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

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 expo

SUBMITTER: Lee SH 

PROVIDER: S-EPMC5299615 | biostudies-literature | 2017 Feb

REPOSITORIES: biostudies-literature