Genomic selection and weighted single-step genome-wide association study of sheep body weight and milk yield: Imputing low-coverage sequencing data with similar genetic background panels

Abstract

Low-coverage whole-genome sequencing (LcWGS), a cost-effective genotyping method, offers greater flexibility in variant detection than SNP chips. However, to our knowledge, no studies have explored the application of LcWGS in sheep. This study aimed to evaluate the feasibility of implementing LcWGS and genotype imputation and assess their applicability in genomic studies of body weight and milk yield in sheep. A total of 45,787 birth weight (BiW), 31,135 weaning daily gain (WDG), 8,928 milk yield (MY), and 4,918 milk yield per unit of metabolic body weight (MWMY) data records were analyzed. Among these, 2,366 sheep had imputed high-density genotypes. Simulated sequencing depths from 0.1× to 3× were imputed using reference panels of 100 to 600 individuals. Genotype concordance with true data improved from 0.8875 to 0.9852 as the sequencing depth and panel size increased. The single-step GBLUP method applied to the imputed data yielded higher accuracy for BiW, WDG, MY, and MWMY than the classical pedigree-based BLUP, and notably increased MY accuracy from 0.61 to 0.66. Furthermore, a weighted single-step genome-wide association study identified key genes associated with BiW (ANKS1B, OPRM1, CSMD1), WDG (TKDP5, GRP, RAX, IGFBP7), MY (CCSER1, FGGY, HOOK1), and MWMY (NDUFA10, ZNF385D, NWD1), revealing the importance of multiple pathways in sheep growth and milk production. This is the first study to assess the feasibility of combining LcWGS with genotype imputation for sheep genomic selection, balancing economic costs and imputation efficiency. Furthermore, we demonstrate an effective approach for identifying genetic variants linked to body weight and milk production, offering a cost-effective strategy for dairy sheep breeding.