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Transcriptome-wide association studies accounting for colocalization using Egger regression
Richard Barfield, Helian Feng, Alexander Gusev, Lang Wu, Wei Zheng, Bogdan Pasaniuc, Peter Kraft
doi: https://doi.org/10.1101/223263
Richard Barfield
1Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
Helian Feng
2Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
Alexander Gusev
3Division of Population Sciences, Dana-Farber Cancer Institute
4Division of Genetics, Brigham & Women’s Hospital
Lang Wu
5Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN, USA
Wei Zheng
5Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN, USA
Bogdan Pasaniuc
6Department of Pathology & Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
7Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA, USA
8Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
Peter Kraft
2Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
9Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
10Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
Posted April 27, 2018.
Transcriptome-wide association studies accounting for colocalization using Egger regression
Richard Barfield, Helian Feng, Alexander Gusev, Lang Wu, Wei Zheng, Bogdan Pasaniuc, Peter Kraft
bioRxiv 223263; doi: https://doi.org/10.1101/223263
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