The Metabochip, a Custom Genotyping Array for Genetic Studies of Metabolic, Cardiovascular, and Anthropometric Traits

PLoS Genetics, Aug 2012

Genome-wide association studies have identified hundreds of loci for type 2 diabetes, coronary artery disease and myocardial infarction, as well as for related traits such as body mass index, glucose and insulin levels, lipid levels, and blood pressure. These studies also have pointed to thousands of loci with promising but not yet compelling association evidence. To establish association at additional loci and to characterize the genome-wide significant loci by fine-mapping, we designed the “Metabochip,” a custom genotyping array that assays nearly 200,000 SNP markers. Here, we describe the Metabochip and its component SNP sets, evaluate its performance in capturing variation across the allele-frequency spectrum, describe solutions to methodological challenges commonly encountered in its analysis, and evaluate its performance as a platform for genotype imputation. The metabochip achieves dramatic cost efficiencies compared to designing single-trait follow-up reagents, and provides the opportunity to compare results across a range of related traits. The metabochip and similar custom genotyping arrays offer a powerful and cost-effective approach to follow-up large-scale genotyping and sequencing studies and advance our understanding of the genetic basis of complex human diseases and traits.

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The Metabochip, a Custom Genotyping Array for Genetic Studies of Metabolic, Cardiovascular, and Anthropometric Traits

The Metabochip, a Custom Genotyping Array for Genetic Studies of Metabolic, Cardiovascular, and Anthropometric Traits Benjamin F. Voight Hyun Min Kang Jun Ding Cameron D. Palmer Carlo Sidore Peter S. Chines Noe l P. Burtt Christian Fuchsberger Yanming Li Jeanette Erdmann Timothy M. Frayling Iris M. Heid Anne U. Jackson Toby Johnson Tuomas O. Kilpela inen Cecilia M. Lindgren Andrew P. Morris Inga Prokopenko Joshua C. Randall Richa Saxena Nicole Soranzo Elizabeth K. Speliotes Tanya M. Teslovich Eleanor Wheeler Jared Maguire Melissa Parkin Simon Potter N. William Rayner Neil Robertson Kathleen Stirrups Wendy Winckler Serena Sanna Antonella Mulas Ramaiah Nagaraja Francesco Cucca Ine s Barroso Panos Deloukas Ruth J. F. Loos Sekar Kathiresan Patricia B. Munroe Christopher Newton-Cheh Arne Pfeufer Nilesh J. Samani Heribert Schunkert Joel N. Hirschhorn David Altshuler Mark I. McCarthy Gonc alo R. Abecasis Michael Boehnke Genome-wide association studies have identified hundreds of loci for type 2 diabetes, coronary artery disease and myocardial infarction, as well as for related traits such as body mass index, glucose and insulin levels, lipid levels, and blood pressure. These studies also have pointed to thousands of loci with promising but not yet compelling association evidence. To establish association at additional loci and to characterize the genome-wide significant loci by fine-mapping, we designed the ''Metabochip,'' a custom genotyping array that assays nearly 200,000 SNP markers. Here, we describe the Metabochip and its component SNP sets, evaluate its performance in capturing variation across the allele-frequency spectrum, describe solutions to methodological challenges commonly encountered in its analysis, and evaluate its performance as a platform for genotype imputation. The metabochip achieves dramatic cost efficiencies compared to designing single-trait follow-up reagents, and provides the opportunity to compare results across a range of related traits. The metabochip and similar custom genotyping arrays offer a powerful and cost-effective approach to follow-up large-scale genotyping and sequencing studies and advance our understanding of the genetic basis of complex human diseases and traits. - Citation: Voight BF, Kang HM, Ding J, Palmer CD, Sidore C, et al. (2012) The Metabochip, a Custom Genotyping Array for Genetic Studies of Metabolic, Cardiovascular, and Anthropometric Traits. PLoS Genet 8(8): e1002793. doi:10.1371/journal.pgen.1002793 Editor: Greg Gibson, Georgia Institute of Technology, United States of America Received December 6, 2011; Accepted May 13, 2012; Published August 2, 2012 Copyright: 2012 Voight et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: Support from the National Institutes of Health (HG000376, HG005214, HG005581, DK062370, NO1-AG-1-2109), the Wellcome Trust (098051), the British Heart Foundation, and the Leicester NIHR Biomedical Research Unit in Cardiovascular Disease is gratefully acknowledged. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: The authors have declared that no competing interests exist. . These authors contributed equally to this work. Recent data emerging from theoretical models [1,2] and empirical observation through genome-wide association studies (GWAS) (for example [3,4]) demonstrate that hundreds of genetic loci contribute to complex traits in humans. These data prompt two questions: (1) can additional genetic loci be identified by follow-up of the most significantly associated variants after initial GWAS meta-analysis? and (2) can further investigation via genetic fine-mapping refine association signals at established genetic loci? Systematically addressing these two questions should help improve understanding of the genetic architecture of complex traits and their shared genetic determinants, and suggest hypotheses and disease mechanisms that can be tested in functional experiments or model systems [5]. Addressing these two questions requires genotyping thousands of individuals at many genetic markers. For most currently available genotyping technologies, this kind of characterization is costprohibitive. To address this need in the context of type 2 diabetes, coronary artery disease and myocardial infarction, and quantitative traits related to these diseases, we designed the Metabochip, a custom genotyping array that provides accurate and cost-effective genotyping of nearly 200,000 single nucleotide polymorphisms (SNPs) chosen based on GWAS meta-analyses of 23 traits (Table 1). Metabochip SNPs were selected from the catalogs developed by the International HapMap [6] and 1000 Genomes [7] Projects, allowing inclusion of SNPs across a wide r (...truncated)


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Benjamin F. Voight, Hyun Min Kang, Jun Ding, Cameron D. Palmer, Carlo Sidore, Peter S. Chines, Noël P. Burtt, Christian Fuchsberger, Yanming Li, Jeanette Erdmann, Timothy M. Frayling, Iris M. Heid, Anne U. Jackson, Toby Johnson, Tuomas O. Kilpeläinen, Cecilia M. Lindgren, Andrew P. Morris, Inga Prokopenko, Joshua C. Randall, Richa Saxena, Nicole Soranzo, Elizabeth K. Speliotes, Tanya M. Teslovich, Eleanor Wheeler, Jared Maguire, Melissa Parkin, Simon Potter, N. William Rayner, Neil Robertson, Kathleen Stirrups, Wendy Winckler, Serena Sanna, Antonella Mulas, Ramaiah Nagaraja, Francesco Cucca, Inês Barroso, Panos Deloukas, Ruth J. F. Loos, Sekar Kathiresan, Patricia B. Munroe, Christopher Newton-Cheh, Arne Pfeufer, Nilesh J. Samani, Heribert Schunkert, Joel N. Hirschhorn, David Altshuler, Mark I. McCarthy, Gonçalo R. Abecasis, Michael Boehnke. The Metabochip, a Custom Genotyping Array for Genetic Studies of Metabolic, Cardiovascular, and Anthropometric Traits, PLoS Genetics, 2012, 8, DOI: 10.1371/journal.pgen.1002793