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

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 and Robinson, Matthew R (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


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
Title Improving genetic prediction by leveraging genetic correlations among human diseases and traits
Journal name Nature Communications   Check publisher's open access policy
ISSN 2041-1723
Publication date 2018-03-07
Year available 2018
Sub-type Article (original research)
DOI 10.1038/s41467-017-02769-6
Open Access Status DOI
Volume 9
Issue 1
Start page 989
Total pages 17
Place of publication London, United Kingdom
Publisher Nature Publishing Group
Language eng
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.
Keyword Genome-Wide Association
Risk Prediction
Increases Accuracy
Complex Traits
Selection
Schizophrenia
Pitfalls
Values
Model
Loci
Q-Index Code C1
Q-Index Status Provisional Code
Grant ID 160103860
1087889
R21ESO25052-01
NWO 480-05-003
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: HERDC Pre-Audit
Queensland Brain Institute Publications
Institute for Molecular Bioscience - Publications
 
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Created: Wed, 14 Mar 2018, 10:05:30 EST