Integrating gene expression data via weighted multiple kernel ridge regression improved accuracy of genomic prediction.

ABSTRACT:

Background

Gene expression profiles hold potentially valuable information for the prediction of breeding values and phenotypes. However, in practical breeding programs, most reference population individuals typically have only genomic data, lacking transcriptomic data. Predicting gene expression based on genetic markers and integrating the genetically predicted gene expression data into genomic prediction may offer a potential solution.

Results

This study extends kernel ridge regression (KRR) to weighted multiple kernel ridge regression (WMKRR), which integrates genomic data and transcriptomic data predicted from genetic markers through a multiple kernel learning (MKL) approach. We evaluated the predictive ability of WMKRR compared to traditional genomic best linear unbiased

SUBMITTER: Wang X 

PROVIDER: S-EPMC12465700 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature