Refining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes

Abstract

We aggregated coding variant data for 81,412 type 2 diabetes cases and 370,832 controls of diverse ancestry, identifying 40 coding variant association signals (p<2.2×10−7): of these, 16 map outside known risk loci. We make two important observations. First, only five of these signals are driven by low-frequency variants: even for these, effect sizes are modest (odds ratio ≤1.29). Second, when we used large-scale genome-wide association data to fine-map the associated variants in their regional context, accounting for the global enrichment of complex trait associations in coding sequence, compelling evidence for coding variant causality was obtained for only 16 signals. At 13 others, the associated coding variants clearly represent “false leads” with potential to generate erroneous mechanistic inference. Coding variant associations offer a direct route to biological insight for complex diseases and identification of validated therapeutic targets: however, appropriate mechanistic inference requires careful specification of their causal contribution to disease predisposition.

Dissecting the genetic relationship between cardiovascular risk factors and Alzheimer’s disease

“…We evaluated complete GWAS results in the form of summary statistics ( p values and odds ratios) for clinically diagnosed AD dementia [24] and eight CV-associated RFs, including BMI [47], T2D [28], CAD [31], WHR [18], and plasma lipid levels (TC, TG, LDL, and HDL [44]). We obtained publicly available AD GWAS summary statistic data from the International Genomics of Alzheimer’s Disease Project (IGAP Stages 1 and 2; for additional details, see Supplemental Information and [24]; Table 1).…”

Functionally-informed fine-mapping and polygenic localization of complex trait heritability

“…Genome-wide association studies of complex traits have been extremely successful in identifying loci harboring causal variants but less successful in fine-mapping the underlying causal variants, making the development of fine-mapping methods a key priority 1,2 . Fine-mapping methods aim to pinpoint causal variants by accounting for linkage disequilibrium (LD) between variants [3][4][5][6][7][8][9][10][11][12] , but have limited power in the presence of strong LD. One way to increase fine-mapping power is to prioritize variants in functional annotations that are enriched for complex trait heritability 7,8,10,[13][14][15][16][17] .…”

Exploring diseases/traits and blood proteins causally related to expression of ACE2, the putative receptor of SARS-CoV-2: A Mendelian Randomization analysis highlights tentative relevance of diabetes-related traits

“…GSMR reported the lowest p-value of 2.49E-04 (beta = 0.1835, 95% CI: 0.0853 to 0.2817). While this study 33 has partial overlap with the trans-ethnic analysis in 2014 31 , the consistent associations provide further support to a causal link between diabetes and expression of ACE2.…”