Mining the human phenome using allelic scores that index biological intermediates
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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; Munafò, Marcus; Whitfield, John B; Medland, Sarah E; Montgomery, Grant W; Timpson, Nicholas J; St Pourcain, Beate; Lawlor, Debbie A; Martin, Nicholas G; Dehghan, Abbas; Hirschhorn, Joel; Smith, George Davey; GIANT Consortium.
In: PLOS GENET, Vol. 9, No. 10, 10.2013, p. e1003919.Research output: SCORING: Contribution to journal › SCORING: Journal article › Research › peer-review
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TY - JOUR
T1 - Mining the human phenome using allelic scores that index biological intermediates
AU - Evans, David M
AU - Brion, Marie Jo A
AU - Paternoster, Lavinia
AU - Kemp, John P
AU - McMahon, George
AU - Munafò, Marcus
AU - Whitfield, John B
AU - Medland, Sarah E
AU - Montgomery, Grant W
AU - Timpson, Nicholas J
AU - St Pourcain, Beate
AU - Lawlor, Debbie A
AU - Martin, Nicholas G
AU - Dehghan, Abbas
AU - Hirschhorn, Joel
AU - Smith, George Davey
AU - GIANT Consortium
AU - Schnabel, Renate
PY - 2013/10
Y1 - 2013/10
N2 - 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.
AB - 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.
KW - Adaptor Proteins, Vesicular Transport/genetics
KW - Alleles
KW - C-Reactive Protein/genetics
KW - Genetic Diseases, Inborn/genetics
KW - Genetic Predisposition to Disease
KW - Genome, Human
KW - Genome-Wide Association Study
KW - Genotype
KW - Humans
KW - Longitudinal Studies
KW - Phenotype
KW - Polymorphism, Single Nucleotide/genetics
U2 - 10.1371/journal.pgen.1003919
DO - 10.1371/journal.pgen.1003919
M3 - SCORING: Journal article
C2 - 24204319
VL - 9
SP - e1003919
JO - PLOS GENET
JF - PLOS GENET
SN - 1553-7404
IS - 10
ER -