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.
Highlights
Sex differences are widespread in nature, observed readily among many human traits and diseases
Many complex human traits are known to be influenced by an impressive number of causal variants each with very small effects, posing great challenges for genome-wide association studies (GWAS)
While GWAS are commonly performed using specific methods in which one single nucleotide polymorphism (SNP) at a time is tested for association with a trait, alternatively we utilize methods more commonly observed in the genomic prediction literature
Summary
Sex differences are widespread in nature, observed readily among many human traits and diseases. Sex differences are likely due to a myriad of factors including differential environmental exposures, unequal gene dosages for sex-linked genes as well as sex-heterogeneity in the architecture of genetic effects at one or more autosomal loci (i.e. gene-by-sex (G×S) interactions). Many traits seem to have between-sex genetic correlation that is evidentially less than one, genomewide association (GWA) studies intended to map G×S interactions have struggled to pinpoint such loci [6,7]. Based on this dichotomy, G×S interactions presumably exist for many traits, but the magnitude of a typical G×S interaction is suspected to be exceedingly small, explaining why such events commonly elude detection, after multiple test correction. Just as numerous small effect causal loci accumulate to affect phenotypic variance, small G×S interactions may accumulate to influence both sex differences and phenotypic variance
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
