Using information of relatives in genomic prediction to apply effective stratified medicine.
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.
School of Environmental and Rural Science, University of New England, NSW 2351, Australia.
S Hong
SH
Lee
School of Environmental and Rural Science, University of New England, NSW 2351, Australia.
W M Shalanee P
WM
Weerasinghe
The Centre of Neurogenetics and Statistical Genomics, Queensland Brain Institute, The University of Queensland, QLD 4072, Australia.
Naomi R
NR
Wray
Faculty of Land and Food Resources, University of Melbourne, Melbourne, Australia.|Department of Primary Industries, Biosciences Research Division, Bundoora, Australia.
Michael E
ME
Goddard
School of Environmental and Rural Science, University of New England, NSW 2351, Australia.
Julius H J
JH
van der Werf
D003198:Computer Simulation; D005190:Family; D030342:Genetic Diseases, Inborn / Q000175:diagnosis*; D023281:Genomics / Q000379:methods*; D006801:Humans; D057285:Precision Medicine / Q000379:methods*; D012372:ROC Curve; D018570:Risk Assessment; D013223:Statistics as Topic