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

Nat Commun
Authors
Keywords
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.

Year of Publication
2018
Journal
Nat Commun
Volume
9
Issue
1
Pages
989
Date Published
2018 03 07
ISSN
2041-1723
DOI
10.1038/s41467-017-02769-6
PubMed ID
29515099
PubMed Central ID
PMC5841449
Links
Grant list
MC_PC_17228 / MRC_ / Medical Research Council / United Kingdom
MC_QA137853 / MRC_ / Medical Research Council / United Kingdom