Sex-specific genetic and transcriptomic liability to neuroticism

Wendt, Frank R.; Pathak, Gita A; Singh, Kritika; Stein, Murray B.; Koenen, Karestan C.; Krystal, John H.; Davis, Lea K.; Polimanti, Renato

doi:10.1101/2022.03.03.22271772

Cited by 1 publication

(1 citation statement)

References 83 publications

(96 reference statements)

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“…On the other hand, the PRSxSex regression approach (P=0.58) and SNP-heritability by Sex approach (P=0.45) produced non-significant results. We note that males had lower prevalence of neuroticism than females (1.6% vs. 2.3% in top score for neuroticism, P<1e-16), as previously reported 42 . Second, systolic blood pressure was assigned to Scenario 2 (Figure 6b).…”

Section: Identifying Gene-sex Interactions Across 33 Diseases/complex...supporting

confidence: 89%

Distinct explanations underlie gene-environment interactions in the UK Biobank

Durvasula,

Price

2023

Preprint

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The role of gene-environment (GxE) interaction in disease and complex trait architectures is widely hypothesized, but currently unknown. Here, we apply three statistical approaches to quantify and distinguish three different types of GxE interaction for a given disease/trait and E variable. First, we detect locus-specific GxE interaction by testing for genetic correlation (rg) < 1 across E bins. Second, we detect genome-wide effects of the E variable on genetic variance by leveraging polygenic risk scores (PRS) to test for significant PRSxE in a regression of phenotypes on PRS, E, and PRSxE, together with differences in SNP-heritability across E bins. Third, we detect genome-wide proportional amplification of genetic and environmental effects as a function of the E variable by testing for significant PRSxE with no differences in SNP-heritability across E bins. Simulations show that these approaches achieve high sensitivity and specificity in distinguishing these three GxE scenarios. We applied our framework to 33 UK Biobank diseases/traits (average N=325K) and 10 E variables spanning lifestyle, diet, and other environmental exposures. First, we identified 19 trait-E pairs with rg significantly < 1 (FDR<5%) (average rg=0.95); for example, white blood cell count had rg=0.95 (s.e. 0.01) between smokers and non-smokers. Second, we identified 28 trait-E pairs with significant PRSxE and significant SNP-heritability differences across E bins; for example, type 2 diabetes had a significant PRSxE for alcohol consumption (P=1e-13) with 4.2x larger SNP-heritability in the largest versus smallest quintiles of alcohol consumption (P<1e-16). Third, we identified 15 trait-E pairs with significant PRSxE with no SNP-heritability differences across E bins; for example, triglyceride levels had a significant PRSxE effect for composite diet score (P=4e-5) with no SNP-heritability differences. Analyses using biological sex as the E variable produced additional significant findings in each of the three scenarios. Overall, we infer a substantial contribution of GxE and GxSex effects to disease and complex trait variance.

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Section: Identifying Gene-sex Interactions Across 33 Diseases/complex...supporting

confidence: 89%