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- Title
Comparison of F -tests for Univariate and Multivariate Mixed-Effect Models in Genome-Wide Association Mapping.
- Authors
Onogi, Akio
- Abstract
Genome-wide association mapping (GWA) has been widely applied to a variety of species to identify genomic regions responsible for quantitative traits. The use of multivariate information could enhance the detection power of GWA. Although mixed-effect models are frequently used for GWA, the utility of F -tests for multivariate mixed-effect models is not well-recognized. Thus, we compared the F -tests for univariate and multivariate mixed-effect models with simulations. The superiority of the multivariate F -test over the univariate test varied depending on three parameters: phenotypic correlation between variates (r), relative size of quantitative trait locus effects between variates (a d), and missing proportion of phenotypic records (m prop). Simulation results showed that, when m prop was low, the multivariate F -test outperformed the univariate test as r and a d differ, and as m prop increased, the multivariate F -test outperformed as a d increased. These observations were consistent with results of the analytical evaluation of the F -value. When m prop was at the maximum, i.e., when no individual had phenotypic values for multiple variates, as in the case of meta-analysis, the multivariate F -test gained more detection power as a d increased. Although using multivariate information in mixed-effect model contexts did not always ensure more detection power than with univariate tests, the multivariate F -test will be a method applied when multivariate data are available because it does not show inflation of signals and could lead to new findings.
- Subjects
F-test (Mathematical statistics); SINGLE nucleotide polymorphisms; STATISTICS; GENOTYPES; PHENOTYPES
- Publication
Frontiers in Genetics, 2019, pN.PAG
- ISSN
1664-8021
- Publication type
Article
- DOI
10.3389/fgene.2019.00030