Fig. 1

Schematic overview of current study. a Data collection. We divided the full variants from WGS data into 11 feature sets (annotation, LD, allele frequency, variant density, p-variants, selection signature, conservation, eQTL, mQTL, OCR and HMRs) from genomics, transcriptomics, metabolomics, and epigenetics data. b. Calculate variants score. For each of the 43 traits, we estimated the variance explained by the random effects associated with each GRM using GREML. Each GREML analysis incorporated two random effects: one based on the targeted GRM and another based on the GRM derived from the remaining variants. We calculated the proportion of genetic variance attributed to the targeted GRM for each trait. To determine the per-variant heritability, we divided the explained variance by the number of variants in the set. Finally, we averaged this value across the 11 functional sets for each variant. c. Validation analysis. To assess the reasonableness of the scores, we established six thresholds: “top-5”, “top-10”, “top-30”, “bottom-5”, “bottom-10” and “bottom-30” We then compared the variance explained by each threshold with the accuracy of the genomic predictions. To ascertain whether there are pertinent QTL enrichments for our top variants, we conducted a QTL enrichment analysis using the Cattle QTL Database