Ridge regression and deep learning models for genome-wide selection of complex traits in New Mexican Chile peppers.

ABSTRACT:

Background

Genomewide prediction estimates the genomic breeding values of selection candidates which can be utilized for population improvement and cultivar development. Ridge regression and deep learning-based selection models were implemented for yield and agronomic traits of 204 chile pepper genotypes evaluated in multi-environment trials in New Mexico, USA.

Results

Accuracy of prediction differed across different models under ten-fold cross-validations, where high prediction accuracy was observed for highly heritable traits such as plant height and plant width. No model was superior across traits using 14,922 SNP markers for genomewide selection. Bayesian ridge regression had the highest average accuracy for first pod date (0.77) and total yield per plant (0.33). Multila

SUBMITTER: Lozada DN 

PROVIDER: S-EPMC10726521 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature