| Title: | Identifying sex-specific genetic effects across 733 traits in UK biobank |
| Journal: | BMC Genomics |
| Published: | 15 Jul 2026 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/42458269/ |
| DOI: | https://doi.org/10.1186/s12864-026-13164-1 |
Publication 20269
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Abstract
Sex-specificity has been reported in a wide range of diseases and complex traits. While sex-specific genetic effects have been documented for certain traits, the genetic mechanisms underlying sex differences in most traits remain largely unexplored. With its large sample size and wide range of diseases and traits, the UK Biobank - a large, prospective cohort study containing health history, phenotypic measurements, and genetic data for over 500,000 individuals - provides an opportunity to explore sexually dimorphic genetic architectures in a large number of traits and diseases. Here, we present a sex-specific analysis of 733 sex-stratified complex trait GWAS for 361,194 white British individuals in the UK Biobank. Among these 733 traits, we detected sex-specific locus in 520 of them. These 520 candidate traits with sex-specific genetic effects were classified to 270 distinct groups. Using a systematic sex-specific discovery-replication analysis, we identify 489 loci showing sex-specific effects on 127 of the candidate trait groups, among which three of them having the most numbers of traits showing replicable signals were further investigated, including fat mass related traits (12 traits), heart disease related traits (7 traits) and body impedance related traits (6 traits). We identify pathways with sex-biased enrichment patterns from exploratory enrichment analyses based on QTL-associated genes. In addition, we present further evidence for significant sex-specific genetic effects in 25 traits by comparing the prediction performance of sex-specific polygenic risk scores (PRS).</p>
5 Authors
- Wei Jiang
- Yueqian Jing
- James Han
- Yixuan Ye
- Hongyu Zhao
1 Application
| Application ID | Title |
|---|---|
| 29900 | Defining physical activity phenotypes based on objective measures and dissecting the genetic and phenotypic relationships of physical activity with diseases and health outcomes |
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