Haplotype-based genome-wide association study identifies loci and candidate genes for milk yield in Holsteins

• Published in: PloS one 2018-02, Vol.13 (2), p.e0192695-e0192695
• Main Authors: Chen, Zhenliang, Yao, Yunqiu, Ma, Peipei, Wang, Qishan, Pan, Yuchun
• Format: Article
• Language: English
• Subjects: Agricultural production; Agriculture; Animal sciences; Animals; Bayesian analysis; Biology; Biology and Life Sciences; Biotechnology; Bovidae; Cattle; Cattle - genetics; Computer and Information Sciences; Dairying; Empirical analysis; Filtration; Gene expression; Gene sequencing; Genes; Genetic aspects; Genome-wide association studies; Genome-Wide Association Study; Genomes; Genomics; Genotyping; Haplotypes; Holstein Friesian cattle; Identification and classification; Laboratories; Marker-assisted selection; Medicine and Health Sciences; Methods; Milk; Milk production; Mutation; Physical Sciences; Physiological aspects; Polymorphism, Single Nucleotide; Quantitative Trait Loci; Single-nucleotide polymorphism; Studies; Yield; Zoology
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0192695

Since milk yield is a highly important economic trait in dairy cattle, the genome-wide association study (GWAS) is vital to explain the genetic architecture underlying milk yield and to perform marker-assisted selection (MAS). In this study, we adopted a haplotype-based empirical Bayesian GWAS to identify the loci and candidate genes for milk yield. A total of 1 092 Holstein cows were sequenced by using the genotyping by genome reducing and sequencing (GGRS) method. After filtering, 164 312 high-confidence SNPs and 13 476 haplotype blocks were identified to use for GWAS. The results indicated that 17 blocks were significantly associated with milk yield. We further identified the nearest gene of each haplotype block and annotated the genes with milk-associated quantitative trait locus (QTL) intervals and ingenuity pathway analysis (IPA) networks. Our analysis showed that four genes, DLGAP1, AP2B1, ITPR2 and THBS4, have relationships with milk yield, while another three, ARHGEF4, TDRD1 and KIF19, were inferred to have potential relationships. Additionally, a network derived from the IPA containing one inferred (ARHGEF4) and all four confirmed genes likely regulates milk yield. Our findings add to the understanding of identifying the causal genes underlying milk production traits and could guide follow up studies for further confirmation of the associated genes, pathways and biological networks.