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1. A compendium of genetic regulatory effects across pig tissues.

Author

Teng, Jinyan, Gao, Yahui, Yin, Hongwei, Bai, Zhonghao, Liu, Shuli, Zeng, Haonan, Bai, Lijing, Cai, Zexi, Zhao, Bingru, Li, Xiujin, Xu, Zhiting, Lin, Qing, Pan, Zhangyuan, Yang, Wenjing, Yu, Xiaoshan, Guan, Dailu, Hou, Yali, Keel, Brittney, Rohrer, Gary, Lindholm-Perry, Amanda, Oliver, William, Ballester, Maria, Crespo-Piazuelo, Daniel, Quintanilla, Raquel, Canela-Xandri, Oriol, Rawlik, Konrad, Xia, Charley, Yao, Yuelin, Zhao, Qianyi, Yao, Wenye, Yang, Liu, Li, Houcheng, Zhang, Huicong, Liao, Wang, Chen, Tianshuo, Karlskov-Mortensen, Peter, Fredholm, Merete, Amills, Marcel, Clop, Alex, Giuffra, Elisabetta, Wu, Jun, Cai, Xiaodian, Diao, Shuqi, Pan, Xiangchun, Wei, Chen, Li, Jinghui, Cheng, Hao, Wang, Sheng, Su, Guosheng, Sahana, Goutam, Lund, Mogens, Dekkers, Jack, Kramer, Luke, Tuggle, Christopher, Corbett, Ryan, Groenen, Martien, Madsen, Ole, Gòdia, Marta, Rocha, Dominique, Charles, Mathieu, Li, Cong-Jun, Pausch, Hubert, Hu, Xiaoxiang, Frantz, Laurent, Luo, Yonglun, Lin, Lin, Zhou, Zhongyin, Zhang, Zhe, Chen, Zitao, Cui, Leilei, Xiang, Ruidong, Shen, Xia, Li, Pinghua, Huang, Ruihua, Tang, Guoqing, Li, Mingzhou, Zhao, Yunxiang, Yi, Guoqiang, Tang, Zhonglin, Jiang, Jicai, Zhao, Fuping, Yuan, Xiaolong, Liu, Xiaohong, Chen, Yaosheng, Xu, Xuewen, Zhao, Shuhong, Zhao, Pengju, Haley, Chris, Zhou, Huaijun, Wang, Qishan, Pan, Yuchun, Ding, Xiangdong, Ma, Li, Li, Jiaqi, Navarro, Pau, Zhang, Qin, Li, Bingjie, Tenesa, Albert, Li, Kui, and Liu, George

Subjects
Swine, Animals, Humans, Gene Expression Regulation, Genotype, Phenotype, Sequence Analysis, RNA, Gene Expression Profiling
Abstract

The Farm Animal Genotype-Tissue Expression (FarmGTEx) project has been established to develop a public resource of genetic regulatory variants in livestock, which is essential for linking genetic polymorphisms to variation in phenotypes, helping fundamental biological discovery and exploitation in animal breeding and human biomedicine. Here we show results from the pilot phase of PigGTEx by processing 5,457 RNA-sequencing and 1,602 whole-genome sequencing samples passing quality control from pigs. We build a pig genotype imputation panel and associate millions of genetic variants with five types of transcriptomic phenotypes in 34 tissues. We evaluate tissue specificity of regulatory effects and elucidate molecular mechanisms of their action using multi-omics data. Leveraging this resource, we decipher regulatory mechanisms underlying 207 pig complex phenotypes and demonstrate the similarity of pigs to humans in gene expression and the genetic regulation behind complex phenotypes, supporting the importance of pigs as a human biomedical model.

Published
2024

4. Integrating Multiple Database Resources to Elucidate the Gene Flow in Southeast Asian Pig Populations

Author

Li, Guangzhen, primary, Liu, Yuqiang, additional, Feng, Xueyan, additional, Diao, Shuqi, additional, Zhong, Zhanming, additional, Li, Bolang, additional, Teng, Jinyan, additional, Zhang, Wenjing, additional, Zeng, Haonan, additional, Cai, Xiaodian, additional, Gao, Yahui, additional, Liu, Xiaohong, additional, Yuan, Xiaolong, additional, Li, Jiaqi, additional, and Zhang, Zhe, additional

Published
2024
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5. A compendium of genetic regulatory effects across pig tissues

Author

National Natural Science Foundation of China, National Key Research and Development Program (China), Guangdong Province, National Supercomputer Center in Guangzhou, Earmarked Fund for China Agriculture Research System, Health Data Research UK, European Commission, Department of Agriculture (US), National Institute of Food and Agriculture (US), Agricultural Research Service (US), Medical Research Council (UK), Australian Research Council, Ballester, María [0000-0002-5413-4640], Crespo-Piazuelo, Daniel [0000-0001-7896-2507], Gòdia, Marta [0000-0002-0439-4014], Teng, Jinyan, Gao, Yahui, Yin, Hongwei, Bai, Zhonghao, Liu, Shuli, Zeng, Haonan, Bai, Lijing, Cai, Zexi, Zhao, Bingru, Li, Xiujin, Xu, Zhiting, Lin, Qing, Pan, Zhangyuan, Yang, Wenjing, Yu, Xiaoshan, Guan, Dailu, Hou, Yali, Keel, Brittney N., Rohrer, Gary A., Lindholm-Perry, Amanda K., Oliver, William T., Ballester, María, Crespo-Piazuelo, Daniel, Quintanilla, Raquel, Canela-Xandri, Oriol, Rawlik, Konrad, Xia, Charley, Yao, Yuelin, Zhao, Qianyi, Yao, Wenye, Yang, Liu, Li, Houcheng, Zhang, Huicong, Liao, Wang, Chen, Tianshuo, Karlskov-Mortensen, Peter, Fredholm, Merete, Amills, Marcel, Clop, Alex, Giuffra, Elisabetta, Wu, Jun, Cai, Xiaodian, Diao, Shuqi, Pan, Xiangchun, Wei, Chen, Li, Jinghui, Cheng, Hao, Wang, Sheng, Su, Guosheng, Sahana, Goutam, Lund, Mogens Sandø, Dekkers, Jack C. M., Kramer, Luke, Tuggle, Christopher K., Corbett, Ryan, Groenen, Martien A. M., Madsen, Ole, Gòdia, Marta, Rocha, Dominique, Charles, Mathieu, Li, Cong-Jun, Pausch, Hubert, Hu, Xiaoxiang, Frantz, Laurent, Luo, Yonglun, Lin, Lin, Zhou, Zhongyin, Zhang, Zhe, Chen, Zitao, Cui, Leilei, Xiang, Ruidong, Shen, Xia, Li, Pinghua, Huang, Ruihua, Tang, Guoqing, Li, Mingzhou, Zhao, Yunxiang, Yi, Guoqiang, Tang, Zhonglin, Jiang, Jicai, Zhao, Fuping, Yuan, Xiaolong, Liu, Xiaohong, Chen, Yaosheng, Xu, Xuewen, Zhao, Shuhong, Zhao, Pengju, Haley, Chris, Zhou, Huaijun, Wang, Qishan, Pan, Yuchun, Ding, Xiangdong, Ma, Li, Li, Jiaqi, Navarro, Pau, Zhang, Qin, Li, Bingjie, Tenesa, Albert, Li, Kui, Liu, George E., Fang, Lingzhao, National Natural Science Foundation of China, National Key Research and Development Program (China), Guangdong Province, National Supercomputer Center in Guangzhou, Earmarked Fund for China Agriculture Research System, Health Data Research UK, European Commission, Department of Agriculture (US), National Institute of Food and Agriculture (US), Agricultural Research Service (US), Medical Research Council (UK), Australian Research Council, Ballester, María [0000-0002-5413-4640], Crespo-Piazuelo, Daniel [0000-0001-7896-2507], Gòdia, Marta [0000-0002-0439-4014], Teng, Jinyan, Gao, Yahui, Yin, Hongwei, Bai, Zhonghao, Liu, Shuli, Zeng, Haonan, Bai, Lijing, Cai, Zexi, Zhao, Bingru, Li, Xiujin, Xu, Zhiting, Lin, Qing, Pan, Zhangyuan, Yang, Wenjing, Yu, Xiaoshan, Guan, Dailu, Hou, Yali, Keel, Brittney N., Rohrer, Gary A., Lindholm-Perry, Amanda K., Oliver, William T., Ballester, María, Crespo-Piazuelo, Daniel, Quintanilla, Raquel, Canela-Xandri, Oriol, Rawlik, Konrad, Xia, Charley, Yao, Yuelin, Zhao, Qianyi, Yao, Wenye, Yang, Liu, Li, Houcheng, Zhang, Huicong, Liao, Wang, Chen, Tianshuo, Karlskov-Mortensen, Peter, Fredholm, Merete, Amills, Marcel, Clop, Alex, Giuffra, Elisabetta, Wu, Jun, Cai, Xiaodian, Diao, Shuqi, Pan, Xiangchun, Wei, Chen, Li, Jinghui, Cheng, Hao, Wang, Sheng, Su, Guosheng, Sahana, Goutam, Lund, Mogens Sandø, Dekkers, Jack C. M., Kramer, Luke, Tuggle, Christopher K., Corbett, Ryan, Groenen, Martien A. M., Madsen, Ole, Gòdia, Marta, Rocha, Dominique, Charles, Mathieu, Li, Cong-Jun, Pausch, Hubert, Hu, Xiaoxiang, Frantz, Laurent, Luo, Yonglun, Lin, Lin, Zhou, Zhongyin, Zhang, Zhe, Chen, Zitao, Cui, Leilei, Xiang, Ruidong, Shen, Xia, Li, Pinghua, Huang, Ruihua, Tang, Guoqing, Li, Mingzhou, Zhao, Yunxiang, Yi, Guoqiang, Tang, Zhonglin, Jiang, Jicai, Zhao, Fuping, Yuan, Xiaolong, Liu, Xiaohong, Chen, Yaosheng, Xu, Xuewen, Zhao, Shuhong, Zhao, Pengju, Haley, Chris, Zhou, Huaijun, Wang, Qishan, Pan, Yuchun, Ding, Xiangdong, Ma, Li, Li, Jiaqi, Navarro, Pau, Zhang, Qin, Li, Bingjie, Tenesa, Albert, Li, Kui, Liu, George E., and Fang, Lingzhao

Abstract

The Farm Animal Genotype-Tissue Expression (FarmGTEx) project has been established to develop a public resource of genetic regulatory variants in livestock, which is essential for linking genetic polymorphisms to variation in phenotypes, helping fundamental biological discovery and exploitation in animal breeding and human biomedicine. Here we show results from the pilot phase of PigGTEx by processing 5,457 RNA-sequencing and 1,602 whole-genome sequencing samples passing quality control from pigs. We build a pig genotype imputation panel and associate millions of genetic variants with five types of transcriptomic phenotypes in 34 tissues. We evaluate tissue specificity of regulatory effects and elucidate molecular mechanisms of their action using multi-omics data. Leveraging this resource, we decipher regulatory mechanisms underlying 207 pig complex phenotypes and demonstrate the similarity of pigs to humans in gene expression and the genetic regulation behind complex phenotypes, supporting the importance of pigs as a human biomedical model.

Published
2024

6. PigBiobank: a valuable resource for understanding genetic and biological mechanisms of diverse complex traits in pigs

Author

National Natural Science Foundation of China, National Key Research and Development Program (China), Guangdong Province, Earmarked Fund for China Agriculture Research System, Ballester, María [0000-0002-5413-4640], Crespo-Piazuelo, Daniel [0000-0001-7896-2507], Amills, Marcel [0000-0002-8999-0770], Clop, Alex [0000-0001-9238-2728], Zeng, Haonan, Zhang, Wenjing, Lin, Qing, Gao, Yahui, Teng, Jinyan, Xu, Zhiting, Cai, Xiaodian, Zhong, Zhanming, Wu, Jun, Liu, Yuqiang, Diao, Shuqi, Wei, Chen, Gong, Wentao, Pan, Xiangchun, Li, Zedong, Huang, Xiaoyu, Chen, Xifan, Du, Jinshi, The PigGTEx Consortium, Zhao, Fuping, Zhao, Yunxiang, Ballester, María, Crespo-Piazuelo, Daniel, Amills, Marcel, Clop, Alex, Karlskov-Mortensen, Peter, Fredholm, Merete, Li, Pinghua, Huang, Ruihua, Tang, Guoqing, Li, Mingzhou, Liu, Xiaohong, Chen, Yaosheng, Zhang, Qin, Li, Jiaqi, Yuan, Xiaolong, Ding, Xiangdong, Fang, Lingzhao, Zhang, Zhe, National Natural Science Foundation of China, National Key Research and Development Program (China), Guangdong Province, Earmarked Fund for China Agriculture Research System, Ballester, María [0000-0002-5413-4640], Crespo-Piazuelo, Daniel [0000-0001-7896-2507], Amills, Marcel [0000-0002-8999-0770], Clop, Alex [0000-0001-9238-2728], Zeng, Haonan, Zhang, Wenjing, Lin, Qing, Gao, Yahui, Teng, Jinyan, Xu, Zhiting, Cai, Xiaodian, Zhong, Zhanming, Wu, Jun, Liu, Yuqiang, Diao, Shuqi, Wei, Chen, Gong, Wentao, Pan, Xiangchun, Li, Zedong, Huang, Xiaoyu, Chen, Xifan, Du, Jinshi, The PigGTEx Consortium, Zhao, Fuping, Zhao, Yunxiang, Ballester, María, Crespo-Piazuelo, Daniel, Amills, Marcel, Clop, Alex, Karlskov-Mortensen, Peter, Fredholm, Merete, Li, Pinghua, Huang, Ruihua, Tang, Guoqing, Li, Mingzhou, Liu, Xiaohong, Chen, Yaosheng, Zhang, Qin, Li, Jiaqi, Yuan, Xiaolong, Ding, Xiangdong, Fang, Lingzhao, and Zhang, Zhe

Abstract

To fully unlock the potential of pigs as both agricultural species for animal-based protein food and biomedical models for human biology and disease, a comprehensive understanding of molecular and cellular mechanisms underlying various complex phenotypes in pigs and how the findings can be translated to other species, especially humans, are urgently needed. Here, within the Farm animal Genotype-Tissue Expression (FarmGTEx) project, we build the PigBiobank (http://pigbiobank.farmgtex.org) to systematically investigate the relationships among genomic variants, regulatory elements, genes, molecular networks, tissues and complex traits in pigs. This first version of the PigBiobank curates 71 885 pigs with both genotypes and phenotypes from over 100 pig breeds worldwide, covering 264 distinct complex traits. The PigBiobank has the following functions: (i) imputed sequence-based genotype-phenotype associations via a standardized and uniform pipeline, (ii) molecular and cellular mechanisms underlying trait-associations via integrating multi-omics data, (iii) cross-species gene mapping of complex traits via transcriptome-wide association studies, and (iv) high-quality results display and visualization. The PigBiobank will be updated timely with the development of the FarmGTEx-PigGTEx project, serving as an open-access and easy-to-use resource for genetically and biologically dissecting complex traits in pigs and translating the findings to other species.

Published
2024

7. PigBiobank:a valuable resource for understanding genetic and biological mechanisms of diverse complex traits in pigs

Author

Zeng, Haonan, Zhang, Wenjing, Lin, Qing, Gao, Yahui, Teng, Jinyan, Xu, Zhiting, Cai, Xiaodian, Zhong, Zhanming, Wu, Jun, Liu, Yuqiang, Diao, Shuqi, Wei, Chen, Gong, Wentao, Pan, Xiangchun, Li, Zedong, Huang, Xiaoyu, Chen, Xifan, Du, Jinshi, Zhao, Fuping, Zhao, Yunxiang, Ballester, Maria, Crespo-Piazuelo, Daniel, Amills, Marcel, Clop, Alex, Karlskov-Mortensen, Peter, Fredholm, Merete, Li, Pinghua, Huang, Ruihua, Tang, Guoqing, Li, Mingzhou, Liu, Xiaohong, Chen, Yaosheng, Zhang, Qin, Li, Jiaqi, Yuan, Xiaolong, Ding, Xiangdong, Fang, Lingzhao, Zhang, Zhe, Zeng, Haonan, Zhang, Wenjing, Lin, Qing, Gao, Yahui, Teng, Jinyan, Xu, Zhiting, Cai, Xiaodian, Zhong, Zhanming, Wu, Jun, Liu, Yuqiang, Diao, Shuqi, Wei, Chen, Gong, Wentao, Pan, Xiangchun, Li, Zedong, Huang, Xiaoyu, Chen, Xifan, Du, Jinshi, Zhao, Fuping, Zhao, Yunxiang, Ballester, Maria, Crespo-Piazuelo, Daniel, Amills, Marcel, Clop, Alex, Karlskov-Mortensen, Peter, Fredholm, Merete, Li, Pinghua, Huang, Ruihua, Tang, Guoqing, Li, Mingzhou, Liu, Xiaohong, Chen, Yaosheng, Zhang, Qin, Li, Jiaqi, Yuan, Xiaolong, Ding, Xiangdong, Fang, Lingzhao, and Zhang, Zhe

Abstract

To fully unlock the potential of pigs as both agricultural species for animal-based protein food and biomedical models for human biology and disease, a comprehensive understanding of molecular and cellular mechanisms underlying various complex phenotypes in pigs and how the findings can be translated to other species, especially humans, are urgently needed. Here, within the Farm animal Genotype-Tissue Expression (FarmGTEx) project, we build the PigBiobank (http://pigbiobank.farmgtex.org) to systematically investigate the relationships among genomic variants, regulatory elements, genes, molecular networks, tissues and complex traits in pigs. This first version of the PigBiobank curates 71 885 pigs with both genotypes and phenotypes from over 100 pig breeds worldwide, covering 264 distinct complex traits. The PigBiobank has the following functions: (i) imputed sequence-based genotype-phenotype associations via a standardized and uniform pipeline, (ii) molecular and cellular mechanisms underlying trait-associations via integrating multi-omics data, (iii) cross-species gene mapping of complex traits via transcriptome-wide association studies, and (iv) high-quality results display and visualization. The PigBiobank will be updated timely with the development of the FarmGTEx-PigGTEx project, serving as an open-access and easy-to-use resource for genetically and biologically dissecting complex traits in pigs and translating the findings to other species.

Published
2024

8. Integrating large‐scale meta‐analysis of genome‐wide association studies improve the genomic prediction accuracy for combined pig populations.

Author

Cai, Xiaodian, Zhang, Wenjing, Gao, Ning, Wei, Chen, Wu, Xibo, Si, Jinglei, Gao, Yahui, Li, Jiaqi, Yin, Tong, and Zhang, Zhe

Subjects
*SINGLE nucleotide polymorphisms, *WHOLE genome sequencing, *GENOME-wide association studies, *SWINE, *FORECASTING
Abstract

The strategy of combining reference populations has been widely recognized as an effective way to enhance the accuracy of genomic prediction (GP). This study investigated the efficiency of genomic prediction using prior information and combined reference population. In total, prior information considering trait‐associated single nucleotide polymorphisms (SNPs) obtained from meta‐analysis of genome‐wide association studies (GWAS meta‐analysis) was incorporated into three models to assess the performance of GP using combined reference populations. Two different Yorkshire populations with imputed whole genome sequence (WGS) data (9,741,620 SNPs), named as P1 (1259 individuals) and P2 (1018 individuals), were used to predict genomic estimated breeding values for three live carcass traits, including backfat thickness, loin muscle area, and loin muscle depth. A 10 × 5 fold cross‐validation was used to evaluate the prediction accuracy of 203 randomly selected candidate pigs from the P2 population and the reference population consisted of the remaining pigs from P2 and the stepwise added pigs from P1. By integrating SNPs with different p‐value thresholds from GWAS meta‐analysis downloaded from PigGTEx Project, the prediction accuracy of GBLUP, genomic feature BLUP (GFBLUP) and GBLUP given genetic architecture (BLUP|GA) were compared. Moreover, we explored effects of reference population size and heritability enrichment of genomic features on the prediction accuracy improvement of GFBLUP and BLUP|GA relative to GBLUP. The prediction accuracy of GBLUP using all WGS markers showed average improvement of 4.380% using the P1 + P2 reference population compared with the P2 reference population. Using the combined reference population, GFBLUP and BLUP|GA yielded 6.179% and 5.525% higher accuracies than GBLUP using all SNPs based on the single reference population, respectively. Positive regression coefficients were estimated in relation to the improvement in prediction accuracy (between GFBLUP/BLUP|GA and GBLUP) and the size of the reference as well as the heritability enrichment of genomic features. Compared to the classic GBLUP model, GFBLUP and BLUP|GA models integrating GWAS meta‐analysis information increase the prediction accuracy and using combined populations with enlarged reference population size further enhances prediction accuracy of the two approaches. The heritability enrichment of genomic features can be used as an indicator to reflect weather prior information is accurately presented. [ABSTRACT FROM AUTHOR]

Published
2024
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9. Preselecting Variants from Large-Scale Genome-Wide Association Study Meta-Analyses Increases the Genomic Prediction Accuracy of Growth and Carcass Traits in Large White Pigs

Author

Wei, Chen, primary, Chang, Chengjie, additional, Zhang, Wenjing, additional, Ren, Duanyang, additional, Cai, Xiaodian, additional, Zhou, Tianru, additional, Shi, Shaolei, additional, Wu, Xibo, additional, Si, Jinglei, additional, Yuan, Xiaolong, additional, Li, Jiaqi, additional, and Zhang, Zhe, additional

Published
2023
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10. Construction of a multi-tissue cell atlas reveals cell-type-specific regulation of molecular and complex phenotypes in pigs

Author

Chen, Lijuan, primary, Li, Houcheng, additional, Teng, Jinyan, additional, Wang, Zhen, additional, Qu, Xiaolu, additional, Chen, Zhe, additional, Cai, Xiaodian, additional, Zeng, Haonan, additional, Bai, Zhonghao, additional, Li, Jinghui, additional, Pan, Xiangchun, additional, Yan, Leyan, additional, Wang, Fei, additional, Lin, Lin, additional, Luo, Yonglun, additional, Sahana, Goutam, additional, Lund, Mogens Sandø, additional, Ballester, Maria, additional, Crespo-Piazuelo, Daniel, additional, Karlskov-Mortensen, Peter, additional, Fredholm, Merete, additional, Clop, Alex, additional, Amills, Marcel, additional, Loving, Crystal, additional, Tuggle, Christopher K., additional, Madsen, Ole, additional, Li, Jiaqi, additional, Zhang, Zhe, additional, Liu, George E., additional, Jiang, Jicai, additional, Fang, Lingzhao, additional, and Yi, Guoqiang, additional

Published
2023
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13. Genomic insight into the multiple-gene introgression of Southeast Asia pig

Author

Li, Guangzhen, primary, Liu, Yuqiang, additional, Feng, Xueyan, additional, Diao, Shuqi, additional, Li, Bolang, additional, Teng, Jinyan, additional, Zhang, Wenjin, additional, Zhong, Zhanming, additional, Zeng, Haonan, additional, Xu, Zhiting, additional, Cai, Xiaodian, additional, Wu, Jun, additional, Su, Zhanqin, additional, Liu, Xiaohong, additional, Yuan, Xiaolong, additional, Li, Jiaqi, additional, and zhang, zhe, additional

Published
2023
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15. A compendium of genetic regulatory effects across pig tissues

Author

Fang, Lingzhao, primary, Teng, Jinyan, additional, Gao, Yahui, additional, Yin, Hongwei, additional, Bai, Zhonghao, additional, Liu, Shuli, additional, Zeng, Haonan, additional, Bai, Lijing, additional, Cai, Zexi, additional, Zhao, Bingru, additional, Li, Xiujin, additional, Xu, Zhiting, additional, Lin, Qing, additional, Pan, Zhangyuan, additional, Yang, Wenjing, additional, Yu, Xiaoshan, additional, Guan, Dailu, additional, Hou, Yali, additional, Keel, Brittney, additional, Rohrer, Gary, additional, Lindholm-Perry, Amanda, additional, Oliver, William, additional, Ballester, Maria, additional, Crespo-Piazuelo, Daniel, additional, Quintanilla, Raquel, additional, Canela-Xandri, Oriol, additional, Rawlik, Konrad, additional, Xia, Charley, additional, Yao, Yuelin, additional, Zhao, Qianyi, additional, Yao, Wenye, additional, Yang, Liu, additional, Li, Houcheng, additional, Zhang, Huicong, additional, Liao, Wang, additional, Chen, Tianshuo, additional, Karlskov-Mortensen, Peter, additional, Fredholm, Merete, additional, Amills, Marcel, additional, Clop, Alex, additional, Giuffra, Elisabetta, additional, Wu, Jun, additional, Cai, Xiaodian, additional, Diao, Shuqi, additional, Pan, Xiangchun, additional, Wei, Chen, additional, Li, Jinghui, additional, Cheng, Hao, additional, Wang, Sheng, additional, Su, Guosheng, additional, Sahana, Goutam, additional, Lund, Mogens, additional, Dekkers, Jack, additional, Kramer, Luke, additional, Tuggle, Christopher, additional, Corbett, Ryan, additional, Groenen, Martien A.M., additional, Madsen, Ole, additional, Gòdia, Marta, additional, Rocha, Dominique, additional, Charles, Mathieu, additional, Li, Cong-jun, additional, Pausch, Hubert, additional, Hu, Xiaoxiang, additional, Frantz, Laurent, additional, Luo, Yonglun, additional, Lin, Lin, additional, Zhou, Zhong-Yin, additional, Zhang, Zhe, additional, Chen, Zitao, additional, Cui, Leilei, additional, Xiang, Ruidong, additional, Shen, Xia, additional, Li, Pinghua, additional, Huang, Ruihua, additional, Tang, Guoqing, additional, Li, Mingzhou, additional, Zhao, Yunxiang, additional, Yi, Guoqiang, additional, Tang, Zhonglin, additional, Jiang, Jicai, additional, Zhao, Fuping, additional, Yuan, Xiaolong, additional, Liu, Xiaohong, additional, Chen, Yaosheng, additional, Xu, Xuewen, additional, Zhao, Shuhong, additional, Zhao, Pengju, additional, Haley, Chris, additional, Zhou, Huaijun, additional, Wang, Qishan, additional, Pan, Yuchun, additional, Ding, Xiangdong, additional, Ma, Li, additional, Li, Jiaqi, additional, Navarro, Pau, additional, Zhang, Qin, additional, Li, Bingjie, additional, Tenesa, Albert, additional, Li, Kui, additional, and Liu, George, additional

Published
2022
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19. Additional file 1 of Impact of linkage disequilibrium heterogeneity along the genome on genomic prediction and heritability estimation

Author

Ren, Duanyang, Cai, Xiaodian, Lin, Qing, Ye, Haoqiang, Teng, Jinyan, Li, Jiaqi, Ding, Xiangdong, and Zhang, Zhe

Abstract

Additional file 1: Table S1. Descriptive statistics of estimated breeding values and their reliabilities. Table S2. Genomic prediction accuracy of GCTA, LDAK, GCTA-LDS and LDAK-LDS based on the high- and medium-density panels for dairy cattle traits. Table S3. Model fit (AIC) of GCTA, LDAK, GCTA-LDS and LDAK-LDS based on the high- and medium-density panels for dairy cattle traits. Table S4. Estimates of SNP-heritability for dairy cattle traits by GCTA, LDAK, GCTA-LDS, and LDAK-LDS based on the high-and medium-density panels

Published
2022
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20. Additional file 2 of Impact of linkage disequilibrium heterogeneity along the genome on genomic prediction and heritability estimation

Author

Ren, Duanyang, Cai, Xiaodian, Lin, Qing, Ye, Haoqiang, Teng, Jinyan, Li, Jiaqi, Ding, Xiangdong, and Zhang, Zhe

Abstract

Additional file 2: Figure S1. Replicate tagging of SNPs on chromosome 29 without and after LD weighting. (a) Represents replicate tagging of SNPs without LD weighting in the high-density panel (300K). (b) Represents replicate tagging of SNPs after LD weighting in the high-density panel. (c) Represents replicate tagging of SNPs without LD weighting in the medium-density panel (50K). (d) Represents replicate tagging of SNPs after LD weighting in the medium-density panel. (e) Represents the distribution of replicate tagging of SNPs without LD weighting in the high-density panel. (f) Represents the distribution of replicate tagging of SNPs after LD weighting in the high-density panel. (g) Represents the distribution of replicate tagging of SNPs without LD weighting in the medium-density panel. (h) Represents the distribution of replicate tagging of SNPs after LD weighting in the medium-density panel. Figure S2. Performance of GCTA, LDAK, GCTA-LDS and LDAK-LDS in terms of genomic prediction (a), heritability estimation (b), and model fit (c) for simulated phenotypes that are controlled by causal variants with different tagging levels. The heritability of all simulated phenotypes was 0.5. Paired t-test was applied to compare the difference between models, with P values adjusted by Bonferroni correction. ***indicates significant differences at P < 0.001, ** significant differences at 0.001 < P < 0.01, * significant differences at 0.01 < P < 0.05, and ns indicates no statistically significant difference. Figure S3. Estimated heritability enrichment of simulated phenotypes in five LD groups. Phenotypes in (a) to (e) were controlled by very weakly, weakly, averagely, strongly, and very strongly tagged causal variants, respectively. Estimates of heritability enrichment were calculated from the GREML-LDS model. SNPs in the high-density panel were used to construct the GRM used in GREML-LDS. The red lines represent the true median of heritability enrichment for simulated traits. Figure S4. Relationship between LD score and minor allele frequency. The 336,977 SNPs in the high-density panel were used to calculate the LD score. The red line is the regression line across all 336,977 SNPs, its positive gradient, is especially apparent for SNPs with a MAF < 0.2.

Published
2022
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21. Genomic Prediction Using LD-Based Haplotypes in Combined Pig Populations

Author

Ye, Haoqiang, Zhang, Zipeng, Ren, Duanyang, Cai, Xiaodian, Zhu, Qianghui, Ding, Xiangdong, Zhang, Hao, Zhang, Zhe, and Li, Jiaqi

Subjects
Genetics, Molecular Medicine, Genetics (clinical)
Abstract

The size of reference population is an important factor affecting genomic prediction. Thus, combining different populations in genomic prediction is an attractive way to improve prediction ability. However, combining multireference population roughly cannot increase the prediction accuracy as well as expected in pig. This may be due to different linkage disequilibrium (LD) pattern differences between population. In this study, we used the imputed whole-genome sequencing (WGS) data to construct LD-based haplotypes for genomic prediction in combined population to explore the impact of different single-nucleotide polymorphism (SNP) densities, variant representation (SNPs or haplotype alleles), and reference population size on the prediction accuracy for reproduction traits. Our results showed that genomic best linear unbiased prediction (GBLUP) using the WGS data can improve prediction accuracy in multi-population but not within-population. Not only the genomic prediction accuracy of the haplotype method using 80 K chip data in multi-population but also GBLUP for the multi-population (3.4–5.9%) was higher than that within-population (1.2–4.3%). More importantly, we have found that using the haplotype method based on the WGS data in multi-population has better genomic prediction performance, and our results showed that building haploblock in this scenario based on low LD threshold (r2 = 0.2–0.3) produced an optimal set of variables for reproduction traits in Yorkshire pig population. Our results suggested that whether the use of the haplotype method based on the chip data or GBLUP (individual SNP method) based on the WGS data were beneficial for genomic prediction in multi-population, while simultaneously combining the haplotype method and WGS data was a better strategy for multi-population genomic evaluation.

Published
2021

22. Evaluation of six machine learning classification algorithms in pig breed identification using SNPs array data.

Author

Liu, Ruiqi, Xu, Zhiting, Teng, Jinyan, Pan, Xiangchun, Lin, Qing, Cai, Xiaodian, Diao, Shuqi, Feng, Xueyan, Yuan, Xiaolong, Li, Jiaqi, and Zhang, Zhe

Subjects
MACHINE learning, CLASSIFICATION algorithms, ARTIFICIAL intelligence, SINGLE nucleotide polymorphisms, ANOMALY detection (Computer security), FEATURE selection, IDENTIFICATION
Abstract

Breed identification utilizing multiple information sources and methods is widely applicated in the field of animal genetics and breeding. Simultaneously, with the development of artificial intelligence, the integration of high‐throughput genomic data and machine learning techniques is increasingly used for breed identification. In this context, we used 654 individuals from 15 pig breeds, evaluating the performance of machine learning and stacking ensemble learning classifiers, as well as the function of feature selection and anomaly detection in different scenarios. Our results showed that, when using a training set of 16 individuals per breed and 32 features (SNPs), the accuracy of breed identification with feature selection (eXtreme Gradient Boosting, XGBoost) could exceed 95.00% (nine breeds), and was improved by 7.04% over the results with random selection. For stacking ensemble learning, feature selection methods (including random selection method) were used before different base learners. When these base learners' training set had 16 individuals per breed and 32 features, the accuracy of stacking ensemble learning improved by 9.24% over the best base learner (nine breeds), but did not significantly increase the advantage over the models with XGBoost feature selection. When using a training set of 16 individuals and 512 features per breed, breed identification with anomaly detection (local outlier factor, LOF) and random selection could achieve an accuracy of 89.06% (15 breeds). These results show that machine learning could be an effective tool for breed identification and this study will also provide useful information for the application of machine learning in animal genetics and breeding. [ABSTRACT FROM AUTHOR]

Published
2023
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