Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/21093
Title: Using information of relatives in genomic prediction to apply effective stratified medicine
Contributor(s): Lee, Sang Hong (author); Weerasinghe, Shalanee (author); Wray, Naomi R (author); Goddard, Michael E (author); Van Der Werf, Julius H (author)orcid 
Publication Date: 2017
Open Access: Yes
DOI: 10.1038/srep42091
Handle Link: https://hdl.handle.net/1959.11/21093
Open Access Link: http://dx.doi.org/10.1038/srep42091
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.
Publication Type: Journal Article
Source of Publication: Scientific Reports, v.7, p. 1-13
Publisher: Nature Publishing Group
Place of Publication: United Kingdom
ISSN: 2045-2322
Field of Research (FOR): 070201 Animal Breeding
Peer Reviewed: Yes
HERDC Category Description: C1 Refereed Article in a Scholarly Journal
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