Prescreening of large-effect markers with multiple strategies improves the accuracy of genomic prediction

Abstract

Presently, integrating multi-omics information into a prediction model has become a ameliorate strategy for genomic selection to improve genomic prediction accuracy. Here, we set the genomic and transcriptomic data as the training population data, using BSLMM, TWAS, and eQTL mapping to prescreen features according to | ^βb|>0, top 1% of phenotypic variation explained (PVE), expression-associated single nucleotide polymorphisms (eSNPs), and egenes (false discovery rate (FDR)<0.01), where these loci were set as extra fixed effects (named GBLUP-Fix) and random effects (GFBLUP) to improve the prediction accuracy in the validation population, respectively. The results suggested that both GBLUP-Fix and GFBLUP models could improve the accuracy of longissimus dorsi muscle (LDM), water holding capacity (WHC), shear force (SF), and pH in Huaxi cattle on average from 2.14 to 8.69%, especially the improvement of GFBLUP-TWAS over GBLUP was 13.66% for SF. These methods also captured more genetic variance than GBLUP. Our study confirmed that multi-omics-assisted large-effects loci prescreening could improve the accuracy of genomic prediction.

Keywords

• multi-omics data
• features prescreening
• eQTL mapping
• Huaxi cattle
• genomic selection