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

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Version: Final published version
Serval ID
serval:BIB_4C41F42457D9
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Improving genetic prediction by leveraging genetic correlations among human diseases and traits.
Journal
Nature communications
Author(s)
Maier R.M., Zhu Z., Lee S.H., Trzaskowski M., Ruderfer D.M., Stahl E.A., Ripke S., Wray N.R., Yang J., Visscher P.M., Robinson M.R.
ISSN
2041-1723 (Electronic)
ISSN-L
2041-1723
Publication state
Published
Issued date
07/03/2018
Peer-reviewed
Oui
Volume
9
Number
1
Pages
989
Language
english
Notes
Publication types: Evaluation Studies ; Journal Article ; Research Support, N.I.H., Extramural
Publication Status: epublish
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.
Keywords
Bipolar Disorder/genetics, Genetic Pleiotropy, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Models, Statistical, Risk Assessment, Schizophrenia/genetics
Pubmed
Web of science
Open Access
Yes
Create date
15/03/2018 20:16
Last modification date
20/08/2019 14:00
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