The Value of Rare Genetic Variation in the Prediction of Common Obesity in European Ancestry Populations
Authors: Wang Zhe , Choi Shing Wan , Chami Nathalie , Boerwinkle Eric , Fornage Myriam , Redline Susan , Bis Joshua C. , Brody Jennifer A. , Psaty Bruce M. , Kim Wonji , McDonald Merry-Lynn N. , Regan Elizabeth A. , Silverman Edwin K. , Liu Ching-Ti , Vasan Ramachandran S. , Kalyani Rita R. , Mathias Rasika A. , Yanek Lisa R. , Arnett Donna K. , Justice Anne E. , North Kari E. , Kaplan Robert , Heckbert Susan R. , de Andrade Mariza , Guo Xiuqing , Lange Leslie A. , Rich Stephen S. , Rotter Jerome I. , Ellinor Patrick T. , Lubitz Steven A. , Blangero John , Shoemaker M. Benjamin , Darbar Dawood , Gladwin Mark T. , Albert Christine M. , Chasman Daniel I. , Jackson Rebecca D. , Kooperberg Charles , Reiner Alexander P. , O’Reilly Paul F. , Loos Ruth J. F. 
Published: May 2022

Polygenic risk scores (PRSs) aggregate the effects of genetic variants across the genome and are used to predict risk of complex diseases, such as obesity. Current PRSs only include common variants (minor allele frequency (MAF) ≥1%), whereas the contribution of rare variants in PRSs to predict disease remains unknown. Here, we examine whether augmenting the standard common variant PRS (PRScommon) with a rare variant PRS (PRSrare) improves prediction of obesity. We used genome-wide genotyped and imputed data on 451,145 European-ancestry participants of the UK Biobank, as well as whole exome sequencing (WES) data on 184,385 participants. We performed single variant analyses (for both common and rare variants) and gene-based analyses (for rare variants) for association with BMI (kg/m2), obesity (BMI ≥ 30 kg/m2), and extreme obesity (BMI ≥ 40 kg/m2). We built PRSscommon and PRSsrare using a range of methods (Clumping+Thresholding [C+T], PRS-CS, lassosum, gene-burden test). We selected the best-performing PRSs and assessed their performance in 36,757 European-ancestry unrelated participants with whole genome sequencing (WGS) data from the Trans-Omics for Precision Medicine (TOPMed) program. The best-performing PRScommon explained 10.1% of variation in BMI, and 18.3% and 22.5% of the susceptibility to obesity and extreme obesity, respectively, whereas the best-performing PRSrare explained 1.49%, and 2.97% and 3.68%, respectively. The PRSrare was associated with an increased risk of obesity and extreme obesity (ORobesity = 1.37 per SDPRS, Pobesity = 1.7x10-85; ORextremeobesity = 1.55 per SDPRS, Pextremeobesity = 3.8x10-40), which was attenuated, after adjusting for PRScommon (ORobesity = 1.08 per SDPRS, Pobesity = 9.8x10-6; ORextremeobesity= 1.09 per SDPRS, Pextremeobesity = 0.02). When PRSrare and PRScommon are combined, the increase in explained variance attributed to PRSrare was small (incremental Nagelkerke R2 = 0.24% for obesity and 0.51% for extreme obesity). Consistently, combining PRSrare to PRScommon provided little improvement to the prediction of obesity (PRSrare AUC = 0.591; PRScommon AUC = 0.708; PRScombined AUC = 0.710). In summary, while rare variants show convincing association with BMI, obesity and extreme obesity, the PRSrare provides limited improvement over PRScommon in the prediction of obesity risk, based on these large populations.