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Mining the human phenome using allelic scores that index biological intermediates.

TitleMining the human phenome using allelic scores that index biological intermediates.
Publication TypeJournal Article
Year of Publication2013
AuthorsEvans DM, Brion MJo A, Paternoster L, Kemp JP, McMahon G, Munafò M, Whitfield JB, Medland SE, Montgomery GW, Timpson NJ, St Pourcain B, Lawlor DA, Martin NG, Dehghan A, Hirschhorn J
Secondary AuthorsSmith G D
Corporate AuthorsGIANT consortium, CRP Consortium, TAG Consortium
JournalPLoS Genet
Volume9
Issue10
Paginatione1003919
Date Published2013 Oct
ISSN1553-7404
KeywordsAdaptor Proteins, Vesicular Transport, Alleles, C-Reactive Protein, Genetic Diseases, Inborn, Genetic Predisposition to Disease, Genome, Human, Genome-Wide Association Study, Genotype, Humans, Longitudinal Studies, Phenotype, Polymorphism, Single Nucleotide
Abstract

It is common practice in genome-wide association studies (GWAS) to focus on the relationship between disease risk and genetic variants one marker at a time. When relevant genes are identified it is often possible to implicate biological intermediates and pathways likely to be involved in disease aetiology. However, single genetic variants typically explain small amounts of disease risk. Our idea is to construct allelic scores that explain greater proportions of the variance in biological intermediates, and subsequently use these scores to data mine GWAS. To investigate the approach's properties, we indexed three biological intermediates where the results of large GWAS meta-analyses were available: body mass index, C-reactive protein and low density lipoprotein levels. We generated allelic scores in the Avon Longitudinal Study of Parents and Children, and in publicly available data from the first Wellcome Trust Case Control Consortium. We compared the explanatory ability of allelic scores in terms of their capacity to proxy for the intermediate of interest, and the extent to which they associated with disease. We found that allelic scores derived from known variants and allelic scores derived from hundreds of thousands of genetic markers explained significant portions of the variance in biological intermediates of interest, and many of these scores showed expected correlations with disease. Genome-wide allelic scores however tended to lack specificity suggesting that they should be used with caution and perhaps only to proxy biological intermediates for which there are no known individual variants. Power calculations confirm the feasibility of extending our strategy to the analysis of tens of thousands of molecular phenotypes in large genome-wide meta-analyses. We conclude that our method represents a simple way in which potentially tens of thousands of molecular phenotypes could be screened for causal relationships with disease without having to expensively measure these variables in individual disease collections.

DOI10.1371/journal.pgen.1003919
Alternate JournalPLoS Genet
PubMed ID24204319
PubMed Central IDPMC3814299
Grant ListDA019951 / DA / NIDA NIH HHS / United States
AA13320 / AA / NIAAA NIH HHS / United States
MC_UU_12013/3 / / Medical Research Council / United Kingdom
G1000758 / / Medical Research Council / United Kingdom
092731 / / Wellcome Trust / United Kingdom
AA13321 / AA / NIAAA NIH HHS / United States
MR/J012165/1 / / Medical Research Council / United Kingdom
AA17688 / AA / NIAAA NIH HHS / United States
DA012854 / DA / NIDA NIH HHS / United States
085515 / / Wellcome Trust / United Kingdom
AA07728 / AA / NIAAA NIH HHS / United States
MC_UU_12013/1 / / Medical Research Council / United Kingdom
090532 / / Wellcome Trust / United Kingdom
AA11998 / AA / NIAAA NIH HHS / United States
MC_UU_12013/4 / / Medical Research Council / United Kingdom
076467 / / Wellcome Trust / United Kingdom
095515 / / Wellcome Trust / United Kingdom
G0600717 / / Medical Research Council / United Kingdom
AA14041 / AA / NIAAA NIH HHS / United States
083431MA / / Wellcome Trust / United Kingdom
AA07535 / AA / NIAAA NIH HHS / United States
MC_UU_12013/5 / / Medical Research Council / United Kingdom
AA13326 / AA / NIAAA NIH HHS / United States
G9815508 / / Medical Research Council / United Kingdom