Chen Zhenliang, Yao Yunqiu, Ma Peipei, Wang Qishan, Pan Yuchun
Department of Animal Science, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, PR China.
Shanghai Key Laboratory of Veterinary Biotechnology, Shanghai, PR China.
PLoS One. 2018 Feb 15;13(2):e0192695. doi: 10.1371/journal.pone.0192695. eCollection 2018.
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.
由于产奶量是奶牛的一个极其重要的经济性状，全基因组关联研究（GWAS）对于解释产奶量背后的遗传结构以及进行标记辅助选择（MAS）至关重要。在本研究中，我们采用基于单倍型的经验贝叶斯GWAS来鉴定产奶量的基因座和候选基因。使用基因组缩减测序基因分型（GGRS）方法对总共1092头荷斯坦奶牛进行了测序。经过筛选，鉴定出164312个高可信度单核苷酸多态性（SNP）和13476个单倍型块用于GWAS。结果表明，17个单倍型块与产奶量显著相关。我们进一步鉴定了每个单倍型块的最邻近基因，并用与牛奶相关的数量性状基因座（QTL）区间和 Ingenuity 通路分析（IPA）网络对这些基因进行了注释。我们的分析表明，四个基因DLGAP1、AP2B1、ITPR2和THBS4与产奶量有关，而另外三个基因ARHGEF4、TDRD1和KIF19被推断具有潜在关系。此外，从IPA衍生的一个包含一个推断基因（ARHGEF4）和所有四个已证实基因的网络可能调节产奶量。我们的研究结果有助于理解确定产奶性状背后的因果基因，并可为后续研究提供指导，以进一步证实相关基因、通路和生物网络。
