Genomic Prediction Including SNP-Specific Variance Predictors.

ABSTRACT: The increasing amount of available biological information on the markers can be used to inform the models applied for genomic selection to improve predictions. The objective of this study was to propose a general model for genomic selection using a link function approach within the hierarchical generalized linear model framework (hglm) that can include external information on the markers. These models can be fitted using the well-established hglm package in R. We also present an R package (CodataGS) to fit these models, which is significantly faster than the hglm package. Simulated data were used to validate the proposed model. We tested categorical, continuous and combination models where the external information on the markers was related to 1) the location of the QTL on the genome with

SUBMITTER: Mouresan EF 

PROVIDER: S-EPMC6778789 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature