TY - JOUR
T1 - The Value of Rare Genetic Variation in the Prediction of Common Obesity in European Ancestry Populations
AU - Wang, Zhe
AU - Choi, Shing Wan
AU - Chami, Nathalie
AU - Boerwinkle, Eric
AU - Fornage, Myriam
AU - Redline, Susan
AU - Bis, Joshua C.
AU - Brody, Jennifer A.
AU - Psaty, Bruce M.
AU - Kim, Wonji
AU - McDonald, Merry Lynn N.
AU - Regan, Elizabeth A.
AU - Silverman, Edwin K.
AU - Liu, Ching Ti
AU - Vasan, Ramachandran S.
AU - Kalyani, Rita R.
AU - Mathias, Rasika A.
AU - Yanek, Lisa R.
AU - Arnett, Donna K.
AU - Justice, Anne E.
AU - North, Kari E.
AU - Kaplan, Robert
AU - Heckbert, Susan R
AU - de Andrade, Mariza
AU - Guo, Xiuqing
AU - Lange, Leslie A.
AU - Rich, Stephen S
AU - Rotter, Jerome I.
AU - Ellinor, Patrick T.
AU - Lubitz, Steven A.
AU - Blangero, John
AU - Shoemaker, M. Benjamin
AU - Darbar, Dawood
AU - Gladwin, Mark T.
AU - Albert, Christine M.
AU - Chasman, Daniel I.
AU - Jackson, Rebecca D.
AU - Kooperberg, Charles
AU - Reiner, Alexander P.
AU - O’Reilly, Paul F.
AU - Loos, Ruth J.F.
N1 - Funding Information:
A full list of study-specific acknowledgments and individual acknowledgments can be found in the Supplementary Information. Whole genome sequencing (WGS) for the Trans-Omics in Precision Medicine (TOPMed) program was supported by the National Heart, Lung and Blood Institute (NHLBI). WGS for “NHLBI TOPMed: Trans-Omics for Precision Medicine Whole Genome Sequencing Project: ARIC” (phs001211.v1.p1) was performed at the Broad Institute of MIT and at the Baylor Human Genome Sequencing Center (3R01HL092577-06S1, HHSN268201500015C, 3U54HG003273-12S). WGS for “NHLBI TOPMed: Mount Sinai BioMe Biobank (BioMe)” (phs001644.v1.p1) was performed at the McDonnell Genome Institute and at the Baylor Human Genome Sequencing Center (HHSN268201600037I, HHSN268201600033I).
Funding Information:
Whole genome sequencing (WGS) for the Trans-Omics in Precision Medicine (TOPMed) program was supported by the National Heart, Lung and Blood Institute (NHLBI). WGS for “NHLBI TOPMed: Trans-Omics for Precision Medicine Whole Genome Sequencing Project: ARIC” (phs001211.v1.p1) was performed at the Broad Institute of MIT and at the Baylor Human Genome Sequencing Center (3R01HL092577-06S1, HHSN268201500015C, 3U54HG003273-12S). WGS for “NHLBI TOPMed: Mount Sinai BioMe Biobank (BioMe)” (phs001644.v1.p1) was performed at the McDonnell Genome Institute and at the Baylor Human Genome Sequencing Center (HHSN268201600037I, HHSN268201600033I).WGS for “NHLBI TOPMed: Coronary Artery Risk Development in Young Adults (CARDIA)” (phs001612.v1.p1) was performed at the Baylor Human Genome Sequencing Center and at the Keck Molecular Genomics Core Facility (HHSN268201600038I, HHSN268201600033I). WGS for “NHLBI TOPMed: The Cleveland Family Study (WGS)” (phs000954.v2.p1) was performed at the University of Washington Northwest Genomics Center (3R01HL098433-05S1). WGS for “NHLBI TOPMed: Cardiovascular Health Study” (phs001368.v1.p1) was performed at the Baylor Human Genome Sequencing Center (HHSN268201500015C, 75N92021D00006). WGS for “NHLBI TOPMed: Genetic Epidemiology of COPD (COPDGene) in the TOPMed Program” (phs000951.v2.p2) was performed at the Broad Institute of MIT and Harvard and the University of Washington Northwest Genomics Center (HHSN268201500014C). WGS for “NHLBI TOPMed: Whole Genome Sequencing and Related Phenotypes in the Framingham Heart Study” (phs000974.v3.p2) was performed at the Broad Institute of MIT and Harvard (3R01HL092577-06S1). WGS for “NHLBI TOPMed: GeneSTAR (Genetic Study of Atherosclerosis Risk)” (phs001218.v1.p1) was performed at the Broad Institute of MIT and Harvard (HHSN268201500014C), at Macrogen Corp (3R01HL112064-04S1) and at Illumina (HL112064). WGS for “NHLBI TOPMed: Genetics of Lipid Lowering Drugs and Diet Network (GOLDN)” (phs001359.v1.p1) was performed at the University of Washington Northwest Genomics Center (3R01HL104135-04S1). WGS for “NHLBI TOPMed: Hispanic Community Health Study/Study of Latinos (HCHS/SOL)” (phs001395.v1.p1) was performed at the Baylor Human Genome Sequencing Center (HHSN268201600033I). WGS for “NHLBI TOPMed: Heart and Vascular Health Study (HVH)” (phs000993.v2.p2) was performed at the Broad Institute of MIT and Harvard and the Baylor Human Genome Sequencing Center (3R01HL092577-06S1, 3U54HG003273-12S2). WGS for “NHLBI TOPMed: Lung Tissue Research Consortium (LTRC)” (phs001662.v2.p1) was performed at the Broad Institute of MIT and Harvard (HHSN268201600034I). WGS for “NHLBI TOPMed: Whole Genome Sequencing of Venous Thromboembolism (WGS of VTE)” (phs001402.v1.p1) was performed at the Baylor Human Genome Sequencing Center (HHSN268201500015C, 3U54HG003273-12S2). WGS for “NHLBI TOPMed: MESA and MESA Family AA-CAC” (phs001416.v1.p1) was performed at the Broad Institute of MIT and Harvard (3U54HG003067-13S1, HHSN268201500014C). WGS for “NHLBI TOPMed: MGH Atrial Fibrillation Study” (phs001062.v3.p2) was performed at the Broad Institute of MIT and Harvard (3R01HL092577-06S1). WGS for “NHLBI TOPMed: Partners Healthcare Biorepository (Partners)” (phs001024.v1.p1) was performed at the Broad Institute of MIT and Harvard (3R01HL092577-06S1). WGS for “NHLBI TOPMed: San Antonio Family Heart Study” (phs001215) was performed at the Illumina Genomic Services (3R01HL113323-03S1). WGS for “NHLBI TOPMed - NHGRI CCDG: The Vanderbilt AF Ablation Registry” (phs000997.v5.p2) was performed at the Broad Institute of MIT and Harvard (3R01HL092577-06S1). WGS for “NHLBI TOPMed: The Vanderbilt Atrial Fibrillation Registry” (phs001032.v3.p2) was performed at the Broad Institute of MIT and Harvard (3R01HL092577-06S1). WGS for “NHLBI TOPMed: Walk-PHaSST Sickle Cell Disease (SCD)” (phs001514.v2.p1) was performed at the Baylor Human Genome Sequencing Center (HHSN268201500015C). WGS for “NHLBI TOPMed: The Women’s Genome Health Study” (phs001040.v3.p1) was performed at the Broad Institute of MIT and Harvard (3R01HL092577-06S1). WGS for “NHLBI TOPMed: Women’s Health Initiative (WHI)” (phs001237.v1.p1) was performed at the Broad Institute of MIT and Harvard (HHSN268201500014C). Core support including centralized genomic read mapping and genotype calling, along with variant quality metrics and filtering were provided by the TOPMed Informatics Research Center (3R01HL-117626-02S1; contract HHSN268201800002I). Core support including phenotype harmonization, data management, sample-identity QC, and general program coordination were provided by the TOPMed Administrative Coordinating Center (R01HL-120393; U01HL-120393; contract HHSN268201800001I). We gratefully acknowledge the studies and participants who provided biological samples and data for TOPMed.
Funding Information:
BP serves on the Steering Committee of the Yale Open Data Access Project funded by Johnson & Johnson. PE has received sponsored research support from Bayer AG and from IBM Research and has also served on advisory boards or consulted for Bayer AG, Quest Diagnostics, MyoKardia and Novartis. SL receives sponsored research support from Bristol Myers Squibb/Pfizer, Bayer AG, Boehringer Ingelheim, Fitbit, and IBM, and has consulted for Bristol Myers Squibb/Pfizer, Blackstone Life Sciences, and Invitae. ES has received grant support from GSK and Bayer.
Publisher Copyright:
Copyright © 2022 Wang, Choi, Chami, Boerwinkle, Fornage, Redline, Bis, Brody, Psaty, Kim, McDonald, Regan, Silverman, Liu, Vasan, Kalyani, Mathias, Yanek, Arnett, Justice, North, Kaplan, Heckbert, de Andrade, Guo, Lange, Rich, Rotter, Ellinor, Lubitz, Blangero, Shoemaker, Darbar, Gladwin, Albert, Chasman, Jackson, Kooperberg, Reiner, O’Reilly and Loos.
PY - 2022/5/3
Y1 - 2022/5/3
N2 - 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.
AB - 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.
KW - BMI - body mass index
KW - C+T
KW - PRS-CS
KW - burden score
KW - lassosum
KW - obesity risk
KW - polygenic risk score
KW - rare variants
UR - http://www.scopus.com/inward/record.url?scp=85130368867&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85130368867&partnerID=8YFLogxK
U2 - 10.3389/fendo.2022.863893
DO - 10.3389/fendo.2022.863893
M3 - Article
AN - SCOPUS:85130368867
SN - 1664-2392
VL - 13
JO - Frontiers in Endocrinology
JF - Frontiers in Endocrinology
M1 - 863893
ER -
