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

Medical Population Genetics, The Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.
PLoS Genetics (Impact Factor: 7.53). 08/2012; 8(8):e1002793. DOI: 10.1371/journal.pgen.1002793
Source: PubMed


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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Available from: Jeanette Erdmann, Oct 06, 2015
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    • "The Cardio-MetaboChip is a custom Illumina iSelect genotyping array of 196,725 variants designed to facilitate cost-effective discovery and fine mapping of cardiovascular and metabolic traits, including T2D [21]. Variants in 257 fine-mapping regions, including 34 established GWAS loci for T2D, were selected from reference panels from the HapMap Project [15] and a pilot release of the 1000 Genomes Project [16]. "
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    ABSTRACT: Genome-wide association studies of type 2 diabetes have been extremely successful in discovering loci that contribute genetic effects to susceptibility to the disease. However, at the vast majority of these loci, the variants and transcripts through which these effects on type 2 diabetes are mediated are unknown, limiting progress in defining the pathophysiological basis of the disease. In this review, we will describe available approaches for assaying genetic variation across loci and discuss statistical methods to determine the most likely causal variants in the region. We will consider the utility of trans-ethnic meta-analysis for fine mapping by leveraging the differences in the structure of linkage disequilibrium between diverse populations. Finally, we will discuss progress in fine-mapping type 2 diabetes susceptibility loci to date and consider the prospects for future efforts to localise causal variants for the disease.
    Current Diabetes Reports 11/2014; 14(11):549. DOI:10.1007/s11892-014-0549-2 · 3.08 Impact Factor
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    • "SNPs that include: (1) " replication " SNPs corresponding to validated associations; (2) a set of 63,450 SNPs that were the most significantly associated with over 20 traits related to coronary artery disease or T2D, including lipids, (3) SNPs previously associated with BMI and waist circumference, as well as 122,241 SNPs to fine-map these loci; and (4) 16,992 other SNPs selected for a variety of reasons, including those that reached genomewide significance in any GWAS (Voight et al., 2012; Shah et al., 2013). Genotyping of the Metabochip was performed as per the manufacturer's protocol. "
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    ABSTRACT: A variety of health-related data are commonly deposited into electronic health records (EHRs), including laboratory, diagnostic, and medication information. The digital nature of EHR data facilitates efficient extraction of these data for research studies, including genome-wide association studies (GWAS). Previous GWAS have identified numerous SNPs associated with variation in total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and triglycerides (TG). These findings have led to the development of specialized genotyping platforms that can be used for fine-mapping and replication in other populations. We have combined the efficiency of EHR data and the economic advantages of the Illumina Metabochip, a custom designed SNP chip targeted to traits related to coronary artery disease, myocardial infarction, and type 2 diabetes, to conduct an array-wide analysis of lipid traits in a population with extreme obesity. Our analyses identified associations with 12 of 21 previously identified lipid-associated SNPs with effect sizes similar to prior results. Association analysis using several approaches to account for lipid-lowering medication use resulted in fewer and less strongly associated SNPs. The availability of phenotype data from the EHR and the economic efficiency of the specialized Metabochip can be exploited to conduct multi-faceted genetic association analyses.
    Frontiers in Genetics 08/2014; 5:222. DOI:10.3389/fgene.2014.00222
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    • "All existing and new study sites in eMERGE II offered existing data on a variety of genotyping platforms and genetic ancestries. With the inclusion of the eMERGE phase I data, a total of 60,766 (47,507 adult and 13,259 pediatric) samples with GWAS-level genotypes or other largescale data [such as Metabochip (Voight et al., 2012)] generated by either Illumina or Affymetrix arrays are available for study in eMERGE phase II. As detailed in a separate manuscript (Verma et al., in press), pooling and merging of these data required imputation and extensive quality control. "
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    ABSTRACT: The electronic MEdical Records & GEnomics (eMERGE) network was established in 2007 by the National Human Genome Research Institute (NHGRI) of the National Institutes of Health (NIH) in part to explore the utility of electronic medical records (EMRs) in genome science. The initial focus was on discovery primarily using the genome-wide association paradigm, but more recently, the network has begun evaluating mechanisms to implement new genomic information coupled to clinical decision support into EMRs. Herein, we describe this evolution including the development of the individual and merged eMERGE genomic datasets, the contribution the network has made toward genomic discovery and human health, and the steps taken toward the next generation genotype-phenotype association studies and clinical implementation.
    Frontiers in Genetics 06/2014; 5:184. DOI:10.3389/fgene.2014.00184
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