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|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)||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||Statistics to Oct 2018:||Visitors: 37
|Appears in Collections:||Journal Article|
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