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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 N., Rohrer, Gary A., Lindholm-Perry, Amanda K., Oliver, William T., 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 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, 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, 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 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

2. 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 N., Rohrer, Gary A., Lindholm-Perry, Amanda K., Oliver, William T., 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 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, 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, 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 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

3. 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

4. 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

5. 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 N., Rohrer, Gary A., Lindholm-Perry, Amanda K., Oliver, William T., 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 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, 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, 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, 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 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, 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

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

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. Cassava genome from a wild ancestor to cultivated varieties

Author

Wang, Wenquan, Feng, Binxiao, Xiao, Jingfa, Xia, Zhiqiang, Zhou, Xincheng, Li, Pinghua, Zhang, Weixiong, Wang, Ying, Møller, Birger Lindberg, Zhang, Peng, Luo, Ming-Cheng, Xiao, Gong, Liu, Jingxing, Yang, Jun, Chen, Songbi, Rabinowicz, Pablo D., Chen, Xin, Zhang, Hong-Bin, Ceballos, Henan, Lou, Qunfeng, Zou, Meiling, Carvalho, Luiz J. C. B., Zeng, Changying, Xia, Jing, Sun, Shixiang, Fu, Yuhua, Wang, Haiyan, Lu, Cheng, Ruan, Mengbin, Zhou, Shuigeng, Wu, Zhicheng, Liu, Hui, Kannangara, Rubini Maya, Jørgensen, Kirsten, Neale, Rebecca Louise, Bonde, Maya, Heinz, Nanna, Zhu, Wenli, Wang, Shujuan, Zhang, Yang, Pan, Kun, Wen, Mingfu, Ma, Ping-An, Li, Zhengxu, Hu, Meizhen, Liao, Wenbin, Hu, Wenbin, Zhang, Shengkui, Pei, Jinli, Guo, Anping, Guo, Jianchun, Zhang, Jiaming, Zhang, Zhengwen, Ye, Jianqiu, Ou, Wenjun, Ma, Yaqin, Liu, Xinyue, Tallon, Luke J., Galens, Kevin, Ott, Sandra, Huang, Jie, Xue, Jingjing, An, Feifei, Yao, Qingqun, Lu, Xiaojing, Fregene, Martin, Augusto Becerra Lopez-Lavalle, L., Wu, Jiajie, You, Frank M., Chen, Meili, Hu, Songnian, Wu, Guojiang, Zhong, Silin, Ling, Peng, Chen, Yeyuan, Wang, Qinghuang, Liu, Guodao, Liu, Bin, Li, Kaimian, Peng, Ming, Wang, Wenquan, Feng, Binxiao, Xiao, Jingfa, Xia, Zhiqiang, Zhou, Xincheng, Li, Pinghua, Zhang, Weixiong, Wang, Ying, Møller, Birger Lindberg, Zhang, Peng, Luo, Ming-Cheng, Xiao, Gong, Liu, Jingxing, Yang, Jun, Chen, Songbi, Rabinowicz, Pablo D., Chen, Xin, Zhang, Hong-Bin, Ceballos, Henan, Lou, Qunfeng, Zou, Meiling, Carvalho, Luiz J. C. B., Zeng, Changying, Xia, Jing, Sun, Shixiang, Fu, Yuhua, Wang, Haiyan, Lu, Cheng, Ruan, Mengbin, Zhou, Shuigeng, Wu, Zhicheng, Liu, Hui, Kannangara, Rubini Maya, Jørgensen, Kirsten, Neale, Rebecca Louise, Bonde, Maya, Heinz, Nanna, Zhu, Wenli, Wang, Shujuan, Zhang, Yang, Pan, Kun, Wen, Mingfu, Ma, Ping-An, Li, Zhengxu, Hu, Meizhen, Liao, Wenbin, Hu, Wenbin, Zhang, Shengkui, Pei, Jinli, Guo, Anping, Guo, Jianchun, Zhang, Jiaming, Zhang, Zhengwen, Ye, Jianqiu, Ou, Wenjun, Ma, Yaqin, Liu, Xinyue, Tallon, Luke J., Galens, Kevin, Ott, Sandra, Huang, Jie, Xue, Jingjing, An, Feifei, Yao, Qingqun, Lu, Xiaojing, Fregene, Martin, Augusto Becerra Lopez-Lavalle, L., Wu, Jiajie, You, Frank M., Chen, Meili, Hu, Songnian, Wu, Guojiang, Zhong, Silin, Ling, Peng, Chen, Yeyuan, Wang, Qinghuang, Liu, Guodao, Liu, Bin, Li, Kaimian, and Peng, Ming

Published
2014

9. Cassava genome from a wild ancestor to cultivated varieties

Author

Wang, Wenquan, Feng, Binxiao, Xiao, Jingfa, Xia, Zhiqiang, Zhou, Xincheng, Li, Pinghua, Zhang, Weixiong, Wang, Ying, Møller, Birger Lindberg, Zhang, Peng, Luo, Ming-Cheng, Xiao, Gong, Liu, Jingxing, Yang, Jun, Chen, Songbi, Rabinowicz, Pablo D., Chen, Xin, Zhang, Hong-Bin, Ceballos, Henan, Lou, Qunfeng, Zou, Meiling, Carvalho, Luiz J. C. B., Zeng, Changying, Xia, Jing, Sun, Shixiang, Fu, Yuhua, Wang, Haiyan, Lu, Cheng, Ruan, Mengbin, Zhou, Shuigeng, Wu, Zhicheng, Liu, Hui, Kannangara, Rubini Maya, Jørgensen, Kirsten, Neale, Rebecca Louise, Bonde, Maya, Heinz, Nanna, Zhu, Wenli, Wang, Shujuan, Zhang, Yang, Pan, Kun, Wen, Mingfu, Ma, Ping-An, Li, Zhengxu, Hu, Meizhen, Liao, Wenbin, Hu, Wenbin, Zhang, Shengkui, Pei, Jinli, Guo, Anping, Guo, Jianchun, Zhang, Jiaming, Zhang, Zhengwen, Ye, Jianqiu, Ou, Wenjun, Ma, Yaqin, Liu, Xinyue, Tallon, Luke J., Galens, Kevin, Ott, Sandra, Huang, Jie, Xue, Jingjing, An, Feifei, Yao, Qingqun, Lu, Xiaojing, Fregene, Martin, Augusto Becerra Lopez-Lavalle, L., Wu, Jiajie, You, Frank M., Chen, Meili, Hu, Songnian, Wu, Guojiang, Zhong, Silin, Ling, Peng, Chen, Yeyuan, Wang, Qinghuang, Liu, Guodao, Liu, Bin, Li, Kaimian, Peng, Ming, Wang, Wenquan, Feng, Binxiao, Xiao, Jingfa, Xia, Zhiqiang, Zhou, Xincheng, Li, Pinghua, Zhang, Weixiong, Wang, Ying, Møller, Birger Lindberg, Zhang, Peng, Luo, Ming-Cheng, Xiao, Gong, Liu, Jingxing, Yang, Jun, Chen, Songbi, Rabinowicz, Pablo D., Chen, Xin, Zhang, Hong-Bin, Ceballos, Henan, Lou, Qunfeng, Zou, Meiling, Carvalho, Luiz J. C. B., Zeng, Changying, Xia, Jing, Sun, Shixiang, Fu, Yuhua, Wang, Haiyan, Lu, Cheng, Ruan, Mengbin, Zhou, Shuigeng, Wu, Zhicheng, Liu, Hui, Kannangara, Rubini Maya, Jørgensen, Kirsten, Neale, Rebecca Louise, Bonde, Maya, Heinz, Nanna, Zhu, Wenli, Wang, Shujuan, Zhang, Yang, Pan, Kun, Wen, Mingfu, Ma, Ping-An, Li, Zhengxu, Hu, Meizhen, Liao, Wenbin, Hu, Wenbin, Zhang, Shengkui, Pei, Jinli, Guo, Anping, Guo, Jianchun, Zhang, Jiaming, Zhang, Zhengwen, Ye, Jianqiu, Ou, Wenjun, Ma, Yaqin, Liu, Xinyue, Tallon, Luke J., Galens, Kevin, Ott, Sandra, Huang, Jie, Xue, Jingjing, An, Feifei, Yao, Qingqun, Lu, Xiaojing, Fregene, Martin, Augusto Becerra Lopez-Lavalle, L., Wu, Jiajie, You, Frank M., Chen, Meili, Hu, Songnian, Wu, Guojiang, Zhong, Silin, Ling, Peng, Chen, Yeyuan, Wang, Qinghuang, Liu, Guodao, Liu, Bin, Li, Kaimian, and Peng, Ming

Published
2014

10. The statistics of identifying differentially expressed genes in Expresso and TM4: a comparison

Author

Sioson, Allan A., Mane, Shrinivasrao P., Li, Pinghua, Sha, Wei, Heath, Lenwood S., Bohnert, Hans J., Grene, Ruth, Sioson, Allan A., Mane, Shrinivasrao P., Li, Pinghua, Sha, Wei, Heath, Lenwood S., Bohnert, Hans J., and Grene, Ruth

Abstract

Background Analysis of DNA microarray data takes as input spot intensity measurements from scanner software and returns differential expression of genes between two conditions, together with a statistical significance assessment. This process typically consists of two steps: data normalization and identification of differentially expressed genes through statistical analysis. The Expresso microarray experiment management system implements these steps with a two-stage, log-linear ANOVA mixed model technique, tailored to individual experimental designs. The complement of tools in TM4, on the other hand, is based on a number of preset design choices that limit its flexibility. In the TM4 microarray analysis suite, normalization, filter, and analysis methods form an analysis pipeline. TM4 computes integrated intensity values (IIV) from the average intensities and spot pixel counts returned by the scanner software as input to its normalization steps. By contrast, Expresso can use either IIV data or median intensity values (MIV). Here, we compare Expresso and TM4 analysis of two experiments and assess the results against qRT-PCR data. Results The Expresso analysis using MIV data consistently identifies more genes as differentially expressed, when compared to Expresso analysis with IIV data. The typical TM4 normalization and filtering pipeline corrects systematic intensity-specific bias on a per microarray basis. Subsequent statistical analysis with Expresso or a TM4 t-test can effectively identify differentially expressed genes. The best agreement with qRT-PCR data is obtained through the use of Expresso analysis and MIV data. Conclusion The results of this research are of practical value to biologists who analyze microarray data sets. The TM4 normalization and filtering pipeline corrects microarray-specific systematic bias and complements the normalization stage in Expresso analysis. The results of Expresso using MIV data have the best agreement with qRT-PCR results. In o

Published
2006
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11. The statistics of identifying differentially expressed genes in Expresso and TM4: a comparison

Author

Computer Science, School of Plant and Environmental Sciences, Fralin Life Sciences Institute, Sioson, Allan A., Mane, Shrinivasrao P., Li, Pinghua, Sha, Wei, Heath, Lenwood S., Bohnert, Hans J., Grene, Ruth, Computer Science, School of Plant and Environmental Sciences, Fralin Life Sciences Institute, Sioson, Allan A., Mane, Shrinivasrao P., Li, Pinghua, Sha, Wei, Heath, Lenwood S., Bohnert, Hans J., and Grene, Ruth

Abstract

Background Analysis of DNA microarray data takes as input spot intensity measurements from scanner software and returns differential expression of genes between two conditions, together with a statistical significance assessment. This process typically consists of two steps: data normalization and identification of differentially expressed genes through statistical analysis. The Expresso microarray experiment management system implements these steps with a two-stage, log-linear ANOVA mixed model technique, tailored to individual experimental designs. The complement of tools in TM4, on the other hand, is based on a number of preset design choices that limit its flexibility. In the TM4 microarray analysis suite, normalization, filter, and analysis methods form an analysis pipeline. TM4 computes integrated intensity values (IIV) from the average intensities and spot pixel counts returned by the scanner software as input to its normalization steps. By contrast, Expresso can use either IIV data or median intensity values (MIV). Here, we compare Expresso and TM4 analysis of two experiments and assess the results against qRT-PCR data. Results The Expresso analysis using MIV data consistently identifies more genes as differentially expressed, when compared to Expresso analysis with IIV data. The typical TM4 normalization and filtering pipeline corrects systematic intensity-specific bias on a per microarray basis. Subsequent statistical analysis with Expresso or a TM4 t-test can effectively identify differentially expressed genes. The best agreement with qRT-PCR data is obtained through the use of Expresso analysis and MIV data. Conclusion The results of this research are of practical value to biologists who analyze microarray data sets. The TM4 normalization and filtering pipeline corrects microarray-specific systematic bias and complements the normalization stage in Expresso analysis. The results of Expresso using MIV data have the best agreement with qRT-PCR results. In o

Published
2006

12. The statistics of identifying differentially expressed genes in Expresso and TM4: a comparison

Author

Computer Science, School of Plant and Environmental Sciences, Fralin Life Sciences Institute, Sioson, Allan A., Mane, Shrinivasrao P., Li, Pinghua, Sha, Wei, Heath, Lenwood S., Bohnert, Hans J., Grene, Ruth, Computer Science, School of Plant and Environmental Sciences, Fralin Life Sciences Institute, Sioson, Allan A., Mane, Shrinivasrao P., Li, Pinghua, Sha, Wei, Heath, Lenwood S., Bohnert, Hans J., and Grene, Ruth

Abstract

Background Analysis of DNA microarray data takes as input spot intensity measurements from scanner software and returns differential expression of genes between two conditions, together with a statistical significance assessment. This process typically consists of two steps: data normalization and identification of differentially expressed genes through statistical analysis. The Expresso microarray experiment management system implements these steps with a two-stage, log-linear ANOVA mixed model technique, tailored to individual experimental designs. The complement of tools in TM4, on the other hand, is based on a number of preset design choices that limit its flexibility. In the TM4 microarray analysis suite, normalization, filter, and analysis methods form an analysis pipeline. TM4 computes integrated intensity values (IIV) from the average intensities and spot pixel counts returned by the scanner software as input to its normalization steps. By contrast, Expresso can use either IIV data or median intensity values (MIV). Here, we compare Expresso and TM4 analysis of two experiments and assess the results against qRT-PCR data. Results The Expresso analysis using MIV data consistently identifies more genes as differentially expressed, when compared to Expresso analysis with IIV data. The typical TM4 normalization and filtering pipeline corrects systematic intensity-specific bias on a per microarray basis. Subsequent statistical analysis with Expresso or a TM4 t-test can effectively identify differentially expressed genes. The best agreement with qRT-PCR data is obtained through the use of Expresso analysis and MIV data. Conclusion The results of this research are of practical value to biologists who analyze microarray data sets. The TM4 normalization and filtering pipeline corrects microarray-specific systematic bias and complements the normalization stage in Expresso analysis. The results of Expresso using MIV data have the best agreement with qRT-PCR results. In o

Published
2006

13. The statistics of identifying differentially expressed genes in Expresso and TM4: a comparison

Author

Computer Science, School of Plant and Environmental Sciences, Fralin Life Sciences Institute, Sioson, Allan A., Mane, Shrinivasrao P., Li, Pinghua, Sha, Wei, Heath, Lenwood S., Bohnert, Hans J., Grene, Ruth, Computer Science, School of Plant and Environmental Sciences, Fralin Life Sciences Institute, Sioson, Allan A., Mane, Shrinivasrao P., Li, Pinghua, Sha, Wei, Heath, Lenwood S., Bohnert, Hans J., and Grene, Ruth

Abstract

Background Analysis of DNA microarray data takes as input spot intensity measurements from scanner software and returns differential expression of genes between two conditions, together with a statistical significance assessment. This process typically consists of two steps: data normalization and identification of differentially expressed genes through statistical analysis. The Expresso microarray experiment management system implements these steps with a two-stage, log-linear ANOVA mixed model technique, tailored to individual experimental designs. The complement of tools in TM4, on the other hand, is based on a number of preset design choices that limit its flexibility. In the TM4 microarray analysis suite, normalization, filter, and analysis methods form an analysis pipeline. TM4 computes integrated intensity values (IIV) from the average intensities and spot pixel counts returned by the scanner software as input to its normalization steps. By contrast, Expresso can use either IIV data or median intensity values (MIV). Here, we compare Expresso and TM4 analysis of two experiments and assess the results against qRT-PCR data. Results The Expresso analysis using MIV data consistently identifies more genes as differentially expressed, when compared to Expresso analysis with IIV data. The typical TM4 normalization and filtering pipeline corrects systematic intensity-specific bias on a per microarray basis. Subsequent statistical analysis with Expresso or a TM4 t-test can effectively identify differentially expressed genes. The best agreement with qRT-PCR data is obtained through the use of Expresso analysis and MIV data. Conclusion The results of this research are of practical value to biologists who analyze microarray data sets. The TM4 normalization and filtering pipeline corrects microarray-specific systematic bias and complements the normalization stage in Expresso analysis. The results of Expresso using MIV data have the best agreement with qRT-PCR results. In o

Published
2006

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