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

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 |Aβb|＞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.