AUTHOR=Onogi Akio 

TITLE=Comparison of F-tests for Univariate and Multivariate Mixed-Effect Models in Genome-Wide Association Mapping

JOURNAL=Frontiers in Genetics

VOLUME=10

YEAR=2019

URL=https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2019.00030

DOI=10.3389/fgene.2019.00030

ISSN=1664-8021

ABSTRACT=<p>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 <italic>F</italic>-tests for multivariate mixed-effect models is not well-recognized. Thus, we compared the <italic>F</italic>-tests for univariate and multivariate mixed-effect models with simulations. The superiority of the multivariate <italic>F</italic>-test over the univariate test varied depending on three parameters: phenotypic correlation between variates (<italic>r</italic>), relative size of quantitative trait locus effects between variates (<italic>a</italic><sub>d</sub>), and missing proportion of phenotypic records (<italic>m</italic><sub>prop</sub>). Simulation results showed that, when <italic>m</italic><sub>prop</sub> was low, the multivariate <italic>F</italic>-test outperformed the univariate test as <italic>r</italic> and <italic>a</italic><sub>d</sub> differ, and as <italic>m</italic><sub>prop</sub> increased, the multivariate <italic>F</italic>-test outperformed as <italic>a</italic><sub>d</sub> increased. These observations were consistent with results of the analytical evaluation of the <italic>F</italic>-value. When <italic>m</italic><sub>prop</sub> was at the maximum, i.e., when no individual had phenotypic values for multiple variates, as in the case of meta-analysis, the multivariate <italic>F</italic>-test gained more detection power as <italic>a</italic><sub>d</sub> increased. Although using multivariate information in mixed-effect model contexts did not always ensure more detection power than with univariate tests, the multivariate <italic>F</italic>-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.</p>