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Improving the Efficiency of Genomic Selection in Chinese Simmental beef cattle

Jiangwei Xia, Yang Wu, Huizhong Fang, Wengang Zhang, Yuxin Song, Lupei Zhang, Xue Gao, Yan Chen, Junya Li, Huijiang Gao
doi: https://doi.org/10.1101/022673
Jiangwei Xia
1Institute of Animal Science, Chinese Academy of Agricultural Science, Beijing 100193, China
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Yang Wu
1Institute of Animal Science, Chinese Academy of Agricultural Science, Beijing 100193, China
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Huizhong Fang
1Institute of Animal Science, Chinese Academy of Agricultural Science, Beijing 100193, China
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Wengang Zhang
1Institute of Animal Science, Chinese Academy of Agricultural Science, Beijing 100193, China
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Yuxin Song
1Institute of Animal Science, Chinese Academy of Agricultural Science, Beijing 100193, China
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Lupei Zhang
1Institute of Animal Science, Chinese Academy of Agricultural Science, Beijing 100193, China
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Xue Gao
1Institute of Animal Science, Chinese Academy of Agricultural Science, Beijing 100193, China
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Yan Chen
1Institute of Animal Science, Chinese Academy of Agricultural Science, Beijing 100193, China
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Junya Li
1Institute of Animal Science, Chinese Academy of Agricultural Science, Beijing 100193, China
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  • For correspondence: gaohj111@sina.com JL1@iascaas.net.cn
Huijiang Gao
1Institute of Animal Science, Chinese Academy of Agricultural Science, Beijing 100193, China
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  • For correspondence: gaohj111@sina.com JL1@iascaas.net.cn
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Abstract

Genomic selection is an accurate and efficient method of estimating genetic merits by using high-density genome-wide single nucleotide polymorphisms (SNPs).In this study, we investigate an approach to increase the efficiency of genomic prediction by using genome-wide markers. The approach is a feature selection based on genomic best linear unbiased prediction (GBLUP),which is a statistical method used to predict breeding values using SNPs for selection in animal and plant breeding. The objective of this study is the choice of kinship matrix for genomic best linear unbiased prediction (GBLUP).The G-matrix is using the information of genome-wide dense markers. We compare three kinds of kinships based on different combinations of centring and scaling of marker genotypes. And find a suitable kinship approach that adjusts for the resource population of Chinese Simmental beef cattle. Single nucleotide polymorphism (SNPs) can be used to estimate kinship matrix and individual inbreeding coefficients more accurately. So in our research a genomic relationship matrix was developed for 1059 Chinese Simmental beef cattle using 640000 single nucleotide polymorphisms and breeding values were estimated using phenotypes about Carcass weight and Sirloin weight. The number of SNPs needed to accurately estimate a genomic relationship matrix was evaluated in this population. Another aim of this study was to optimize the selection of markers and determine the required number of SNPs for estimation of kinship in the Chinese Simmental beef cattle.

We find that the feature selection of GBLUP using Xu’s and the Astle and Balding’s kinships model performed similarly well, and were the best-performing methods in our study. Inbreeding and kinship matrix can be estimated with high accuracy using ≥12,000s in Chinese Simmental beef cattle.

Footnotes

  • ↵1 Emails of other authors: Jiangwei Xia: jiangwei_xia06{at}126.com, Yang Wu: wuyangf7{at}126.com, Huizhong Fan: fanhuizhong1990{at}126.com, Wenggan Zhang: zhangwengang_19{at}sina.com, Yunxin Song: 2361278555{at}qq.com, Lupei Zhang: lpzhang{at}iascaas.net.cn, Xue Gao: gaoxue76{at}126.com, Yan Chen: chenyan0204{at}163.com

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted July 17, 2015.
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Improving the Efficiency of Genomic Selection in Chinese Simmental beef cattle
Jiangwei Xia, Yang Wu, Huizhong Fang, Wengang Zhang, Yuxin Song, Lupei Zhang, Xue Gao, Yan Chen, Junya Li, Huijiang Gao
bioRxiv 022673; doi: https://doi.org/10.1101/022673
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Improving the Efficiency of Genomic Selection in Chinese Simmental beef cattle
Jiangwei Xia, Yang Wu, Huizhong Fang, Wengang Zhang, Yuxin Song, Lupei Zhang, Xue Gao, Yan Chen, Junya Li, Huijiang Gao
bioRxiv 022673; doi: https://doi.org/10.1101/022673

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