Integration of rare expression outlier-associated variants improves polygenic risk prediction

Published in The American Journal of Human Genetics, 2022

Polygenic risk score (PRS), an approach to estimate genetic liability to complex traits by aggregating the effects across multiple genetic variants, has attracted increasing research interest. However, most existing PRSs focused on common variants, despite the well-established roles of specific rare genetic variants in common complex traits and diseases. To address this, we propose the independent outlier gene count (IOGC) score, an approach to aggregate the effects of rare variants that show expression outliers in a population-scale transcriptome sequencing study. We demonstrate that the high burden of the rare expression outlier variants on complex traits and the IOGC score improves polygenic prediction.

Congrats, Craig and the team!

IOGC paper Figure 1A

Reference: C. Smail, N. M. Ferraro, Q. Hui, M. G. Durrant, M. Aguirre, Y. Tanigawa, M. R. Keever-Keigher, A. S. Rao, J. M. Justesen, X. Li, M. J. Gloudemans, T. L. Assimes, C. Kooperberg, A. P. Reiner, J. Huang, C. J. O'Donnell, Y. V. Sun, Million Veteran Program, M. A. Rivas, S. B. Montgomery, Integration of rare expression outlier-associated variants improves polygenic risk prediction. Am J Hum Genet. 109, 1055-1064 (2022). https://doi.org/10.1016/j.ajhg.2022.04.015 (full text)