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Peng W, Zhang Y, Gao L, Wang S, Liu M, Sun E, Lu K, Zhang Y, Li B, Li G, Cao J, Yang M. Examination of homozygosity runs and selection signatures in native goat breeds of Henan, China. BMC Genomics 2024; 25:1184. [PMID: 39643897 PMCID: PMC11624592 DOI: 10.1186/s12864-024-11098-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2024] [Accepted: 11/27/2024] [Indexed: 12/09/2024] Open
Abstract
Understanding the genomic characteristics of livestock is crucial for improving breeding efficiency and conservation efforts. However, there is a relative lack of information on the genetic makeup of local goat breeds in Henan, China. In this study, we identified runs of homozygosity (ROH), genomic inbreeding coefficients (FROH), and selection signatures in four breeds including Funiu White (FNW), Huai (HG), Lushan Bullleg (LS), and Taihang black (THB). The genomic analysis utilized a dataset of 46,278 SNP markers and 102 animals. A total of 342, 567, 1285, and 180 ROH segments were detected in FNW, HG, LS, and THB, respectively, with an average of 15.55, 29.84, 32.95, and 8.18 segments per individual. The lengths of ROH segments varied from 69.36 Mb in THB to 417.06 Mb in LS, with the most common lengths being 2-4 Mb and 4-8 Mb. The highest number of longest ROH segments (> 16 Mb) were found in LS (328) and the highest average FROH value was observed in LS (0.173), followed by HG (0.128), while the lowest FROH values were in THB (0.029) and FNW (0.070). Furthermore, the analysis of ROH islands and Composite Likelihood Ratio (CLR) identified a total of 175 significant genes. Among these, 25 genes were found to overlap, detected by both methods. These genes were associated with a diverse range of traits including reproductive ability (GPRIN3), weight (CCSER1), immune response (HERC5 and TIGD2), embryo development (NAP1L5), environmental adaptation (KLHL3, TRHDE, and IFNGR1), and milk characteristics (FAM13A). Significant Gene Ontology (GO) terms related to embryo skeletal system morphogenesis, brain ventricle development, and growth were also identified. This study helps reveal the genetic architecture of Henan goat breeds and provides valuable insights for the effective conservation and breeding programs of local goat breeds in Henan.
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Affiliation(s)
- Weifeng Peng
- College of Life Science and Agronomy, Zhoukou Normal University, Zhoukou, China.
| | - Yiyuan Zhang
- State Key Laboratory for Sheep Genetic Improvement and Healthy Production, Xinjiang Academy of Agricultural and Reclamation Sciences, Shihezi, China
| | - Lei Gao
- State Key Laboratory for Sheep Genetic Improvement and Healthy Production, Xinjiang Academy of Agricultural and Reclamation Sciences, Shihezi, China
| | - Shuping Wang
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, China
| | - Mengting Liu
- College of Life Science and Agronomy, Zhoukou Normal University, Zhoukou, China
| | - Enrui Sun
- College of Life Science and Agronomy, Zhoukou Normal University, Zhoukou, China
| | - Kaixin Lu
- College of Life Science and Agronomy, Zhoukou Normal University, Zhoukou, China
| | - Yunxia Zhang
- College of Life Science and Agronomy, Zhoukou Normal University, Zhoukou, China
| | - Bing Li
- College of Life Science and Agronomy, Zhoukou Normal University, Zhoukou, China
| | - Guoyin Li
- College of Life Science and Agronomy, Zhoukou Normal University, Zhoukou, China
| | - Jingya Cao
- College of Life Science and Agronomy, Zhoukou Normal University, Zhoukou, China
| | - Mingsheng Yang
- College of Life Science and Agronomy, Zhoukou Normal University, Zhoukou, China.
- Field Observation and Research Station of Green Agriculture in Dancheng County, Zhoukou, China.
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Wang H, Wu H, Zhang W, Jiang J, Qian H, Man C, Gao H, Chen Q, Du L, Chen S, Wang F. Development and validation of a 5K low-density SNP chip for Hainan cattle. BMC Genomics 2024; 25:873. [PMID: 39294563 PMCID: PMC11409743 DOI: 10.1186/s12864-024-10753-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Accepted: 09/02/2024] [Indexed: 09/20/2024] Open
Abstract
BACKGROUND This study aimed to design and develop a 5K low-density liquid chip for Hainan cattle utilizing targeted capture sequencing technology. The chip incorporates a substantial number of functional single nucleotide polymorphism (SNP) loci derived from public literature, including SNP loci significantly associated with immunity, heat stress, meat quality, reproduction, and other traits. Additionally, SNPs located in the coding regions of immune-related genes from the Bovine Genome Variation Database (BGVD) and Hainan cattle-specific SNP loci were included. RESULTS A total of 5,293 SNPs were selected, resulting in 9,837 DNA probes with a coverage rate of 85.69%, thereby creating a Hainan cattle-specific 5K Genotyping by Target Sequencing (GBTS) liquid chip. Evaluation with 152 cattle samples demonstrated excellent clustering performance and a detection rate ranging from 96.60 to 99.07%, with 94.5% of SNP sites exhibiting polymorphism. The chip achieved 100% gender coverage and displayed a heterozygosity rate between 14.20% and 29.65%, with a repeatability rate of 99.65-99.85%. Analyses using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) revealed the potential regulatory roles of exonic SNPs in immune response pathways. CONCLUSION The development and validation of the 5K GBTS liquid chip for Hainan cattle represent a valuable tool for genome analysis and genetic diversity assessment. Furthermore, it facilitates breed identification, gender determination, and kinship analysis, providing a foundation for the efficient utilization and development of local cattle genetic resources.
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Affiliation(s)
- Huan Wang
- Hainan Key Lab of Tropical Animal Reproduction, Breeding and Epidemic Disease Research, College of Tropical Agriculture and Forestry, Hainan University, Haikou, Hainan, PR China
| | - Hui Wu
- Xinjiang Barkol Kazakh Autonomous County Animal Husbandry Veterinary Station, Barkol Kazakh Autonomous County, Xinjiang, PR China
| | - Wencan Zhang
- Hainan Key Lab of Tropical Animal Reproduction, Breeding and Epidemic Disease Research, College of Tropical Agriculture and Forestry, Hainan University, Haikou, Hainan, PR China
| | - Junming Jiang
- Hainan Key Lab of Tropical Animal Reproduction, Breeding and Epidemic Disease Research, College of Tropical Agriculture and Forestry, Hainan University, Haikou, Hainan, PR China
| | - Hejie Qian
- Hainan Key Lab of Tropical Animal Reproduction, Breeding and Epidemic Disease Research, College of Tropical Agriculture and Forestry, Hainan University, Haikou, Hainan, PR China
| | - Churiga Man
- Hainan Key Lab of Tropical Animal Reproduction, Breeding and Epidemic Disease Research, College of Tropical Agriculture and Forestry, Hainan University, Haikou, Hainan, PR China
| | - Hongyan Gao
- Hainan Key Lab of Tropical Animal Reproduction, Breeding and Epidemic Disease Research, College of Tropical Agriculture and Forestry, Hainan University, Haikou, Hainan, PR China
| | - Qiaoling Chen
- Hainan Key Lab of Tropical Animal Reproduction, Breeding and Epidemic Disease Research, College of Tropical Agriculture and Forestry, Hainan University, Haikou, Hainan, PR China
| | - Li Du
- Hainan Key Lab of Tropical Animal Reproduction, Breeding and Epidemic Disease Research, College of Tropical Agriculture and Forestry, Hainan University, Haikou, Hainan, PR China
| | - Si Chen
- Hainan Key Lab of Tropical Animal Reproduction, Breeding and Epidemic Disease Research, College of Tropical Agriculture and Forestry, Hainan University, Haikou, Hainan, PR China.
| | - Fengyang Wang
- Hainan Key Lab of Tropical Animal Reproduction, Breeding and Epidemic Disease Research, College of Tropical Agriculture and Forestry, Hainan University, Haikou, Hainan, PR China.
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Qi L, Xiao L, Fu R, Nie Q, Zhang X, Luo W. Genetic characteristics and selection signatures between Southern Chinese local and commercial chickens. Poult Sci 2024; 103:103863. [PMID: 38810566 PMCID: PMC11166977 DOI: 10.1016/j.psj.2024.103863] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Revised: 04/25/2024] [Accepted: 05/13/2024] [Indexed: 05/31/2024] Open
Abstract
The introduction of exotic breeds and the cultivation of new lines by breeding companies have posed challenges to native chickens in South China, including loss of breed characteristics, decreased genetic diversity, and declining purity. Understanding the population genetic structure and genetic diversity of native chickens in South China is crucial for further advancements in breeding efforts. In this study, we analyzed the population genetic structure and genetic diversity of 321 individuals from 10 different breeds in South China. By comparing commercial chickens with native ones, we identified selection signatures occurring between local chickens and commercial breeds. The analysis of population genetic structure revealed that the native chicken populations in South China exhibited a considerable level of genetic diversity. Moreover, the commercial lines of Xiaobai chicken and Huangma chicken displayed even higher levels of genetic diversity, which distinguished them from other native varieties at the clustering level. However, certain individuals within these commercial varieties showed a discernible genetic relationship with the native populations. Notably, both commercial varieties also retained a significant degree of genetic similarity to their respective native counterparts. In order to investigate the genomic changes occurring during the commercialization of native chickens, we employed 4 methods (Fst, ROD, XPCLR, and XPEHH) to identify potential candidate regions displaying selective signatures in Southern Chinese native chicken population. A total of 168 (identified by Fst and ROD) and 86 (identified by XPCLR and XPEHH) overlapping genes were discovered. Functional annotation analysis revealed that these genes may be associated with reproduction and growth (SAMSN1, HYLS1, ROBO3, FGF14, PRSS23), musculoskeletal development (DNER, MYBPC1, DGKB, ORC1, KLF10), disease resistance and environmental adaptability (PUS3, CRB2, CALD1, USP15, SGCD, LTBP1), as well as egg production (ADGRB3, ACSF3). Overall, native chickens in South China harbor numerous selective sweep regions compared to commercial chickens, enriching valuable genomic resources for future genetic research and breeding conservation.
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Affiliation(s)
- Lin Qi
- State Key Laboratory of Livestock and Poultry Breeding, & Lingnan Guangdong Laboratory of Agriculture, South China Agricultural University, Guangzhou 510642, China; Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, and Key Lab of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture and Rural Affair, South China Agricultural University, 510642, China; Department of Animal Genetics, Breeding and Reproduction, College of Animal Science, South China Agricultural University, Guangzhou Guangzhou 510642, China
| | - Liangchao Xiao
- State Key Laboratory of Livestock and Poultry Breeding, & Lingnan Guangdong Laboratory of Agriculture, South China Agricultural University, Guangzhou 510642, China; Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, and Key Lab of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture and Rural Affair, South China Agricultural University, 510642, China; Department of Animal Genetics, Breeding and Reproduction, College of Animal Science, South China Agricultural University, Guangzhou Guangzhou 510642, China
| | - Rong Fu
- State Key Laboratory of Livestock and Poultry Breeding, & Lingnan Guangdong Laboratory of Agriculture, South China Agricultural University, Guangzhou 510642, China; Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, and Key Lab of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture and Rural Affair, South China Agricultural University, 510642, China; Department of Animal Genetics, Breeding and Reproduction, College of Animal Science, South China Agricultural University, Guangzhou Guangzhou 510642, China
| | - Qinghua Nie
- State Key Laboratory of Livestock and Poultry Breeding, & Lingnan Guangdong Laboratory of Agriculture, South China Agricultural University, Guangzhou 510642, China; Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, and Key Lab of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture and Rural Affair, South China Agricultural University, 510642, China; Department of Animal Genetics, Breeding and Reproduction, College of Animal Science, South China Agricultural University, Guangzhou Guangzhou 510642, China
| | - Xiquan Zhang
- State Key Laboratory of Livestock and Poultry Breeding, & Lingnan Guangdong Laboratory of Agriculture, South China Agricultural University, Guangzhou 510642, China; Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, and Key Lab of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture and Rural Affair, South China Agricultural University, 510642, China; Department of Animal Genetics, Breeding and Reproduction, College of Animal Science, South China Agricultural University, Guangzhou Guangzhou 510642, China
| | - Wen Luo
- State Key Laboratory of Livestock and Poultry Breeding, & Lingnan Guangdong Laboratory of Agriculture, South China Agricultural University, Guangzhou 510642, China; Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, and Key Lab of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture and Rural Affair, South China Agricultural University, 510642, China; Department of Animal Genetics, Breeding and Reproduction, College of Animal Science, South China Agricultural University, Guangzhou Guangzhou 510642, China.
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Dementieva NV, Shcherbakov YS, Stanishevskaya OI, Vakhrameev AB, Larkina TA, Dysin AP, Nikolaeva OA, Ryabova AE, Azovtseva AI, Mitrofanova OV, Peglivanyan GK, Reinbach NR, Griffin DK, Romanov MN. Large-scale genome-wide SNP analysis reveals the rugged (and ragged) landscape of global ancestry, phylogeny, and demographic history in chicken breeds. J Zhejiang Univ Sci B 2024; 25:324-340. [PMID: 38584094 PMCID: PMC11009443 DOI: 10.1631/jzus.b2300443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 10/10/2023] [Indexed: 04/09/2024]
Abstract
The worldwide chicken gene pool encompasses a remarkable, but shrinking, number of divergently selected breeds of diverse origin. This study was a large-scale genome-wide analysis of the landscape of the complex molecular architecture, genetic variability, and detailed structure among 49 populations. These populations represent a significant sample of the world's chicken breeds from Europe (Russia, Czech Republic, France, Spain, UK, etc.), Asia (China), North America (USA), and Oceania (Australia). Based on the results of breed genotyping using the Illumina 60K single nucleotide polymorphism (SNP) chip, a bioinformatic analysis was carried out. This included the calculation of heterozygosity/homozygosity statistics, inbreeding coefficients, and effective population size. It also included assessment of linkage disequilibrium and construction of phylogenetic trees. Using multidimensional scaling, principal component analysis, and ADMIXTURE-assisted global ancestry analysis, we explored the genetic structure of populations and subpopulations in each breed. An overall 49-population phylogeny analysis was also performed, and a refined evolutionary model of chicken breed formation was proposed, which included egg, meat, dual-purpose types, and ambiguous breeds. Such a large-scale survey of genetic resources in poultry farming using modern genomic methods is of great interest both from the viewpoint of a general understanding of the genetics of the domestic chicken and for the further development of genomic technologies and approaches in poultry breeding. In general, whole genome SNP genotyping of promising chicken breeds from the worldwide gene pool will promote the further development of modern genomic science as applied to poultry.
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Affiliation(s)
- Natalia V Dementieva
- Russian Research Institute of Farm Animal Genetics and Breeding ‒ Branch of the L. K. Ernst Federal Research Centre for Animal Husbandry, Pushkin, St. Petersburg, 196601, Russia.
| | - Yuri S Shcherbakov
- Russian Research Institute of Farm Animal Genetics and Breeding ‒ Branch of the L. K. Ernst Federal Research Centre for Animal Husbandry, Pushkin, St. Petersburg, 196601, Russia
| | - Olga I Stanishevskaya
- Russian Research Institute of Farm Animal Genetics and Breeding ‒ Branch of the L. K. Ernst Federal Research Centre for Animal Husbandry, Pushkin, St. Petersburg, 196601, Russia
| | - Anatoly B Vakhrameev
- Russian Research Institute of Farm Animal Genetics and Breeding ‒ Branch of the L. K. Ernst Federal Research Centre for Animal Husbandry, Pushkin, St. Petersburg, 196601, Russia
| | - Tatiana A Larkina
- Russian Research Institute of Farm Animal Genetics and Breeding ‒ Branch of the L. K. Ernst Federal Research Centre for Animal Husbandry, Pushkin, St. Petersburg, 196601, Russia
| | - Artem P Dysin
- Russian Research Institute of Farm Animal Genetics and Breeding ‒ Branch of the L. K. Ernst Federal Research Centre for Animal Husbandry, Pushkin, St. Petersburg, 196601, Russia
| | - Olga A Nikolaeva
- Russian Research Institute of Farm Animal Genetics and Breeding ‒ Branch of the L. K. Ernst Federal Research Centre for Animal Husbandry, Pushkin, St. Petersburg, 196601, Russia
| | - Anna E Ryabova
- Russian Research Institute of Farm Animal Genetics and Breeding ‒ Branch of the L. K. Ernst Federal Research Centre for Animal Husbandry, Pushkin, St. Petersburg, 196601, Russia
| | - Anastasiia I Azovtseva
- Russian Research Institute of Farm Animal Genetics and Breeding ‒ Branch of the L. K. Ernst Federal Research Centre for Animal Husbandry, Pushkin, St. Petersburg, 196601, Russia
| | - Olga V Mitrofanova
- Russian Research Institute of Farm Animal Genetics and Breeding ‒ Branch of the L. K. Ernst Federal Research Centre for Animal Husbandry, Pushkin, St. Petersburg, 196601, Russia
| | - Grigoriy K Peglivanyan
- Russian Research Institute of Farm Animal Genetics and Breeding ‒ Branch of the L. K. Ernst Federal Research Centre for Animal Husbandry, Pushkin, St. Petersburg, 196601, Russia
| | - Natalia R Reinbach
- Russian Research Institute of Farm Animal Genetics and Breeding ‒ Branch of the L. K. Ernst Federal Research Centre for Animal Husbandry, Pushkin, St. Petersburg, 196601, Russia
| | - Darren K Griffin
- School of Biosciences, University of Kent, Canterbury, CT2 7NJ, UK. ,
| | - Michael N Romanov
- School of Biosciences, University of Kent, Canterbury, CT2 7NJ, UK. ,
- L K. Ernst Federal Research Center for Animal Husbandry, Dubrovitsy, Podolsk, Moscow Oblast, 142132, Russia. ,
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Dorji J, Reverter A, Alexandre PA, Chamberlain AJ, Vander-Jagt CJ, Kijas J, Porto-Neto LR. Ancestral alleles defined for 70 million cattle variants using a population-based likelihood ratio test. Genet Sel Evol 2024; 56:11. [PMID: 38321371 PMCID: PMC10848479 DOI: 10.1186/s12711-024-00879-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Accepted: 01/30/2024] [Indexed: 02/08/2024] Open
Abstract
BACKGROUND The study of ancestral alleles provides insights into the evolutionary history, selection, and genetic structures of a population. In cattle, ancestral alleles are widely used in genetic analyses, including the detection of signatures of selection, determination of breed ancestry, and identification of admixture. Having a comprehensive list of ancestral alleles is expected to improve the accuracy of these genetic analyses. However, the list of ancestral alleles in cattle, especially at the whole genome sequence level, is far from complete. In fact, the current largest list of ancestral alleles (~ 42 million) represents less than 28% of the total number of detected variants in cattle. To address this issue and develop a genomic resource for evolutionary studies, we determined ancestral alleles in cattle by comparing prior derived whole-genome sequence variants to an out-species group using a population-based likelihood ratio test. RESULTS Our study determined and makes available the largest list of ancestral alleles in cattle to date (70.1 million) and includes 2.3 million on the X chromosome. There was high concordance (97.6%) of the determined ancestral alleles with those from previous studies when only high-probability ancestral alleles were considered (29.8 million positions) and another 23.5 million high-confidence ancestral alleles were novel, expanding the available reference list to improve the accuracies of genetic analyses involving ancestral alleles. The high concordance of the results with previous studies implies that our approach using genomic sequence variants and a likelihood ratio test to determine ancestral alleles is appropriate. CONCLUSIONS Considering the high concordance of ancestral alleles across studies, the ancestral alleles determined in this study including those not previously listed, particularly those with high-probability estimates, may be used for further genetic analyses with reasonable accuracy. Our approach that used predetermined variants in species and the likelihood ratio test to determine ancestral alleles is applicable to other species for which sequence level genotypes are available.
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Affiliation(s)
- Jigme Dorji
- CSIRO, Agriculture & Food, St. Lucia, QLD, 4067, Australia.
| | | | | | - Amanda J Chamberlain
- AgriBio, Centre for AgriBioscience, Agriculture Victoria, Bundoora, VIC, 3083, Australia
| | - Christy J Vander-Jagt
- AgriBio, Centre for AgriBioscience, Agriculture Victoria, Bundoora, VIC, 3083, Australia
| | - James Kijas
- CSIRO, Agriculture & Food, St. Lucia, QLD, 4067, Australia
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Guan S, Li W, Jin H, Zhang L, Liu G. Development and Validation of a 54K Genome-Wide Liquid SNP Chip Panel by Target Sequencing for Dairy Goat. Genes (Basel) 2023; 14:genes14051122. [PMID: 37239482 DOI: 10.3390/genes14051122] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 05/18/2023] [Accepted: 05/19/2023] [Indexed: 05/28/2023] Open
Abstract
As an important genotyping platform, SNP chips are essential for implementing genomic selection. In this article, we introduced the development of a liquid SNP chip panel for dairy goats. This panel contains 54,188 SNPs based on genotyping by targeted sequencing (GBTS) technology. The source of SNPs in the panel were from the whole-genome resequencing of 110 dairy goats from three European and two Chinese indigenous dairy goat breeds. The performance of this liquid SNP chip panel was evaluated by genotyping 200 additional goats. Fifteen of them were randomly selected for whole-genome resequencing. The average capture ratio of the panel design loci was 98.41%, and the genotype concordance with resequencing reached 98.02%. We further used this chip panel to conduct genome-wide association studies (GWAS) to detect genetic loci that affect coat color in dairy goats. A single significant association signal for hair color was found on chromosome 8 at 31.52-35.02 Mb. The TYRP1 gene, which is associated with coat color in goats, was identified to be located at this genomic region (chromosome 8: 31,500,048-31,519,064). The emergence of high-precision and low-cost liquid microarrays will improve the analysis of genomics and breeding efficiency of dairy goats.
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Affiliation(s)
- Shengyu Guan
- State Key Laboratory of Animal Biotech Breeding, Key Laboratory of Animal Genetics and Breeding of the Ministry of Agriculture, Beijing Key Laboratory for Animal Genetic Improvement, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
| | - Weining Li
- State Key Laboratory of Animal Biotech Breeding, Key Laboratory of Animal Genetics and Breeding of the Ministry of Agriculture, Beijing Key Laboratory for Animal Genetic Improvement, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
| | - Hai Jin
- Inner Mongolia Academy of Agricultural & Animal Husbandry Sciences, Hohhot 010031, China
| | - Lu Zhang
- State Key Laboratory of Animal Biotech Breeding, Key Laboratory of Animal Genetics and Breeding of the Ministry of Agriculture, Beijing Key Laboratory for Animal Genetic Improvement, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
| | - Guoshi Liu
- State Key Laboratory of Animal Biotech Breeding, Key Laboratory of Animal Genetics and Breeding of the Ministry of Agriculture, Beijing Key Laboratory for Animal Genetic Improvement, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
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