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

Author(s): Smail, C; Ferraro, NM; Hui, Q; Durrant, MG; Aguirre, M; Tanigawa, Y; Keever-Keigher, MR; Rao, AS; Justesen, JM; Li, X; Gloudemans, MJ; Assimes, TL; Kooperberg, C; Reiner, AP; Huang, J; O'Donnell, CJ; Sun, YV; Million Veteran Program; Rivas, MA; Montgomery, SB;
Year: 2022;  
Journal: American Journal of Human Genetics;  
Volume: 109;  
Issue: 6;  

Polygenic risk scores (PRSs) quantify the contribution of multiple genetic loci to an individual’s likelihood of a complex trait or disease. However, existing PRSs estimate this likelihood with common genetic variants, excluding the impact of rare variants. Here, we report on a method to identify rare variants associated with outlier gene expression and integrate their impact into PRS predictions for body mass index (BMI), obesity, and bariatric surgery. Between the top and bottom 10%, we observed a 20.8% increase in risk for obesity (p = 3 × 10-14), 62.3% increase in risk for severe obesity (p = 1 × 10-6), and median 5.29 years earlier onset for bariatric surgery (p = 0.008), as a function of expression outlier-associated rare variant burden when controlling for common variant PRS. We show that these predictions were more significant than integrating the effects of rare protein-truncating variants (PTVs), observing a mean 19% increase in phenotypic variance explained with expression outlier-associated rare variants when compared with PTVs (p = 2 × 10-15). We replicated these findings by using data from the Million Veteran Program and demonstrated that PRSs across multiple traits and diseases can benefit from the inclusion of expression outlier-associated rare variants identified through population-scale transcriptome sequencing.