population structure;
genomic breed composition;
cattle;
Angus;
Brahman;
PREDICTION;
D O I:
10.3389/fgene.2018.00090
中图分类号:
Q3 [遗传学];
学科分类号:
071007 ;
090102 ;
摘要:
Crossbreeding is a common strategy used in tropical and subtropical regions to enhance beef production, and having accurate knowledge of breed composition is essential for the success of a crossbreeding program. Although pedigree records have been traditionally used to obtain the breed composition of crossbred cattle, the accuracy of pedigree-based breed composition can be reduced by inaccurate and/or incomplete records and Mendelian sampling. Breed composition estimation from genomic data has multiple advantages including higher accuracy without being affected by missing, incomplete, or inaccurate records and the ability to be used as independent authentication of breed in breed-labeled beef products. The present study was conducted with 676 Angus-Brahman crossbred cattle with genotype and pedigree information to evaluate the feasibility and accuracy of using genomic data to determine breed composition. We used genomic data in parametric and non-parametric methods to detect population structure due to differences in breed composition while accounting for the confounding effect of close familial relationships. By applying principal component analysis (PCA) and the maximum likelihood method of ADMIXTURE to genomic data, it was possible to successfully characterize population structure resulting from heterogeneous breed ancestry, while accounting for close familial relationships. PCA results offered additional insight into the different hierarchies of genetic variation structuring. The first principal component was strongly correlated with Angus-Brahman proportions, and the second represented variation within animals that have a relatively more extended Brangus lineage-indicating the presence of a distinct pattern of genetic variation in these cattle. Although there was strong agreement between breed proportions estimated from pedigree and genetic information, there were significant discrepancies between these two methods for certain animals. This was most likely due to inaccuracies in the pedigree-based estimation of breed composition, which supported the case for using genomic information to complement and/or replace pedigree information when estimating breed composition. Comparison with a supervised analysis where purebreds are used as the training set suggest that accurate predictions can be achieved even in the absence of purebred population information.
机构:
Chungnam Natl Univ, Dept Bio Big Data & Precis Agr, Daejeon 34134, South KoreaChungnam Natl Univ, Dept Bio Big Data & Precis Agr, Daejeon 34134, South Korea
Hong, Euiseo
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机构:
Chung, Yoonji
Dinh, Phuong Thanh N.
论文数: 0引用数: 0
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机构:
Chungnam Natl Univ, Dept Bio AI Convergence, Daejeon 34134, South KoreaChungnam Natl Univ, Dept Bio Big Data & Precis Agr, Daejeon 34134, South Korea
Dinh, Phuong Thanh N.
Kim, Yoonsik
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机构:
Chungnam Natl Univ, Inst Agr Sci, Daejeon 34134, South KoreaChungnam Natl Univ, Dept Bio Big Data & Precis Agr, Daejeon 34134, South Korea
Kim, Yoonsik
Maeng, Suyeon
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机构:
Chungnam Natl Univ, Div Anim & Dairy Sci, Daejeon 34134, South KoreaChungnam Natl Univ, Dept Bio Big Data & Precis Agr, Daejeon 34134, South Korea
Maeng, Suyeon
Choi, Young jae
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h-index: 0
机构:
Chungnam Natl Univ, Dept Bio AI Convergence, Daejeon 34134, South KoreaChungnam Natl Univ, Dept Bio Big Data & Precis Agr, Daejeon 34134, South Korea
Choi, Young jae
Lee, Jaeho
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机构:
Chungnam Natl Univ, Dept Bio AI Convergence, Daejeon 34134, South KoreaChungnam Natl Univ, Dept Bio Big Data & Precis Agr, Daejeon 34134, South Korea
Lee, Jaeho
Jeong, Woonyoung
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机构:
Chungnam Natl Univ, Dept Bio Big Data & Precis Agr, Daejeon 34134, South KoreaChungnam Natl Univ, Dept Bio Big Data & Precis Agr, Daejeon 34134, South Korea
Jeong, Woonyoung
Choi, Hyunji
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机构:
Natl Inst Anim Sci, Div Anim Genom & Bioinformat, Wanju 55365, South KoreaChungnam Natl Univ, Dept Bio Big Data & Precis Agr, Daejeon 34134, South Korea
Choi, Hyunji
Lee, Seung Hwan
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机构:
Chungnam Natl Univ, Div Anim & Dairy Sci, Daejeon 34134, South KoreaChungnam Natl Univ, Dept Bio Big Data & Precis Agr, Daejeon 34134, South Korea