Mining the Human Phenome Using Allelic Scores That Index Biological Intermediates

Evans, David M., Brion, Marie Jo A., Paternoster, Lavinia, Kemp, John P., McMahon, George, Munafo, Marcus, Whitfield, John B., Medland, Sarah E., Montgomery, Grant W., The GIANT consortium, The CRP consortium, The TAG Consortium, Timpson, Nicholas J., St. Pourcain, Beate, Lawlor, Debbie A., Martin, Nicholas G., Dehghan, Abbas, Hirschhorn, Joel and Davey Smith, George (2013) Mining the Human Phenome Using Allelic Scores That Index Biological Intermediates. PLoS Genetics, 9 10: e1003919.1-e1003919.15. doi:10.1371/journal.pgen.1003919


Author Evans, David M.
Brion, Marie Jo A.
Paternoster, Lavinia
Kemp, John P.
McMahon, George
Munafo, Marcus
Whitfield, John B.
Medland, Sarah E.
Montgomery, Grant W.
The GIANT consortium
The CRP consortium
The TAG Consortium
Timpson, Nicholas J.
St. Pourcain, Beate
Lawlor, Debbie A.
Martin, Nicholas G.
Dehghan, Abbas
Hirschhorn, Joel
Davey Smith, George
Title Mining the Human Phenome Using Allelic Scores That Index Biological Intermediates
Journal name PLoS Genetics   Check publisher's open access policy
ISSN 1553-7390
Publication date 2013-01-01
Year available 2013
Sub-type Article (original research)
DOI 10.1371/journal.pgen.1003919
Open Access Status DOI
Volume 9
Issue 10
Start page e1003919.1
End page e1003919.15
Total pages 16
Place of publication San Francisco, CA United States
Publisher Public Library of Science
Language eng
Subject 1311 Genetics
1312 Molecular Biology
1105 Dentistry
1306 Cancer Research
2716 Genetics (clinical)
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.
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Queensland Brain Institute Publications
Official 2014 Collection
UQ Diamantina Institute Publications
 
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