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

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.

泌乳量是奶牛极为关键的经济性状，全基因组关联分析（Genome-Wide Association Study, GWAS）对于解析泌乳量的遗传调控机制、开展标记辅助选择（Marker-Assisted Selection, MAS）均具有重要价值。本研究采用基于单倍型的经验贝叶斯全基因组关联分析方法，筛选与奶牛泌乳量相关的位点及候选基因。研究共对1092头荷斯坦奶牛采用基因组简化基因分型测序（Genotyping by Genome Reducing and Sequencing, GGRS）技术进行测序，经质量过滤后，共获得164312个高置信度单核苷酸多态性（Single Nucleotide Polymorphism, SNP）位点与13476个单倍型区块用于后续GWAS分析。结果显示，共有17个单倍型区块与泌乳量显著相关。研究团队进一步鉴定了每个单倍型区块的邻近基因，并针对这些基因开展了泌乳相关数量性状位点（Quantitative Trait Locus, QTL）区间注释以及经典通路分析（Ingenuity Pathway Analysis, IPA）网络构建。分析表明，DLGAP1、AP2B1、ITPR2与THBS4这4个基因与泌乳量存在明确关联；另有ARHGEF4、TDRD1与KIF19这3个基因被推测存在潜在关联。此外，由IPA构建的调控网络包含1个推测关联基因ARHGEF4以及全部4个已验证关联基因，该网络可能参与调控奶牛泌乳量。本研究结果深化了对泌乳性状因果基因的认知，可为后续验证关联基因、通路及生物网络的相关研究提供理论指导。