Improving genetic prediction by leveraging genetic correlations among human diseases and traits
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Author
Maier, Robert M.
Zhu, Zhihong
Lee, Sang Hong
Trzaskowski, Maciej
Ruderfer, Douglas M.
Stahl, Eli A.
Ripke, Stephan
Wray, Naomi R.
Yang, Jian
Visscher, Peter M.
Robinson, Matthew R.
Note: Order does not necessarily reflect citation order of authors.
Published Version
https://doi.org/10.1038/s41467-017-02769-6Metadata
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Maier, R. M., Z. Zhu, S. H. Lee, M. Trzaskowski, D. M. Ruderfer, E. A. Stahl, S. Ripke, et al. 2018. “Improving genetic prediction by leveraging genetic correlations among human diseases and traits.” Nature Communications 9 (1): 989. doi:10.1038/s41467-017-02769-6. http://dx.doi.org/10.1038/s41467-017-02769-6.Abstract
Genomic prediction has the potential to contribute to precision medicine. However, to date, the utility of such predictors is limited due to low accuracy for most traits. Here theory and simulation study are used to demonstrate that widespread pleiotropy among phenotypes can be utilised to improve genomic risk prediction. We show how a genetic predictor can be created as a weighted index that combines published genome-wide association study (GWAS) summary statistics across many different traits. We apply this framework to predict risk of schizophrenia and bipolar disorder in the Psychiatric Genomics consortium data, finding substantial heterogeneity in prediction accuracy increases across cohorts. For six additional phenotypes in the UK Biobank data, we find increases in prediction accuracy ranging from 0.7% for height to 47% for type 2 diabetes, when using a multi-trait predictor that combines published summary statistics from multiple traits, as compared to a predictor based only on one trait.Other Sources
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5841449/pdf/Terms of Use
This article is made available under the terms and conditions applicable to Other Posted Material, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#LAACitable link to this page
http://nrs.harvard.edu/urn-3:HUL.InstRepos:35982028
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