Genetic association of DDIT3, RPL23A, SESN2 and NR4A1 genes with milk yield and composition in dairy cattle

Abstract

Previously, we identified by RNA sequencing that DDIT3, RPL23A, SESN2 and NR4A1 genes were significantly differentially expressed between the mammary glands of lactating Holstein cows with extremely high and low milk protein and fat percentages; thus, these four genes are considered as promising candidates potentially affecting milk yield and composition traits in dairy cattle. In the present study, we further verified whether these genes have genetic effects on milk traits in a Chinese Holstein population. By re-sequencing part of the non-coding and the entire coding regions of the DDIT3, RPL23A, SESN2 and NR4A1 genes, a total of 35 SNPs and three insertions/deletions were identified, of which three were found in DDIT3, 12 in RPL23A, 16 in SESN2 and seven in NR4A1. Moreover, two of the insertions/deletions-g.125714860_125714872del and g.125714806delinsCCCC in SESN2-were novel and have not been reported previously. Subsequent single SNP analyses revealed multiple significant association with all 35 SNPs and three indels regressed against the dairy production traits (P-value\xa0=\xa0<0.0001-0.0493). In addition, with a linkage disequilibrium analysis, we found one, one, three, and one haplotype blocks in the DDIT3, RPL23A, SESN2 and NR4A1 genes respectively. Haplotype-based association analyses revealed that some\xa0haplotypes were also significantly associated with milk production traits (P-value\xa0=\xa0<0.0001-0.0461). We also found that 12 SNPs and two indels (two in DDIT3, two in RPL23A, nine in SESN2 and one in NR4A1) altered the specific transcription factor binding sites in the promoter, thereby regulating promoter activity, suggesting that they might be promising potential functional variants for milk traits. In summary, our findings first determined the genetic associations of DDIT3, RPL23A, SESN2 and NR4A1 with milk yield and composition traits in dairy cattle and also suggested potentially causal variants, which require in-depth validation.