Improving genetic prediction by leveraging genetic correlations among human diseases and traits

Robert M. Maier (), Zhihong Zhu, Sang Hong Lee, Maciej Trzaskowski, Douglas M. Ruderfer, Eli A. Stahl, Stephan Ripke, Naomi R. Wray, Jian Yang, Peter M. Visscher () and Matthew R. Robinson ()
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Robert M. Maier: University of Queensland
Zhihong Zhu: University of Queensland
Sang Hong Lee: University of Queensland
Maciej Trzaskowski: University of Queensland
Douglas M. Ruderfer: Vanderbilt University Medical Center
Eli A. Stahl: Icahn School of Medicine at Mount Sinai
Stephan Ripke: Broad Institute
Naomi R. Wray: University of Queensland
Jian Yang: University of Queensland
Peter M. Visscher: University of Queensland
Matthew R. Robinson: University of Queensland

Nature Communications, 2018, vol. 9, issue 1, 1-17

Abstract: 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.

Date: 2018
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DOI: 10.1038/s41467-017-02769-6

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