Transcriptomic signatures across human tissues identify functional rare genetic variation
Nicole M. Ferraro, Benjamin J. Strober, Jonah Einson, Nathan S. Abell, François Aguet, Alvaro Barbeira, Margot Brandt, Maja Bućan, Stephane E. Castel, Joe R. Davis, Emily Greenwald, Gaelen T. Hess, Austin T. Hilliard, Rachel L. Kember, Bence Kotis, YoSon Park, Gina M. Peloso, Shweta Ramdas, Alexandra J. Scott, Craig Smail, Emily K. Tsang, Seyedeh M. Zekavat, Marcello Ziosi, Aradhana, TOPMed Lipids Working Group, Kristin Ardlie, Themistocles L. Assimes, Michael C. Bassik, Christopher D. Brown, Adolfo Correa, Ira M. Hall, Hae Kyung Im, Xin Li, Pradeep Natarajan, Tuuli Lappalainen, Pejman Mohammadi, Stephen B. Montgomery, Alexis Battle, François Aguet, Shankara Anand, Kristin Ardlie, Stacey Gabriel, Gad Getz, Aaron Graubert, Kane Hadley, Robert E. Handsaker, Katherine Huang, Seva Kashin, Xiao Li, Daniel G. MacArthur, Samuel R. Meier, Jared L. Nedzel, Duyen T. Nguyen, Ayellet V. Segrè, Ellen Todres, Brunilda Balliu, Alvaro N. Barbeira, Alexis Battle, Rodrigo Bonazzola, Andrew Brown, Christopher D. Brown, Stephane E. Castel, Donald F. Conrad, Daniel J. Cotter, Nancy J. Cox, Sayantan Das, Olivia M. de Goede, Emmanouil T. Dermitzakis, Jonah Einson, Barbara E. Engelhardt, Eleazar Eskin, Tiffany Eulalio, Nicole M. Ferraro, Elise D. Flynn, Laure Frésard, Eric R. Gamazon, Diego Garrido-Martín, Nicole R. Gay, Michael J. Gloudemans, Roderic Guigó, Andrew R. Hame, Yuan He, Paul Hoffman, Farhad Hormozdiari, Lei Hou, Hae Kyung Im, Brian Jo, Silva Kasela, Manolis Kellis, Sarah Kim-Hellmuth, Alan Kwong, Tuuli Lappalainen, Xin Li, Yanyu Liang, Serghei Mangul, Pejman Mohammadi, Stephen B. Montgomery, Manuel Muñoz-Aguirre, Daniel Nachun, Andrew B. Nobel
Abstract
Rare genetic variants are abundant across the human genome, and identifying their function and phenotypic impact is a major challenge. Measuring aberrant gene expression has aided in identifying functional, large-effect rare variants (RVs). Here, we expanded detection of genetically driven transcriptome abnormalities by analyzing gene expression, allele-specific expression, and alternative splicing from multitissue RNA-sequencing data, and demonstrate that each signal informs unique classes of RVs. We developed Watershed, a probabilistic model that integrates multiple genomic and transcriptomic signals to predict variant function, validated these predictions in additional cohorts and through experimental assays, and used them to assess RVs in the UK Biobank, the Million Veterans Program, and the Jackson Heart Study. Our results link thousands of RVs to diverse molecular effects and provide evidence to associate RVs affecting the transcriptome with human traits.