A comprehensive reanalysis of publicly available GWAS datasets reveals an X chromosome rare regulatory variant associated with high risk for type 2 diabetes

Abstract

A comprehensive reanalysis of publicly available GWAS datasets reveals an X chromosome rare regulatory variant associated with high risk for type 2 diabetes. AbstractThe reanalysis of publicly available GWAS data represents a powerful and costeffective opportunity to gain insights into the genetics and pathophysiology of complex diseases. We demonstrate this by gathering and reanalyzing public type 2 diabetes (T2D) GWAS data for 70,127 subjects, using an innovative imputation and association strategy based on multiple reference panels (1000G and UK10K). This approach led us replicate and fine map 50 known T2D loci, and identify seven novel associated regions: five driven by common variants in or near LYPLAL1, NEUROG3, CAMKK2, ABO and GIP genes; one by a low frequency variant near EHMT2; and one driven by a rare variant in chromosome Xq23, associated with a 2.7-fold increased risk for T2D in males, and located within an active enhancer associated with the expression of Angiotensin II Receptor type 2 gene (AGTR2), a known modulator of insulin sensitivity. We further show that the risk T allele reduces binding of a nuclear protein, resulting in increased enhancer activity in muscle cells. Beyond providing novel insights into the genetics and pathophysiology of T2D, these results also underscore the value of reanalyzing publicly available data using novel analytical approaches.

Multivariable MR can mitigate bias in two-sample MR using covariable-adjusted summary associations

“…95% CI, 95% confidence interval for the estimated effect. Effects estimated using 424 SNPs identified across both SBP and BMI for unadjusted estimates, and 490 for adjusted estimates, summary data used was obtained from: UK Biobank (SBP unadjusted), International Consortium of Blood Pressure (SBP adjusted) (11), 70KforT2D (type-2 diabetes) (24), UK Biobank (BMI). Standard errors displayed in brackets.…”

“…To estimate the effect of SBP on T2D we used summary data for BMI and SBP from an unadjusted GWAS extracted from UK Biobank and hosted on OpenGWAS (23) and from a GWAS study adjusted for BMI using data from UK Biobank and the International Consortium of Blood Pressure-Genome Wide Association Studies (ICBP) (11). GWAS results for T2D, unadjusted for BMI, were obtained from the 70KforT2D GWAS due to lack of overlap with UK Biobank (24).…”

“…The causal effects were estimated using inverse variance weighting (IVW), genetic variants were selected on the basis that they were robustly associated with the exposure in a GWAS of that exposure, based on a genome wide significant p-value < 5e-08. Each study applied its own quality control, details of which can be found in the original publications (11,12,23,25,26). Genome-wide significant SNPs were clumped to remove SNPs in LD with each other (R2 threshold for considering LD was set to 0.001).…”