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Exome array analysis identifies new loci and low-frequency variants influencing insulin processing and secretion

Abstract

Insulin secretion has a crucial role in glucose homeostasis, and failure to secrete sufficient insulin is a hallmark of type 2 diabetes. Genome-wide association studies (GWAS) have identified loci contributing to insulin processing and secretion1,2; however, a substantial fraction of the genetic contribution remains undefined. To examine low-frequency (minor allele frequency (MAF) 0.5–5%) and rare (MAF < 0.5%) nonsynonymous variants, we analyzed exome array data in 8,229 nondiabetic Finnish males using the Illumina HumanExome Beadchip. We identified low-frequency coding variants associated with fasting proinsulin concentrations at the SGSM2 and MADD GWAS loci and three new genes with low-frequency variants associated with fasting proinsulin or insulinogenic index: TBC1D30, KANK1 and PAM. We also show that the interpretation of single-variant and gene-based tests needs to consider the effects of noncoding SNPs both nearby and megabases away. This study demonstrates that exome array genotyping is a valuable approach to identify low-frequency variants that contribute to complex traits.

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Figure 1: Manhattan plot for the fasting proinsulin analysis.
Figure 2: MADD is located in a region of unusually high LD on chromosome 11 at 46–57 Mb.

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Acknowledgements

This study was supported by the Academy of Finland (contract 124243) (to M.L.), the Finnish Heart Foundation (to M.L.), the Finnish Diabetes Foundation (to M.L.), Tekes (contract 1510/31/06) (to M.L.), the Commission of the European Community (HEALTH-F2-2007-201681) (to M.L.) and US National Institutes of Health grants DK093757 (to K.L.M.), DK072193 (to K.L.M.), DK062370 (to M.B.) and 1Z01 HG000024 (to F.S.C.). Genotyping was conducted at the Genetic Resources Core Facility (GRCF) at the Johns Hopkins Institute of Genetic Medicine.

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Contributions

J.R.H. led statistical analysis, and J.R.H., A.U.J., H.M.S., X.S., L.Y. and C.F. performed statistical analysis. J.R.H., M.P.F., M.L.B. and P.S.C. performed bioinformatics analysis. A.S., H.C., J.K. and M.L. obtained and analyzed phenotype data. J.M.R., H.L., I.M., R.I., E.W.P. and K.F.D. generated genotype data. B.M.N., M.J.D. and G.R.A. designed the genotyping array. H.M.K. and G.R.A. developed statistical analysis tools. J.K. and M.L. designed and supervised the METSIM study. J.R.H., M.P.F., M.L.B., M.B. and K.L.M. drafted the manuscript, and all authors reviewed the manuscript. J.R.H., A.U.J., M.P.F., M.L.B., A.S., H.M.S., X.S., L.Y., C.F., T.M.T., L.J.S., F.S.C., G.R.A., R.M.W., M.B., M.L. and K.L.M. contributed to discussion and interpreted the data. M.B., M.L. and K.L.M. designed and supervised the study.

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Correspondence to Karen L Mohlke.

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The authors declare no competing financial interests.

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Huyghe, J., Jackson, A., Fogarty, M. et al. Exome array analysis identifies new loci and low-frequency variants influencing insulin processing and secretion. Nat Genet 45, 197–201 (2013). https://doi.org/10.1038/ng.2507

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