Mining the human phenome using allelic scores that index biological intermediates

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dc.contributor.authorEvans, D.
dc.contributor.authorBrion, M.
dc.contributor.authorPaternoster, L.
dc.contributor.authorKemp, J.
dc.contributor.authorMcMahon, G.
dc.contributor.authorMunafo, M.
dc.contributor.authorWhitfield, J.
dc.contributor.authorMedland, S.
dc.contributor.authorMontgomery, G.
dc.contributor.authorGIANT consortium,
dc.contributor.authorCRP consortium,
dc.contributor.authorTAG Consortium,
dc.contributor.authorTimpson, N.
dc.contributor.authorSt Pourcain, B.
dc.contributor.authorLawlor, D.
dc.contributor.authorMartin, N.
dc.contributor.authorDehghan, A.
dc.contributor.authorHirschhorn, J.
dc.contributor.authorSmith, G.
dc.contributor.editorGojobori, T.
dc.date.issued2013
dc.descriptionGIANT Consortium contributor; Lyle Palmer for the University of Adelaide
dc.description.abstractIt 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.
dc.description.statementofresponsibilityDavid M. Evans, Marie Jo A. Brion, Lavinia Paternoster, John P. Kemp, George McMahon, Marcus Munafo, John B. Whitfield, Sarah E. Medland, Grant W. Montgomery, The GIANT consortium, The CRP consortium, The TAG Consortium, Nicholas J. Timpson, Beate St. Pourcain, Debbie A. Lawlor, Nicholas G. Martin, Abbas Dehghan, Joel Hirschhorn, George Davey Smith
dc.identifier.citationPLoS Genetics, 2013; 9(10):e1003919-1-e1003919-15
dc.identifier.doi10.1371/journal.pgen.1003919
dc.identifier.issn1553-7390
dc.identifier.issn1553-7404
dc.identifier.orcidLawlor, D. [0000-0002-6793-2262]
dc.identifier.urihttp://hdl.handle.net/2440/97956
dc.language.isoen
dc.publisherPublic Library of Science
dc.relation.granthttp://purl.org/au-research/grants/nhmrc/241944
dc.relation.granthttp://purl.org/au-research/grants/nhmrc/339462
dc.relation.granthttp://purl.org/au-research/grants/nhmrc/389927
dc.relation.granthttp://purl.org/au-research/grants/nhmrc/389875
dc.relation.granthttp://purl.org/au-research/grants/nhmrc/389891
dc.relation.granthttp://purl.org/au-research/grants/nhmrc/389892
dc.relation.granthttp://purl.org/au-research/grants/nhmrc/389938
dc.relation.granthttp://purl.org/au-research/grants/nhmrc/442915
dc.relation.granthttp://purl.org/au-research/grants/nhmrc/442981
dc.relation.granthttp://purl.org/au-research/grants/nhmrc/496739
dc.relation.granthttp://purl.org/au-research/grants/nhmrc/552485
dc.relation.granthttp://purl.org/au-research/grants/nhmrc/552498
dc.relation.granthttp://purl.org/au-research/grants/arc/A7960034
dc.relation.granthttp://purl.org/au-research/grants/arc/A79906588
dc.relation.granthttp://purl.org/au-research/grants/arc/A79801419
dc.relation.granthttp://purl.org/au-research/grants/arc/DP0770096
dc.relation.granthttp://purl.org/au-research/grants/arc/DP0212016
dc.relation.granthttp://purl.org/au-research/grants/arc/DP0343921
dc.rights© 2013 Evans 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.
dc.source.urihttps://doi.org/10.1371/journal.pgen.1003919
dc.subjectGenome-Wide Association Study
dc.titleMining the human phenome using allelic scores that index biological intermediates
dc.typeJournal article
pubs.publication-statusPublished

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