Genomic prediction accuracy for switchgrass traits related to bioenergy within differentiated populations.

ABSTRACT:

Background

Switchgrass breeders need to improve the rates of genetic gain in many bioenergy-related traits in order to create improved cultivars that are higher yielding and have optimal biomass composition. One way to achieve this is through genomic selection. However, the heritability of traits needs to be determined as well as the accuracy of prediction in order to determine if efficient selection is possible.

Results

Using five distinct switchgrass populations comprised of three lowland, one upland and one hybrid accession, the accuracy of genomic predictions under different cross-validation strategies and prediction methods was investigated. Individual genotypes were collected using GBS while kin-BLUP, partial least squares, sparse partial least squares, and BayesB meth

SUBMITTER: Fiedler JD 

PROVIDER: S-EPMC6038187 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature