Projects per year
Abstract / Description of output
Genome-wide association studies (GWAS) promised to translate their findings intoclinically beneficial improvements of patient management by tailoring disease management to the individual through the prediction of disease risk. However, the ability to translate genetic findings from GWAS into predictive tools that are of clinical utility and which may inform clinical practice has, so far, been encouraging but limited. Here we propose to use a more powerful statistical approach, the use of which has traditionally been limited due to computational requirements and lack of sufficiently large individual level genotyped cohorts, but which improve the prediction of multiple medically relevant phenotypes using the same panel of SNPs. As a proof of principle, we used a shared panel of 319,038 common SNPs with MAF > 0.05 to train the prediction models in 114,264 unrelated White-British individuals for height and four obesity related traits (body mass index, basal metabolic rate, body fat percentage, and waist-to-hip ratio). We obtained prediction accuracies that ranged between 46% and 75% of the maximum achievable given the captured heritable component. For height, this represents an improvement in prediction accuracy of up to 69% (184% more phenotypic variance explained) over SNPs reported to be robustlyassociated with height in a previous GWAS meta-analysis of similar size. Acrosspopulation predictions in White non-British individuals were similar to those in White-British whilst those in Asian and Black individuals were informative but less accurate. We estimate that the genotyping of circa 500,000 unrelated individuals will yield predictions between 66% and 82% of the SNP-heritability captured by common variants in our array. Prediction accuracies did not improve when including rarer SNPs or when fitting multiple traits jointly in multivariate models.
Original language | English |
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Article number | e0166755 |
Journal | PLoS ONE |
Volume | 11 |
Issue number | 12 |
DOIs | |
Publication status | Published - 15 Dec 2016 |
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Dive into the research topics of 'Improved genetic profiling of anthropometric traits using a big data approach'. Together they form a unique fingerprint.Projects
- 3 Finished
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Roslin Led Application: Genomic prediction of anthropomorphic traits using hundreds of thousands of individuals
1/11/15 → 31/07/19
Project: Research
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Estimation of the genetic correlation among human cancers and identification of pleiotropic cancer loci
Tenesa, A., Law, A. & Woolliams, J.
14/10/13 → 13/10/16
Project: Research
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ISP1: Analysis and prediction in complex animal systems
Tenesa, A., Archibald, A., Beard, P., Bishop, S., Bronsvoort, M., Burt, D., Freeman, T., Haley, C., Hocking, P., Houston, R., Hume, D., Joshi, A., Law, A., Michoel, T., Summers, K., Vernimmen, D., Watson, M., Wiener, P., Wilson, A., Woolliams, J., Ait-Ali, T., Barnett, M., Carlisle, A., Finlayson, H., Haga, I., Karavolos, M., Matika, O., Paterson, T., Paton, B., Pong-Wong, R., Robert, C. & Robertson, G.
1/04/12 → 31/03/17
Project: Research