Identification of rare loss-of-function genetic variation regulating body fat distribution

This is the final version. Available on open access from Oxford University Press via the DOI in this recordData Availability: This research was conducted using the UK Biobank resource (application Nos. 44448 and 9905). Access to the UK Biobank genotype and phenotype data is open to all approved health researchers (http://www.ukbiobank.ac.uk/).CONTEXT: Biological and translational insights from large-scale, array-based genetic studies of fat distribution, a key determinant of metabolic health, have been limited by the difficulty in linking predominantly non-coding variants to specific gene targets. Rare coding variant analyses provide greater confidence that a specific gene is involved, but do not necessarily indicate whether gain or loss-of-function (LoF) would be of most therapeutic benefit. OBJECTIVE, DESIGN AND SETTING: To identify genes/proteins involved in determining fat distribution, we combined the power of genome-wide analysis of array-based rare, non-synonymous variants in 450,562 individuals of UK Biobank with exome-sequence-based rare loss of function gene burden testing in 184,246 individuals. RESULTS: The data indicates that loss-of-function of four genes (PLIN1 [LoF variants, p=5.86×10 -7], INSR [LoF variants, p=6.21×10 -7], ACVR1C [LoF + Moderate impact variants, p=1.68×10 -7; Moderate impact variants, p=4.57×10 -7] and PDE3B [LoF variants, p=1.41×10 -6]) is associated with a beneficial impact on WHRadjBMI and increased gluteofemoral fat mass, whereas LoF of PLIN4 [LoF variants, p=5.86×10 -7] adversely affects these parameters. Phenotypic follow-up suggests that LoF of PLIN1, PDE3B and ACVR1C favourably affects metabolic phenotypes (e.g. triglyceride [TG] and HDL cholesterol concentrations) and reduces the risk of cardiovascular disease, whereas PLIN4 LoF has adverse health consequences. INSR LoF is associated with lower TG and HDL levels but may increase the risk of type 2 diabetes. CONCLUSION: This study robustly implicates these genes in the regulation of fat distribution, providing new and in some cases somewhat counter-intuitive insight into the potential consequences of targeting these molecules therapeutically.Medical Research Council (MRC)National Institute for Health Research (NIHR)Wellcome TrustResearch Englan

Large-scale association analyses identify new loci influencing glycemic traits and provide insight into the underlying biological pathways

Through genome-wide association meta-analyses of up to 133,010 individuals of European ancestry without diabetes, including individuals newly genotyped using the Metabochip, we have increased the number of confirmed loci influencing glycemic traits to 53, of which 33 also increase type 2 diabetes risk (q < 0.05). Loci influencing fasting insulin concentration showed association with lipid levels and fat distribution, suggesting impact on insulin resistance. Gene-based analyses identified further biologically plausible loci, suggesting that additional loci beyond those reaching genome-wide significance are likely to represent real associations. This conclusion is supported by an excess of directionally consistent and nominally significant signals between discovery and follow-up studies. Functional analysis of these newly discovered loci will further improve our understanding of glycemic control