Deciphering Sex-Specific Genetic Architectures Using Local Bayesian Regressions.

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.

Untangling the complex relationships between incident gout risk, serum urate, and its comorbidities.

BACKGROUND: Many gout comorbidities (e.g., hypertension) are correlated with serum urate. In this investigation, we identified risk factors (e.g., systolic blood pressure [SBP]), that (1) are associated with incident gout, (2) have effects on gout risk that cannot be fully explained by correlated differences in serum urate, and (3) may modulate the relationship between gout and serum urate. METHODS: Using data from the Atherosclerosis Risk in Communities (ARIC) study, we estimated the unadjusted associations between gout and risk factors by calculating ORs and using chi-square tests. The adjusted associations were analyzed using logistic regression by sequentially adding (1) one risk factor at a time or (2) all risk factors, to a baseline model that includes serum urate only. Stepwise selection was used to select main effects. Two-way interactions of variables from the main effects model were also analyzed. RESULTS: Average gout incidence was 2.7 per 1000 people per year. Serum urate was highly associated with incident gout, with odd ratios of 3.16 [95% CI 2.11, 4.76] and 25.9 [95% CI 17.2, 38.4] for moderately high (6-8 mg/dl) and high serum urate (> 8 mg/dl), relative to normal serum urate (< 6 mg/dl), respectively. Ethnicity and SBP were independently and additively associated with gout after accounting for serum urate levels. No significant interactions were found between serum urate and ethnicity or SBP. CONCLUSIONS: Ethnicity and hypertension are predictive of gout risk, and the associations cannot be fully explained by serum urate. For serum urate levels near the crystallization threshold (6-8 mg/dl) African Americans and people with hypertension are at two to three times greater risk for developing gout. The gout risk for this group appears to increase before the onset of severe hyperuricemia.