Cargando…
Comparison of F-tests for Univariate and Multivariate Mixed-Effect Models in Genome-Wide Association Mapping
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 o...
Autor principal: | Onogi, Akio |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6369166/ https://www.ncbi.nlm.nih.gov/pubmed/30778369 http://dx.doi.org/10.3389/fgene.2019.00030 |
Ejemplares similares
-
MetaPhat: Detecting and Decomposing Multivariate Associations From Univariate Genome-Wide Association Statistics
por: Lin, Jake, et al.
Publicado: (2020) -
How Well Can Multivariate and Univariate GWAS Distinguish Between True and Spurious Pleiotropy?
por: Fernandes, Samuel B., et al.
Publicado: (2021) -
Univariate and Multivariate QTL Analyses Reveal Covariance Among Mineral Elements in the Rice Ionome
por: Liu, Huan, et al.
Publicado: (2021) -
An empirical comparison of univariate versus multivariate methods for the analysis of brain–behavior mapping
por: Ivanova, Maria V., et al.
Publicado: (2020) -
Accuracies of univariate and multivariate genomic prediction models in African cassava
por: Okeke, Uche Godfrey, et al.
Publicado: (2017)