Mining the Human Phenome Using Allelic Scores That Index Biological Intermediates
Evans, D. M., Brion, M. J. A., Paternoster, L., Kemp, J. P., McMahon, G., Munafò, M., Whitfield, J. B., Medland, S. E., Montgomery, G. W., Timpson, N. J., St Pourcain, B., Lawlor, D. A., Martin, N. G., Dehghan, A., Hirschhorn, J., Davey Smith, G., The GIANT consortium, The CRP consortium, & The TAG Consortium
Mining the Human Phenome Using Allelic Scores That Index Biological Intermediates. PLoS Genet, 9
(10): e1003919. doi:10.1371/journal.pgen.1003919.
Author SummaryThe standard approach in genome-wide association studies is to analyse the relationship between genetic variants and disease one marker at a time. Significant associations between markers and disease are then used as evidence to implicate biological intermediates and pathways likely to be involved in disease aetiology. However, single genetic variants typically only explain small amounts of disease risk. Our idea is to construct allelic scores that explain greater proportions of the variance in biological intermediates than single markers, and then use these scores to data mine genome-wide association studies. We show how allelic scores derived from known variants as well as allelic scores derived from hundreds of thousands of genetic markers across the genome explain significant portions of the variance in body mass index, levels of C-reactive protein, and LDLc cholesterol, and many of these scores show expected correlations with disease. Power calculations confirm the feasibility of scaling our strategy to the analysis of tens of thousands of molecular phenotypes in large genome-wide meta-analyses. Our method represents a simple way in which tens of thousands of molecular phenotypes could be screened for potential causal relationships with disease.