Using information of relatives in genomic prediction to apply effective stratified medicine

Lee, S. Hong, Weerasinghe, W. M. Shalanee P., Wray, Naomi R., Goddard, Michael E. and Van Der Werf, Julius H. J. (2017) Using information of relatives in genomic prediction to apply effective stratified medicine. Scientific Reports, 7 . doi:10.1038/srep42091


Author Lee, S. Hong
Weerasinghe, W. M. Shalanee P.
Wray, Naomi R.
Goddard, Michael E.
Van Der Werf, Julius H. J.
Title Using information of relatives in genomic prediction to apply effective stratified medicine
Journal name Scientific Reports   Check publisher's open access policy
ISSN 2045-2322
Publication date 2017-02-09
Year available 2017
Sub-type Article (original research)
DOI 10.1038/srep42091
Open Access Status DOI
Volume 7
Total pages 13
Place of publication London, United Kingdom
Publisher Nature Publishing Group
Language eng
Formatted abstract
Genomic prediction shows promise for personalised medicine in which diagnosis and treatment are tailored to individuals based on their genetic profiles for complex diseases. We present a theoretical framework to demonstrate that prediction accuracy can be improved by targeting more informative individuals in the data set used to generate the predictors ("discovery sample") to include those with genetically close relationships with the subjects put forward for risk prediction. Increase of prediction accuracy from closer relationships is achieved under an additive model and does not rely on any family or interaction effects. Using theory, simulations and real data analyses, we show that the predictive accuracy or the area under the receiver operating characteristic curve (AUC) increased exponentially with decreasing effective size (Ne), i.e. when individuals are closely related. For example, with the sample size of discovery set N = 3000, heritability h2 = 0.5 and population prevalence K = 0.1, AUC value approached to 0.9 and the top percentile of the estimated genetic profile scores had 23 times higher proportion of cases than the general population. This suggests that there is considerable room to increase prediction accuracy by using a design that does not exclude closer relationships.
Keyword Multidisciplinary Sciences
Science & Technology - Other Topics
Q-Index Code C1
Q-Index Status Provisional Code
Grant ID 1080157
DP160102126
N01-HC-25195
N02-HL-64278
RC2 AG033067
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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