Whole-genome sequencing of eight goat populations for the detection of selection signatures underlying production and adaptive traits

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作者
Xiaolong Wang
Jing Liu
Guangxian Zhou
Jiazhong Guo
Hailong Yan
Yiyuan Niu
Yan Li
Chao Yuan
Rongqing Geng
Xianyong Lan
Xiaopeng An
Xingui Tian
Huangkai Zhou
Jiuzhou Song
Yu Jiang
Yulin Chen
机构
[1] College of Animal Science and Technology,Department of Animal and Avian Sciences
[2] Northwest A&F University,undefined
[3] College of Animal Science and Technology,undefined
[4] Sichuan Agricultural University,undefined
[5] College of Life Science,undefined
[6] Yulin University,undefined
[7] Lanzhou Institute of Husbandry and Pharmaceutical Sciences of CAAS,undefined
[8] College of Pharmacy,undefined
[9] Yancheng Teachers University,undefined
[10] Guizhou University,undefined
[11] Guangzhou Gene de-novo Biotechnology Co. Ltd.,undefined
[12] University of Maryland,undefined
[13] College Park,undefined
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摘要
The goat (Capra hircus) is one of the first farm animals that have undergone domestication and extensive natural and artificial selection by adapting to various environments, which in turn has resulted in its high level of phenotypic diversity. Here, we generated medium-coverage (9–13×) sequences from eight domesticated goat breeds, representing morphologically or geographically specific populations, to identify genomic regions representing selection signatures. We discovered ~10 million single nucleotide polymorphisms (SNPs) for each breed. By combining two approaches, ZHp and di values, we identified 22 genomic regions that may have contributed to the phenotypes in coat color patterns, body size, cashmere traits, as well as high altitude adaptation in goat populations. Candidate genes underlying strong selection signatures including coloration (ASIP, KITLG, HTT, GNA11, and OSTM1), body size (TBX15, DGCR8, CDC25A, and RDH16), cashmere traits (LHX2, FGF9, and WNT2), and hypoxia adaptation (CDK2, SOCS2, NOXA1, and ENPEP) were identified. We also identified candidate functional SNPs within selected genes that may be important for each trait. Our results demonstrated the potential of using sequence data in identifying genomic regions that are responsible for agriculturally significant phenotypes in goats, which in turn can be used in the selection of goat breeds for environmental adaptation and domestication.
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