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A scalable Bayesian method for integrating functional information in genome-wide association studies
Jingjing Yang, Lars G. Fritsche, Xiang Zhou, Gonçalo Abecasis, International Age-related Macular Degeneration Genomics Consortium (IAMDGC)
doi: https://doi.org/10.1101/101691
Jingjing Yang
1Center for Statistical Genetics, Department of Biostatistics, University of Michigan School of Public Health, 1415 Washington Heights, Ann Arbor, MI 48109, USA.
Lars G. Fritsche
1Center for Statistical Genetics, Department of Biostatistics, University of Michigan School of Public Health, 1415 Washington Heights, Ann Arbor, MI 48109, USA.
2K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health, NTNU, Norwegian University of Science and Technology, Trondheim, Norway.
Xiang Zhou
1Center for Statistical Genetics, Department of Biostatistics, University of Michigan School of Public Health, 1415 Washington Heights, Ann Arbor, MI 48109, USA.
Gonçalo Abecasis
1Center for Statistical Genetics, Department of Biostatistics, University of Michigan School of Public Health, 1415 Washington Heights, Ann Arbor, MI 48109, USA.
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Posted February 03, 2017.
A scalable Bayesian method for integrating functional information in genome-wide association studies
Jingjing Yang, Lars G. Fritsche, Xiang Zhou, Gonçalo Abecasis, International Age-related Macular Degeneration Genomics Consortium (IAMDGC)
bioRxiv 101691; doi: https://doi.org/10.1101/101691
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