Multi-omics-data-assisted genomic feature markers preselection improves the accuracy of genomic prediction.

ABSTRACT:

Background

Presently, multi-omics data (e.g., genomics, transcriptomics, proteomics, and metabolomics) are available to improve genomic predictors. Omics data not only offers new data layers for genomic prediction but also provides a bridge between organismal phenotypes and genome variation that cannot be readily captured at the genome sequence level. Therefore, using multi-omics data to select feature markers is a feasible strategy to improve the accuracy of genomic prediction. In this study, simultaneously using whole-genome sequencing (WGS) and gene expression level data, four strategies for single-nucleotide polymorphism (SNP) preselection were investigated for genomic predictions in the Drosophila Genetic Reference Panel.

Results

Using genomic best linear unbiased predi

SUBMITTER: Ye S 

PROVIDER: S-EPMC7708144 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature