The Value of Rare Genetic Variation in the Prediction of Common Obesity in European Ancestry Populations

Abstract

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.

Trans‐ethnic polygenic risk scores for body mass index: An international hundred K+ cohorts consortium study

“…The performance of the trans‐ethnic PRS in different cohorts is comparable to the published PRS models for the prediction of obesity in populations with European ancestries, for example, AUC = 0.708 in the European‐ancestry participants of the UK Biobank, 31 AUC = 0.619 to 0.704 in the Quebec Family Study 32 . In our study, LDpred outperformed LDpred‐inf with the fractions of causal markers (1, 0.3, 0.1, 0.03), but not with the other fractions (0.01, 0.003, and 0.001) lower than the above.…”

Trans‐ethnic polygenic risk scores for body mass index

“…The BMI‐associated genetic markers identified in the previous GWASs enable the development of BMI‐PRS. In contrast, including rare variants associated with BMI has no obvious improvement of PRS models built using common variants 36 …”

“…In contrast, including rare variants associated with BMI has no obvious improvement of PRS models built using common variants. 36 The development of a PRS model for BMI holds great potential for personalized medicine approaches in obesity management, facilitating targeted prevention strategies, precise intervention and prediction of treatment responsiveness. By identifying individuals at high genetic risk for obesity, healthcare systems can allocate resources more efficiently, focusing on interventions and support for those who need them most.…”

“…In contrast, including rare variants associated with BMI has no obvious improvement of PRS models built using common variants. 36 The development of a PRS model for BMI holds great potential for personalized medicine approaches in obesity management, facilitating targeted prevention strategies, precise intervention and prediction of treatment responsiveness.…”

Ensembled best subset selection using summary statistics for polygenic risk prediction

“…We compare ALL-Sum's performance with four alternative methods -Clumping and Thresholding (C+T) 10,11 , LDPred2 14 , Lassosum2 20 , and Stacked C+T 29 . Variants of these methods have been commonly used in practice 3,[34][35][36][37][38] , and we take them to be representative of the broad classes of methods previously discussed. We first evaluate the five methods in large-scale analyses of simulated GWAS data based on European ancestry.…”