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GWAS Study

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

Wang Z, Choi SW, Chami N et al.

35592775 PubMed ID
GWAS Study Type
451145 Participants
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Chapter I

Publication Details

Comprehensive information about this research publication

Authors

WZ
Wang Z
CS
Choi SW
CN
Chami N
BE
Boerwinkle E
FM
Fornage M
RS
Redline S
BJ
Bis JC
BJ
Brody JA
PB
Psaty BM
KW
Kim W
MM
McDonald MN
RE
Regan EA
SE
Silverman EK
LC
Liu CT
VR
Vasan RS
KR
Kalyani RR
MR
Mathias RA
YL
Yanek LR
AD
Arnett DK
JA
Justice AE
NK
North KE
KR
Kaplan R
HS
Heckbert SR
DA
de Andrade M
GX
Guo X
LL
Lange LA
RS
Rich SS
RJ
Rotter JI
EP
Ellinor PT
LS
Lubitz SA
BJ
Blangero J
SM
Shoemaker MB
DD
Darbar D
GM
Gladwin MT
AC
Albert CM
CD
Chasman DI
JR
Jackson RD
KC
Kooperberg C
RA
Reiner AP
OP
O'Reilly PF
LR
Loos RJF
Chapter II

Abstract

Summary of the research findings

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.

451,145 European ancestry individuals

Chapter III

Study Statistics

Key metrics and study information

451145
Total Participants
GWAS
Study Type
No
Replicated
European
Ancestry
U.K.
Recruitment Country
Chapter IV

Analysis

Comprehensive review of health and genetic findings

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