In:
PLOS Genetics, Public Library of Science (PLoS), Vol. 18, No. 10 ( 2022-10-27), p. e1010443-
Abstract:
Multi-population cohorts offer unprecedented opportunities for profiling disease risk in large samples, however, heterogeneous risk effects underlying complex traits across populations make integrative prediction challenging. In this study, we propose a novel Bayesian probability framework, the Prism Vote (PV), to construct risk predictions in heterogeneous genetic data. The PV views the trait of an individual as a composite risk from subpopulations, in which stratum-specific predictors can be formed in data of more homogeneous genetic structure. Since each individual is described by a composition of subpopulation memberships, the framework enables individualized risk characterization. Simulations demonstrated that the PV framework applied with alternative prediction methods significantly improved prediction accuracy in mixed and admixed populations. The advantage of PV enlarges as genetic heterogeneity and sample size increase. In two real genome-wide association data consists of multiple populations, we showed that the framework considerably enhanced prediction accuracy of the linear mixed model in five-group cross validations. The proposed method offers a new aspect to analyze individual’s disease risk and improve accuracy for predicting complex traits in genotype data.
Type of Medium:
Online Resource
ISSN:
1553-7404
DOI:
10.1371/journal.pgen.1010443
DOI:
10.1371/journal.pgen.1010443.g001
DOI:
10.1371/journal.pgen.1010443.g002
DOI:
10.1371/journal.pgen.1010443.g003
DOI:
10.1371/journal.pgen.1010443.g004
DOI:
10.1371/journal.pgen.1010443.g005
DOI:
10.1371/journal.pgen.1010443.g006
DOI:
10.1371/journal.pgen.1010443.s001
DOI:
10.1371/journal.pgen.1010443.s002
DOI:
10.1371/journal.pgen.1010443.s003
DOI:
10.1371/journal.pgen.1010443.s004
DOI:
10.1371/journal.pgen.1010443.s005
DOI:
10.1371/journal.pgen.1010443.s006
DOI:
10.1371/journal.pgen.1010443.s007
DOI:
10.1371/journal.pgen.1010443.s008
DOI:
10.1371/journal.pgen.1010443.s009
DOI:
10.1371/journal.pgen.1010443.s010
DOI:
10.1371/journal.pgen.1010443.s011
DOI:
10.1371/journal.pgen.1010443.s012
Language:
English
Publisher:
Public Library of Science (PLoS)
Publication Date:
2022
detail.hit.zdb_id:
2186725-2
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