The functional impact of rare variation across the regulatory cascade
Taibo Li, Nicole M. Ferraro, Benjamin J. Strober, François Aguet, Silva Kasela, Marios Arvanitis, Bohan Ni, Laurens Wiel, Elliot A. Hershberg, Kristin Ardlie, Dan E. Arking, Rebecca Beer, Jennifer A. Brody, Thomas W. Blackwell, Clary B. Clish, Stacey Gabriel, Robert E. Gerszten, Xiuqing Guo, Namrata Gupta, W. Craig Johnson, Tuuli Lappalainen, Henry J. Lin, Ching‐Ti Liu, Deborah A. Nickerson, George Papanicolaou, Jonathan K. Pritchard, Pankaj Qasba, Ali Shojaie, J. G. Smith, Nona Sotoodehnia, Kent D. Taylor, Russell P. Tracy, David Van Den Berg, Matthew T. Wheeler, Stephen S. Rich, Jerome I. Rotter, Alexis Battle, Stephen B. Montgomery
Abstract
Each human genome has tens of thousands of rare genetic variants; however, identifying impactful rare variants remains a major challenge. We demonstrate how use of personal multi-omics can enable identification of impactful rare variants by using the Multi-Ethnic Study of Atherosclerosis, which included several hundred individuals, with whole-genome sequencing, transcriptomes, methylomes, and proteomes collected across two time points, 10 years apart. We evaluated each multi-omics phenotype's ability to separately and jointly inform functional rare variation. By combining expression and protein data, we observed rare stop variants 62 times and rare frameshift variants 216 times as frequently as controls, compared to 13-27 times as frequently for expression or protein effects alone. We extended a Bayesian hierarchical model, "Watershed," to prioritize specific rare variants underlying multi-omics signals across the regulatory cascade. With this approach, we identified rare variants that exhibited large effect sizes on multiple complex traits including height, schizophrenia, and Alzheimer's disease.