Open AccessHaplotype-based genome-wide association study identifies loci and candidate genes for milk yield in Holsteins
Author(s) -
Zhenliang Chen,
Yunqiu Yao,
Peipei Ma,
Qishan Wang,
Yuchun Pan
Publication year - 2018
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0192695
Subject(s) - genome wide association study , quantitative trait locus , biology , haplotype , candidate gene , genetics , single nucleotide polymorphism , genetic association , gene , genetic architecture , genome , genotyping , locus (genetics) , computational biology , allele , genotype
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.