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

TitleThe Value of Rare Genetic Variation in the Prediction of Common Obesity in European Ancestry Populations.
Publication TypeJournal Article
Year of Publication2022
AuthorsWang, Z, Choi, SWan, Chami, N, Boerwinkle, E, Fornage, M, Redline, S, Bis, JC, Brody, JA, Psaty, BM, Kim, W, McDonald, M-LN, Regan, EA, Silverman, EK, Liu, C-T, Vasan, RS, Kalyani, RR, Mathias, RA, Yanek, LR, Arnett, DK, Justice, AE, North, KE, Kaplan, R, Heckbert, SR, de Andrade, M, Guo, X, Lange, LA, Rich, SS, Rotter, JI, Ellinor, PT, Lubitz, SA, Blangero, J, M Shoemaker, B, Darbar, D, Gladwin, MT, Albert, CM, Chasman, DI, Jackson, RD, Kooperberg, C, Reiner, AP, O'Reilly, PF, Loos, RJF
JournalFront Endocrinol (Lausanne)
Date Published2022
KeywordsGene Frequency, Genetic Variation, Genome-Wide Association Study, Humans, Obesity, Whole Genome Sequencing
Abstract<p>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 (PRS) with a rare variant PRS (PRS) 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/m), obesity (BMI ≥ 30 kg/m), and extreme obesity (BMI ≥ 40 kg/m). We built PRSs and PRSs 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 PRS 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 PRS explained 1.49%, and 2.97% and 3.68%, respectively. The PRS was associated with an increased risk of obesity and extreme obesity (OR = 1.37 per SD, = 1.7x10; OR = 1.55 per SD, = 3.8x10), which was attenuated, after adjusting for PRS (OR = 1.08 per SD, = 9.8x10; OR= 1.09 per SD, = 0.02). When PRS and PRS are combined, the increase in explained variance attributed to PRS was small (incremental Nagelkerke R = 0.24% for obesity and 0.51% for extreme obesity). Consistently, combining PRS to PRS provided little improvement to the prediction of obesity (PRS AUC = 0.591; PRS AUC = 0.708; PRS AUC = 0.710). In summary, while rare variants show convincing association with BMI, obesity and extreme obesity, the PRS provides limited improvement over PRS in the prediction of obesity risk, based on these large populations.</p>
Alternate JournalFront Endocrinol (Lausanne)
PubMed ID35592775
PubMed Central IDPMC9110787
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