Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2013 Feb;56(2):298-310.
doi: 10.1007/s00125-012-2756-1. Epub 2012 Nov 19.

Exome sequencing-driven discovery of coding polymorphisms associated with common metabolic phenotypes

A Albrechtsen  1 , N Grarup, Y Li, T Sparsø, G Tian, H Cao, T Jiang, S Y Kim, T Korneliussen, Q Li, C Nie, R Wu, L Skotte, A P Morris, C Ladenvall, S Cauchi, A Stančáková, G Andersen, A Astrup, K Banasik, A J Bennett, L Bolund, G Charpentier, Y Chen, J M Dekker, A S F Doney, M Dorkhan, T Forsen, T M Frayling, C J Groves, Y Gui, G Hallmans, A T Hattersley, K He, G A Hitman, J Holmkvist, S Huang, H Jiang, X Jin, J M Justesen, K Kristiansen, J Kuusisto, M Lajer, O Lantieri, W Li, H Liang, Q Liao, X Liu, T Ma, X Ma, M P Manijak, M Marre, J Mokrosiński, A D Morris, B Mu, A A Nielsen, G Nijpels, P Nilsson, C N A Palmer, N W Rayner, F Renström, R Ribel-Madsen, N Robertson, O Rolandsson, P Rossing, T W Schwartz; D.E.S.I.R. Study Group; P E Slagboom, M Sterner; DIAGRAM Consortium; M Tang, L Tarnow, T Tuomi, E van't Riet, N van Leeuwen, T V Varga, M A Vestmar, M Walker, B Wang, Y Wang, H Wu, F Xi, L Yengo, C Yu, X Zhang, J Zhang, Q Zhang, W Zhang, H Zheng, Y Zhou, D Altshuler, L M 't Hart, P W Franks, B Balkau, P Froguel, M I McCarthy, M Laakso, L Groop, C Christensen, I Brandslund, T Lauritzen, D R Witte, A Linneberg, T Jørgensen, T Hansen, J Wang, R Nielsen, O Pedersen
Collaborators, Affiliations

Exome sequencing-driven discovery of coding polymorphisms associated with common metabolic phenotypes

A Albrechtsen et al. Diabetologia. 2013 Feb.

Abstract

Aims/hypothesis: Human complex metabolic traits are in part regulated by genetic determinants. Here we applied exome sequencing to identify novel associations of coding polymorphisms at minor allele frequencies (MAFs) >1% with common metabolic phenotypes.

Methods: The study comprised three stages. We performed medium-depth (8×) whole exome sequencing in 1,000 cases with type 2 diabetes, BMI >27.5 kg/m(2) and hypertension and in 1,000 controls (stage 1). We selected 16,192 polymorphisms nominally associated (p < 0.05) with case-control status, from four selected annotation categories or from loci reported to associate with metabolic traits. These variants were genotyped in 15,989 Danes to search for association with 12 metabolic phenotypes (stage 2). In stage 3, polymorphisms showing potential associations were genotyped in a further 63,896 Europeans.

Results: Exome sequencing identified 70,182 polymorphisms with MAF >1%. In stage 2 we identified 51 potential associations with one or more of eight metabolic phenotypes covered by 45 unique polymorphisms. In meta-analyses of stage 2 and stage 3 results, we demonstrated robust associations for coding polymorphisms in CD300LG (fasting HDL-cholesterol: MAF 3.5%, p = 8.5 × 10(-14)), COBLL1 (type 2 diabetes: MAF 12.5%, OR 0.88, p = 1.2 × 10(-11)) and MACF1 (type 2 diabetes: MAF 23.4%, OR 1.10, p = 8.2 × 10(-10)).

Conclusions/interpretation: We applied exome sequencing as a basis for finding genetic determinants of metabolic traits and show the existence of low-frequency and common coding polymorphisms with impact on common metabolic traits. Based on our study, coding polymorphisms with MAF above 1% do not seem to have particularly high effect sizes on the measured metabolic traits.

PubMed Disclaimer

Figures

Fig. 1
Fig. 1
Overview of the study. UTR, untranslated region
Fig. 2
Fig. 2
Manhattan plots of 16,192 SNPs for 12 metabolic traits in up to 15,989 Danish individuals (stage 2). For each of the traits the −log10(p) was plotted against the chromosome position. SNPs that have been established as known genome-wide associated signals for each trait are marked in orange. The dotted line indicates Bonferroni correction significance threshold corrected for 16,192 SNPs and 12 traits. The association analyses were performed with logistic or linear regression adjusted for first principal component and sex. All p values were corrected by genomic control
See this image and copyright information in PMC

References

    1. Manolio TA, Brooks LD, Collins FS. A HapMap harvest of insights into the genetics of common disease. J Clin Invest. 2008;118:1590–1605. doi: 10.1172/JCI34772. - DOI - PMC - PubMed
    1. Voight BF, Scott LJ, Steinthorsdottir V, et al. Twelve type 2 diabetes susceptibility loci identified through large-scale association analysis. Nat Genet. 2010;42:579–589. doi: 10.1038/ng.609. - DOI - PMC - PubMed
    1. Kooner JS, Saleheen D, Sim X, et al. Genome-wide association study in individuals of South Asian ancestry identifies six new type 2 diabetes susceptibility loci. Nat Genet. 2011;43:984–989. doi: 10.1038/ng.921. - DOI - PMC - PubMed
    1. Dupuis J, Langenberg C, Prokopenko I, et al. New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk. Nat Genet. 2010;42:105–116. doi: 10.1038/ng.520. - DOI - PMC - PubMed
    1. Grarup N, Sparsø T, Hansen T. Physiologic characterization of type 2 diabetes-related loci. Curr Diab Rep. 2010;10:485–497. doi: 10.1007/s11892-010-0154-y. - DOI - PMC - PubMed

Publication types