Predictive accuracy of combined genetic and environmental risk scores

Dudbridge, Frank, Pashayan, Nora and Yang, Jian (2017) Predictive accuracy of combined genetic and environmental risk scores. Genetic Epidemiology, 42 1: 4-19. doi:10.1002/gepi.22092

Author Dudbridge, Frank
Pashayan, Nora
Yang, Jian
Title Predictive accuracy of combined genetic and environmental risk scores
Journal name Genetic Epidemiology   Check publisher's open access policy
ISSN 1098-2272
Publication date 2017-11-26
Year available 2018
Sub-type Article (original research)
DOI 10.1002/gepi.22092
Open Access Status Not yet assessed
Volume 42
Issue 1
Start page 4
End page 19
Total pages 16
Place of publication Hoboken, NJ United States
Publisher John Wiley & Sons
Language eng
Subject 2713 Epidemiology
2716 Genetics (clinical)
Abstract The substantial heritability of most complex diseases suggests that genetic data could provide useful risk prediction. To date the performance of genetic risk scores has fallen short of the potential implied by heritability, but this can be explained by insufficient sample sizes for estimating highly polygenic models. When risk predictors already exist based on environment or lifestyle, two key questions are to what extent can they be improved by adding genetic information, and what is the ultimate potential of combined genetic and environmental risk scores? Here, we extend previous work on the predictive accuracy of polygenic scores to allow for an environmental score that may be correlated with the polygenic score, for example when the environmental factors mediate the genetic risk. We derive common measures of predictive accuracy and improvement as functions of the training sample size, chip heritabilities of disease and environmental score, and genetic correlation between disease and environmental risk factors. We consider simple addition of the two scores and a weighted sum that accounts for their correlation. Using examples from studies of cardiovascular disease and breast cancer, we show that improvements in discrimination are generally small but reasonable degrees of reclassification could be obtained with current sample sizes. Correlation between genetic and environmental scores has only minor effects on numerical results in realistic scenarios. In the longer term, as the accuracy of polygenic scores improves they will come to dominate the predictive accuracy compared to environmental scores.
Keyword ROC curve
polygenic score
risk prediction
risk score
Q-Index Code C1
Q-Index Status Provisional Code
Grant ID MR/K006215/1
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, 29 Nov 2017, 12:00:36 EST