| Title: | Deciphering Sex-Specific Genetic Architectures Using Local Bayesian Regressions |
| Journal: | Genetics |
| Published: | 1 May 2020 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/32198180/ |
| DOI: | https://doi.org/10.1534/genetics.120.303120 |
| URL: | https://academic.oup.com/genetics/article-pdf/215/1/231/37820613/genetics0231.pdf |
Publication 4628
WARNING: the interactive features of this website use CSS3, which your browser does not support. To use the full features of this website, please update your browser.
Abstract
Many complex human traits exhibit differences between sexes. While numerous factors likely contribute to this phenomenon, growing evidence from genome-wide studies suggest a partial explanation: that males and females from the same population possess differing genetic architectures. Despite this, mapping gene-by-sex (G×S) interactions remains a challenge likely because the magnitude of such an interaction is typically and exceedingly small; traditional genome-wide association techniques may be underpowered to detect such events, due partly to the burden of multiple test correction. Here, we developed a local Bayesian regression (LBR) method to estimate sex-specific SNP marker effects after fully accounting for local linkage-disequilibrium (LD) patterns. This enabled us to infer sex-specific effects and G×S interactions either at the single SNP level, or by aggregating the effects of multiple SNPs to make inferences at the level of small LD-based regions. Using simulations in which there was imperfect LD between SNPs and causal variants, we showed that aggregating sex-specific marker effects with LBR provides improved power and resolution to detect G×S interactions over traditional single-SNP-based tests. When using LBR to analyze traits from the UK Biobank, we detected a relatively large G×S interaction impacting bone mineral density within ABO, and replicated many previously detected large-magnitude G×S interactions impacting waist-to-hip ratio. We also discovered many new G×S interactions impacting such traits as height and body mass index (BMI) within regions of the genome where both male- and female-specific effects explain a small proportion of phenotypic variance (R2 < 1 × 10-4), but are enriched in known expression quantitative trait loci.</p>
5 Authors
- Scott A Funkhouser
- Ana I Vazquez
- Juan P Steibel
- Catherine W Ernst
- Gustavo de Los Campos
Enabling scientific discoveries that improve human health