    • Identify maximal posterior probability models
    • Perform model averaging to account for uncertainty

G data Endometriosis Genotype and Phenotype Data prep Data Preprocessing and Stratification by Stage data->prep prior Incorporate Functional Annotation Priors prep->prior search MOSS Algorithm for Model Space Exploration prior->search posterior Posterior Probability Calculation search->posterior multi Multi-SNP Model Selection posterior->multi

Figure 2: Bayesian Graphical Model Workflow for Endometriosis GWAS

Adaptive Rank Truncated Product Method for Pathway Analysis

Pathway-Based Meta-Analysis Framework

The Adaptive Rank Truncated Product (ARTP) method provides a powerful approach for pathway-based meta-analysis using summary statistics from GWAS. This method enables multi-marker testing procedures that integrate information across multiple genetic variants within biological pathways, offering enhanced power to detect subtle polygenic effects [30] [31]. For endometriosis research, pathway analysis is particularly valuable given the involvement of multiple biological processes, including sex steroid hormone signaling and immune function.

The ARTP2 method, an enhanced version of the original algorithm, allows for association testing on user-defined genes or pathways without assuming independence between genes, making it suitable for analyzing overlapping functional pathways [30]. This approach can leverage summary statistics from trans-ancestry meta-analyses, facilitating the identification of biological pathways enriched for endometriosis risk variants across diverse populations.

The summary-based Adaptive Rank Truncated Product (sARTP) method enables pathway meta-analysis using only SNP-level summary statistics in combination with genotype correlation estimated from a reference panel [31]. This approach has been validated through comprehensive applications, including a pathway-based meta-analysis of type 2 diabetes that identified 43 significant pathways, demonstrating its utility for complex disease genetics.

For endometriosis research, sARTP enables the integration of summary statistics from multiple ancestries to identify conserved biological pathways, even when individual variant effects are heterogeneous across populations. This method is particularly valuable for trans-ancestry analysis where individual-level genotype data may not be available for all cohorts.

Experimental Protocol: Pathway Analysis for Endometriosis Genetics

Required Input Data:

  • GWAS summary statistics from endometriosis meta-analysis
  • Pathway definitions from databases (KEGG, Reactome, GO)
  • Reference panel for LD estimation (e.g., 1000 Genomes)

Step-by-Step Procedure:

  • Pathway Definition and Annotation

    • Download curated pathway definitions from MSigDB or KEGG
    • Annotate SNPs to genes based on genomic position (±50kb from transcription start/end sites)
    • Exclude genes in established endometriosis loci to avoid bias

  • Summary Statistics Processing

    • Harmonize summary statistics across studies (allele coding, strand orientation)
    • Calculate Z-scores from effect estimates and standard errors
    • Filter SNPs with MAF < 0.01 and imputation quality < 0.6

  • ARTP2 Execution

    • Run ARTP2 with pathway definition file: Rscript ARTP2.R --sumstats endometriosis.txt --pathway hormone_pathways.txt --out pathway_results
    • Set significance threshold for gene-based tests (P < 0.05)
    • Perform 10,000 permutations to estimate empirical P-values

  • Results Interpretation

    • Apply Bonferroni correction for multiple pathway testing
    • Calculate false discovery rates (FDR) to control Type I error
    • Annotate significant pathways with biological relevance to endometriosis

G input GWAS Summary Statistics and Pathway Databases annot SNP to Gene Annotation and Pathway Mapping input->annot artp ARTP2 Pathway Enrichment Analysis annot->artp perm Empirical P-value Calculation via Permutation artp->perm corr Multiple Testing Correction perm->corr bio Biological Interpretation and Prioritization corr->bio

Figure 3: ARTP2 Pathway Analysis Workflow for Endometriosis Genetics

Integrated Analytical Framework for Trans-ancestry Endometriosis Research

Synergistic Application of Core Methods

The integration of PRS-CSx, Bayesian modeling, and ARTP2 methods creates a powerful analytical framework for trans-ancestry endometriosis research. These approaches address complementary aspects of genetic analysis: PRS-CSx enables cross-population risk prediction, Bayesian methods identify multi-SNP associations, and ARTP2 elucidates biological pathways. When applied synergistically, these methods provide a comprehensive understanding of endometriosis genetics across diverse populations.

A recommended analytical sequence begins with trans-ancestry meta-analysis to identify robust genetic associations, followed by Bayesian graphical modeling to refine multi-SNP models, then pathway analysis to identify biological mechanisms, and finally PRS construction for risk prediction. This integrated approach leverages the strengths of each method while mitigating their individual limitations.

Research Reagent Solutions

Table 3: Essential Research Reagents and Computational Tools

Category Item Specification/Version Function Application Notes
Software Packages PRS-CSx Python implementation Bayesian polygenic prediction Requires LD reference panels
genMOSS R package Bayesian graphical models MCMC for high-dimensional space
ARTP2 R package Pathway enrichment analysis Accepts summary statistics
BGLR R package Bayesian regression models Implements Bayesian Alphabet
Reference Data 1000 Genomes Phase 3 LD reference panels Multi-ancestry foundation
GWAS Catalog Current release Prior knowledge base Informed priors for Bayesian methods
KEGG/Reactome Current release Pathway definitions Biological context for ARTP2
Quality Control PLINK v1.9/v2.0 Genotype processing Data preprocessing and QC
R v4.0+ Statistical computing Primary analysis environment

The integration of PRS-CSx, Bayesian modeling, and Adaptive Rank Truncated Product methods represents a significant advancement in trans-ancestry endometriosis research. These core analytical frameworks address critical challenges in complex disease genetics, including population diversity, polygenic architecture, and biological interpretation. By providing detailed protocols and implementation guidelines, this article enables researchers to apply these sophisticated methods to advance our understanding of endometriosis genetics across diverse global populations.

Future methodological developments will likely focus on enhancing cross-population portability, integrating multi-omics data, and improving computational efficiency for large-scale biobank data. As these methods evolve, they will continue to transform endometriosis research, ultimately contributing to improved risk prediction, clinical stratification, and targeted therapeutic development for this complex gynecological disorder.

Multi-ancestry genome-wide association studies (GWAS) represent a transformative approach in genetic epidemiology, addressing historical biases toward European-ancestry populations that have limited the generalizability of genetic discoveries [32]. By integrating data from diverse ancestral backgrounds, researchers can leverage differences in linkage disequilibrium (LD) patterns, allele frequencies, and genetic architectures to enhance variant discovery, improve fine-mapping resolution, and develop more portable polygenic risk scores [32] [33]. This Application Note provides detailed methodologies for implementing multi-ancestry GWAS approaches, with specific application to endometriosis research, a complex gynecological condition affecting approximately 10% of reproductive-aged women worldwide [12] [6].

The strategic integration of diverse genetic data addresses crucial limitations of single-ancestry studies while unlocking new biological insights. For endometriosis, recent multi-ancestry efforts in approximately 1.4 million women have identified 80 genome-wide significant associations, 37 of which are novel, demonstrating the substantial discovery potential of diverse cohorts [12]. Furthermore, cross-ancestry fine-mapping has proven particularly valuable for narrowing candidate causal variants within associated loci, with studies reporting 19 of 113 independent signals pinpointed within 95% credible sets [33].

Key Methodological Approaches

Two primary computational strategies dominate multi-ancestry GWAS implementations, each with distinct advantages and considerations for endometriosis research.

Pooled Analysis (Mega-Analysis)

Pooled analysis combines individual-level genetic data from all ancestral backgrounds into a single unified dataset, typically incorporating principal components or mixed-effects models to account for population stratification [32] [34]. This approach maximizes statistical power through increased sample size and efficiently handles admixed individuals without requiring arbitrary ancestry categorizations [35].

Recent evaluations demonstrate that pooled analysis generally provides superior statistical power compared to meta-analysis approaches across various ancestry compositions and trait architectures, particularly when allele frequencies differ across populations [32] [34]. The method maintains well-controlled type I error rates in realistic scenarios with proper stratification control [35]. Implementation typically employs mixed-effect models (e.g., REGENIE) to account for population structure and relatedness, especially important in biobank-scale datasets where cryptic relatedness is common [32].

Meta-Analysis

Meta-analysis conducts separate GWAS within defined ancestry groups and subsequently combines summary statistics using fixed-effect or random-effects models [32] [36]. This approach effectively captures fine-scale population structure within homogeneous groups and facilitates data sharing when individual-level data are restricted [34].

Advanced meta-analysis extensions like MR-MEGA leverage allele-frequency differences among contributing studies to enhance power and handle admixed individuals [32]. However, this method introduces additional parameters that can reduce power, particularly with complex admixture patterns [32]. Limitations include reduced effectiveness of population structure correction in smaller cohorts and potential exclusion of individuals who don't fit neatly into predefined ancestry categories [32] [36].

Table 1: Comparison of Multi-ancestry GWAS Methodological Approaches

Feature Pooled Analysis Meta-Analysis MR-MEGA
Data Structure Individual-level data combined Summary statistics combined Summary statistics combined with ancestry parameters
Population Structure Control Principal components, mixed models Within-group corrections Leverages allele frequency differences
Handling of Admixed Individuals Direct inclusion Challenging, often excluded Specifically designed for admixture
Statistical Power Generally higher [32] [34] Moderate Variable, reduced with complex admixture [32]
Implementation Complexity Higher computational demands Lower, facilitates distributed analysis Moderate, requires careful parameterization
Data Sharing Considerations Requires individual data access Can use summary statistics Can use summary statistics

Comparative Performance in Real-World Applications

Empirical evaluations across multiple biobanks demonstrate the practical implications of method selection. In analyses of eight continuous and five binary traits from the UK Biobank (N ≈ 324,000) and All of Us Research Program (N ≈ 207,000), pooled analysis consistently exhibited better statistical power while effectively controlling for population stratification [34] [35]. Similarly, in the Hyperglycemia and Adverse Pregnancy Outcome Study, heterogeneous ancestry mega-analysis identified significantly more associations with maternal glucose measures compared to homogeneous ancestry meta-analysis, including biologically credible signals at the MTNR1B locus that were missed by the meta-analysis approach [36].

Experimental Protocols for Multi-ancestry GWAS

Protocol 1: Pooled Analysis Implementation

Sample Preparation and Quality Control
  • Perform joint genotype calling across all samples regardless of ancestry
  • Apply standard QC filters: call rate >98%, Hardy-Weinberg equilibrium p > 1×10⁻⁶, minor allele frequency >1%
  • Conduct principal component analysis (PCA) on LD-pruned variants to visualize genetic relationships
  • Remove outliers exceeding 6 standard deviations from cluster centers
  • Ancestry Inference: Use AIPS (Ancestry Inference using Principal Component Analysis and Spatial Analysis) with LD-pruned SNPs, excluding lactase (2q21), MHC, and inversion regions (8p23, 17q21.31) [36]
Imputation and Phasing
  • Align all samples collectively to cosmopolitan reference panels (TOPMed Freeze 8 GRCh38 recommended) [36] [33]
  • Perform phasing using Eagle v2.4 or newer [36]
  • Conduct imputation with Minimac4 using R-square filter >0.30 [36]
  • Retain variants with MAF >0.01 for analysis
Association Testing
  • Implement mixed-effect models using REGENIE or similar tools to account for population structure and relatedness [32]
  • Include principal components as covariates (typically 10-20 PCs)
  • For binary traits like endometriosis, use logistic mixed models to address case-control imbalances [32]
  • Apply genome-wide significance threshold of p < 5×10⁻⁸

Protocol 2: Meta-Analysis Implementation

Ancestry-Specific Processing
  • Define homogeneous ancestry groups using genetic clustering algorithms [36]
  • Process each ancestry group independently through alignment, phasing, and imputation
  • Use ancestry-specific reference panels (CAAPA for African, HRC for European, GAsP for Asian, 1000G Phase 3 for admixed American) [36]
  • Apply within-group QC filters: HWE p > 0.001, MAF > 0.01
Stratified Association Analysis
  • Conduct GWAS within each ancestry group using linear or logistic regression
  • Include ancestry-specific principal components as covariates
  • Apply genomic control correction within each group (λ ≈ 1.0)
  • Harmonize effect alleles and directions across studies
  • Perform fixed-effect meta-analysis using inverse variance weighting [32]
  • Assess heterogeneity with I² statistics
  • Implement random-effects models when significant heterogeneity is detected

meta_analysis_workflow cluster_ancestry_groups Ancestry-Specific Processing start Multi-ancestry Cohort pc1 Ancestry Stratification (PCA Clustering) start->pc1 pc2 Ancestry-Specific Imputation pc1->pc2 pc3 Within-Group GWAS pc2->pc3 pc4 Summary Statistics Harmonization pc3->pc4 pc5 Meta-Analysis (Inverse Variance Weighting) pc4->pc5 end Cross-ancestry Association Results pc5->end

Graph 1: Meta-analysis workflow for multi-ancestry GWAS. The process involves ancestry stratification, group-specific imputation and association testing, followed by summary statistics harmonization and cross-ancestry integration.

Application to Endometriosis Research

Current Genetic Landscape

Endometriosis exhibits substantial heritability, yet previous GWAS have explained only ~5% of disease variance [2]. Recent multi-ancestry efforts have dramatically expanded our understanding, with the largest study to date (N ≈ 1.4 million women) identifying 80 genome-wide significant associations, including 37 novel loci and 5 inaugural adenomyosis variants [12]. Functional annotation revealed enrichment in pathways involved in immune regulation, tissue remodeling, and cell differentiation, providing mechanistic insights into disease pathogenesis [12].

Multi-ancestry Analytical Framework for Endometriosis

endometriosis_framework cluster_omics Multi-omics Integration data Endometriosis Cohorts (UK Biobank, FinnGen, All of Us) step1 Multi-ancestry GWAS Integration data->step1 step2 Cross-ancestry Fine-mapping step1->step2 step3 Functional Annotation (eQTL/pQTL Colocalization) step2->step3 step4 Pathway Enrichment & Gene Prioritization step3->step4 step5 Therapeutic Target Identification step4->step5 mr Mendelian Randomization step5->mr

Graph 2: Comprehensive analytical framework for multi-ancestry endometriosis research, integrating genetic discovery with functional annotation and therapeutic prioritization.

Cross-ancestry Fine-mapping Protocol

Credible Set Calculation
  • Apply Sum of Single Effects (SuSiE) framework to define 95% credible sets for associated loci [33]
  • Integrate LD information from multi-ancestry reference panels
  • Prioritize variants with posterior probability >0.95 for functional validation
Colocalization Analysis
  • Integrate endometriosis GWAS with expression (eQTL) and protein (pQTL) quantitative trait loci
  • Calculate posterior probabilities for shared causal variants (PPH4 > 0.8)
  • Annotate variants with regulatory potential using epigenomic datasets

Therapeutic Target Prioritization

Multi-ancestry endometriosis studies have successfully identified potential therapeutic targets through Mendelian randomization and colocalization analysis. Recent research implicated RSPO3 and FLT1 as potential therapeutic candidates, with external validation confirming robust associations for RSPO3 [6]. Drug-repurposing analyses have highlighted interventions currently used for breast cancer and preterm birth prevention as promising candidates [12].

Table 2: Key Research Reagent Solutions for Multi-ancestry Endometriosis GWAS

Reagent/Resource Function Specifications Application in Endometriosis
TOPMed Freeze 8 Cosmopolitan reference panel Diverse ancestries, whole genome sequencing Unified imputation for heterogeneous cohorts [36] [33]
Multi-Ethnic Genotyping Array (MEGA) Genome-wide variant screening ~2M markers optimized for diverse populations Initial genotyping of multi-ancestry cohorts [36]
SOMAscan V4 Plasma protein quantification 4,907 protein targets pQTL mapping for therapeutic target identification [6]
REGENIE Mixed-model GWAS Handles relatedness, population structure Association testing in pooled analyses [32]
FUMA Functional annotation Integrates multiple genomic databases Prioritization of endometriosis-associated loci [37]
PoPS Gene prioritization Polygenic Priority Score algorithm Identification of endometriosis effector genes [33]

Data Interpretation Guidelines

Quality Control Metrics

  • Genomic Inflation: Acceptable λGC < 1.1 for well-controlled studies [33]
  • LD Score Regression: Intercept should approximate 1.0, indicating minimal confounding [37]
  • Heterogeneity Assessment: Cochran's Q and I² statistics for cross-ancestry consistency evaluation

Replication Standards

  • Cross-ancestry Validation: Variants should replicate at nominal significance (p < 0.05) in independent cohorts [37]
  • Directional Consistency: Effects should align across ancestries with concordant directions
  • Functional Corroboration: Colocalization with molecular QTLs strengthens causal inference

Multi-ancestry GWAS integration represents a paradigm shift in endometriosis genetics, enhancing discovery power and biological insights while promoting health equity. The methodological framework outlined in this Application Note provides researchers with standardized protocols for implementing these approaches, from initial study design through functional interpretation. As diverse biobanks continue to expand, these strategies will be essential for translating genetic discoveries into clinically actionable insights for endometriosis diagnosis, treatment, and prevention.

SNP-Centric, Gene-Centric, and Pathway-Centric Approaches for Data Integration

The integration of multi-ancestry genome-wide association studies (GWAS) has become crucial for advancing our understanding of complex diseases like endometriosis. Endometriosis, affecting approximately 5-10% of reproductive-age women, is now recognized as a systemic inflammatory disease rather than merely a localized pelvic condition [38]. Its etiopathogenesis involves a complex interplay between genetic inheritance and environmental influences, with GWAS having identified numerous disease risk loci [38]. However, traditional single-ancestry genetic studies have limitations in generalizability and power, creating an urgent need for sophisticated trans-ancestry integration methods that can leverage diverse genetic datasets [39].

This protocol details three complementary analytical frameworks—SNP-centric, gene-centric, and pathway-centric approaches—for integrating trans-ancestry genetic data in endometriosis research. Each method offers distinct advantages for aggregating association signals across different ancestral populations, including African, East Asian, and European cohorts [39]. By implementing these strategies, researchers can enhance detection efficiency, improve biological interpretation, and identify novel therapeutic targets for this complex gynecological disorder.

Trans-Ancestry Integration Frameworks

Foundational Principles

The trans-ancestry integration framework operates under the Trans-Ancestry Gene Consistency (TAGC) assumption, which posits that a specific subset of genes within a pathway is associated with endometriosis across various ancestry groups, though association strengths may differ due to genetic and environmental variations [39]. This assumption is biologically plausible since functional variants, particularly common ones, are often shared among diverse populations [39]. The integration strategies are categorized by the level at which genetic data is combined: SNP-level, gene-level, or pathway-level.

Comparative Framework Characteristics

Table 1: Comparison of Trans-Ancestry Integration Approaches

Approach Integration Level Key Methodology Primary Advantage Best Use Case
SNP-Centric Individual SNPs Consolidates SA-SNP summary statistics to generate TA-SNP statistics [39] Maximizes fine-mapping resolution Identifying specific causal variants
Gene-Centric Gene-level Aggregates SA-SNP data within genes to produce SA-gene statistics, then unifies across ancestries [39] Balances resolution and biological interpretability Candidate gene prioritization
Pathway-Centric Pathway-level Integrates p-values from pathway analyses across each SA-GWAS [39] Captures polygenic effects across biological systems Pathway identification and therapeutic targeting

SNP-Centric Integration Protocol

The SNP-centric approach begins with consolidating single-ancestry SNP-level (SA-SNP) summary data from multiple genome-wide association studies to generate trans-ancestry SNP-level (TA-SNP) summary statistics [39].

SNP_Centric SA_GWAS_1 Single-Ancestry GWAS 1 (European) SNP_Harmonization SNP Effect Size Harmonization SA_GWAS_1->SNP_Harmonization SA_GWAS_2 Single-Ancestry GWAS 2 (East Asian) SA_GWAS_2->SNP_Harmonization SA_GWAS_3 Single-Ancestry GWAS 3 (African) SA_GWAS_3->SNP_Harmonization Effect_Model Apply Trans-Ancestry Effect Size Model SNP_Harmonization->Effect_Model TA_SNP_Output Trans-Ancestry SNP Statistics Effect_Model->TA_SNP_Output Gene_Aggregation Gene-Level Aggregation TA_SNP_Output->Gene_Aggregation TA_Gene_Output Trans-Ancestry Gene Statistics Gene_Aggregation->TA_Gene_Output

Step-by-Step Methodology

Step 1: Data Preparation and Harmonization

  • Collect summary statistics from each single-ancestry GWAS, including effect sizes (β̂), standard errors (Ï„), and p-values for all SNPs [39]
  • Harmonize effect directions across datasets, accounting for potential strand issues and allele frequency differences
  • Exclude SNPs with allele frequency differences >0.2 between any pairwise datasets to minimize population stratification bias [10]

Step 2: Effect Size Modeling

  • Model SNP marginal effects across populations using a linear function of principal components representing genetic variation [39]
  • Alternatively, apply joint normal distribution modeling of conditional effects while maintaining consistent correlation structure genome-wide [39]
  • For endometriosis applications, prioritize SNPs with P < 5×10⁻⁸ and LD clumping (r² < 0.001, distance = 1 Mb) [6]

Step 3: Trans-Ancestry SNP Statistic Calculation

  • Apply inverse variance-weighted meta-analysis to generate trans-ancestry z-scores: Z_TA = (Σ w_i * Z_i) / √(Σ w_i²) where wi = 1/SEi [39]
  • Calculate trans-ancestry p-values from the aggregated z-scores

Step 4: Gene-Level Aggregation

  • Assign TA-SNP statistics to genes using a 50 kb window around gene boundaries [39]
  • Apply the Adaptive Rank Truncated Product (ARTP) method to aggregate SNP signals within each gene [39]
  • Generate trans-ancestry gene-level (TA-gene) statistics for downstream pathway analysis
Technical Considerations

For endometriosis research, incorporate genomic predictors beyond GWAS summary statistics, including:

  • Promoter capture Hi-C data defining conformation genes (cGene) [38]
  • Expression quantitative trait loci (eQTL) data defining expression genes (eGene) [38]
  • Protein quantitative trait loci (pQTL) data for protein abundance insights [6]

Validate SNP-gene assignments using endometriosis-relevant tissues (uterus, endometrium) from GTEx v8 dataset [10].

Gene-Centric Integration Protocol

The gene-centric approach first aggregates single-ancestry SNP data within genes, then unifies these gene-level statistics across ancestry groups [39].

Gene_Centric SA_GWAS_Data Single-Ancestry GWAS Summary Data SNP_to_Gene SNP-to-Gene Assignment (50kb Window) SA_GWAS_Data->SNP_to_Gene SA_Gene_Stats Single-Ancestry Gene Statistics SNP_to_Gene->SA_Gene_Stats Cross_Ancestry Cross-Ancestry Gene-Level Integration SA_Gene_Stats->Cross_Ancestry TA_Gene_Stats Trans-Ancestry Gene Statistics Cross_Ancestry->TA_Gene_Stats Pathway_ARTP Pathway Analysis using ARTP Framework TA_Gene_Stats->Pathway_ARTP Significant_Pathways Significant Pathways Pathway_ARTP->Significant_Pathways

Step-by-Step Methodology

Step 1: Single-Ancestry Gene-Level Analysis

  • Assign SNPs to genes based on physical proximity (50 kb upstream/downstream from gene boundaries) [39]
  • For each ancestry group, compute gene-level association statistics using the ARTP method [39]
  • Account for linkage disequilibrium patterns specific to each population using appropriate reference panels

Step 2: Gene-Level Statistics Integration

  • Apply the TAGC assumption that a consistent set of genes drives associations across ancestries [39]
  • Use meta-analysis methods robust to heterogeneity (e.g., Han-Eskin random effects model) to combine gene-level statistics
  • For endometriosis, prioritize genes showing consistent directional effects across populations

Step 3: Biological Validation and Prioritization

  • Integrate functional genomics data including endometriosis eQTLs, mQTLs, and pQTLs [10]
  • Implement Bayesian approaches to combine computational results with prior knowledge from databases and literature [40]
  • For endometriosis, apply multi-omic SMR analysis to test causal relationships between gene expression and disease risk [10]
Endometriosis-Specific Applications

Gene-centric integration has identified specific endometriosis-risk genes including:

  • MAP3K5: Displays contrasting methylation patterns linked to endometriosis risk [10]
  • THRB and ENG: Validated as risk factors in FinnGen R10 and UK Biobank cohorts [10]
  • RSPO3: Identified through Mendelian randomization as a potential therapeutic target [6]

Pathway-Centric Integration Protocol

The pathway-centric approach conducts pathway analysis separately for each ancestry group, then integrates the results across populations [39].

Pathway_Centric Ancestry_1 European GWAS Data Pathway_Analysis_1 Pathway Analysis (ARTP Method) Ancestry_1->Pathway_Analysis_1 Ancestry_2 East Asian GWAS Data Pathway_Analysis_2 Pathway Analysis (ARTP Method) Ancestry_2->Pathway_Analysis_2 Ancestry_3 African GWAS Data Pathway_Analysis_3 Pathway Analysis (ARTP Method) Ancestry_3->Pathway_Analysis_3 SA_Pathway_Pvals Single-Ancestry Pathway P-values Pathway_Analysis_1->SA_Pathway_Pvals Pathway_Analysis_2->SA_Pathway_Pvals Pathway_Analysis_3->SA_Pathway_Pvals Pvalue_Integration Pathway P-value Integration SA_Pathway_Pvals->Pvalue_Integration TA_Pathway_Results Trans-Ancestry Pathway Results Pvalue_Integration->TA_Pathway_Results Endo_Pathways Endometriosis-Relevant Pathways: - Neutrophil Degranulation - Hormone Metabolism - Cell Adhesion/Migration TA_Pathway_Results->Endo_Pathways

Step-by-Step Methodology

Step 1: Single-Ancestry Pathway Analysis

  • For each ancestry-specific GWAS, implement the ARTP method for pathway analysis [39]
  • Define pathways using curated databases (KEGG, GO, MSigDB) with endometriosis-relevant terms
  • Include 6,970+ pathways for comprehensive coverage of biological processes [39]

Step 2: Pathway P-value Integration

  • Apply Fisher's combined probability test or similar methods to integrate pathway p-values across ancestries
  • Account for potential heterogeneity in pathway effect sizes across populations
  • Use empirical null distributions to control for multiple testing

Step 3: Endometriosis-Specific Pathway Enrichment

  • Conduct target set enrichment analysis quantifying the degree to which predefined gene lists are enriched [38]
  • For endometriosis, examine pathways including neutrophil degranulation, hormone metabolism, and cell adhesion [38]
  • Implement pathway crosstalk-based attack analysis to identify critical nodes like AKT1 in endometriosis pathogenesis [38]
Key Endometriosis Pathways Identified

Table 2: Endometriosis-Relevant Pathways Identified Through Trans-Ancestry Integration

Pathway Category Specific Pathways Biological Significance in Endometriosis Therapeutic Implications
Inflammatory Processes Neutrophil Degranulation [38] Facilitates metastasis-like spread to distant organs Potential for immunomodulator repurposing
Hormone Metabolism Estrogen Receptor Signaling [38] Drives lesion establishment and growth ESR1-targeting agents in clinical trials
Cell Processes PI3K/AKT/mTOR [38], Cell Adhesion/Migration [2] Promotes lesion survival and invasion AKT1 inhibitors, anti-adhesion therapies
Stress Response Autophagy [2] Supports cell survival in ectopic locations Novel therapeutic target
Immune Function Macrophage Biology [2] Creates pro-inflammatory microenvironment Immunomodulatory approaches

The Scientist's Toolkit

Table 3: Key Research Reagent Solutions for Trans-Ancestry Endometriosis Studies

Resource Category Specific Resource Function in Analysis Access Information
GWAS Summary Data UK Biobank (ukb-b-10903) [6] Endometriosis case-control data https://www.ukbiobank.ac.uk/
Multi-Ancestry Data FinnGen R10/R12 [10] [6] Validation cohorts for trans-ancestry analysis https://www.finngen.fi/en
QTL Databases eQTLGen [10], GTEx v8 [10] Expression quantitative trait loci data https://www.eqtlgen.org/
Pathway Databases KEGG, MSigDB [38] Curated biological pathways for enrichment analysis https://www.genome.jp/kegg/
Analysis Tools SMR software [10], ARTP method [39] Multi-omic integration and pathway analysis https://cnsgenomics.com/software/smr/
Prior Knowledge Bases CellAge [10], STRING [38] Cellular aging genes and protein interaction networks https://genomics.senescence.info/cells/
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Implementation Considerations

Sample Size Requirements

  • For trans-ancestry endometriosis studies, prioritize large sample sizes (20,000+ cases) [6]
  • Ensure sufficient representation from diverse ancestral backgrounds
  • Account for potential heterogeneity in endometriosis subphenotypes

Quality Control Measures

  • Implement rigorous QC filters for allele frequency differences (>0.2) between datasets [10]
  • Exclude SNPs with potential strand issues or poor imputation quality
  • Validate findings in independent cohorts when possible

Endometriosis-Specific Considerations

  • Account for disease heterogeneity (ovarian vs. peritoneal, stages I-IV) [40]
  • Consider hormonal influences across menstrual cycle phases [38]
  • Incorporate relevant tissues (uterus, endometrium) in functional validation

The integration of SNP-centric, gene-centric, and pathway-centric approaches provides a powerful framework for advancing trans-ancestry endometriosis research. By leveraging diverse genomic datasets and multi-omic integration strategies, researchers can overcome limitations of single-ancestry studies, enhance discovery power, and identify biologically relevant mechanisms driving endometriosis pathogenesis. The protocols outlined here provide a roadmap for implementing these approaches, with specific considerations for endometriosis applications. As multi-ancestry resources continue to expand, these methods will become increasingly essential for translating genetic discoveries into improved diagnostics and therapeutics for this complex gynecological disorder.

The integration of transcriptome-wide and proteome-wide association studies represents a transformative approach in complex disease research, enabling the identification of functionally relevant molecular mechanisms that transcend genomic associations alone. This integrated framework is particularly powerful when applied to endometriosis, a heritable inflammatory condition affecting 5-10% of reproductive-aged women worldwide, with an estimated heritability of 47-52% [19] [41]. While genome-wide association studies (GWAS) have successfully identified multiple risk loci for endometriosis, these predominantly lie in non-coding regions, suggesting regulatory functions that can only be fully elucidated through multi-omics integration [41]. This protocol details comprehensive methodologies for trans-ancestry meta-analysis coupled with transcriptomic and proteomic profiling to bridge the gap between genetic susceptibility and functional pathophysiology in endometriosis research.

Quantitative Data Synthesis from Multi-Omics Endometriosis Studies

Table 1: Key Findings from Multi-Omics Endometriosis Studies

Study Type Sample Size Key Quantitative Findings Significance
GWAS Meta-analysis [21] 17,045 cases; 191,596 controls 5 novel loci (FN1, CCDC170, ESR1, SYNE1, FSHB); 19 independent SNPs explaining ≤5.19% variance P < 5 × 10-8; highlights genes in sex steroid hormone pathways
Proteomics [42] 39 samples across cohorts 73,218 tryptic peptides; 8,032 unique proteins quantified; 41 ubiquitinated fibrosis-related proteins identified Proteins with FC >1.5, p < 0.05 considered significant
Ubiquitylomics [42] [43] 5 normal; 6 EU/EC pairs 1,647 ubiquitinated lysine sites (EC vs NC); 1,698 sites (EC vs EU); 8,407 Kub peptides total Correlation coefficients: 0.32 (EC/NC) and 0.36 (EC/EU) for ubiquitinated fibrosis proteins
Transcriptomics [42] 6 NC; 6 EU; 10 EC 41 differentially expressed genes in menstrual stem cells; 16,383 characterized transcripts FDR < 0.1; genes involved in proliferation, migration, steroid response
Multi-omics SMR [10] 21,779 cases; 449,087 controls 196 CpG sites in 78 genes; 18 eQTL-associated genes; 7 pQTL-associated proteins PSMR < 0.05; PHEIDI > 0.05; PPH4 > 0.5 for colocalization

Table 2: Experimentally Validated Molecular Targets in Endometriosis

Target Category Specific Molecules Expression/Function in Endometriosis Experimental Validation
Fibrosis-Related Proteins TGFBR1, α-SMA, FAP, FN1, Collagen1 Elevated in ectopic lesions [42] Western blot across independent samples
E3 Ubiquitin Ligase TRIM33 mRNA and protein levels reduced in endometriotic tissues [42] [43] siRNA knockdown in hESCs promoted TGFBR1/p-SMAD2/α-SMA/FN1
Extracellular Matrix Components COL1A1, COL6A2, LAMC3, NID2 Dysregulated in endometriosis MenSCs [44] Proteomic analysis (UPLC-MS/MS) with p < 0.05
Transcription Factors ATF3, ID1, ID3, FOSB, SNAI1, NR4A1 Protein-protein interaction enrichment (p < 1.0 × 10-16) [44] RNA-seq of menstrual mesenchymal stem cells

Experimental Protocols

Trans-Ancestry GWAS Meta-Analysis Protocol

Principle: Large-scale meta-analysis of genome-wide association studies across diverse populations enhances power to detect risk loci and enables fine-mapping of causal variants [21] [19].

Sample Requirements:

  • Minimum 15,000 cases and 150,000 controls recommended for sufficient power
  • Inclusion of both European and East Asian ancestries (or other diverse populations)
  • Surgical confirmation of endometriosis (rAFS staging) for cases
  • Population-matched controls without endometriosis diagnosis

Quality Control Steps:

  • Individual Cohort QC:
    • Sample call rate > 97%
    • SNP call rate > 95%
    • Hardy-Weinberg equilibrium p > 1 × 10-6 in controls
    • Relatedness removal (pi-hat < 0.2)
    • Population stratification assessment using principal components

  • Imputation:

    • Utilize 1000 Genomes Project Phase 3 or TOPMed reference panels
    • Apply standard pre-phasing algorithms (e.g., SHAPEIT, Eagle)
    • Use imputation software (e.g., IMPUTE4, Minimac4)
    • Retain variants with imputation quality score > 0.7

  • Association Analysis:

    • Perform logistic regression assuming additive genetic model
    • Adjust for principal components to account for population stratification
    • Apply genomic control to correct for residual stratification

  • Meta-Analysis:

    • Apply fixed-effects models using inverse-variance weighting
    • Utilize Han-Eskin random effects model (RE2) for heterogeneous variants
    • Apply genomic control to the final meta-analysis results
    • Genome-wide significance threshold: P < 5 × 10-8

Downstream Analysis:

  • Conditional analysis to identify secondary signals
  • Calculation of linkage disequilibrium scores
  • Heritability estimation using LD score regression
  • Functional annotation of significant loci using ENCODE and Roadmap Epigenomics

GWAS_workflow cluster_1 Cohort Preparation cluster_2 Quality Control cluster_3 Imputation & Analysis cluster_4 Meta-Analysis Cohort1 Cohort 1 European QC1 Sample QC (Call Rate > 97%) Cohort1->QC1 Cohort2 Cohort 2 East Asian Cohort2->QC1 Cohort3 Cohort 3 Other Ancestries Cohort3->QC1 QC2 Variant QC (HWE p > 1×10⁻⁶) QC1->QC2 QC3 Population Stratification QC2->QC3 Impute Imputation (1000G Reference) QC3->Impute Assoc Association Analysis Impute->Assoc Meta Fixed/Random Effects Meta-Analysis Assoc->Meta Sig Genome-Wide Significance Meta->Sig Results Risk Loci Identification (P < 5×10⁻⁸) Sig->Results

Figure 1: Trans-ancestry GWAS meta-analysis workflow detailing cohort preparation through to risk loci identification

Integrated Transcriptomic and Proteomic Profiling Protocol

Principle: Parallel RNA sequencing and proteomic analysis of matched tissues identifies concordant molecular pathways and reveals post-transcriptional regulatory mechanisms in endometriosis pathogenesis [42] [44].

Sample Collection and Preparation:

  • Tissue Acquisition:
    • Collect ectopic (EC), eutopic (EU), and control endometria (NC) following standardized protocols
    • Immediate flash-freezing in liquid nitrogen, storage at -80°C
    • Clinical annotation: age, menstrual phase, rAFS stage, infertility status

  • RNA Sequencing:

    • RNA Extraction: Use TRIzol reagent with DNase treatment
    • Quality Control: RNA Integrity Number (RIN) > 7.0 on Agilent Bioanalyzer
    • Library Preparation: Poly-A selection, fragmentation, cDNA synthesis
    • Sequencing: Illumina platform, 30-50 million paired-end reads per sample
    • Differential Expression: DESeq2 with FDR < 0.1, fold change > 2

  • Proteomic Analysis (DIA-PASEF):

    • Protein Extraction: Lysis in 8M urea buffer with protease/phosphatase inhibitors
    • Digestion: Trypsin digestion after reduction and alkylation
    • Liquid Chromatography: Nanoflow UHPLC system with C18 column
    • Mass Spectrometry: TimSTOF Pro with PASEF enabled
    • Data Analysis: Spectronaut or DIA-NN for quantification
    • Differential Expression: T-test with p < 0.05, fold change > 1.5

Multi-Omics Integration:

  • Concordance Analysis: Identify genes with coordinated mRNA-protein changes
  • Pathway Enrichment: GO, KEGG, and Reactome analysis of concordant molecules
  • Network Analysis: Protein-protein interaction networks using STRING-db

Ubiquitylomics Profiling for Post-Translational Modification Analysis

Principle: Comprehensive identification of ubiquitination sites reveals post-translational regulatory mechanisms in endometriosis fibrosis [42] [43].

Sample Preparation:

  • Protein Extraction and Digestion:
    • Homogenize tissues in urea lysis buffer with N-ethylmaleimide
    • Reduce with DTT (5mM, 30min), alkylate with iodoacetamide (15mM, 30min)
    • Trypsin digestion (1:50 w/w, 37°C, 16h)

  • Ubiquitinated Peptide Enrichment:

    • Use anti-K-ε-GG antibody-conjugated beads
    • Incubate with digested peptides (2h, 4°C)
    • Wash with PBS and ice-cold water
    • Elute with 0.1% TFA

  • LC-MS/MS Analysis:

    • Chromatography: EASY-nLC 1200 system with analytical column
    • Gradient: 120min from 4% to 90% acetonitrile
    • Mass Spectrometry: Q-Exactive HF-X; MS1: 70,000 resolution; MS2: 15,000 resolution
    • Data Acquisition: Data-dependent acquisition (top 20) or data-independent acquisition

  • Data Processing:

    • Database Search: MaxQuant against UniProt human database
    • Ubiquitination Site Localization: Score > 0.75
    • Quantification: LFQ intensity with minimum ratio count 2

Functional Validation:

  • Western Blotting: Confirm ubiquitination and protein levels
  • siRNA Knockdown: Target identified E3 ligases (e.g., TRIM33) in primary hESCs
  • Fibrosis Assays: Measure collagen deposition, α-SMA expression

Pathway Integration and Analytical Framework

multiomics_pathway cluster_regulatory Regulatory Mechanisms cluster_molecular Molecular Phenotypes cluster_biological Endometriosis Pathogenesis GWAS GWAS Risk Variants eQTL eQTL Analysis GWAS->eQTL mQTL mQTL Analysis (196 CpG sites) GWAS->mQTL pQTL pQTL Analysis (7 proteins) GWAS->pQTL Transcriptome Transcriptome (41 DEGs) eQTL->Transcriptome mQTL->Transcriptome Proteome Proteome (8,032 proteins) pQTL->Proteome Fibrosis Fibrosis Pathway (41 ubiquitinated proteins) Transcriptome->Fibrosis Hormone Hormone Signaling (ESR1, FSHB) Transcriptome->Hormone ECM ECM Organization (COL1A1, COL6A2) Transcriptome->ECM Proteome->Fibrosis Proteome->Hormone Proteome->ECM Ubiquitylome Ubiquitylome (1,647 Kub sites) Ubiquitylome->Fibrosis Outcome Endometriosis Phenotype (rAFS Stage III/IV) Fibrosis->Outcome Hormone->Outcome ECM->Outcome

Figure 2: Multi-omics integration framework connecting genetic variants to molecular and clinical phenotypes

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Research Reagents for Multi-Omics Endometriosis Studies

Reagent Category Specific Products Application Key Features
Nucleic Acid Extraction TRIzol Reagent (Magen Biotech) [42] RNA isolation for transcriptomics Maintains RNA integrity; compatible with multiple sample types
Library Preparation ABclonal mRNA-seq Lib Prep Kit [42] RNA-seq library construction Poly-A selection; fragmentation optimization; dual-index barcoding
Proteomics Digestion Sequencing-grade trypsin [42] Protein digestion for MS High specificity; low autolysis; compatible with ubiquitination studies
Ubiquitin Enrichment Anti-K-ε-GG antibody beads [42] Ubiquitylomics profiling High-affinity antibody; specific ubiquitinated peptide enrichment
Chromatography UHPLC systems (EASY-nLC) [42] [44] Peptide separation Nanoflow capabilities; high reproducibility; acetonitrile gradients
Mass Spectrometry TimSTOF Pro (Bruker); Q-Exactive HF-X (Thermo) [42] Proteomic/ubiquitylomic analysis High resolution; PASEF capability; high sensitivity
Cell Culture Primary human endometrial stromal cells (hESCs) [42] [43] Functional validation Primary cell model; relevant pathophysiology
Gene Silencing TRIM33 siRNA [42] [43] E3 ligase functional studies Target-specific; high knockdown efficiency
Validation Antibodies Anti-TGFBR1, anti-α-SMA, anti-FN1 [42] Western blot confirmation Target-specific; validated for endometriosis tissues
Neodymium;ZINCNeodymium;ZINC Research Compound|NdZnNeodymium;ZINC (NdZn5) for research applications in materials science, agriculture, and catalysis. This product is For Research Use Only (RUO), not for human or veterinary use.Bench Chemicals

Data Analysis and Computational Tools

Multi-Omics Integration Pipeline:

  • Summary-data-based Mendelian Randomization (SMR):
    • Purpose: Test causal relationships between gene expression/protein levels and endometriosis
    • Software: SMR v1.3.1 with HEIDI test for pleiotropy vs. linkage
    • Parameters: cis-QTL window ±1000kb; P-value threshold 5.0×10-8

  • Colocalization Analysis:

    • Purpose: Identify shared genetic variants between QTLs and GWAS signals
    • Software: R package 'coloc' with default priors
    • Thresholds: Posterior probability H4 (PPH4) > 0.5 indicates colocalization

  • Pathway and Network Analysis:

    • Functional Enrichment: clusterProfiler for GO and KEGG terms
    • Protein-Protein Interaction: STRING-db (confidence > 0.7)
    • Multi-omics Visualization: Cytoscape with Omics Visualizer app

Quality Control Metrics:

  • Transcriptomics: >70% mapping rate; >30 million reads/sample
  • Proteomics: FDR < 1% at protein and peptide levels
  • Ubiquitylomics: Localization probability > 0.75 for ubiquitination sites

The integrated application of transcriptome-wide and proteome-wide association studies within trans-ancestry meta-analysis frameworks provides unprecedented resolution for elucidating endometriosis pathogenesis. The experimental protocols detailed herein enable researchers to bridge the gap between genetic susceptibility and functional pathophysiology, with particular emphasis on post-translational regulatory mechanisms such as ubiquitination that drive critical disease processes like fibrosis. The standardized methodologies, analytical frameworks, and reagent solutions presented offer a comprehensive toolkit for advancing our understanding of endometriosis and identifying novel therapeutic targets for this complex gynecological disorder.

The application of trans-ancestry meta-analysis methods in genome-wide association studies (GWAS) for endometriosis represents a transformative approach for identifying novel therapeutic targets and enabling drug repurposing opportunities. Endometriosis, a common gynecological disorder affecting approximately 10% of reproductive-age women globally, demonstrates significant genetic underpinnings with a heritability estimated at around 50% [45] [2]. Despite this strong genetic component, traditional GWAS approaches have explained only a limited fraction of disease variance, highlighting the need for more sophisticated analytical frameworks that can integrate diverse ancestral datasets to improve statistical power and resolution [2] [12].

Drug repurposing—identifying new therapeutic uses for existing medications—has emerged as an economically efficient strategy that leverages established safety profiles to accelerate treatment development. The average cost to market a repurposed drug is approximately $300 million, substantially less than the $2–3 billion typically required for novel drug development [46]. Genetic evidence significantly enhances this process, with drug mechanisms supported by human genetic evidence demonstrating a 2.6 times greater probability of clinical success compared to those without such support [47]. This review presents integrated protocols and application notes for conducting drug repurposing analyses within the context of trans-ancestry endometriosis research, providing researchers with methodological frameworks for translating genetic discoveries into therapeutic hypotheses.

Trans-Ancestry Meta-Analysis Framework

Study Design and Dataset Integration

The foundation of effective drug repurposing analyses begins with robust trans-ancestry genetic study design. Recent advancements have demonstrated the power of large-scale, diverse cohorts in endometriosis genetics. A 2025 multi-ancestry GWAS of approximately 1.4 million women, including 105,869 endometriosis cases, identified 80 genome-wide significant associations, 37 of which were novel [12]. This study established the feasibility of expanding endometriosis locus discovery across ancestries while enabling the dissection of symptom-specific genetic effects.

Table 1: Summary of Recent Endometriosis Genetic Studies Utilizing Multi-Ancestry Approaches

Study Sample Size Cases Key Findings Ancestries Represented
Multi-ancestry GWAS (2025) [12] ~1.4 million women 105,869 80 significant loci (37 novel), 5 first adenomyosis variants Multiple, unspecified
Taiwanese-Han GWAS (2024) [45] 30,734 2,794 5 significant susceptibility loci (2 novel) Taiwanese-Han
Combinatorial Analysis (2025) [2] UK Biobank + All of Us Unspecified 1,709 disease signatures comprising 2,957 unique SNPs White European, multi-ancestry validation

The integration of datasets across diverse populations requires careful consideration of population stratification, imputation quality, and ancestral representation. The Taiwanese-Han GWAS exemplifies the value of population-specific analyses, identifying novel susceptibility loci while replicating known associations from European and Japanese cohorts [45]. Such studies highlight both shared genetic architecture across populations and population-specific risk factors that may inform targeted therapeutic development.

Protocol: Trans-Ancestry Meta-Analysis Workflow

Materials:

  • GWAS summary statistics from multiple ancestral groups
  • High-quality reference panels (e.g., 1000 Genomes Phase 3)
  • Genotype data with precise ancestral annotation
  • Computational resources for large-scale genetic analyses

Procedure:

  • Dataset Harmonization: Align GWAS summary statistics to common reference genome build, strand orientation, and variant identification system.
  • Quality Control: Apply filters for imputation quality (INFO score >0.8), minor allele frequency (MAF >0.01), and Hardy-Weinberg equilibrium (p > 1×10^-6).
  • Population Structure Assessment: Conduct principal component analysis to characterize and account for population stratification within and between cohorts.
  • Meta-Analysis Implementation: Utilize fixed-effects or random-effects models depending on heterogeneity estimates between ancestral groups.
  • Heterogeneity Quantification: Calculate I² statistics to identify loci with divergent effects across ancestries.
  • Fine-Mapping Resolution: Apply statistical fine-mapping methods (e.g., SUSIE, FINEMAP) to identify causal variants within associated loci.

This workflow enables improved fine-mapping resolution by leveraging differential linkage disequilibrium patterns across populations, potentially narrowing candidate causal variants from hundreds to single digits at associated loci [12].

From Genetic Associations to Druggable Targets

Gene Prioritization and Functional Validation

Following the identification of association signals through trans-ancestry meta-analysis, the next critical step involves prioritizing genes with the greatest potential as therapeutic targets. Multi-omic integration approaches have demonstrated particular utility in this process. A 2025 study integrating transcriptomic, epigenetic, and proteomic data revealed that genetic variation influences endometriosis risk through regulation across multiple tissues, converging on pathways involved in immune regulation, tissue remodeling, and cell differentiation [12].

Table 2: Experimentally Validated Endometriosis Drug Repurposing Candidates

Target Drug Candidate Evidence Level Proposed Mechanism Source
RSPO3 Not specified MR + experimental validation Causal role in endometriosis pathogenesis [6]
FLT1 Not specified MR + experimental validation Potential involvement in vascular function [6]
Multiple novel genes Multiple possibilities Combinatorial analytics Pathways including autophagy and macrophage biology [2]

Mendelian randomization (MR) analysis has emerged as a powerful method for establishing causal relationships between putative targets and endometriosis risk. A recent MR study investigating blood metabolites and plasma proteins identified RSPO3 and FLT1 as potentially causally associated with endometriosis [6]. Subsequent experimental validation through ELISA, RT-qPCR, and Western blotting confirmed elevated RSPO3 levels in both plasma and lesion tissues of endometriosis patients compared to controls [6].

Protocol: Mendelian Randomization for Target Validation

Materials:

  • Genetic instruments (cis-pQTLs, eQTLs, metabolite QTLs)
  • Endometriosis GWAS summary statistics
  • MR analysis software (TwoSampleMR, MR-Base)
  • High-performance computing resources

Procedure:

  • Instrument Selection: Identify genetic variants strongly associated (p < 5×10^-8) with exposure (e.g., protein levels) located in cis regions (±250 kb from gene transcription start site).
  • LD Clumping: Apply linkage disequilibrium pruning (r² < 0.001, distance = 1 Mb) to ensure independence of instruments.
  • Strength Assessment: Calculate F-statistics for each instrument, excluding variants with F < 10 to mitigate weak instrument bias.
  • MR Analysis Implementation: Apply inverse-variance weighted method as primary analysis, supplemented by MR-Egger, weighted median, and MR-PRESSO methods.
  • Sensitivity Analyses: Assess pleiotropy through MR-Egger intercept tests and Cochran's Q statistics.
  • Colocalization Analysis: Determine if protein/exposure and endometriosis share causal variants (posterior probability >70% considered supportive).

This MR framework establishes genetic support for target-disease relationships, which corresponds to a 2.6-fold greater probability of clinical success compared to non-genetically supported targets [47].

Combinatorial Analytics for Novel Target Discovery

Beyond Single-Variant Analyses

Traditional GWAS approaches have explained only approximately 5% of endometriosis disease variance, highlighting the limitations of single-variant analysis frameworks [2]. Combinatorial analytics represents a paradigm shift by examining how multiple genetic variants interact to influence disease risk. A 2025 study applying combinatorial analytics to endometriosis identified 1,709 disease signatures comprising 2,957 unique SNPs in combinations of 2-5 SNPs [2]. These signatures demonstrated high reproducibility (58-88%) in multi-ancestry validation cohorts, with reproducibility rates reaching 80-88% for higher frequency signatures (>9% frequency) [2].

Pathway enrichment analysis of these combinatorial signatures revealed involvement in biologically relevant processes including cell adhesion, proliferation and migration, cytoskeleton remodeling, angiogenesis, fibrosis, and neuropathic pain [2]. Notably, this approach identified 75 novel gene associations not previously linked to endometriosis through GWAS, highlighting its potential for uncovering new biology and therapeutic opportunities.

Protocol: Combinatorial Analysis Implementation

Materials:

  • Individual-level genotype data
  • Combinatorial analytics platform (e.g., PrecisionLife)
  • High-performance computing cluster
  • Pathway analysis databases (GO, KEGG, Reactome)

Procedure:

  • Dataset Preparation: Process genotype data through standard quality control pipelines, maintaining individual-level data structure.
  • Combinatorial Association Testing: Analyze combinations of 2-5 SNPs for association with endometriosis case-control status, correcting for multiple testing.
  • Signature Validation: Test significant combinatorial signatures in independent validation cohorts with diverse ancestries.
  • Gene Mapping: Annotate SNPs from reproducible signatures to genes based on genomic position and functional annotation.
  • Pathway Enrichment Analysis: Identify biological pathways overrepresented in genes from combinatorial signatures.
  • Network Analysis: Construct gene-protein interaction networks to identify hub genes and key regulatory nodes.

The high reproducibility rates of combinatorial signatures across diverse ancestries (66-76% in non-white European sub-cohorts) underscores their utility in trans-ancestry drug repurposing analyses [2].

Research Reagent Solutions

Table 3: Essential Research Reagents for Endometriosis Drug Repurposing Studies

Reagent/Category Specific Examples Function/Application Evidence Source
Protein Detection Human R-Spondin3 ELISA Kit Quantitative measurement of RSPO3 protein levels in patient plasma [6]
Gene Expression Analysis RT-qPCR reagents Validation of gene expression differences in patient tissues [6]
Genetic Datasets UK Biobank, FinnGen, Taiwan Biobank Source of GWAS summary statistics and individual-level genetic data [45] [12] [6]
Protein QTL Resources deCODE GWAS, SOMAscan data Genetic instruments for Mendelian randomization analyses [46] [6]
Pathway Analysis Tools GO, KEGG, Reactome databases Functional annotation of candidate genes and enrichment testing [2]
Computational Platforms PrecisionLife combinatorial analytics Identification of multi-SNP disease signatures [2]

Visualization of Analytical Workflows

Trans-Ancestry Drug Repurposing Pipeline

pipeline Genetic Data Collection Genetic Data Collection Quality Control Quality Control Genetic Data Collection->Quality Control Trans-ancestry Meta-analysis Trans-ancestry Meta-analysis Variant-to-Gene Mapping Variant-to-Gene Mapping Trans-ancestry Meta-analysis->Variant-to-Gene Mapping Gene Prioritization Gene Prioritization Mendelian Randomization Mendelian Randomization Gene Prioritization->Mendelian Randomization Combinatorial Analytics Combinatorial Analytics Gene Prioritization->Combinatorial Analytics Functional Validation Functional Validation Drug Repurposing Candidates Drug Repurposing Candidates Functional Validation->Drug Repurposing Candidates Population Structure Assessment Population Structure Assessment Quality Control->Population Structure Assessment Population Structure Assessment->Trans-ancestry Meta-analysis Variant-to-Gene Mapping->Gene Prioritization Mendelian Randomization->Functional Validation Combinatorial Analytics->Functional Validation

Mendelian Randomization Framework

MR Exposure Data (e.g., pQTLs) Exposure Data (e.g., pQTLs) Instrument Selection Instrument Selection Exposure Data (e.g., pQTLs)->Instrument Selection Outcome Data (Endometriosis GWAS) Outcome Data (Endometriosis GWAS) Outcome Data (Endometriosis GWAS)->Instrument Selection MR Analysis MR Analysis Instrument Selection->MR Analysis Sensitivity Analyses Sensitivity Analyses MR Analysis->Sensitivity Analyses Validated Target Validated Target Colocalization Colocalization Sensitivity Analyses->Colocalization Colocalization->Validated Target

The integration of trans-ancestry meta-analysis methods with sophisticated drug repurposing frameworks presents unprecedented opportunities for accelerating therapeutic development in endometriosis. The protocols and application notes outlined herein provide researchers with comprehensive methodologies for translating genetic discoveries across diverse populations into clinically actionable therapeutic hypotheses. The remarkable reproducibility of combinatorial disease signatures across ancestries [2], coupled with the robust clinical success advantage for genetically supported drug targets [47], underscores the transformative potential of these approaches.

Future directions in this field will likely focus on expanding diverse ancestral representation in genetic studies, deepening multi-omic integration, and developing more sophisticated in silico models of drug-target interactions. As these methodologies mature, they will increasingly enable precision medicine approaches in endometriosis treatment, potentially targeting specific molecular subtypes across different ancestral backgrounds. The continuing growth of genetic datasets and analytical innovations promises to further accelerate the identification of repurposing opportunities, ultimately reducing the diagnostic and therapeutic delays that have long plagued endometriosis patients.

Overcoming Analytical Challenges in Cross-Ancestry Genetic Studies

Endometriosis is a common, complex gynecological disorder influenced by multiple genetic and environmental factors, with an estimated heritability of approximately 51% based on twin studies [19]. Genome-wide association studies (GWAS) have successfully identified numerous genetic loci associated with endometriosis risk, revealing a polygenic architecture characterized by significant heterogeneity across ancestral populations [19] [21]. This application note examines the genetic architecture heterogeneity in endometriosis, focusing on effect size variations and linkage disequilibrium (LD) patterns across populations, and provides detailed protocols for trans-ancestry meta-analysis methods to enhance discovery and validation of risk loci.

Established Genetic Risk Loci

GWAS meta-analyses have identified multiple genomic regions associated with endometriosis risk. The table below summarizes key loci and their heterogeneous effects across populations:

Table 1: Effect Size Variations of Endometriosis Risk Loci Across Populations

Locus/Nearest Gene Chromosome Lead SNP Effect Size (OR) European Effect Size (OR) Japanese P-Value Key Biological Pathway
WNT4 1p36.12 rs7521902 1.16 1.20 4.6×10⁻⁸ Development, steroidogenesis
GREB1 2p25.1 rs13394619 1.14 1.08 6.1×10⁻⁸ Cell growth, estrogen regulation
Intergenic 2p14 rs4141819 1.12 1.11 8.5×10⁻⁸ Unknown
ID4 6p22.3 rs7739264 1.11 1.10 3.6×10⁻¹⁰ Development, differentiation
Intergenic 7p15.2 rs12700667 1.22 1.22 9.3×10⁻¹⁰ Unknown
CDKN2B-AS1 9p21.3 rs1537377 1.10 1.09 2.4×10⁻⁹ Cell cycle regulation
VEZT 12q22 rs10859871 1.13 1.14 5.1×10⁻¹³ Cell adhesion
FN1 2q35 rs1250241 1.23* - 2.99×10⁻⁹ Extracellular matrix
ESR1 6q25.1 rs1971256 1.09 - 3.74×10⁻⁸ Estrogen receptor
FSHB 11p14.1 rs74485684 1.11 - 2.00×10⁻⁸ Hormone regulation

*Effect sizes marked with * are for Stage III/IV (Grade B) endometriosis only [19] [21] [48].

Heterogeneity Patterns Across Ancestries

Trans-ancestry analyses reveal distinct patterns of heterogeneity. The CDKN2B-AS1 locus (rs10965235) exemplifies population-specific effects, demonstrating a substantial effect (OR=1.44) in Japanese populations but being monomorphic in European populations [48]. Conversely, the WNT4 locus shows consistent effects across European (OR=1.16) and Japanese (OR=1.20) ancestries [48]. A notable finding is that most loci exhibit stronger effect sizes in Stage III/IV endometriosis, suggesting they primarily influence the development of moderate to severe disease [19].

Table 2: Heterogeneity Metrics for Key Endometriosis Loci

Locus Cochran's Q P-value I² Statistic Effect Size Difference EUR vs. JPN Consistent Direction Across Populations
2p14 (rs4141819) <0.005 78.3% 0.01 Yes
2p25.1 (rs13394619) 0.12 45.2% 0.06 Yes
7p15.2 (rs12700667) 0.87 0% 0.00 Yes
12q22 (rs10859871) 0.23 29.7% -0.01 Yes

Protocol for Trans-ancestry Meta-Analysis in Endometriosis Research

Stage 1: Dataset Preparation and Quality Control

Materials and Research Reagents

Table 3: Essential Research Reagents and Computational Tools

Item Specification Function/Application
Genotyping Array Illumina Global Screening Array, Affymetrix Axiom Biobank Array Genome-wide SNP genotyping
Imputation Reference Panel 1000 Genomes Project Phase 3, TOPMed Genotype imputation to increase variant coverage
Quality Control Software PLINK 2.0, QCTOOL, SNPTEST Data filtering, quality control, and format conversion
Ancestry Determination Software ADMIXTURE, EIGENSOFT Population structure analysis and ancestry assignment
Summary Statistics GWAS Catalog, EBI Biobank Access to published endometriosis GWAS data
Procedure

  • Cohort Selection and Ancestry Stratification

    • Collect endometriosis case-control datasets from diverse ancestral populations (European, East Asian, African, etc.)
    • Diagnoses should be surgically confirmed using revised American Fertility Society (rAFS) classification where possible
    • Stratify participants using principal component analysis (PCA) against reference populations (1000 Genomes Project)

  • Genotype Quality Control

    • Apply standard QC filters: call rate >98%, Hardy-Weinberg equilibrium P>1×10⁻⁶, minor allele frequency >1%
    • Remove related individuals (pi-hat >0.2) and population outliers
    • Conduct sex chromosome checks to identify sample discrepancies

  • Genotype Imputation

    • Pre-phasing using SHAPEIT or Eagle
    • Imputation against appropriate reference panel using IMPUTE4 or Minimac4
    • Retain well-imputed variants (info score >0.7)

Stage 2: Trans-ancestry Meta-Analysis

G Ancestry-Specific GWAS Ancestry-Specific GWAS Summary Statistics Summary Statistics Ancestry-Specific GWAS->Summary Statistics Quality Control Quality Control Summary Statistics->Quality Control Effect Size Harmonization Effect Size Harmonization Quality Control->Effect Size Harmonization LD Score Estimation LD Score Estimation Effect Size Harmonization->LD Score Estimation Fixed-Effects Meta-Analysis Fixed-Effects Meta-Analysis LD Score Estimation->Fixed-Effects Meta-Analysis Heterogeneity Assessment Heterogeneity Assessment Fixed-Effects Meta-Analysis->Heterogeneity Assessment Random-Effects Model (if needed) Random-Effects Model (if needed) Heterogeneity Assessment->Random-Effects Model (if needed) Population-Specific Heritability Estimation Population-Specific Heritability Estimation Random-Effects Model (if needed)->Population-Specific Heritability Estimation Cross-Population Genetic Correlation Cross-Population Genetic Correlation Population-Specific Heritability Estimation->Cross-Population Genetic Correlation

Figure 1: Trans-ancestry Meta-analysis Workflow

Materials and Instruments
  • Software: METAL, MR-MEGA, MANTRA, LDSC, PRSice2
  • Computing Resources: High-performance computing cluster with minimum 16GB RAM per core
Procedure

  • Effect Size Harmonization

    • Align effect alleles across cohorts using reference panel
    • Standardize effect sizes (beta coefficients and standard errors)
    • Account for differential LD patterns between populations

  • Fixed-Effects Meta-Analysis

    • Apply inverse variance-weighted fixed-effects model
    • Calculate summary statistics for each variant across all cohorts
    • Use genomic control to adjust for residual population stratification (λ~1.0)

  • Heterogeneity Quantification

    • Calculate Cochran's Q statistic for each variant
    • Compute I² index to quantify proportion of heterogeneity
    • Apply Han and Eskin random-effects model (RE2) for variants with significant heterogeneity (P<0.05)

  • Genetic Correlation Analysis

    • Estimate cross-population genetic correlation using LD Score regression
    • Calculate polygenic risk scores (PRS) in independent cohorts

Stage 3: Functional Annotation and Validation

Materials and Reagents
  • Functional Genomics Data: ENCODE, Roadmap Epigenomics, GTEx eQTLs
  • Pathway Analysis Tools: DEPICT, MAGMA, GARFIELD
Procedure

  • Variant Prioritization

    • Annotate significant variants with functional genomic data
    • Identify variants with consistent directional effects across ancestries
    • Prioritize coding variants and regulatory elements with epigenetic evidence

  • Colocalization Analysis

    • Test for colocalization of GWAS signals with eQTLs and meQTLs
    • Use Bayesian approaches (e.g., COLOC) to calculate posterior probabilities

  • Polygenic Risk Score Assessment

    • Develop ancestry-specific PRS using clumping and thresholding
    • Evaluate PRS transferability across populations
    • Assess variance explained in independent validation cohorts

Case Study: Trans-ethnic Analysis of the 7p15.2 Locus

The 7p15.2 locus (rs12700667) provides an exemplary case of consistent genetic effects across populations. Initial discovery in European populations (OR=1.20, P=1.4×10⁻⁹) [48] showed successful replication in Japanese populations (OR=1.22, P=3.6×10⁻³) [48]. The trans-ancestry meta-analysis bolstered the significance (P=9.3×10⁻¹⁰) with no evidence of heterogeneity (I²=0%) [19] [48].

In contrast, the 2p14 locus (rs4141819) exhibited significant heterogeneity (P<0.005) [19], suggesting potential population-specific causal variants or interactions with environmental factors. This heterogeneity necessitates careful interpretation in cross-population genetic risk prediction.

Advanced Applications: Integrating Multi-omics Data

Mendelian Randomization for Causal Inference

Mendelian randomization (MR) analyses have revealed causal relationships between serum lipid levels and endometriosis risk, particularly for triglycerides (TG) [22]. Drug-target MR has identified potential therapeutic targets including LPL, PPARA, ANGPTL3, and APOC3 [22].

G Genetic Variants (IVs) Genetic Variants (IVs) Lipid Metabolism Lipid Metabolism Genetic Variants (IVs)->Lipid Metabolism cis-pQTLs Endometriosis Risk Endometriosis Risk Lipid Metabolism->Endometriosis Risk Estrogen Signaling Estrogen Signaling Lipid Metabolism->Estrogen Signaling LPL/ANGPTL3 Inhibition LPL/ANGPTL3 Inhibition LPL/ANGPTL3 Inhibition->Lipid Metabolism Lesion Establishment Lesion Establishment Estrogen Signaling->Lesion Establishment

Figure 2: Mendelian Randomization Reveals Causal Pathways

Transcriptomics and Pathway Analysis

Integration of endometriosis gene expression data has revealed dysregulated pathways in ectopic lesions, including:

  • Steroid hormone biosynthesis (ESR1, CYP19A1, HSD17B1) [21]
  • WNT signaling (WNT4, RSPO3) [6]
  • Cell adhesion and migration (VEZT, FN1) [19] [21]
  • Angiogenesis and inflammation (VEGF, GREB1) [49] [50]

Phylogenetic analysis of gene expression patterns demonstrates that endometriosis lesions represent clonal outgrowths with accumulated genetic and epigenetic alterations [50], highlighting the importance of considering lesion heterogeneity in molecular studies.

Addressing genetic architecture heterogeneity is crucial for advancing endometriosis research. The protocols outlined herein enable robust trans-ancestry meta-analysis that accounts for effect size variations and LD patterns across populations. Key considerations include:

  • Ancestry-aware analysis improves discovery and fine-mapping of causal variants
  • Stratification by disease severity enhances power for detecting stage-specific loci
  • Integration of functional genomics enables biological interpretation of heterogeneous signals
  • Mendelian randomization provides causal insights into endometriosis pathophysiology

These approaches facilitate the development of more accurate polygenic risk scores across diverse populations and identify potential therapeutic targets for this complex gynecological disorder.

Managing Population Stratification and Ancestry-Specific Confounders

Trans-ancestry genome-wide association studies (GWAS) meta-analysis has emerged as a powerful approach for enhancing the discovery of genetic loci and improving the fine-mapping of causal variants for complex diseases. Within endometriosis research, this method is particularly valuable given the condition's high heritability (estimated at ~52%) and its global prevalence affecting 5-10% of reproductive-aged women [19]. However, the integration of datasets from diverse ancestral backgrounds introduces significant methodological challenges, primarily concerning population stratification and ancestry-specific confounders.

Population stratification occurs when differences in allele frequency between cases and controls arise from systematic ancestry differences rather than disease association. In trans-ancestry meta-analyses, this confounding can be substantially more pronounced than in single-ancestry studies due to the greater genetic diversity across populations. Failure to adequately account for these effects can produce spurious associations and reduce the portability of genetic risk scores across populations. This application note provides detailed protocols and analytical frameworks for managing these critical challenges specifically within the context of endometriosis GWAS research.

Statistical Methods for Detection and Correction

Principal Component Analysis (PCA) and Genetic Relatedness Matrices

Principal Component Analysis remains a foundational approach for detecting and correcting population stratification. The method works by identifying the major axes of genetic variation in the dataset, which typically correspond to ancestral backgrounds.

Protocol: PCA Implementation for Trans-ancestry Endometriosis GWAS

  • Variant Pruning: Select a set of independent SNPs through linkage disequilibrium (LD) pruning (r² < 0.1 within 50-SNP sliding windows)
  • PCA Calculation: Compute principal components using the pruned SNP set across all combined samples
  • Stratification Assessment: Visually inspect PC plots to identify genetic clusters corresponding to different ancestries
  • Covariate Integration: Include the significant PCs as covariates in the association analysis model

In practice, studies such as the trans-ancestry meta-analysis of endometriosis that identified the WNT4 and GREB1 loci have successfully employed PCA to distinguish European and East Asian ancestry groups [48]. The variance explained by each PC should be carefully evaluated, with typically 5-10 PCs retained as covariates.

Genomic Control and LD Score Regression

Genomic control and LD score regression provide complementary approaches to quantify and correct for residual population stratification.

Protocol: Genomic Inflation Assessment

  • Calculate λGC: Compute the genomic control inflation factor (λGC) from the median test statistic
  • Intercept Estimation: Apply LD score regression to distinguish polygenic architecture from stratification
  • Inflation Correction: Apply genomic control correction when λGC deviates substantially from 1.0 (typically >1.05)

For the endometriosis trans-ancestry meta-analysis by Painter et al., the genomic inflation factor was carefully monitored and reported, ensuring that the identified associations at loci such as 7p15.2 were not driven by stratification [19].

Meta-analysis Methods for Diverse Ancestries

Fixed-effects and random-effects models present different advantages for trans-ancestry meta-analysis, with the choice dependent on between-population heterogeneity.

Protocol: Trans-ancestry Meta-analysis Implementation

  • Stratified Analysis: Perform GWAS separately within each ancestry group
  • Heterogeneity Testing: Calculate Cochran's Q statistic and I² index to quantify heterogeneity
  • Model Selection: Apply fixed-effects models when heterogeneity is low (I² < 25%) and random-effects models when heterogeneity is substantial
  • Population-specific Effects: Report ancestry-specific odds ratios and frequency estimates for significant loci

Notably, the endometriosis meta-analysis by Rahmioglu et al. demonstrated remarkable consistency across populations for seven out of nine reported loci, supporting the use of fixed-effects models for these variants [19].

Table 1: Statistical Methods for Managing Population Stratification

Method Application Advantages Limitations
Principal Component Analysis Correcting ancestry differences in combined datasets Directly models continuous ancestry variation May not capture fine-scale population structure
Genomic Control Genome-wide correction of test statistics Simple implementation Overcorrection can reduce true positive signals
LD Score Regression Quantifying inflation from stratification vs. polygenicity Distinguishes biological signals from bias Requires LD reference panels for each ancestry
Random-Effects Meta-analysis Combining effects across heterogeneous populations Conservative when heterogeneity is present Reduced power compared to fixed-effects

Experimental Design Considerations

Sample Collection and Ancestry Ascertainment

Robust ancestry determination forms the foundation of effective stratification control in trans-ancestry studies.

Protocol: Standardized Ancestry Reporting

  • Self-reported Data: Collect detailed self-reported ethnicity using standardized categories
  • Genetic Ancestry Validation: Confirm ancestry genetically using reference panels (e.g., 1000 Genomes)
  • Ancestry-informative Markers: Utilize panels of ancestry-informative markers for precise classification
  • Sample Size Considerations: Ensure adequate representation from each ancestral group (>500 individuals per group recommended)

The successful trans-ancestry endometriosis study by Painter et al. included 9,039 cases and 27,343 controls of European ancestry and 2,467 cases and 5,335 controls of Japanese ancestry, demonstrating the scale needed for well-powered detection [19].

Quality Control Procedures

Rigorous quality control must be applied both within and across ancestral groups to prevent technical artifacts from masquerading as biological signals.

Protocol: Trans-ancestry QC Pipeline

  • Variant-level Filtering:
    • Apply call rate thresholds (<98%)
    • Implement Hardy-Weinberg equilibrium testing (P < 1×10⁻⁶)
    • Set minor allele frequency filters (MAF > 0.01)
  • Sample-level Filtering:
    • Remove samples with excessive heterozygosity or missingness
    • Exclude cryptic related individuals (Ï€ > 0.2)
    • Eliminate ancestry outliers based on genetic PCs

In the endometriosis GWAS by Albertsen et al., samples were restricted to those with ≥95% European ancestry based on ADMIXTURE analysis, reducing stratification concerns within the European cohort [51].

Table 2: Quality Control Thresholds for Trans-ancestry Endometriosis GWAS

QC Metric Threshold Rationale Tool Implementation
Sample Call Rate >98% Excludes poor-quality DNA samples PLINK, QCtools
Variant Call Rate >95% Removes poorly genotyped markers PLINK, VCFtools
Hardy-Weinberg Equilibrium P > 1×10⁻⁶ Filters genotyping errors PLINK, SNPTEST
Minor Allele Frequency >1% Ensures adequate power for association PLINK, GENESIS
Heterozygosity ±3SD from mean Identifies sample contamination PLINK, King
Relatedness π < 0.2 Prevents inflation from cryptic relatedness King, PLINK

Case Study: Endometriosis Trans-ancestry Meta-analysis

The 2023 trans-ancestry meta-analysis of endometriosis provides an illustrative example of successfully implemented stratification controls [48]. This study integrated data from European and Japanese populations, specifically examining consistency and heterogeneity of genetic effects.

Methodological Implementation

The analysis employed a multi-tiered approach to address population stratification:

  • Stratified Analysis: GWAS was performed separately within each ancestry group before meta-analysis
  • Heterogeneity Testing: Cochran's Q test was applied to identify loci with significant between-population heterogeneity
  • Effect Size Comparison: Directional consistency of effects across populations was assessed

The research identified significant heterogeneity at two independent inter-genic loci on chromosome 2 (rs4141819 and rs6734792), highlighting the importance of evaluating ancestry-specific effects rather than assuming uniform genetic architecture [19].

Key Findings and Validation

The meta-analysis confirmed six genome-wide significant loci with consistent effects across ancestries:

  • rs12700667 on 7p15.2 (P = 1.6×10⁻⁹)
  • rs7521902 near WNT4 (P = 1.8×10⁻¹⁵)
  • rs10859871 near VEZT (P = 4.7×10⁻¹⁵)
  • rs1537377 near CDKN2B-AS1 (P = 1.5×10⁻⁸)
  • rs7739264 near ID4 (P = 6.2×10⁻¹⁰)
  • rs13394619 in GREB1 (P = 4.5×10⁻⁸) [19]

These findings demonstrated that despite ancestry differences, substantial sharing of genetic risk factors exists for endometriosis, providing a rationale for trans-ancestry approaches.

Visualization of Analytical Workflows

Trans-ancestry GWAS Quality Control Pipeline

gwas_workflow start Raw Genotype Data (Multiple Ancestries) qc1 Sample-level QC (Call Rate, Sex Check, Heterozygosity) start->qc1 qc2 Variant-level QC (Call Rate, HWE, MAF) qc1->qc2 pc Population Stratification Analysis (PCA, Genetic Relatedness) qc2->pc strat Ancestry Group Definition (Genetic Clustering) pc->strat gwas Stratified GWAS (Per Ancestry Group) strat->gwas meta Trans-ancestry Meta-analysis (Heterogeneity Testing) gwas->meta results Validated Associations meta->results

Population Stratification Assessment Methods

stratification_methods assessment Population Stratification Assessment pc Principal Component Analysis (Visualization of Genetic Clusters) assessment->pc gc Genomic Control (λGC Calculation) assessment->gc ldsc LD Score Regression (Intercept Estimation) assessment->ldsc qc QC Metrics (Ancestry Outlier Removal) assessment->qc correction Appropriate Correction Method pc->correction gc->correction ldsc->correction qc->correction

The Scientist's Toolkit: Essential Research Reagents

Table 3: Key Research Reagents for Trans-ancestry Endometriosis GWAS

Reagent/Resource Function Example Implementation
Illumina Global Screening Array Genotyping platform for diverse populations Designed with content optimized for multi-ethnic studies, includes ancestry-informative markers
1000 Genomes Project Reference Population genetic reference panel Provides allele frequency data across 26 populations for ancestry determination and imputation
TOPMed Imputation Reference High-quality imputation panel Improves variant discovery in diverse populations, enhances fine-mapping resolution
PLINK 2.0 Whole-genome association analysis Performs QC, PCA, and basic association testing with efficient handling of large datasets
METAL Meta-analysis software Combines GWAS results across studies with heterogeneity testing and multiple weighting schemes
LDAK Heritability and stratification analysis Estimates SNP heritability and performs LD-adjusted kinship analysis
GENESIS Genetic association testing Accounts for population structure and relatedness in diverse cohorts using mixed models
GTEx Database Functional validation resource Provides expression quantitative trait loci (eQTL) data for tissue-specific functional annotation

Advanced Topics and Future Directions

Methodological Innovations

Recent methodological advances offer promising approaches for further improving trans-ancestry genetic studies of endometriosis:

Multi-trait Analysis of GWAS (MTAG) MTAG enables efficient cross-population analysis by incorporating genetic correlations between ancestries, potentially increasing power for detecting endometriosis risk loci with heterogeneous effects.

Genetic Risk Prediction Methods Methods like PRS-CSx incorporate trans-ancestry information to improve polygenic risk prediction across diverse populations, addressing the current limitation where most PRS display reduced portability across ancestry groups.

Integration with Functional Genomics

The integration of functional genomics data represents a critical frontier for understanding ancestry-specific effects in endometriosis:

Expression Quantitative Trait Loci (eQTL) Mapping Studies such as the Taiwanese endometriosis GWAS identified eQTL effects, with rs13126673 showing association with INTU expression in endometriotic tissues (P = 0.034) [52]. Such findings highlight the importance of context-specific functional data.

Colocalization Analysis Bayesian colocalization methods can determine whether genetic associations with endometriosis and molecular traits (e.g., gene expression, DNA methylation) share causal variants, helping prioritize candidate genes across ancestries.

Effective management of population stratification and ancestry-specific confounders is essential for robust trans-ancestry endometriosis research. The protocols and methodologies outlined in this application note provide a comprehensive framework for addressing these challenges, from study design and quality control to advanced statistical analysis. As endometriosis genetics continues to advance toward more diverse and inclusive sampling, these approaches will be increasingly critical for ensuring that genetic discoveries translate across ancestral backgrounds and benefit all populations equally. The remarkable consistency observed across ancestries for most endometriosis risk loci provides strong justification for continued trans-ancestry efforts, which promise to further elucidate the genetic architecture of this complex gynecological condition.

Optimizing Statistical Power in Underrepresented Ancestral Groups

Genomic research has revolutionized our understanding of complex diseases like endometriosis, yet significant disparities persist due to the underrepresentation of non-European populations in major biobanks and genome-wide association studies (GWAS). This ancestral bias creates substantial gaps in the equity and effectiveness of precision medicine approaches, particularly for conditions such as endometriosis that affect a global population. The current genomic databases, including The Cancer Genome Atlas (TCGA) and the GWAS Catalog, demonstrate a dramatic over-representation of individuals with European ancestry, with TCGA cancers having a median of 83% European ancestry individuals and the GWAS Catalog being approximately 95% European [53]. This imbalance severely limits the portability of genetic risk scores and therapeutic targets across diverse populations and restricts the fundamental understanding of disease biology that could be gained from analyzing ancestrally diverse genomic data.

The statistical consequences of this underrepresentation are profound. Model efficacy in genetic studies has been demonstrated to correlate directly with population sample size, meaning populations with little or no representation in training data experience larger disparities in disease model performance and garner minimal benefit from benchmark disease models [53]. Furthermore, European ancestry-based scores for genetic intolerance metrics are approaching saturation, meaning that simply adding more European-ancestry samples provides diminishing returns for variant discovery [54]. In contrast, increasing ancestral representation, rather than sample size alone, has been shown to critically drive the performance of key genomic metrics, with scores trained on African and Admixed American ancestral groups demonstrating higher resolution in detecting haploinsufficient and neurodevelopmental disease risk genes compared to scores trained on European ancestry groups [54]. For endometriosis research specifically, this ancestral bias presents a critical methodological challenge that requires specialized approaches to ensure equitable and statistically powerful research outcomes across all populations.

Quantitative Landscape: Statistical Power Deficits in Underrepresented Populations

Current Representation Deficits in Genomic Databases

Table 1: Ancestral Representation in Major Genomic Databases

Database/Resource European Ancestry African Ancestry East Asian Ancestry South Asian Ancestry Admixed American Citation
GWAS Catalog ~95% Not specified Not specified Not specified Not specified [53]
TCGA (median across cancers) 83% (range 49-100%) Not specified Not specified Not specified Not specified [53]
gnomAD v2 (exomes) 56,885 (NFE) + 10,824 (Finnish) 8,128 9,197 15,308 17,296 (Latino) [54]
UK Biobank (exomes) 437,812 (95.06%) 8,701 (1.89%) 2,150 (0.47%) 9,217 (2.00%) Not specified [54]

The representation disparities shown in Table 1 have direct consequences for statistical power. African ancestry cohorts exhibit approximately 1.8-fold enrichment of common missense variants compared to non-Finnish European cohorts, highlighting the substantial genetic diversity being missed in current studies [54]. This diversity is crucial for comprehensive gene discovery, as demonstrated by the fact that missense tolerance ratio (MTR) metrics trained on just 43,000 multi-ancestry exomes demonstrated greater predictive power than when trained on a nearly 10-fold larger dataset of 440,000 non-Finnish European exomes [54].

Impact on Genetic Discovery and Metric Performance

Table 2: Performance Comparison of Genetic Intolerance Metrics by Ancestry

Ancestry Group Sample Size (gnomAD) AUC for NDD Genes (RVIS) AUC for Haploinsufficient Genes Fold Enrichment of Common Missense Variants Citation
African (AFR) 8,128 Highest (0.71-0.85) Moderate 1.8x [54]
Admixed American (AMR) 17,296 High Moderate Not specified [54]
South Asian (SAS) 15,308 High Moderate Not specified [54]
Non-Finnish European (NFE) 56,885 Lower than AFR Moderate Reference [54]
Finnish (FIN) 10,824 Lower than AFR Moderate Lowest [54]

The data in Table 2 demonstrates that diverse ancestral representation significantly enhances the resolution of genic intolerance metrics. For instance, Residual Variance Intolerance Score (RVIS) metrics derived from African ancestry cohorts consistently achieved the highest area under the ROC curve (AUC) for detecting neurodevelopmental disorder (NDD) genes compared to European-based scores across multiple validation sets [54]. This pattern holds true despite the considerably smaller sample sizes for non-European groups, highlighting that diversity, rather than simply sample size, drives discovery power.

Methodological Framework: Protocols for Enhancing Statistical Power

Protocol 1: Trans-ethnic Meta-Analysis for Endometriosis GWAS

Purpose: To identify endometriosis risk loci with improved portability across diverse ancestral groups through trans-ethnic meta-analysis approaches.

Materials:

  • GWAS summary statistics from diverse ancestral groups
  • Computational resources for large-scale genomic analysis
  • Trans-ethnic meta-analysis software (e.g., TEMR [55])

Procedure:

  • Data Collection and Harmonization: Collect GWAS summary statistics from endometriosis studies across diverse ancestral groups. The largest current endometriosis GWAS meta-analysis includes 17,045 cases and 191,596 controls of European and Japanese ancestries, representing approximately 93% European and 7% Japanese descent [21]. Ensure uniform variant annotation and coordinate systems across datasets.

  • Ancestry-Specific Quality Control: Apply stringent quality control metrics separately for each ancestral group, including:

    • Hardy-Weinberg equilibrium testing (p > 1×10^-6)
    • Minor allele frequency filtering (MAF > 0.01)
    • Imputation quality scores (INFO > 0.8)
    • Removal of strand-ambiguous variants

  • Trans-ethnic Fixed-Effects Meta-analysis: Perform fixed-effects meta-analysis using inverse-variance weighting to combine effects across ancestries:

    • Apply genomic control to each ancestry-specific dataset to account for population structure
    • Use tools such as METAL or MR-MEGA for trans-ethnic implementation
    • Set genome-wide significance threshold at p < 5×10^-8

  • Heterogeneity Assessment: Evaluate heterogeneity in effect sizes across ancestries using Cochran's Q statistic and I² values. Variants showing significant heterogeneity (p < 0.005) require careful interpretation in context of potential ancestry-specific effects [19].

  • Conditional Analysis for Secondary Signals: Identify independent association signals through stepwise conditional analysis within associated loci, as demonstrated in the identification of 19 independent SNPs for endometriosis [21].

  • Validation in Admixed Cohorts: Validate identified loci in admixed populations such as the All of Us cohort, which includes multi-ancestry participants [2].

G Start Collect GWAS Summary Statistics Harmonize Data Harmonization and QC Start->Harmonize AncestryMeta Ancestry-Specific Meta-Analysis Harmonize->AncestryMeta TransEthnic Trans-ethnic Fixed-Effects Meta-analysis AncestryMeta->TransEthnic Heterogeneity Heterogeneity Assessment TransEthnic->Heterogeneity Conditional Conditional Analysis for Secondary Signals Heterogeneity->Conditional Validate Validation in Admixed Cohorts Conditional->Validate

Diagram Title: Trans-ethnic GWAS Meta-analysis Workflow

Protocol 2: Combinatorial Analytics for Cross-ancestry Validation

Purpose: To identify reproducible multi-SNP disease signatures across diverse ancestries using combinatorial analytics approaches.

Materials:

  • Individual-level genotype data from diverse cohorts
  • Combinatorial analytics platform (e.g., PrecisionLife)
  • High-performance computing resources

Procedure:

  • Cohort Preparation and Stratification: Prepare genotype data from diverse ancestral groups, such as UK Biobank (European ancestry) and All of Us (multi-ancestry) cohorts [2]. Stratify data by ancestral background using genetic principal components.

  • Combinatorial Association Testing: Implement combinatorial analytics to identify multi-SNP signatures associated with endometriosis risk:

    • Test combinations of 2-5 SNPs for association with endometriosis status
    • Adjust for multiple testing using false discovery rate control
    • Apply significance threshold of p < 0.05 for signature detection

  • Cross-ancestry Replication Testing: Test signatures identified in one ancestral group for replication in other ancestries:

    • Calculate reproducibility rates as the percentage of signatures replicating
    • Focus on high-frequency signatures (>9% frequency) for prioritization
    • Consider signatures with >80% reproducibility as robust cross-ancestry signals

  • Pathway Enrichment Analysis: Perform functional annotation of genes mapped from reproducing signatures using:

    • Gene ontology (GO) enrichment analysis
    • Pathway databases such as KEGG and Reactome
    • Hallmark gene sets from MSigDB [5]

  • Novel Gene Prioritization: Prioritize novel genes identified through combinatorial approaches that do not overlap with previous GWAS findings, such as the 75 novel genes recently identified for endometriosis [2].

Protocol 3: Multi-tissue eQTL Mapping for Functional Validation

Purpose: To characterize the functional impact of endometriosis-associated variants through multi-tissue expression quantitative trait loci (eQTL) analysis.

Materials:

  • Endometriosis-associated variants from GWAS
  • Multi-tissue eQTL data (e.g., GTEx v8 database)
  • Functional genomics computational resources

Procedure:

  • Variant Selection and Annotation: Curate endometriosis-associated variants from GWAS Catalog (EFO_0001065), retaining only genome-wide significant variants (p < 5×10^-8) with valid rsIDs [5].

  • Tissue-specific eQTL Analysis: Cross-reference variants with eQTL data from six physiologically relevant tissues:

    • Reproductive tissues: uterus, ovary, vagina
    • Gastrointestinal tissues: sigmoid colon, ileum
    • Systemic tissue: peripheral blood

  • Significance Thresholding: Retain only significant eQTLs with false discovery rate (FDR) adjusted p-value < 0.05.

  • Effect Size Quantification: Extract slope values for significant eQTLs, representing the direction and magnitude of regulatory effects.

  • Functional Prioritization: Prioritize genes based on:

    • Frequency of regulation by multiple eQTL variants
    • Magnitude of regulatory effect (absolute slope values)
    • Biological relevance to endometriosis pathways

  • Hallmark Pathway Mapping: Map regulated genes to established biological pathways using:

    • MSigDB Hallmark gene sets
    • Cancer Hallmarks analytics platform
    • Custom endometriosis-relevant pathways

G Start Curate GWAS Variants (p < 5×10⁻⁸) Annotate Variant Functional Annotation Start->Annotate eQTL Multi-tissue eQTL Mapping (GTEx) Annotate->eQTL Filter FDR Filtering (FDR < 0.05) eQTL->Filter Effect Effect Size Quantification Filter->Effect Pathway Hallmark Pathway Mapping Effect->Pathway

Diagram Title: Multi-tissue eQTL Analysis Workflow

Implementation Tools: Research Reagent Solutions

Table 3: Essential Research Reagents and Computational Tools

Tool/Resource Type Primary Function Application in Endometriosis Research Citation
PhyloFrame Computational Method Equitable machine learning for genomic medicine Corrects ancestral bias in disease signatures; improves predictions across ancestries for breast, thyroid, and uterine cancers [53]
TEMR (Trans-ethnic MR) Statistical Method Mendelian randomization for underrepresented populations Improves statistical power for causal inference in non-European populations using trans-ethnic genetic correlations [55]
PrecisionLife Combinatorial Analytics Analytical Platform Identifies multi-SNP disease signatures Discovered 1,709 endometriosis disease signatures with high cross-ancestry reproducibility (58-88%) [2]
GTEx Database v8 Data Resource Multi-tissue gene expression reference Enables eQTL mapping of endometriosis variants across uterus, ovary, colon, and blood tissues [5]
PASS Software Statistical Tool Sample size and power analysis Calculates required sample sizes for achieving sufficient statistical power (≥0.90) in genetic studies [56]
MONITOR Software Statistical Tool Power analysis for monitoring programs Estimates statistical power for trend detection; adaptable to genetic study design [57]
RVIS (Residual Variance Intolerance Score) Genomic Metric Gene-level intolerance to variation Prioritizes candidate genes; African-ancestry versions show improved performance [54]
MTR (Missense Tolerance Ratio) Genomic Metric Sub-genic intolerance to missense variation Identifies protein domains intolerant to variation; benefits from diverse training data [54]

Signaling Pathways and Biological Mechanisms

The integration of trans-ancestry genetic findings with functional genomics has revealed several key biological pathways in endometriosis pathogenesis that demonstrate consistency across diverse populations:

Hormone Signaling Pathways

Novel endometriosis risk loci identified through trans-ethnic approaches implicate genes involved in sex steroid hormone pathways, including ESR1 (estrogen receptor 1), FSHB (follicle-stimulating hormone subunit beta), and CCDC170 (coiled-coil domain containing 170) [21]. These findings highlight the conserved role of hormonal regulation in endometriosis across ancestries. The ESR1 locus in particular contains multiple independent association signals identified through conditional analysis in trans-ethnic datasets [21].

Tissue Remodeling and Adhesion Pathways

Genes identified through combinatorial analytics approaches show strong enrichment in pathways involved in cell adhesion, proliferation, migration, and cytoskeleton remodeling [2]. These processes are fundamental to the establishment and survival of ectopic endometrial lesions. Multi-tissue eQTL analysis further demonstrates that endometriosis risk variants regulate key genes in these pathways, including FN1 (fibronectin 1) and CLDN23 (claudin 23), with effect sizes showing consistency across diverse populations [5].

Immune Modulation and Inflammatory Response

Immune-related pathways predominate in the regulatory profiles of eQTL-associated genes in both peripheral blood and gastrointestinal tissues [5]. Key regulators such as MICB (MHC class I polypeptide-related sequence B) demonstrate consistent effects across tissues and are involved in immune evasion mechanisms relevant to endometriosis pathogenesis. The reproducibility of these findings across ancestries suggests fundamental immune mechanisms in disease development.

Novel Biological Processes

Combinatorial analytics approaches have identified novel gene associations not previously detected through GWAS, providing new insights into autophagy and macrophage biology in endometriosis [2]. These discoveries highlight the value of diverse ancestral representation in uncovering previously overlooked biological mechanisms, potentially offering new targets for therapeutic intervention.

G Hormone Hormone Signaling ESR1, FSHB, CCDC170 Endometriosis Endometriosis Pathogenesis Hormone->Endometriosis Tissue Tissue Remodeling FN1, CLDN23 Tissue->Endometriosis Immune Immune Modulation MICB, Immune Evasion Immune->Endometriosis Novel Novel Processes Autophagy, Macrophage Biology Novel->Endometriosis

Diagram Title: Cross-ancestry Endometriosis Pathways

Optimizing statistical power in underrepresented ancestral groups requires both methodological innovations and a fundamental shift in research practices. The protocols outlined here provide a framework for enhancing discovery and equity in endometriosis genetics research. Key principles include prioritizing ancestral diversity over mere sample size increases, implementing cross-ancestry validation as a standard practice, and integrating functional genomics to interpret findings across diverse populations.

The field is moving toward approaches that explicitly account for and leverage human genetic diversity, as demonstrated by methods like PhyloFrame that create ancestry-aware disease signatures without requiring ancestry labels in training data [53]. Future directions should include the development of specialized statistical methods for admixed populations, increased investment in diverse biobanks, and standardized reporting of ancestry-specific and trans-ancestry findings. Through these approaches, endometriosis research can achieve both improved scientific understanding and greater equity in precision medicine applications across all ancestral backgrounds.

Trans-ancestry meta-analysis has emerged as a powerful strategy to enhance the resolution of fine-mapping causal variants in genome-wide association studies (GWAS). By leveraging genetic differences across diverse populations, researchers can overcome limitations imposed by linkage disequilibrium (LD) patterns in single-ancestry studies. This Application Note provides detailed protocols for implementing trans-ancestry fine-mapping approaches, with specific application to endometriosis research. We present quantitative comparisons of fine-mapping performance, experimental workflows for cross-population analysis, and essential reagent solutions to facilitate implementation in research and drug discovery settings.

Endometriosis is a heritable hormone-dependent gynecological disorder affecting 6-10% of women of reproductive age, characterized by severe pelvic pain and reduced fertility [58]. Genome-wide association studies have identified numerous loci associated with endometriosis risk, yet identifying precise causal variants remains challenging due to extensive LD in single populations [58] [59].

Trans-ancestry meta-analysis leverages differential LD patterns across populations to improve fine-mapping resolution. When causal variants are shared across populations but tagged by different haplotype structures due to varying LD patterns, combining data from diverse ancestry groups enables more precise identification of causal variants [60] [61]. This approach is particularly valuable for endometriosis research, where previous studies have identified risk loci in genes involved in sex steroid hormone pathways including FN1, CCDC170, ESR1, SYNE1, and FSHB [58].

Table 1: Performance Comparison of Fine-Mapping Approaches in Simulated Data

Method Single-Ancestry Credible Set Size Trans-Ancestry Credible Set Size Causal Variants Identified (PIP >0.5) Computational Requirements
MESuSiE 44 (EUR), 21 (EAS) 54 regions 25 High
SuSiE 44 (EUR), 21 (EAS) - Fewer than MESuSiE Moderate
MR-MEGA - 6 novel loci detected Improved over fixed-effects Low
Fixed-effect meta-analysis - 13 novel loci Standard approach Low

Trans-Ancestry Fine-Mapping Workflow

The following diagram illustrates the comprehensive workflow for trans-ancestry fine-mapping in endometriosis research:

workflow Start Start: Collect GWAS Summary Statistics Pop1 European Ancestry GWAS Data Start->Pop1 Pop2 East Asian Ancestry GWAS Data Start->Pop2 Pop3 African Ancestry GWAS Data Start->Pop3 QC Quality Control & Harmonization Pop1->QC Pop2->QC Pop3->QC MetaAnalysis Trans-ancestry Meta-analysis QC->MetaAnalysis Heterogeneity Heterogeneity Assessment MetaAnalysis->Heterogeneity FineMapping Cross-population Fine-mapping Heterogeneity->FineMapping CredibleSets Credible Set Definition FineMapping->CredibleSets FunctionalVal Functional Validation CredibleSets->FunctionalVal End End: Causal Variants FunctionalVal->End

Diagram 1: Trans-ancestry fine-mapping workflow. The process begins with collection of GWAS summary statistics from diverse populations, proceeds through quality control and meta-analysis, and culminates in functional validation of identified causal variants.

Protocol: Trans-ancestry Meta-analysis Implementation

Purpose: To identify and fine-map endometriosis risk loci through trans-ancestry meta-analysis.

Materials:

  • GWAS summary statistics from minimum two ancestry groups
  • Reference panels matching ancestral backgrounds (1000 Genomes, gnomAD)
  • Computational resources for large-scale genetic analysis

Procedure:

  • Data Collection and Harmonization

    • Collect GWAS summary statistics from European, East Asian, and African ancestry cohorts
    • Apply stringent quality control: imputation quality >0.8, minor allele frequency >0.01, Hardy-Weinberg equilibrium p-value >1×10⁻⁶
    • Harmonize effect alleles across studies using reference panels
    • Annotate variants with functional information using ANNOVAR or similar tools [62]

  • Trans-ancestry Meta-analysis

    • Perform fixed-effects meta-analysis using inverse-variance weighting with METAL software
    • Conduct trans-ethnic meta-regression with MR-MEGA to account for heterogeneity correlated with ancestry [60]
    • Apply genomic control correction to test statistics (λ~1.0 indicates proper correction)
    • Define genome-wide significance threshold of p<5×10⁻⁸

  • Heterogeneity Assessment

    • Calculate Cochran's Q statistic to quantify heterogeneity
    • Apply I² statistic to measure proportion of heterogeneity
    • Identify loci with ancestry-correlated heterogeneity using MR-MEGA [60]

  • Fine-mapping Implementation

    • For each significant locus, define 1 Mb flanking regions around lead SNPs
    • Implement multivariate fine-methods such as MESuSiE for cross-population analysis [61]
    • Compute posterior inclusion probabilities (PIP) for each variant
    • Define 95% credible sets containing variants with cumulative PIP ≥0.95

  • Functional Annotation

    • Annotate fine-mapped variants with regulatory data (ENCODE, Roadmap Epigenomics)
    • Perform colocalization with expression QTLs (eQTLs) from relevant tissues (uterus, ovaries)
    • Conduct pathway enrichment analysis using tools like ARTP3 for trans-ancestry pathway analysis [63] [39]

Causal Variant Identification Logic

The following diagram illustrates the logical framework for identifying causal variants through trans-ancestry approaches:

logic Start Shared Causal Variant Hypothesis LD1 Population A: Extended LD Region Start->LD1 LD2 Population B: Different LD Pattern Start->LD2 Overlap Overlapping Region Across Populations LD1->Overlap LD2->Overlap FineMap Refined Credible Set Overlap->FineMap PIP High-PIP Variants FineMap->PIP Functional Functional Evidence PIP->Functional Causal Causal Variant Identification Functional->Causal

Diagram 2: Causal variant identification logic. Differential LD patterns across populations help narrow the candidate region, with functional evidence confirming true causal variants.

Application to Endometriosis Research

In endometriosis, previous trans-ancestry analyses have identified 19 independent SNPs robustly associated with disease risk, explaining up to 5.19% of variance [58]. The following table summarizes key endometriosis loci identified through trans-ancestry approaches:

Table 2: Endometriosis Risk Loci Identified Through Trans-ancestry Meta-analysis

Locus Gene Lead SNP Odds Ratio P-value Function
6q25.1 CCDC170 rs1971256 1.09 3.74×10⁻⁸ Hormone metabolism
6q25.1 SYNE1 rs71575922 1.11 2.02×10⁻⁸ Nuclear organization
11p14.1 FSHB rs74485684 1.11 2.00×10⁻⁸ Gonadotropin subunit
2q35 FN1 rs1250241 1.23 2.99×10⁻⁹ Extracellular matrix
7p12.3 - rs74491657 1.46 4.71×10⁻⁹ Unknown

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Research Reagents and Tools for Trans-ancestry Fine-Mapping

Reagent/Tool Function Application Notes
MR-MEGA Trans-ethnic meta-regression Accounts for heterogeneity correlated with ancestry [60]
MESuSiE Cross-population fine-mapping Identifies shared and ancestry-specific causal signals [61]
METAL Fixed-effects meta-analysis Standard for GWAS meta-analysis [58] [61]
ARTP3 Trans-ancestry pathway analysis Integrates SNP signals across ancestry groups [63] [39]
1000 Genomes Project Reference panel Provides LD information for diverse populations [58]
ANNOVAR Functional variant annotation Prioritizes variants by functional impact [62]
FINEMAP Bayesian fine-mapping Computes posterior inclusion probabilities

Protocol: Trans-ancestry Pathway Analysis for Endometriosis

Purpose: To identify biological pathways associated with endometriosis through trans-ancestry pathway analysis.

Materials:

  • GWAS summary statistics from multiple ancestry groups
  • Pathway databases (KEGG, Reactome, MSigDB)
  • High-performance computing cluster

Procedure:

  • Data Integration

    • Apply SNP-centric, gene-centric, or pathway-centric integration approaches [63] [39]
    • Assign SNPs to genes using 50 kb flanking regions
    • Account for overlapping gene assignments

  • Pathway Analysis

    • Analyze 6,970 pathways from MSigDB or similar databases
    • Implement Adaptive Rank Truncated Product (ARTP) method for signal aggregation
    • Use resampling procedures to control Type I error rate
    • Apply false discovery rate (FDR) correction for multiple testing

  • Interpretation

    • Focus on pathways relevant to endometriosis pathogenesis (hormone metabolism, inflammation, cell adhesion)
    • Validate findings in independent cohorts
    • Integrate with single-cell RNA sequencing data from endometriosis lesions

Discussion

Trans-ancestry approaches substantially improve fine-mapping resolution for endometriosis risk loci by leveraging differential LD patterns across populations. Methods such as MR-MEGA and MESuSiE demonstrate superior performance compared to single-ancestry approaches, reducing credible set sizes and increasing the probability of identifying causal variants [60] [61]. For endometriosis research, these approaches have highlighted the importance of genes involved in sex steroid hormone signaling, including ESR1 and FSHB [58].

Implementation requires careful attention to quality control, ancestry representation, and functional validation. Future directions include integrating trans-ancestry fine-mapping with single-cell epigenomics in endometriosis-relevant tissues and developing polygenic risk scores that transfer across populations for improved risk prediction and clinical translation.

Polygenic risk scores (PRS) have emerged as powerful tools in human genetics, quantifying an individual's inherited susceptibility to complex diseases based on the cumulative effect of numerous genetic variants. However, a significant challenge hindering their equitable clinical application is the sharply reduced accuracy of PRS when applied to non-European populations [64] [65]. This performance disparity stems largely from the historical underrepresentation of diverse populations in genome-wide association studies (GWAS), which are the foundation for calculating these scores. Consequently, PRS developed primarily in European cohorts capture patterns of genetic variation and linkage disequilibrium (LD) specific to that ancestry, limiting their transferability [66] [67].

Enhancing the cross-population accuracy of PRS is not merely a technical statistical challenge but a critical imperative for global health equity. This document details advanced methodologies and protocols for improving PRS transferability, framed within the specific context of endometriosis research. Endometriosis, a common gynecological condition affecting ~10% of women, has a substantial genetic component, but its genetic architecture has been predominantly studied in populations of European ancestry [19] [2]. We focus on trans-ancestry meta-analysis approaches that leverage genetic data from diverse populations to build more portable and powerful risk prediction models.

Background: The Challenge of PRS Portability

The reduced portability of PRS across populations is attributed to several key factors:

  • Differences in Linkage Disequilibrium (LD): LD patterns, the non-random correlation of genetic variants in a population, vary substantially across ancestries. Effect sizes estimated in one population may not accurately tag causal variants in another due to these differing LD structures [65].
  • Allele Frequency Differences: The frequency of risk alleles can vary greatly between populations. A variant common in one ancestry might be rare or absent in another, leading to missed heritability when the PRS is transferred [67].
  • Population-Specific Genetic Effects: In some cases, the causal effects of variants may themselves differ between populations due to distinct environmental or genetic backgrounds [64].
  • Underrepresentation in GWAS: As of recent assessments, individuals of European ancestry constitute a disproportionate majority of GWAS participants, creating a fundamental bias in the initial discovery of risk loci [65] [67].

Table 1: Key Factors Limiting PRS Transferability and Their Consequences.

Factor Description Impact on PRS Accuracy
LD Structure Variation Differences in correlation patterns between genetic variants across populations. Effect sizes from a source population poorly tag causal variants in the target population.
Allele Frequency Divergence Varying frequencies of risk alleles across ancestral groups. Reduces variance explained by the PRS and fails to capture population-specific risk.
Varying Causal Effects True biological effect of a variant may differ across ancestries. Introduces systematic bias in risk prediction if not accounted for.
Limited Diversity in GWAS Over-reliance on European-ancestry discovery cohorts. Fundamental data limitation; models are not trained to recognize risk variants in other groups.

Advanced Methods for Enhancing Cross-Population PRS

Several sophisticated statistical methods have been developed to address the limitations of PRS transferability. These approaches can be broadly categorized into those that leverage multi-ancestry GWAS summary statistics and those that employ novel modeling techniques.

Multi-ancestry GWAS Integration

Integrating genetic data from multiple populations during the discovery phase is a foundational strategy. A multi-ancestry meta-analysis for endometriosis, encompassing over 1.4 million women (including 105,869 cases), has identified 80 genome-wide significant loci, 37 of which are novel [13]. This expanded genetic map across ancestries provides a more robust set of variants for PRS construction.

Statistical Methods for Effect Size Estimation and LD Adjustment

Simply pooling data is insufficient; methods must explicitly account for inter-ancestry differences.

  • SDPRX: This method automatically adjusts for LD differences between populations and characterizes the joint distribution of a variant's effect sizes in two populations (e.g., both null, population-specific, or shared with correlation). It has been shown to improve prediction performance in non-European populations for various complex traits [64].
  • PolyPred/PolyPred+: This framework combines two predictors to improve cross-population PRS. The first leverages functionally informed fine-mapping to estimate causal effects directly, making it less sensitive to LD differences. The second is an established predictor like BOLT-LMM. When a large training sample is available in the non-European target population, PolyPred+ further incorporates this data, leading to significant improvements in accuracy (e.g., +24% versus BOLT-LMM in East Asians) [65].
  • PRS-CSx: A Bayesian method that jointly models GWAS summary statistics from multiple populations using a shared continuous shrinkage prior. This allows for the sharing of information between datasets while accounting for population-specific allele frequencies and LD patterns from 1000 Genomes Project reference panels. It has demonstrated efficacy for traits like Type 2 Diabetes across diverse populations [67].
  • GPSMult: A framework for creating a unified polygenic score that integrates GWAS data not only for the primary disease across five ancestries but also for ten related risk factors. Applied to coronary artery disease, this approach significantly outperformed previous scores across all tested ancestries [66].

Table 2: Comparison of Advanced Statistical Methods for Cross-Population PRS.

Method Core Principle Key Inputs Reported Improvement
SDPRX Models joint effect size distribution across populations; auto-adjusts for LD. GWAS summary stats from two populations. Improved accuracy over existing methods in non-European populations via simulations and real traits [64].
PolyPred/PolyPred+ Combines fine-mapping-based causal effect estimates with standard PRS; can integrate target population data. GWAS summary stats; (Optional) Target population genotype data. +7% to +32% relative improvement vs. BOLT-LMM in Africans and South Asians; +24% in East Asians with PolyPred+ [65].
PRS-CSx Bayesian modeling with a shared continuous shrinkage prior across multiple populations. GWAS summary stats from multiple populations; Population-matched LD reference panels. Effective for T2D prediction in trans-ancestry cohorts (European, African, Hispanic/Latino) [67].
GPSMult Integrates cross-ancestry GWAS for the primary trait and multiple genetically correlated risk factors. Large-scale GWAS summary stats for a disease and its risk factors across ancestries. Outperformed all previously published CAD PRS in multi-ethnic validation datasets [66].

Combinatorial and Functional Genomics Approaches

Moving beyond common variant-based PRS, novel analytical frameworks are emerging.

  • Combinatorial Analytics: Platforms like PrecisionLife identify multi-SNP disease signatures (combinations of 2-5 SNPs) associated with disease. This approach has identified 1,709 such signatures for endometriosis, many of which show high reproducibility (66-88%) in multi-ancestry cohorts, including non-European sub-groups. This method can reveal novel biological insights and genetic risk factors overlooked by standard GWAS [2].
  • Functional Fine-Mapping and Prioritization: Methods like PolyPred leverage functionally informed fine-mapping to prioritize putative causal variants. Integrating data from projects like ENCODE, which has shown that ~80% of non-coding regions have functionality, helps interpret cross-population signals and prioritize variants in regulatory elements [19] [65].

Application Notes & Protocols for Endometriosis Research

This section provides a detailed, actionable protocol for developing and validating a trans-ancestry PRS for endometriosis.

Protocol: Constructing a Trans-ancestry PRS for Endometriosis

Objective: To develop a polygenic risk score for endometriosis with improved predictive accuracy across diverse populations by integrating multi-ancestry GWAS summary statistics using the PRS-CSx method.

workflow Start Start: Define Study Objective DataCol 1. Data Collection Start->DataCol GWASEur Endometriosis GWAS (European Ancestry) DataCol->GWASEur GWASNonEur Endometriosis GWAS (Non-European Ancestries) DataCol->GWASNonEur LDRef 1KG LD Reference Panels (Ancestry-Matched) DataCol->LDRef QC 2. Data QC & Harmonization GWASEur->QC GWASNonEur->QC LDRef->QC PRSConstruction 3. PRS Construction (PRS-CSx Method) QC->PRSConstruction ModelEval 4. Model Evaluation PRSConstruction->ModelEval Application 5. Biological Application ModelEval->Application End End: Report & Disseminate Application->End

Diagram 1: Trans-ancestry PRS development workflow.

Materials and Reagents

Table 3: Research Reagent Solutions for Trans-ancestry PRS Analysis.

Item Function/Description Example Sources
GWAS Summary Statistics Effect sizes, standard errors, and p-values for genetic variants associated with endometriosis. International Endogene Consortium [19], FinnGen, Biobank Japan, All of Us [13].
LD Reference Panels Genotype data used to estimate population-specific linkage disequilibrium patterns. 1000 Genomes Project (1KG) [67], HRC, ancestry-specific reference panels.
Genotyped Target Cohorts Independent datasets with individual-level genotype and phenotype data for validation. UK Biobank, All of Us, Taiwan Biobank, etc. [67] [13].
High-Performance Computing (HPC) Cluster Essential for running computationally intensive genetic analyses and Bayesian methods. Local institutional HPC or cloud computing services (e.g., AWS, Google Cloud).
Analysis Software & Packages Specialized tools for PRS construction and analysis. PRS-CSx [67], SDPRX [64], PolyFun/PolyPred [65], PLINK, R/Bioconductor.
Step-by-Step Procedure

  • Data Collection and Curation

    • GWAS Summary Statistics: Collect summary statistics from large-scale endometriosis GWAS across multiple ancestries. A recent meta-analysis included data from the UK Biobank, FinnGen, the Million Veteran Program, All of Us, Biobank Japan, and the International Endogene Consortium, totaling over 100,000 cases [13].
    • LD Reference Panels: Download pre-computed LD reference panels from the 1000 Genomes Project that match the ancestries of your GWAS data (e.g., EUR, AFR, EAS, SAS).
    • Target Validation Cohort: Identify one or more genotyped cohorts with endometriosis phenotyping for score validation. These should be independent of the discovery GWAS.

  • Data Quality Control and Harmonization

    • Variant Filtering: Restrict analysis to well-imputed, common (MAF > 1%) HapMap3 SNPs to ensure robustness and reduce computational burden [67].
    • Genomic Build and Allele Harmonization: Ensure all summary statistics and LD panels are on the same genomic build (e.g., GRCh37/hg19). Harmonize allele strands and orientations across all files to prevent errors from flipped alleles.

  • PRS Construction using PRS-CSx

    • Command: python PRS-CSx.py --ref_dir=[PATH_TO_LD] --bim_prefix=[TARGET_BIM] --sst_file=[EUR_SUMSTATS],[NON_EUR_SUMSTATS] --n_gwas=[EUR_N],[NON_EUR_N] --out_dir=[OUTPUT_DIR]
    • Parameters: The --phi parameter can be set to auto for automatic learning of the shrinkage parameter. Specify the population labels for each set of summary statistics.
    • Output: The software will generate a single set of variant weights that integrate information across all input ancestries.

  • PRS Calculation and Validation

    • Score Calculation: In the target validation cohort, calculate the individual-level PRS as the weighted sum of effect alleles: ( PRSi = \sum{j=1}^{M} wj * G{ij} ), where ( wj ) is the weight from PRS-CSx for SNP ( j ), and ( G{ij} ) is the genotype dosage for individual ( i ) and SNP ( j ).
    • Association Analysis: Fit a logistic regression model: ( Logit(Endometriosis) ~ PRS + Age + GenotypingArray + PC1..PC10 ). Assess the predictive power using the Odds Ratio (OR) per standard deviation increase in the PRS and the incremental Nagelkerke's R².
    • Stratified Analysis: Evaluate performance within specific ancestry groups and, if data permits, by endometriosis sub-phenotypes (e.g., rAFS Stage III/IV) [19].

  • Biological Interpretation and Downstream Analysis

    • Pathway Enrichment: Perform gene set enrichment analysis on genes mapped to the top-weighted variants in the PRS. For endometriosis, expected pathways include immune regulation, tissue remodeling, cell adhesion, and hormone signaling [2] [13].
    • Mendelian Randomization (MR): Use the genetic variants in the PRS as instrumental variables in MR analyses to investigate causal relationships between endometriosis and potential biomarkers or co-morbidities. This has identified proteins like RSPO3 as potential therapeutic targets [6].
    • Drug Repurposing: Connect the prioritized genes from the PRS to known drug targets via databases like DrugBank. The multi-ancestry endometriosis GWAS has highlighted potential drug repurposing opportunities for agents used in breast cancer and preterm birth prevention [13].

Protocol: Experimental Validation of Prioritated Targets

Objective: To experimentally validate a candidate protein target (e.g., RSPO3) identified through trans-ancestry PRS and downstream MR analysis in clinical endometriosis samples [6].

validation Start Start: Identify Candidate Target (e.g., via MR) SampleCol 1. Clinical Sample Collection Start->SampleCol GroupEM Endometriosis Patients (n=20) SampleCol->GroupEM GroupCtrl Control Patients (n=20) SampleCol->GroupCtrl ProteinAssay 2. Protein Level Assay (ELISA) GroupEM->ProteinAssay RNAAssay 3. Gene Expression Assay (RT-qPCR) GroupEM->RNAAssay GroupCtrl->ProteinAssay GroupCtrl->RNAAssay Analysis 4. Data Analysis ProteinAssay->Analysis RNAAssay->Analysis Confirm Target Confirmed Analysis->Confirm Confirm->Start No End End: Propose for Therapeutic Development Confirm->End Yes

Diagram 2: Experimental validation workflow for candidate targets.

Materials
  • Human Samples: Blood plasma and ectopic/ectopic endometrial tissue from surgically confirmed endometriosis patients and matched controls (e.g., n=20 per group). Obtain informed consent and IRB approval.
  • Reagents: Human R-Spondin3 (RSPO3) ELISA Kit, RNeasy Kit for RNA extraction, cDNA synthesis kit, TaqMan probes for RSPO3 and a housekeeping gene (e.g., GAPDH).
  • Equipment: Microplate reader, real-time PCR system, Western blot apparatus.
Step-by-Step Procedure
  • Sample Collection: Collect blood and tissue samples from patients and controls, ensuring strict adherence to exclusion criteria (e.g., no hormonal drug use in the last 6 months) [6].
  • Protein Quantification (ELISA):
    • Process plasma samples by centrifugation.
    • Use a double-antibody sandwich ELISA kit following the manufacturer's protocol to quantify RSPO3 concentration in plasma from cases and controls.
    • Measure absorbance at 450nm and calculate concentrations from the standard curve.
  • Gene Expression Analysis (RT-qPCR):
    • Extract total RNA from tissue samples.
    • Synthesize cDNA.
    • Perform quantitative PCR using gene-specific probes for RSPO3. Calculate relative expression using the 2^(-ΔΔCt) method normalized to the control group.
  • Data Analysis:
    • Use a t-test or Mann-Whitney U test to compare plasma RSPO3 levels and tissue mRNA expression between endometriosis cases and controls.
    • A statistically significant increase (p < 0.05) in the case group confirms the association predicted by the genetic analysis [6].

The equitable application of polygenic risk scores in clinical practice hinges on our ability to improve their accuracy across the full spectrum of human genetic diversity. Methods such as SDPRX, PolyPred/PolyPred+, and PRS-CSx provide powerful statistical frameworks for achieving this by explicitly modeling ancestral differences and leveraging diverse data. Within endometriosis research, the ongoing generation of large-scale multi-ancestry GWAS data [13], combined with these advanced methods and subsequent experimental validation [6], creates a transformative pathway. This integrated approach promises not only to improve risk prediction for all women but also to uncover novel biology and therapeutic targets, ultimately advancing precision medicine for this common and debilitating condition.

Validation Frameworks and Performance Metrics for Trans-Ancestry Methods

Benchmarking Trans-Ancestry PRS Against Ancestry-Specific Models

Within the specific context of endometriosis genetics research, a disease with complex heritability and significant diagnostic challenges, the need for precise genetic risk prediction is paramount [8] [19]. Genome-wide association studies (GWAS) have successfully identified multiple loci associated with endometriosis risk [21]. However, the predominant reliance on European-ancestry cohorts has limited the generalizability of resulting polygenic risk scores (PRS) across diverse populations, a critical issue for global drug development and clinical application [68].

Trans-ancestry meta-analysis methods present a promising solution to mitigate these biases. These approaches leverage genetic data from multiple populations to enhance the discovery of risk loci and improve the portability of PRS [61]. This protocol details a comprehensive framework for benchmarking trans-ancestry PRS models against ancestry-specific alternatives in endometriosis research, providing drug development professionals with standardized methods for evaluating genetic risk prediction tools.

Key Concepts and Background

Polygenic Risk Scores (PRS) in Context

PRS quantify an individual's genetic susceptibility to a trait by aggregating the effects of numerous genetic variants, typically identified through GWAS [69]. Traditional PRS methods, such as clumping and thresholding (C+T), often demonstrate reduced predictive accuracy when applied to populations not represented in the original training GWAS, particularly for complex diseases like endometriosis [68] [69].

Endometriosis Genetic Architecture

Endometriosis is a heritable, estrogen-dependent inflammatory disease affecting approximately 6-10% of women of reproductive age [19] [22]. Its genetic architecture is complex, with a common SNP-based heritability estimated at 0.26 [21]. Large-scale meta-analyses have identified numerous susceptibility loci, many implicating genes involved in sex steroid hormone pathways (e.g., WNT4, VEZT, ESR1, FSHB), highlighting potential therapeutic targets [21].

Table 1: Key Endometriosis Susceptibility Loci from GWAS Meta-Analyses

Locus Nearest Gene Reported Function Population P-value Reference
7p15.2 - Inter-genic European 1.6 × 10⁻⁹ [19]
1p36.12 WNT4 Developmental pathways European 1.8 × 10⁻¹⁵ [19] [21]
12q22 VEZT Cell adhesion European 4.7 × 10⁻¹⁵ [19]
9p21.3 CDKN2B-AS1 Cell cycle regulation Japanese 5.57 × 10⁻¹² [19]
6q25.1 CCDC170/ESR1 Hormone metabolism Trans-ancestry 3.74 × 10⁻⁸ [21]
11p14.1 FSHB Hormone metabolism Trans-ancestry 2.00 × 10⁻⁸ [21]

Experimental Design and Workflow

This section outlines the core experimental workflow for benchmarking PRS models, from data preparation through to performance evaluation. The following diagram illustrates the complete process:

G Start Start: Study Design DataPrep Data Preparation GWAS Summary Statistics & LD Reference Panels Start->DataPrep PRSConstruction PRS Construction Ancestry-Specific & Trans-ancestry DataPrep->PRSConstruction ModelTuning Model Tuning Parameter Optimization PRSConstruction->ModelTuning Validation Validation Benchmarking in Independent Cohorts ModelTuning->Validation Eval Performance Evaluation Statistical & Clinical Metrics Validation->Eval End End: Model Selection Eval->End

Data Preparation and Quality Control

GWAS Summary Statistics

  • Source: Curate endometriosis GWAS summary statistics from diverse populations (e.g., European, East Asian). Publicly available data can be sourced from the GWAS Catalog (EFO_0001065 for endometriosis) and large-scale consortia like the International Endogene Consortium [8] [19].
  • Quality Control: Apply standard filters: remove variants with low minor allele frequency (MAF < 0.01), imputation quality score (INFO < 0.8), and missingness per SNP (>5%) [70].
  • Harmonization: Ensure consistent allele coding and effect size directions across datasets. Strand ambiguous SNPs should be flagged and handled with care during meta-analysis.

Linkage Disequilibrium (LD) Reference Panels

  • Population-Matched Panels: Utilize 1000 Genomes Project or population-specific biobanks (e.g., UK Biobank, Taiwan Biobank, Biobank Japan) to obtain LD structure information [68] [61].
  • Sample Size Considerations: LD reference panels should contain sufficient sample sizes (typically > 1,000 individuals) to ensure accurate estimation of correlation structures between variants.
PRS Construction Methods

Ancestry-Specific PRS

  • Model Fitting: Construct PRS using GWAS summary statistics and LD information from a single ancestry group. Methods include LDpred2, lassosum, and PRS-CS, which account for LD patterns specific to the target population [70].
  • Implementation: For European-specific PRS in endometriosis, use summary statistics from European-ancestry GWAS meta-analyses [21].

Trans-Ancestry PRS

  • PRS-CSx: This method leverages genetic architecture across multiple populations by learning a continuous shrinkage prior based on LD patterns from different ancestry groups [68]. It improves the portability of PRS by accounting for population-specific LD and varying causal effect sizes.
  • Ensemble Methods: Approaches like PUMAS-ensemble combine multiple PRS models through a regression framework using only summary statistics, enhancing predictive performance without requiring external individual-level data for fitting [70].
Model Tuning and Validation Framework

Parameter Optimization

  • Tuning Strategies: Use cross-validation approaches implemented in methods like PUMAS, which perform Monte Carlo cross-validation using GWAS summary statistics alone [70]. This is particularly valuable when independent tuning datasets are unavailable.
  • Hyperparameters: Fine-tune model-specific parameters (e.g., global shrinkage parameters in PRS-CS, sparsity parameters in lassosum) to optimize performance for endometriosis prediction.

Benchmarking Cohorts

  • Independent Validation: Utilize completely independent datasets not included in the original GWAS. For endometriosis, suitable cohorts include the Taiwan Biobank for East Asian populations and the UK Biobank for European populations [68].
  • Phenotype Refinement: Where possible, stratify analyses by endometriosis subphenotypes (e.g., rAFS Stage III/IV disease) as genetic effects may be stronger for severe disease [19] [21].

Benchmarking Metrics and Performance Evaluation

Statistical Metrics

Evaluate PRS performance using multiple statistical measures to ensure comprehensive assessment. The following diagram illustrates the relationship between evaluation components:

G PRSBenchmarking PRS Benchmarking Statistical Statistical Metrics PRSBenchmarking->Statistical Clinical Clinical Utility PRSBenchmarking->Clinical Technical Technical Performance PRSBenchmarking->Technical R2 Variance Explained (R²) Statistical->R2 AUC Area Under Curve (AUC) Statistical->AUC OR Odds Ratio (OR) Statistical->OR Stratification Risk Stratification Clinical->Stratification Calibration Calibration Clinical->Calibration Portability Portability Technical->Portability

Primary Metrics

  • Variance Explained (R²): For continuous traits, report the covariate-adjusted R² value. In trans-ancestry PRS for LDL cholesterol, multi-ancestry PRS achieved R² = 6.7% in East Asian populations, outperforming European-specific PRS (R² = 4.5%) though still lower than ancestry-specific PRS (R² = 9.3%) [68].
  • Area Under the Curve (AUC): For binary endometriosis classification, calculate AUC with 95% confidence intervals.
  • Odds Ratios (OR): Report ORs across PRS deciles/quintiles. In kidney stone disease, the highest quintile of a cross-population PRS showed an OR of 1.83 (1.68-1.98) compared to the middle quintile [61].
Performance Comparison Table

Table 2: Example PRS Performance Comparison for Complex Traits (Adapted from Published Studies)

Trait Population Ancestry-Specific PRS (R²) Trans-Ancestry PRS (R²) European PRS (R²) Effect Size Contrast Reference
LDL Cholesterol East Asian (TWB) 9.3% 6.7% 4.5% 0.82 vs 0.76 vs 0.59* [68]
LDL Cholesterol East Asian (UKB) 8.6% 7.8% 6.2% - [68]
Kidney Stone Disease Trans-ancestry - PRS-CSxEAS&EUR (Superior) - OR: 1.83 (1.68-1.98) [61]
*Mean difference in LDL levels between extreme PRS deciles
OR for highest vs. middle PRS quintile

Advanced Applications in Endometriosis Research

Functional Validation through Single-Cell PRS

The scPRS framework represents a cutting-edge advancement that integrates single-cell epigenomics with PRS calculation [69]. This approach is particularly relevant for endometriosis given its tissue-specific pathophysiology.

Workflow Implementation

  • Reference Atlas: Generate or utilize existing single-cell chromatin accessibility (scATAC-seq) data from endometriosis-relevant tissues (uterus, ovary, pelvic peritoneum) [69].
  • Cell-Type-Specific PRS: Compute PRS for individual cells by masking variants outside open chromatin regions in each cell type, enabling identification of disease-critical cellular contexts.
  • Biological Discovery: Prioritize causal cell types and cell-type-specific regulatory elements for endometriosis risk variants, potentially revealing novel therapeutic targets.
Integration with Mendelian Randomization

Trans-ancestry Mendelian randomization can elucidate potential causal relationships between modifiable risk factors and endometriosis [22]. For instance, a recent trans-ethnic MR study investigated the causal effects of serum lipids on endometriosis risk, identifying triglyceride-lowering gene targets (LPL, PPARA, ANGPTL3, APOC3) as potential therapeutic avenues [22].

The Scientist's Toolkit

Table 3: Essential Research Reagents and Computational Tools

Category Item Function/Application Example Resources
Data Sources GWAS Summary Statistics Effect size estimates for variants GWAS Catalog (EFO_0001065), IEC, BBJ [8] [19]
LD Reference Panels Population-specific linkage patterns 1000 Genomes Project, UK Biobank, TWB [68] [61]
Software Tools PRS-CSx Trans-ancestry PRS construction GitHub: getian107/PRScsx [68]
PUMAS/PUMAS-ensemble Summary-statistics-based tuning & ensemble learning [70]
scPRS Single-cell PRS calculation [69]
TwoSampleMR Mendelian randomization analysis [22]
Biobanks Taiwan Biobank (TWB) East Asian validation cohort [68]
UK Biobank (UKB) European validation cohort [68] [70]
Biobank Japan (BBJ) East Asian GWAS data [61]

Benchmarking trans-ancestry PRS against ancestry-specific models represents a critical methodological advancement in endometriosis genetics. The protocols outlined here provide a rigorous framework for evaluating PRS performance across diverse populations, directly addressing the critical need for portable genetic risk tools in global drug development programs. As trans-ancestry resources expand, these approaches will become increasingly integral to identifying valid therapeutic targets and developing stratified treatment strategies for endometriosis and other complex genetic disorders.

This application note details a combinatorial analytics approach for identifying and validating multi-Single Nucleotide Polymorphism (SNP) signatures in endometriosis research. Conventional genome-wide association studies (GWAS) have explained only approximately 5% of disease variance in endometriosis, revealing the need for more sophisticated analytical methods to capture its complex genetic architecture [2]. Combinatorial analytics addresses this limitation by detecting synergistic effects between multiple genetic variants that are undetectable through single-variant analysis.

The protocol outlined herein enabled the identification of 1,709 reproducible disease signatures comprising 2,957 unique SNPs in combinations of 2-5 SNPs, demonstrating significant enrichment (58-88%) across diverse ancestry cohorts [2]. This approach has revealed novel biological pathways and potential therapeutic targets, moving beyond the constraints of traditional GWAS and providing a framework for precision medicine in endometriosis and other complex disorders.

Endometriosis affects approximately 10% of women of reproductive age globally, yet diagnosis is typically delayed by 7-10 years due to limited understanding of its pathogenesis and lack of non-invasive diagnostic tools [2] [49]. While familial aggregation and twin studies provide evidence of a strong heritable component, traditional GWAS approaches have identified only 42 genomic loci associated with endometriosis risk, collectively explaining just 5% of disease variance [2].

Combinatorial analytics represents a paradigm shift by analyzing how multiple SNPs interact to influence disease risk, potentially capturing non-linear genetic effects that single-variant approaches miss. This method aligns with broader efforts in trans-ancestry genetic research, which aims to improve the generalizability of findings across diverse populations and enhance the biological relevance of discovered associations [49] [71].

Experimental Protocols

Core Analytical Workflow for Combinatorial Signature Identification

G Combinatorial Analytics Workflow for Multi-SNP Signature Validation cluster_0 Primary Discovery Phase cluster_1 Validation Phase START Input: GWAS Summary Statistics & Cohort Data Step1 1. Combinatorial Analysis Using PrecisionLife Platform START->Step1 Step2 2. Identify Multi-SNP Disease Signatures (2-5 SNPs) Step1->Step2 Step1->Step2 Step3 3. Pathway Enrichment Analysis Step2->Step3 Step2->Step3 Step4 4. Cross-Cohort Validation (UKB → All of Us) Step3->Step4 Step5 5. Ancestry-Specific Validation Analysis Step4->Step5 Step4->Step5 Step6 6. Novel Gene Identification & Prioritization Step5->Step6 Step5->Step6 END Output: Validated Genetic Risk Factors & Therapeutic Targets Step6->END

Detailed Methodological Protocols

Protocol 1: Combinatorial Signature Discovery

Objective: Identify statistically significant multi-SNP combinations associated with endometriosis risk.

Materials:

  • UK Biobank (UKB) genetic dataset (White European cohort)
  • PrecisionLife combinatorial analytics platform
  • High-performance computing infrastructure

Procedure:

  • Data Preparation: Curate quality-controlled genotyping data from 21,779 endometriosis cases and 449,087 controls of European ancestry [2] [10].
  • Combinatorial Analysis: Execute the PrecisionLife platform to systematically test all possible 2-5 way SNP combinations for association with endometriosis status.
  • Statistical Thresholding: Apply false discovery rate (FDR) correction to identify signatures with significant association (p<0.04) after multiple testing correction [2].
  • Signature Characterization: Compile all significant signatures and catalog constituent SNPs, allele frequencies, and odds ratios.

Output: 1,709 disease signatures comprising 2,957 unique SNPs.

Protocol 2: Cross-Ancestry Validation

Objective: Validate discovered signatures in diverse genetic backgrounds.

Materials:

  • All of Us (AoU) Research Program dataset (multi-ancestry American cohort)
  • Population structure covariates
  • Statistical analysis software (R/Python)

Procedure:

  • Cohort Stratification: Divide the AoU cohort by ancestral background (European, African, Asian, Admixed American).
  • Signature Testing: Test each signature identified in Protocol 1 for association with endometriosis in each ancestral group, controlling for population structure.
  • Reproducibility Calculation: Calculate the percentage of signatures that show consistent directional effects and statistical significance (p<0.04) in the validation cohort.
  • Frequency Stratification: Analyze reproducibility rates by signature frequency categories (>9%, 4-9%, <4%).

Output: Reproducibility metrics across ancestry groups and signature frequency categories.

Protocol 3: Biological Pathway Analysis

Objective: Interpret validated signatures in functional biological context.

Materials:

  • Pathway databases (KEGG, Reactome, GO)
  • Gene annotation tools
  • Functional genomics resources

Procedure:

  • Gene Mapping: Map SNPs from reproducing signatures to genes based on genomic position and regulatory potential.
  • Pathway Enrichment: Perform overrepresentation analysis for mapped genes against reference pathway databases.
  • Multi-omics Integration: Correlate genetic findings with expression quantitative trait loci (eQTLs), methylation QTLs (mQTLs), and protein QTLs (pQTLs) where available [10].
  • Therapeutic Prioritization: Prioritize genes based on pathway relevance, druggability, and novelty to endometriosis.

Output: Annotated list of biological pathways and prioritized therapeutic targets.

Results & Data Presentation

Signature Validation Performance Across Ancestries

Table 1: Reproduction Rates of Multi-SNP Signatures by Population and Frequency

Signature Frequency All of Us Overall Non-European Cohorts Key Findings
>9% (High Frequency) 80-88% (p<0.01) 66-76% (p<0.04) Highest reproducibility across all groups
4-9% (Medium Frequency) 70-78% (p<0.03) 60-70% (p<0.04) Moderate but significant reproduction
<4% (Low Frequency) 58-65% (p<0.04) 55-62% (p<0.05) Lowest but above-chance reproduction

The validation demonstrated exceptionally high reproducibility for frequent signatures, with one 2-SNP signature achieving individual significance in the AoU cohort [2]. Notably, reproducibility remained substantial even in non-European populations, supporting the trans-ancestry robustness of the combinatorial approach.

Novel Gene Discovery Outcomes

Table 2: Gene Categories Identified Through Combinatorial Analysis

Gene Category Count Examples Biological Significance
Previously Established GWAS Genes 7 Known from meta-GWAS Validation of existing findings
Literature-Associated with Endometriosis 16 Documented in OpenTargets Confirmation of prior evidence
Novel Gene Associations 75 MAP3K5, autophagy and macrophage genes New biological mechanisms
High-Priority Therapeutic Targets 9 Characterized novel genes Credible drug discovery/repurposing candidates

The combinatorial method identified 75 novel gene associations not detected by previous GWAS, significantly expanding the known genetic landscape of endometriosis [2]. Several of these novel genes implicate previously underappreciated biological processes in endometriosis, including autophagy and macrophage biology.

Biological Pathway Integration

G Key Pathways Implicated by Combinatorial Analysis in Endometriosis cluster_0 Established Pathways cluster_1 Novel Pathway Insights Genetics Multi-SNP Signatures (Combinatorial Risk Factors) Pathways Key Dysregulated Pathways Genetics->Pathways CellAdhesion Cell Adhesion & Migration Pathways->CellAdhesion Cytoskeleton Cytoskeleton Remodeling Pathways->Cytoskeleton Angiogenesis Angiogenesis Pathways->Angiogenesis Fibrosis Fibrosis Processes Pathways->Fibrosis NeuropathicPain Neuropathic Pain Signaling Pathways->NeuropathicPain Autophagy Autophagy (Novel Finding) Pathways->Autophagy Macrophage Macrophage Biology (Novel Finding) Pathways->Macrophage CellAdhesion->Cytoskeleton Cytoskeleton->Angiogenesis Angiogenesis->Fibrosis Fibrosis->NeuropathicPain Autophagy->Macrophage

The pathway analysis revealed enrichment in several biologically relevant processes. Cell adhesion, proliferation, and migration pathways align with the invasive nature of endometriotic lesions. Cytoskeleton remodeling and angiogenesis are essential for lesion establishment and maintenance. The novel associations with autophagy and macrophage biology suggest involvement of cellular clearance mechanisms and immune microenvironment modulation in disease pathogenesis [2] [10].

Integration with multi-omic data through summary-based Mendelian randomization (SMR) approaches has further validated these pathways, demonstrating coordinated effects across methylation, gene expression, and protein abundance layers [10].

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Research Materials and Platforms for Combinatorial Analytics

Resource Category Specific Solutions Application in Workflow
Analytical Platforms PrecisionLife combinatorial analytics platform Primary analysis of multi-SNP combinations
Genetic Datasets UK Biobank, All of Us Research Program, FinnGen Discovery and validation cohorts
Bioinformatics Tools SMR software (v1.3.1), PRS-CSx, LOG-TRAM Multi-omic integration and trans-ancestry optimization
Pathway Databases CellAge, KEGG, Reactome, GO, GTEx Biological interpretation and functional annotation
Quality Control Tools PLINK, R/bioconductor packages Data preprocessing and population structure control

The PrecisionLife platform served as the core analytical engine, specifically designed to detect combinatorial effects in complex disease datasets [2]. The integration of large-scale biobanks provided both discovery (UK Biobank) and validation (All of Us) cohorts with sufficient sample size and ancestral diversity. Specialized statistical genetics tools like PRS-CSx and LOG-TRAM enabled effective cross-population analysis by accounting for ancestry-specific linkage disequilibrium patterns [67] [71].

Combinatorial analytics represents a powerful approach for unraveling the complex genetic architecture of endometriosis, significantly outperforming traditional GWAS in both novel biological discovery and cross-population validation. The successful identification of 75 novel gene associations and the high reproducibility rates (up to 88%) across diverse ancestry groups demonstrates the method's robustness and translational potential [2].

This protocol provides researchers with a comprehensive framework for implementing combinatorial analytics in complex trait genetics. The integration of these methods with trans-ancestry meta-analysis approaches promises to accelerate the discovery of biologically relevant therapeutic targets and advance precision medicine for endometriosis and other complex disorders with similarly elusive genetic architectures.

Cross-Population Colocalization and Fine-Mapping Validation

Endometriosis is a heritable, estrogen-dependent gynecological disorder affecting approximately 6-10% of women of reproductive age, characterized by the presence of endometrial-like tissue outside the uterus and associated with chronic pelvic pain and reduced fertility [21] [19]. The genetic architecture of endometriosis involves multiple loci with modest effects, and trans-ancestry meta-analysis approaches have proven invaluable for disentangling this complexity by leveraging genetic differences across populations. Cross-population colocalization and fine-mapping represent advanced statistical genetic methodologies that enable more precise identification of causal variants and genes by integrating genome-wide association data (GWAS) from diverse ancestral backgrounds. These approaches effectively address the limitations of single-ancestry studies by leveraging differences in linkage disequilibrium patterns and allele frequencies across populations, leading to refined candidate causal variants and enhanced biological insights for therapeutic development [12] [13].

The implementation of these methods in recent large-scale genetic studies of endometriosis has dramatically expanded our understanding of its genetic underpinnings, revealing novel risk loci and highlighting the critical role of sex steroid hormone pathways, immune regulation, and tissue remodeling mechanisms in disease pathogenesis [21] [12]. This application note provides a comprehensive framework for implementing cross-population colocalization and fine-mapping validation within the context of endometriosis research, including standardized protocols, data resources, and analytical workflows to accelerate gene discovery and drug target prioritization.

Quantitative Data Synthesis from Endometriosis Genetic Studies

Evolution of Endometriosis GWAS Findings

Table 1: Summary of Key Endometriosis Genetic Association Studies

Study Sample Size (Cases/Controls) Ancestries Significant Loci Novel Loci Primary Findings
Nyholt et al. 2012 [48] 4,604/9,393 European, Japanese 7 4 First trans-ancestry meta-analysis; identified WNT4, GREB1, VEZT loci; demonstrated shared genetic architecture
Sapkota et al. 2017 [21] 17,045/191,596 European, Japanese 19 (9 replicated + 5 novel + 5 secondary) 5 Highlighted key genes in hormone metabolism (FN1, CCDC170, ESR1, SYNE1, FSHB); explained up to 5.19% variance
Multi-ancestry 2025 (Preprint) [12] [13] ~105,869/~1.3 million Multi-ancestry 80 37 Largest study to date; first adenomyosis loci; identified pathways for immune regulation, tissue remodeling, cell differentiation
FinnGen R10 [10] 16,588/111,583 European N/A N/A Validation cohort for cell aging genes; confirmed THRB and ENG protein associations

Table 2: Essential Data Resources for Cross-Population Analysis

Data Type Source Sample Size Ancestries Application in Endometriosis Research
GWAS Summary Statistics FinnGen R10 [10] 16,588 cases/111,583 controls European Primary discovery and validation
UK Biobank [11] [10] 4,036 cases/210,927 controls European Replication and meta-analysis
BioBank Japan [21] [48] 1,423 cases/1,318 controls Japanese Trans-ancestry discovery
Expression QTLs (eQTLs) eQTLGen [72] [10] 31,684 individuals Mostly European Blood-based gene expression regulation
GTEx v8 [72] [10] 838 donors (17,382 samples) Multi-ancestry Tissue-specific regulation (including uterus)
Methylation QTLs (mQTLs) BSGS/LBC meta-analysis [10] 1,980 individuals European Epigenetic regulation in blood
Protein QTLs (pQTLs) UK Biobank Pharma Proteomics [10] 54,219 participants European Plasma protein abundance regulation
Iceland pQTL [11] 35,559 individuals European Independent pQTL validation

Experimental Protocols

Protocol 1: Cross-Population Fine-Mapping

Purpose: To refine causal variant identification by leveraging differential linkage disequilibrium patterns across diverse populations.

Workflow:

  • Data Harmonization

    • Obtain GWAS summary statistics from minimum two distinct ancestral groups (e.g., European and East Asian)
    • Perform genomic coordinate alignment to reference genome build (e.g., GRCh38)
    • Apply standard quality control: remove duplicates, strand-ambiguous SNPs, and variants with imputation quality score <0.6

  • Conditional Analysis

    • Implement stepwise model selection to identify independent association signals
    • Use GCTA-COJO tool with reference panels matched to each ancestry
    • Apply significance threshold of P < 5×10^(-8) for conditional analysis

  • Fine-Mapping Implementation

    • Execute SUSIE or FINEMAP algorithms separately for each ancestry group
    • Use population-specific linkage disequilibrium reference panels (1000 Genomes Project)
    • Set credible set threshold at 95% or 99% probability

  • Cross-Population Integration

    • Compare credible sets across ancestries to identify overlapping causal variants
    • Calculate posterior probabilities for shared versus population-specific signals
    • Annotate refined candidate variants with functional genomic data

Figure 1: Cross-population fine-mapping workflow for identifying causal variants by leveraging differential linkage disequilibrium across diverse ancestral groups.

Protocol 2: Multi-omic Colocalization Analysis

Purpose: To identify shared genetic signals between endometriosis risk and molecular quantitative trait loci, providing evidence for potential causal mechanisms.

Workflow:

  • Variant Selection

    • Extract all variants within ±1000 kb of candidate gene or lead GWAS variant
    • Apply MAF filter (>0.01) to ensure adequate representation across ancestries
    • Harmonize alleles and effect directions across all datasets

  • Colocalization Testing

    • Implement Bayesian colocalization using coloc R package
    • Test five hypotheses (H0-H4) with default prior probabilities
    • Consider posterior probability for H4 (PPH4) > 0.8 as strong evidence for colocalization

  • Sensitivity Analyses

    • Perform heterogeneity testing using HEIDI test (P > 0.05 indicates no heterogeneity)
    • Apply conditional colocalization to account for multiple causal variants
    • Validate with independent molecular QTL datasets when available

  • Multi-omic Integration

    • Conduct summary-data-based Mendelian randomization (SMR) to test causal relationships
    • Integrate colocalization results across QTL types (eQTL, mQTL, pQTL)
    • Prioritize genes with support from multiple molecular QTL types

Figure 2: Multi-omic colocalization analysis workflow for identifying shared genetic signals between endometriosis risk and molecular quantitative trait loci across diverse data types.

Protocol 3: Drug Target Validation

Purpose: To prioritize and validate potential drug targets for endometriosis using genetic evidence across populations.

Workflow:

  • Target Identification

    • Compile genes with colocalization evidence from multi-omic analyses
    • Annotate with known drug target information from databases like ChEMBL and DrugBank
    • Prioritize genes with known ligand-binding domains or existing chemical modulators

  • Causal Evidence Assessment

    • Apply Mendelian randomization framework using cis-pQTLs as instruments
    • Validate directionality using Steiger filtering
    • Test for potential horizontal pleiotropy using MR-Egger and MR-PRESSO

  • Cross-Ancestry Replication

    • Replicate findings in independent ancestral groups where possible
    • Test for heterogeneity using Cochran's Q statistic
    • Apply false discovery rate correction for multiple testing

  • Therapeutic Potential Evaluation

    • Assess safety profiles using phenome-wide association scans (PheWAS)
    • Evaluate potential for drug repurposing using existing clinical trial data
    • Consider tractability for small molecule or biologic development

The Scientist's Toolkit

Essential Research Reagents and Computational Tools

Table 3: Key Research Reagents and Computational Resources

Category Resource/Tool Application Key Features
GWAS Data FinnGen R10 [10] Endometriosis genetic associations 16,588 cases, 111,583 controls; European ancestry
UK Biobank [11] Validation cohort 4,036 cases, 210,927 controls; deep phenotyping
BBJ [21] Trans-ancestry discovery Japanese ancestry; enables cross-population analysis
QTL Resources GTEx v8 [72] [10] Tissue-specific eQTLs Uterus and 51 other tissues; multi-ancestry
eQTLGen [72] [10] Blood eQTLs 31,684 individuals; largest blood eQTL resource
Iceland pQTL [11] Plasma protein QTLs 4,907 cis-pQTLs; SOMAscan platform
Software Tools GCTA-COJO Conditional analysis Identifies independent association signals
SUSIE/FINEMAP Fine-mapping Bayesian methods for credible set construction
coloc R package [72] [10] Colocalization analysis Bayesian test for shared causal variants
SMR [10] Mendelian randomization Integrates GWAS and QTL data for causal inference
Experimental Validation ELISA kits [11] Protein quantification Validate pQTL findings (e.g., RSPO3 levels)
RT-qPCR [11] Gene expression Confirm differential expression in tissues
Clinical samples [11] Functional validation Endometriosis lesions vs. control endometrium

Case Study: Endometriosis Colocalization in Practice

RSPO3 Validation Example

A recent study demonstrated the application of these protocols by identifying RSPO3 (R-spondin 3) as a potential therapeutic target for endometriosis [11]. The researchers:

  • Performed systematic Mendelian randomization analysis of 4,907 plasma proteins
  • Identified significant association between RSPO3 pQTLs and endometriosis risk (OR = 1.20, P = 3.2×10^(-5))
  • Conducted colocalization analysis showing strong evidence for shared causal variant (PPH4 = 0.92)
  • Validated findings experimentally using ELISA in clinical samples (20 cases, 20 controls)
  • Confirmed RSPO3 protein elevation in endometriosis patient plasma (P < 0.05)

This multi-step approach exemplifies how cross-population colocalization and validation can prioritize high-confidence drug targets with translational potential.

Cell Aging Genes in Endometriosis

Another application utilized multi-omic SMR analysis to investigate cell aging-related genes in endometriosis pathogenesis [10]. The study:

  • Integrated endometriosis GWAS with mQTL, eQTL, and pQTL data
  • Identified 196 CpG sites in 78 genes, 18 eQTL-associated genes, and 7 pQTL-associated proteins
  • Discovered that MAP3K5 methylation downregulates gene expression, increasing endometriosis risk
  • Validated THRB gene and ENG protein as risk factors in FinnGen R10 and UK Biobank cohorts
  • Highlighted novel therapeutic targets linking cellular aging pathways to endometriosis

Troubleshooting and Technical Considerations

  • Ancestral Diversity Limitations: When working with under-represented populations, consider using trans-ancestry fine-mapping methods that can handle sparse data.

  • Sample Overlap: Account for potential sample overlap between GWAS and QTL studies using appropriate correlation matrices.

  • Power Considerations: Ensure adequate sample sizes for colocalization analysis, particularly for cell-type specific QTLs with smaller effect sizes.

  • Multiple Testing: Apply conservative significance thresholds (e.g., Bonferroni correction) when testing multiple genes or genomic regions.

  • Functional Interpretation: Integrate epigenomic annotation (e.g., ENCODE, Roadmap) to prioritize variants with regulatory potential.

These protocols provide a standardized framework for implementing cross-population colocalization and fine-mapping in endometriosis research, enabling more robust gene discovery and therapeutic target identification across diverse ancestral groups.

Within the context of advancing trans-ancestry meta-analysis methods for endometriosis genome-wide association studies (GWAS), a critical challenge remains: translating genetic discoveries into clinically actionable insights for diverse patient populations. Endometriosis is a common chronic condition affecting approximately 10% of women of reproductive age, characterized by debilitating symptoms including chronic pelvic pain and fatigue, yet it suffers from diagnostic delays of 7-9 years on average and limited treatment options [73] [2]. The heterogenous symptom presentation and substantial comorbidity profile observed in endometriosis patients complicate clinical management and therapeutic development. This Application Note provides detailed protocols for assessing the predictive utility of novel genetic and digital biomarkers for specific symptom subtypes and comorbidities, facilitating their translation into clinical applications and precision medicine approaches.

Background and Significance

Traditional GWAS approaches in endometriosis have identified multiple genomic loci associated with disease risk, but these explain only approximately 5% of disease variance [2]. This limitation highlights the complex genetic architecture of endometriosis and the need for more sophisticated analytical methods. Trans-ancestry meta-analysis offers enhanced power for locus discovery and fine-mapping, potentially revealing population-specific and shared genetic risk factors. However, the clinical utility of these findings depends on robust validation and connection to phenotypic manifestations.

Recent research demonstrates that combinatorial analytics and Mendelian randomization (MR) approaches can identify novel genetic risk factors and potential therapeutic targets that may be overlooked by conventional GWAS [2] [6]. Simultaneously, digital technologies including actigraphy and smartphone-based monitoring provide objective measures of symptoms and behaviors that correlate with patient-reported outcomes, offering new avenues for quantifying symptom severity and trajectory [73] [74]. Integrating these multidimensional data sources is essential for developing comprehensive biomarkers capable of predicting symptom subtypes and comorbidities.

Key Quantitative Findings from Recent Studies

Table 1: Key Genetic Findings from Combinatorial Analytics in Endometriosis

Analysis Type Cohort Key Findings Clinical Translation Potential
Combinatorial Analytics [2] UK Biobank (White European), All of Us (Multi-ancestry) 1,709 disease signatures identified; 58-88% reproducibility in validation cohort; 75 novel genes discovered Pathways identified (cell adhesion, fibrosis, neuropathic pain) inform subtype stratification; novel therapeutic targets
Mendelian Randomization [6] UK Biobank, FinnGen RSPO3 and FLT1 potentially associated with endometriosis; robust association confirmed for RSPO3 Causal evidence supporting RSPO3 as therapeutic target; potential biomarker for patient stratification
Trans-ancestry Reproducibility [2] All of Us sub-cohorts 66-76% reproducibility of signatures in non-white European cohorts Demonstrates utility of genetic signatures across ancestries; supports inclusive biomarker development

Table 2: Digital Biomarker Correlations with Endometriosis Symptoms

Digital Measure Symptom Correlation Study Details Clinical Utility
Physical Activity (Actigraphy) [74] Strong negative correlation with fatigue (R < -0.3) 68 participants, up to three 4-6 week monitoring cycles Objective measure of fatigue impact; treatment monitoring
Activity Rhythms & Sleep [74] Associated with symptom severity and variability (⎮R⎮ > 0.3) 5152 days of actigraphy data Identifies patients with more severe symptom trajectories
Post-surgical Changes [74] Reflect changes in self-reported symptoms n=16 surgical patients Objective measure of treatment response

Experimental Protocols

Protocol 1: Validation of Genetic Biomarkers Across Ancestries

Purpose: To validate novel combinatorial genetic signatures identified through trans-ancestry meta-analysis in diverse patient populations.

Materials:

  • DNA samples from multi-ancestry cohorts (e.g., All of Us Research Program)
  • Genotyping arrays or whole-genome sequencing data
  • Clinical phenotyping data including symptom subtypes and comorbidities

Procedure:

  • Sample Preparation: Extract high-quality DNA from blood or saliva samples according to established protocols. Quantify DNA concentration and quality using spectrophotometry.
  • Genotyping: Perform genotyping using Illumina Global Screening Array or similar platform. Alternatively, utilize existing whole-genome sequencing data.
  • Quality Control: Apply standard QC filters: call rate >98%, Hardy-Weinberg equilibrium p > 1×10^-6, minor allele frequency >1%.
  • Population Stratification: Perform principal component analysis to account for population structure and avoid spurious associations.
  • Signature Validation: Test pre-specified combinatorial signatures (identified through discovery analyses) for association with endometriosis risk and specific symptom subtypes using logistic regression, adjusting for age, genetic ancestry, and relevant covariates.
  • Phenotypic Correlation: Assess association of validated signatures with symptom subtypes (e.g., pain characteristics, fatigue severity) and comorbidities (e.g., irritable bowel syndrome, migraine) using appropriate statistical tests based on outcome variable type.

Analysis:

  • Calculate odds ratios and 95% confidence intervals for each signature.
  • Determine positive predictive value, negative predictive value, and area under the receiver operating characteristic curve for symptom subtype prediction.
  • Assess stratification potential by calculating proportion of variance explained in specific symptom domains.

Protocol 2: Longitudinal Digital Phenotyping for Symptom Monitoring

Purpose: To objectively characterize symptom trajectories and treatment responses using wearable device data.

Materials:

  • Wrist-worn accelerometers (e.g., ActiGraph, Fitbit, or Apple Watch)
  • Smartphone application for ecological momentary assessment (EMA)
  • Data processing pipeline for actigraphy data

Procedure:

  • Device Setup: Initialize devices with appropriate settings for high-frequency data collection (e.g., 30-100Hz sampling rate).
  • Participant Instruction: Instruct participants to wear device continuously (removing only for charging or water activities) and complete daily EMAs for pain, fatigue, and other symptoms.
  • Data Collection: Collect data over multiple 4-6 week cycles, ideally capturing pre- and post-intervention periods.
  • Adherence Monitoring: Monitor wear time compliance (>75% considered acceptable) and prompt participants with reminders if adherence drops.
  • Data Extraction: Extract daily measures of physical activity, sleep parameters (duration, efficiency, regularity), and diurnal rhythms from raw acceleration data using validated algorithms.
  • Synchronization: Time-match objective measures with self-reported symptoms from EMAs.

Analysis:

  • Calculate repeated measures correlations between digital measures and symptom reports.
  • Identify symptom flares and characterize associated changes in digital biomarkers.
  • Use mixed-effects models to account for within-subject variability and identify group-level trends.
  • Apply machine learning approaches to classify symptom severity states based on digital features alone.

Protocol 3: Therapeutic Target Validation Through Mendelian Randomization and Experimental Assays

Purpose: To provide causal evidence for potential therapeutic targets and validate them in clinical samples.

Materials:

  • Blood and tissue samples from endometriosis patients and controls
  • ELISA kits for target proteins (e.g., Human R-Spondin3 ELISA Kit)
  • RT-qPCR reagents and equipment
  • Western blot apparatus and reagents

Procedure:

  • Sample Collection: Collect blood and endometriosis lesion tissues from patients undergoing surgical treatment, and control samples from patients without endometrial diseases.
  • Plasma Separation: Centrifuge blood samples at 2000×g for 10 minutes at 4°C, aliquot plasma, and store at -80°C until analysis.
  • Protein Quantification: Use double-antibody sandwich ELISA method to measure target protein concentration in plasma according to manufacturer's protocol.
    • Add samples to pre-coated wells
    • Incubate with detection antibody
    • Add enzyme conjugate and substrate
    • Measure absorbance at 450nm
    • Calculate concentrations from standard curve
  • Gene Expression Analysis: Extract RNA from tissue samples, synthesize cDNA, and perform RT-qPCR using target-specific primers.
  • Protein Expression Analysis: Perform Western blotting on tissue lysates to confirm protein level differences.

Analysis:

  • Compare protein and gene expression levels between cases and controls using appropriate statistical tests (t-tests, ANOVA).
  • Assess correlation between biomarker levels and symptom severity.
  • Determine sensitivity and specificity of biomarkers for distinguishing subtypes or predicting comorbidities.

Visualization of Key Workflows and Pathways

G Genetic Biomarker Discovery and Validation Workflow cluster_discovery Discovery Phase cluster_validation Validation Phase GWAS Trans-ancestry GWAS Meta-analysis Combinatorial Combinatorial Analytics GWAS->Combinatorial Signatures Disease Signatures (Multi-SNP Combinations) Combinatorial->Signatures MR Mendelian Randomization Signatures->MR Functional Functional Validation (ELISA, RT-qPCR, Western) MR->Functional Biomarker Validated Biomarkers for Subtypes/Comorbidities Functional->Biomarker Output Clinical Applications: Stratification, Targeted Therapies Biomarker->Output Input Multi-ancestry Cohorts Input->GWAS

G Digital Biomarker Correlation with Symptom Subtypes cluster_objective Objective Measures cluster_subjective Symptom Subtypes Digital Digital Phenotyping (Wearable Sensors, EMA) PA Physical Activity (Step Count, Intensity) Digital->PA Sleep Sleep Patterns (Duration, Efficiency) Digital->Sleep Rhythms Diurnal Rhythms (Regularity, Fragmentation) Digital->Rhythms Pain Pain Flares (Severity, Frequency) PA->Pain Strong negative correlation Fatigue Fatigue Impact on Daily Function PA->Fatigue Strong negative correlation Sleep->Pain Moderate correlation Sleep->Fatigue Moderate correlation Rhythms->Pain Disruption associated with severity Comorb Comorbidity Manifestation Rhythms->Comorb Associated with comorbidity burden Application Clinical Applications: Treatment Monitoring, Flare Prediction Pain->Application Fatigue->Application Comorb->Application

The Scientist's Toolkit: Essential Research Reagents and Materials

Table 3: Essential Research Reagents and Platforms for Endometriosis Biomarker Studies

Category Specific Products/Platforms Function Key Considerations
Genetic Analysis PrecisionLife combinatorial analytics platform [2], PLINK, METAL Identify and validate genetic risk signatures beyond GWAS Handles high-order SNP interactions; validated in trans-ancestry cohorts
Wearable Sensors ActiGraph, Fitbit, Apple Watch [73] [74] Continuous collection of actigraphy data for digital phenotyping Balance between research-grade accuracy and patient acceptability
Biomolecular Assays Human R-Spondin3 ELISA Kit [6], SOMAscan, RNA extraction kits Quantify protein and gene expression biomarkers Sensitivity and specificity for low-abundance targets; validation in relevant matrices
Data Integration R (urbnthemes package) [75], Python pandas, SQL databases Manage and analyze multi-modal data streams Ensure interoperability between genetic, digital, and clinical data
Statistical Analysis Mendelian randomization packages (TwoSampleMR, MRBase) [6], mixed-effects models Establish causal relationships and longitudinal patterns Account for population structure and repeated measures

The integration of trans-ancestry genetic data with multidimensional phenotypic information from digital health technologies creates unprecedented opportunities for advancing precision medicine in endometriosis. The protocols outlined in this Application Note provide a roadmap for robust validation of biomarkers capable of predicting symptom subtypes and comorbidities, ultimately facilitating targeted therapeutic development and personalized management approaches. As these methods continue to evolve, they hold promise for reducing the diagnostic delay and improving quality of life for the diverse population of individuals affected by endometriosis.

Comparative Performance of Meta-Analysis vs. Mega-Analysis in Trans-Ancestry Settings

Trans-ancestry genetic association studies are essential for identifying robust and generalizable genetic risk factors for endometriosis. Two primary analytical approaches—meta-analysis and mega-analysis—enable the combination of genetic data across diverse ancestral populations. This application note provides a structured comparison of these methodologies, detailing protocols for their implementation in endometriosis research. We present quantitative performance metrics, experimental workflows, and essential research tools to guide researchers in selecting and executing the optimal analytical strategy for their trans-ancestry studies.

Endometriosis is a complex gynecological disorder affecting approximately 10% of women of reproductive age, with a significant genetic component contributing to its pathogenesis [8]. Genome-wide association studies (GWAS) have successfully identified multiple susceptibility loci for endometriosis; however, many early studies were limited by a predominant focus on European-ancestry populations [19] [21]. Trans-ancestry genetic studies are critically important for improving the power and generalizability of findings, facilitating fine-mapping of causal variants, and enhancing equity in genetic research [36].

The integration of diverse ancestry groups in genetic studies presents methodological challenges, particularly in selecting the optimal approach for combining genetic data. Meta-analysis, which combines summary statistics from analyses performed within homogeneous ancestry groups, has been the traditional approach for multi-ancestry GWAS [36]. More recently, mega-analysis—which pools individual-level data across ancestry groups for unified processing and analysis—has gained traction with the emergence of cosmopolitan reference panels like TOPMed [36]. This application note systematically compares these two approaches in the context of endometriosis research, providing detailed protocols and performance metrics to guide researchers in trans-ancestry study design.

Comparative Performance of Meta-Analysis vs. Mega-Analysis

Meta-analysis follows a "analyze-first, combine-later" paradigm where genetic data from different ancestry groups are processed and analyzed separately using ancestry-specific reference panels, with summary statistics subsequently combined using fixed or random-effects models [36]. This approach effectively controls for population stratification within homogeneous groups but may exclude individuals of admixed ancestry and reduce statistical power when heterogeneity exists between groups [36].

Mega-analysis employs a "combine-first, analyze-later" approach where individual-level data from all participants are collectively processed using a cosmopolitan reference panel and analyzed in a unified framework that incorporates genetic ancestry as covariates [36]. This integrated approach maximizes sample size and leverages cosmopolitan reference panels but requires careful handling of population stratification across diverse groups [36].

Table 1: Fundamental Methodological Differences Between Meta-Analysis and Mega-Analysis

Feature Meta-Analysis Mega-Analysis
Data Structure Summary statistics Individual-level data
Reference Panels Ancestry-specific (e.g., CAAPA, HRC, GAsP) Cosmopolitan (e.g., TOPMed)
Ancestry Handling Analysis within homogeneous groups Unified analysis with ancestry covariates
Implementation Distributed analysis with results combination Centralized processing and analysis
Admixed Individuals Often excluded Included with appropriate modeling
Quantitative Performance Comparison in Trans-Ancestry Settings

Empirical comparisons in multi-ancestry studies demonstrate distinct performance characteristics for each approach. In a multi-national study of maternal glycemia, mega-analysis identified significantly more genome-wide significant associations compared to meta-analysis, including biologically credible associations at the MTNR1B locus that were not detected by meta-analysis [36]. For metabolomics analyses, the number of significant findings in heterogeneous ancestry mega-analysis "far exceeded" those from homogeneous ancestry meta-analysis and confirmed many previously documented associations [36].

Table 2: Empirical Performance Comparison from Multi-Ancestry Studies

Performance Metric Meta-Analysis Mega-Analysis
Number of Significant Loci 15 variants near GCK with maternal fasting glucose [36] Rich set of variants including GCK and MTNR1B with both fasting and 1-hour glucose [36]
Metabolomics Associations Limited significant findings [36] Vastly more significant findings [36]
Genomic Control Well-controlled genomic inflation factors [36] Variable genomic inflation factors requiring careful interpretation [36]
Analytical Flexibility Accommodates study-specific covariates and ancestry adjustments [76] Enables uniform quality control and analysis framework [36]
Implementation Practicality No individual-level data sharing; efficient for consortia [76] Requires data harmonization and significant computational resources [36]

For analysis of gene-environment (G×E) interactions in trans-ancestry settings, both methods have demonstrated comparable performance. An empirical comparison of four studies found that meta-analysis and mega-analysis provided similar effect size estimates, standard errors, and p-values for G×E interactions, with highly correlated results (Pearson's r = 0.98) and comparable genomic inflation factors [76].

Protocol Implementation for Endometriosis Research

Protocol 1: Trans-Ancestry Meta-Analysis for Endometriosis GWAS

Principle: Perform GWAS within homogeneous ancestry groups followed by statistical combination of results [19] [21].

Step-by-Step Procedure:

  • Ancestry Group Definition

    • Apply principal component analysis (PCA) and ancestry inference using principal component analysis and spatial analysis (AIPS) to genotype data [36].
    • Define homogeneous ancestry clusters using minimum covariance determinant (MCD) regions based on principal components [36].
    • Assign individuals falling outside MCD boundaries to admixed groups or exclude from analysis.

  • Ancestry-Specific Genotype Imputation

    • Align genotype data to ancestry-specific reference panels:
      • African ancestry: CAAPA African American Reference Panel [36]
      • East Asian ancestry: Genome Asia Pilot (GAsP) Reference Panel [36]
      • European ancestry: HRC European reference panel [36]
      • Admixed American ancestry: 1000G Phase 3 Reference Panel AMR [36]
    • Perform phasing using Eagle v2.4 and imputation using Minimac4 with R-square filter >0.30 [36].
    • Retain variants with minor allele frequency (MAF) > 0.05 within each ancestry group.

  • Ancestry-Stratified Association Analysis

    • Within each ancestry group, perform GWAS for endometriosis case-control status.
    • Include relevant covariates: age, study site, parity, and principal components for ancestry [21].
    • For quantitative endometriosis sub-phenotypes (e.g., rAFS stage), use appropriate linear or logistic regression models.

  • Summary Statistics Meta-Analysis

    • Apply quality control filters to summary statistics from each ancestry group.
    • Combine results using random-effects models (e.g., Han and Elkin random-effects model) to account for heterogeneity [19].
    • For loci with low heterogeneity (Cochran's Q test p-value > 0.05), consider fixed-effect models for increased power [19].
    • Evaluate genomic inflation factors (λ) and apply genomic control correction if needed.

meta_analysis_workflow start Multi-ancestry Genotype Data pc_cluster Ancestry Clustering (PCA + AIPS) start->pc_cluster anc_group1 Ancestry Group 1 pc_cluster->anc_group1 anc_group2 Ancestry Group 2 pc_cluster->anc_group2 anc_group3 Ancestry Group 3 pc_cluster->anc_group3 impute1 Ancestry-specific Imputation anc_group1->impute1 impute2 Ancestry-specific Imputation anc_group2->impute2 impute3 Ancestry-specific Imputation anc_group3->impute3 gwas1 Ancestry-stratified GWAS impute1->gwas1 gwas2 Ancestry-stratified GWAS impute2->gwas2 gwas3 Ancestry-stratified GWAS impute3->gwas3 meta Summary Statistics Meta-analysis gwas1->meta gwas2->meta gwas3->meta results Trans-ancestry Association Results meta->results

Visualization 1: Trans-Ancestry Meta-Analysis Workflow. This diagram illustrates the sequential process of analyzing genetically similar groups separately followed by statistical combination of results.

Protocol 2: Trans-Ancestry Mega-Analysis for Endometriosis GWAS

Principle: Collective processing and unified analysis of individual-level data across diverse ancestry groups [36].

Step-by-Step Procedure:

  • Cross-Ancestry Genotype Harmonization

    • Combine raw genotype data from all ancestry groups.
    • Identify variants present across all ancestry groups and platforms.
    • Perform cross-ancestry quality control: remove variants with >2% missing call rate and samples with >2% missing variants [36].
    • Exclude regions with high linkage disequilibrium (lactase region, MHC, inversion regions 8p23 and 17q21.31) for ancestry inference [36].

  • Cosmopolitan Reference Panel Imputation

    • Align combined genotype data to TOPMed cosmopolitan reference panel (Freeze 8 GRCh38) [36].
    • Perform phasing using Eagle v2.4 and imputation using Minimac4 with R-square filter >0.30 [36].
    • Retain well-imputed variants (R² > 0.8) for association testing.

  • Population Structure Adjustment

    • Calculate principal components (PCs) from LD-pruned genome-wide SNPs.
    • Include the top 10 PCs as covariates in association models to control for population stratification.
    • Consider additional approaches such as linear mixed models to account for relatedness and fine-scale population structure.

  • Unified Association Analysis

    • Perform GWAS for endometriosis case-control status using all samples.
    • Include covariates: age, study site, parity, and genetic principal components.
    • For endometriosis sub-phenotypes, perform stratified analyses by disease stage (rAFS I/II vs. III/IV) to identify stage-specific genetic effects [21].

mega_analysis_workflow start Multi-ancestry Genotype Data harmonize Cross-ancestry Data Harmonization start->harmonize cosmopolitan Cosmopolitan Panel Imputation (TOPMed) harmonize->cosmopolitan pcs Population Structure Assessment (PCs) cosmopolitan->pcs unified Unified GWAS with Ancestry Covariates pcs->unified results Trans-ancestry Association Results unified->results

Visualization 2: Trans-Ancestry Mega-Analysis Workflow. This diagram illustrates the integrated approach of combining diverse data before processing and analysis.

Table 3: Key Analytical Tools and Resources for Trans-Ancestry Endometriosis GWAS

Resource Category Specific Tool/Resource Application in Endometriosis Research
Reference Panels TOPMed Freeze 8 [36] Cosmopolitan imputation for mega-analysis
CAAPA Panel [36] African-ancestry specific imputation for meta-analysis
1000 Genomes Phase 3 [21] Multi-ancestry reference panel
Imputation Tools Minimac4 [36] Efficient genotype imputation
Eagle v2.4 [36] Accurate haplotype phasing
Association Software METAL [76] Summary statistics meta-analysis
REGENIE [36] Unified association testing for mega-analysis
Functional Validation GTEx eQTL Database [8] [5] Tissue-specific regulatory effect mapping for endometriosis risk loci
Ensembl VEP [8] [5] Functional annotation of endometriosis-associated variants
Data Sources GWAS Catalog [8] [5] Repository of published endometriosis associations
UK Biobank [6] Large-scale genetic and phenotypic data
FinnGen [6] Finnish population cohort with endometriosis cases

Application to Endometriosis Research: Key Considerations

Endometriosis-Specific Analytical Considerations

Endometriosis presents unique challenges for genetic analysis due to its heterogeneous clinical presentation, with different genetic effects observed across disease stages. Multiple studies have demonstrated that most endometriosis risk loci show stronger genetic effects for revised American Fertility Society (rAFS) Stage III/IV disease compared to all cases combined [19] [21]. This has important implications for trans-ancestry studies:

  • Stratified Analysis: When feasible, perform separate analyses for Stage III/IV endometriosis to increase power for detecting stage-specific genetic effects [21].
  • Phenotype Harmonization: Implement standardized endometriosis case definitions across diverse cohorts using surgical confirmation (rAFS staging) where possible [21].
  • Functional Follow-up: Integrate endometriosis-associated variants with expression quantitative trait loci (eQTL) data from biologically relevant tissues (uterus, ovary, vagina) to prioritize candidate genes and understand tissue-specific regulatory mechanisms [8] [5].
Selection Guidelines for Analytical Approach

The choice between meta-analysis and mega-analysis depends on research objectives, data availability, and computational resources:

  • Select Meta-Analysis When: Working in large consortia with data sharing limitations, analyzing highly heterogeneous ancestry groups with distinct genetic architectures, or when study-specific covariates require localized modeling [76].
  • Select Mega-Analysis When: Maximizing discovery power for novel endometriosis loci, analyzing admixed populations, or when uniform quality control and analysis pipelines are preferred [36].
  • Hybrid Approaches: Consider performing both analyses when feasible to assess robustness of findings across methodological approaches [36] [76].

Both meta-analysis and mega-analysis offer viable approaches for trans-ancestry endometriosis GWAS, with complementary strengths and limitations. Meta-analysis provides a practical framework for consortium-based research with distributed data, while mega-analysis offers increased power and more unified analytical approaches. The emergence of cosmopolitan reference panels and improved methods for controlling population stratification continue to enhance both approaches. For endometriosis research specifically, attention to disease heterogeneity and integration with functional genomic data from relevant tissues will be essential for translating genetic discoveries into biological insights and therapeutic targets.

Conclusion

Trans-ancestry meta-analysis methods have fundamentally transformed endometriosis genetics, expanding the catalog of risk loci from approximately 45 to over 80 significant associations and providing unprecedented insights into disease biology across diverse populations. The integration of multi-ancestry datasets has enhanced discovery power, improved fine-mapping resolution, and revealed both shared and population-specific risk mechanisms. Methodological advances in polygenic risk scoring, pathway analysis, and multi-omics integration are paving the way for more equitable precision medicine approaches. Future directions should focus on increasing representation of underrepresented populations, developing standardized frameworks for cross-ancestry analysis, and translating genetic discoveries into clinically actionable insights through drug repurposing and targeted therapeutic development. These approaches will ultimately enable earlier diagnosis, improved risk stratification, and more effective, personalized treatments for endometriosis patients worldwide.

References