Deciphering Sex-Specific Genetic Architectures Using Local Bayesian Regressions. Issue 1 (1st May 2020)

• Record Type: Journal Article
• Authors: Funkhouser, Scott A; Vazquez, Ana I; Steibel, Juan P; Ernst, Catherine W; los Campos, Gustavo de
• Is Part Of: Genetics. Volume 215:Issue 1(2020)
• Page Start: 231
• Page End: 241
• Publication Date: 2020-05-01
• Subjects: Bayesian methods -- gene-by-sex interactions -- GWAS; Genetics -- Periodicals; 576.5
• DOI: 10.1534/genetics.120.303120
• Languages: English
• ISSNs: 0016-6731

Abstract

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 (R 2 < 1 × 10 −4 ), but are enriched in known expression quantitative trait loci.