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Integrating Predicted Transcriptome From Multiple Tissues Improves Association Detection
View ORCID ProfileAlvaro N. Barbeira, Milton D. Pividori, Jiamao Zheng, Heather E. Wheeler, Dan L. Nicolae, Hae Kyung Im
doi: https://doi.org/10.1101/292649
Alvaro N. Barbeira
1Section of Genetic Medicine, The University of Chicago, Chicago, IL, USA
Milton D. Pividori
1Section of Genetic Medicine, The University of Chicago, Chicago, IL, USA
Jiamao Zheng
1Section of Genetic Medicine, The University of Chicago, Chicago, IL, USA
Heather E. Wheeler
2Department of Biology, Loyola University Chicago, Chicago, IL, USA
3Department of Computer Science, Loyola University Chicago, Chicago, IL, USA
Dan L. Nicolae
1Section of Genetic Medicine, The University of Chicago, Chicago, IL, USA
4Department of Statistics, The University of Chicago, Chicago, IL, USA
5Department of Human Genetics, The University of Chicago, Chicago, IL, USA
Hae Kyung Im
1Section of Genetic Medicine, The University of Chicago, Chicago, IL, USA
5Department of Human Genetics, The University of Chicago, Chicago, IL, USA
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Posted March 31, 2018.
Integrating Predicted Transcriptome From Multiple Tissues Improves Association Detection
Alvaro N. Barbeira, Milton D. Pividori, Jiamao Zheng, Heather E. Wheeler, Dan L. Nicolae, Hae Kyung Im
bioRxiv 292649; doi: https://doi.org/10.1101/292649
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