Title | Mining the human phenome using allelic scores that index biological intermediates. |
Publication Type | Journal Article |
Year of Publication | 2013 |
Authors | Evans 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 Authors | Smith G D |
Corporate Authors | GIANT consortium, CRP Consortium, TAG Consortium |
Journal | PLoS Genet |
Volume | 9 |
Issue | 10 |
Pagination | e1003919 |
Date Published | 2013 Oct |
ISSN | 1553-7404 |
Keywords | Adaptor 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. |
DOI | 10.1371/journal.pgen.1003919 |
Alternate Journal | PLoS Genet |
PubMed ID | 24204319 |
PubMed Central ID | PMC3814299 |
Grant List | DA019951 / 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 |