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.

Genome-wide association studies (GWAS) have identified >500 common variants associated with quantitative metabolic traits, but in aggregate such variants explain at most 20–30% of the heritable component of population variation in these traits. To further investigate the impact of genotypic variation on metabolic traits, we conducted re-sequencing studies in >6,000 members of a Finnish population cohort (The Northern Finland Birth Cohort of 1966 [NFBC]) and a type 2 diabetes case-control sample (The Finland-United States Investigation of NIDDM Genetics [FUSION] study). By sequencing the coding sequence and 5′ and 3′ untranslated regions of 78 genes at 17 GWAS loci associated with one or more of six metabolic traits (serum levels of fasting HDL-C, LDL-C, total cholesterol, triglycerides, plasma glucose, and insulin), and conducting both single-variant and gene-level association tests, we obtained a more complete understanding of phenotype-genotype associations at eight of these loci. At all eight of these loci, the identification of new associations provides significant evidence for multiple genetic signals to one or more phenotypes, and at two loci, in the genes ABCA1 and CETP, we found significant gene-level evidence of association to non-synonymous variants with MAF<1%. Additionally, two potentially deleterious variants that demonstrated significant associations (rs138726309, a missense variant in G6PC2, and rs28933094, a missense variant in LIPC) were considerably more common in these Finnish samples than in European reference populations, supporting our prior hypothesis that deleterious variants could attain high frequencies in this isolated population, likely due to the effects of population bottlenecks. Our results highlight the value of large, well-phenotyped samples for rare-variant association analysis, and the challenge of evaluating the phenotypic impact of such variants.

People in developing countries have faced multigenerational undernutrition and are currently undergoing major lifestyle changes, contributing to an epidemic of metabolic diseases, though the underlying mechanisms remain unclear. Using a Wistar rat model of undernutrition over 50 generations, we show that Undernourished rats exhibit low birth-weight, high visceral adiposity (DXA/MRI), and insulin resistance (hyperinsulinemic-euglycemic clamps), compared to age-/gender-matched control rats. Undernourished rats also have higher circulating insulin, homocysteine, endotoxin and leptin levels, lower adiponectin, vitamin B12 and folate levels, and an 8-fold increased susceptibility to Streptozotocin-induced diabetes compared to control rats. Importantly, these metabolic abnormalities are not reversed after two generations of unrestricted access to commercial chow (nutrient recuperation). Altered epigenetic signatures in insulin-2 gene promoter region of Undernourished rats are not reversed by nutrient recuperation, and may contribute to the persistent detrimental metabolic profiles in similar multigenerational undernourished human populations.

86As yet undiscovered rare variants are hypothesized to substantially influence an 87 individual's risk for common diseases and traits, but sequencing studies aiming to 88 identify such variants have generally been underpowered. In isolated populations that 89 have expanded rapidly after a population bottleneck, deleterious alleles that passed 90 through the bottleneck may be maintained at much higher frequencies than in other 91 populations. In an exome sequencing study of nearly 20,000 cohort participants from 92 northern and eastern Finnish populations that exemplify this phenomenon, most novel 93 trait-associated deleterious variants are seen only in Finland or display frequencies more 94 than 20 times higher than in other European populations. These enriched alleles underlie 95 34 novel associations with 21 disease-related quantitative traits and demonstrate a 96 geographical clustering equivalent to that of Mendelian disease mutations characteristic 97 of the Finnish population. Sequencing studies in populations without this unique history 98 would require hundreds of thousands to millions of participants for comparable power for 99 these variants. 100 101 (defined here as MAF≤1%) which are not well-tagged by the single-nucleotide 109 polymorphisms (SNPs) on genome-wide genotyping arrays are probably responsible for 110 much of the heritability that remains unexplained 2 . Additionally, because purifying 111 selection acts to remove deleterious alleles from the population, most variants that exert a 112 sizable effect on complex traits, and that likely offer the best prospect for revealing 113 biological mechanisms, should be particularly rare. 114 115 Rare variants are unevenly distributed between populations and difficult to represent 116 effectively on commercial genotyping arrays, as evidenced by relatively sparse 117 association findings even from large array-based studies of coding variants 3-6 . 118Discovering rare variant associations will therefore almost certainly require exome or 119 genome sequencing of very large numbers of individuals. However, the sample size 120 required to reliably identify rare-variant associations remains uncertain; most sequencing 121 studies to date have identified few novel associations, and theoretical analyses confirm 122 that they have been underpowered to do so 7 . These analyses also suggest that power to 123 detect rare variant associations varies enormously between populations that have 124 expanded in isolation from recent bottlenecks compared to those that have not. 125 126In isolated populations that expand rapidly following a bottleneck, alleles that pass 127 through the bottleneck often rise to a much higher frequency than in other populations [8][9][10] . 128If the bottleneck was recent, even deleterious alleles under negative selection may remain 129 relatively frequent in these populations, resulting in increased power to detect association 130 with disease-related traits. The Finnish population exemplifies this type of history. It 131 5 grew from bottle...