Use of the Multivariate Discriminant Analysis for Genome-Wide Association Studies in Cattle.

ABSTRACT: Genome-wide association studies (GWAS) are traditionally carried out by using the single marker regression model that, if a small number of individuals is involved, often lead to very few associations. The Bayesian methods, such as BayesR, have obtained encouraging results when they are applied to the GWAS. However, these approaches, require that an a priori posterior inclusion probability threshold be fixed, thus arbitrarily affecting the obtained associations. To partially overcome these problems, a multivariate statistical algorithm was proposed. The basic idea was that animals with different phenotypic values of a specific trait share different allelic combinations for genes involved in its determinism. Three multivariate techniques were used to highlight the differences between the in

SUBMITTER: Manca E 

PROVIDER: S-EPMC7460480 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature