Genome-Wide Detection of CNVs and Their Association with Complex Traits in Holsteins

Copy number variation (CNV) is an important type of genetic variation contributing to phenotypic differences among mammals. Up to now, GWAS analysis using CNV called by array CGH is lacking in livestock like Holstein cattle. The objectives of this work are to identify CNVs using high-density aCGH data and explore functional CNVs which are associated with complex traits by GWAS method in Holstein cattle. In this study, we reported a systematic CNV association analysis of CNVs and 39 complex production traits in Holsteins. This research identified 1043 CNV regions (CNVRs) by array CGH data in 47 Holstein bulls. Using a genome-wide association analysis (GWAS) approach, we identified 79 significant CNVRs associated with at least one complex traits after false discovery rate (FDR) correction. Notably, 24 CNVRs were markedly related to daughter pregnancy rate (DPR). This study observed the pleiotropy phenomenon of 39 CNV loci which can simultaneously regulate at least 2 complex traits. In summary, the significant CNVs identified in this research could be utilized additional molecular markers for genetic improvement programs in Holsteins. Overall design: Whole genome high-denstiy CGH arrays manufactured by NimbleGen containing 2,166,464 oligonucleotide probes were designed and fabricated on a single slide to provide an evenly distributed coverage on cattle UMD3.1 with an average interval of ~1.2 kb between probes. A total of 48 samples were analyzed. 47 of them were male Holsteins for test samples. Six of them have been uploaded in previous work. Their GSE numbers are GSM1537659, GSM1537660, GSM1537661, GSM1537662, GSM1537663, GSM1537664 in Platform GPL11314. Herein, we uploaded other 41 test samples (semen-BTHO-7~semen-BTHO-47) and one reference sample (L1 Dominette 01449, a Hereford cow of European descent and named its result as DTTRACE).

拷贝数变异（Copy number variation, CNV）作为一种重要的遗传变异形式，对哺乳动物表型差异的形成具有显著贡献。迄今为止，在诸如荷斯坦牛等家畜中，基于基因芯片比较基因组杂交（array CGH）技术进行的拷贝数变异关联分析尚显不足。本研究旨在利用高密度aCGH数据识别CNV，并通过GWAS方法探索与复杂性状相关的功能CNV。本研究对荷斯坦牛中的39种复杂生产性状进行了系统性的CNV关联分析。通过基因芯片CGH数据，本研究在47头荷斯坦公牛中识别出1043个CNV区域（CNVRs）。采用全基因组关联分析（GWAS）方法，经假发现率（FDR）校正后，我们识别出至少与一种复杂性状相关的79个显著CNVRs。值得注意的是，其中24个CNVRs与后代妊娠率（DPR）显著相关。本研究观察到39个CNV位点表现出多效性现象，可同时调节至少2种复杂性状。总之，本研究中确定的显著CNVs可以作为荷斯坦牛遗传改良计划中的额外分子标记。总体设计：由NimbleGen公司制造的包含2,166,464个寡核苷酸探针的全基因组高密度CGH阵列，在一个芯片上设计和制造，以提供对牛UMD3.1基因组的均匀覆盖，探针间平均间隔约为1.2 kb。共分析了48个样本，其中47个为荷斯坦公牛的测试样本。其中6个样本已在先前工作中上传，其GSE编号分别为GSM1537659、GSM1537660、GSM1537661、GSM1537662、GSM1537663、GSM1537664，在平台GPL11314上。在此，我们上传了其他41个测试样本（精子-BTHO-7~精子-BTHO-47）和1个参考样本（L1 Dominette 01449，欧洲血统的荷斯坦牛，命名为DTTRACE）。