The genetic architecture of common traits, including the number,
frequency, and effect sizes of inherited variants that contribute to individual
risk, has been long debated. Genome-wide association studies have identified
scores of common variants associated with type 2 diabetes, but in aggregate,
these explain only a fraction of heritability. To test the hypothesis that
lower-frequency variants explain much of the remainder, the GoT2D and T2D-GENES
consortia performed whole genome sequencing in 2,657 Europeans with and without
diabetes, and exome sequencing in a total of 12,940 subjects from five ancestral
groups. To increase statistical power, we expanded sample size via genotyping
and imputation in a further 111,548 subjects. Variants associated with type 2
diabetes after sequencing were overwhelmingly common and most fell within
regions previously identified by genome-wide association studies. Comprehensive
enumeration of sequence variation is necessary to identify functional alleles
that provide important clues to disease pathophysiology, but large-scale
sequencing does not support a major role for lower-frequency variants in
predisposition to type 2 diabetes.
To further understanding of the genetic basis of type 2 diabetes (T2D) susceptibility, we aggregated published meta-analyses of genome-wide association studies (GWAS) including 26,488 cases and 83,964 controls of European, East Asian, South Asian, and Mexican and Mexican American ancestry. We observed significant excess in directional consistency of T2D risk alleles across ancestry groups, even at SNPs demonstrating only weak evidence of association. By following up the strongest signals of association from the trans-ethnic meta-analysis in an additional 21,491 cases and 55,647 controls of European ancestry, we identified seven novel T2D susceptibility loci. Furthermore, we observed considerable improvements in fine-mapping resolution of common variant association signals at several T2D susceptibility loci. These observations highlight the benefits of trans-ethnic GWAS for the discovery and characterisation of complex trait loci, and emphasize an exciting opportunity to extend insight into the genetic architecture and pathogenesis of human diseases across populations of diverse ancestry.
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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.