{"title":"Rare variant analyses provide insights into the genetic architecture of endometriosis.","authors":"Yuanyuan Gui, Yuhan Xie, Xiaomei Tong, Hongyu Zhao","doi":"10.1186/s40246-026-00934-x","DOIUrl":null,"url":null,"abstract":"<p><p>BACKGROUND: Endometriosis (EMs) is a prevalent gynecologic disorder that often causes severe chronic pelvic pain and infertility. Although genome-wide association studies have identified common variants of EMs, the contribution of rare variants remains unclear. RESULTS: We conducted an exome-wide analysis in 87,100 unrelated female participants of European ancestry from the UK Biobank. Single variant association tests for rare variants and gene-based analyses were performed, with replication in the FinnGen and Biobank Japan cohorts. No single rare variants achieved genome-wide significance ([Formula: see text]). While gene-based analyses identified SOGA1 significantly associated with EMs at the Bonferroni-corrected threshold of [Formula: see text], which was replicated in both external cohorts. Functional analyses indicated involvement of implicated genes and interaction networks in regulating gluconeogenesis, muscle filament sliding, and autophagosome. We also observed expression differences of SOGA1 across various cell types and found significant cell type-specific enrichments for EMs. CONCLUSIONS: This study establishes the contribution of rare genetic variants to endometriosis pathogenesis through the discovery of a novel susceptibility gene, advancing the genetic understanding of this disease. </p>","PeriodicalId":13183,"journal":{"name":"Human Genomics","volume":" ","pages":""},"PeriodicalIF":4.1000,"publicationDate":"2026-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13001359/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Human Genomics","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1186/s40246-026-00934-x","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"GENETICS & HEREDITY","Score":null,"Total":0}
引用次数: 0
Abstract
BACKGROUND: Endometriosis (EMs) is a prevalent gynecologic disorder that often causes severe chronic pelvic pain and infertility. Although genome-wide association studies have identified common variants of EMs, the contribution of rare variants remains unclear. RESULTS: We conducted an exome-wide analysis in 87,100 unrelated female participants of European ancestry from the UK Biobank. Single variant association tests for rare variants and gene-based analyses were performed, with replication in the FinnGen and Biobank Japan cohorts. No single rare variants achieved genome-wide significance ([Formula: see text]). While gene-based analyses identified SOGA1 significantly associated with EMs at the Bonferroni-corrected threshold of [Formula: see text], which was replicated in both external cohorts. Functional analyses indicated involvement of implicated genes and interaction networks in regulating gluconeogenesis, muscle filament sliding, and autophagosome. We also observed expression differences of SOGA1 across various cell types and found significant cell type-specific enrichments for EMs. CONCLUSIONS: This study establishes the contribution of rare genetic variants to endometriosis pathogenesis through the discovery of a novel susceptibility gene, advancing the genetic understanding of this disease.
期刊介绍:
Human Genomics is a peer-reviewed, open access, online journal that focuses on the application of genomic analysis in all aspects of human health and disease, as well as genomic analysis of drug efficacy and safety, and comparative genomics.
Topics covered by the journal include, but are not limited to: pharmacogenomics, genome-wide association studies, genome-wide sequencing, exome sequencing, next-generation deep-sequencing, functional genomics, epigenomics, translational genomics, expression profiling, proteomics, bioinformatics, animal models, statistical genetics, genetic epidemiology, human population genetics and comparative genomics.