Bayesian model comparison for rare-variant association studies.
GWAS
aggregation techniques
gene-based analysis
rare variants
Journal
American journal of human genetics
ISSN: 1537-6605
Titre abrégé: Am J Hum Genet
Pays: United States
ID NLM: 0370475
Informations de publication
Date de publication:
02 12 2021
02 12 2021
Historique:
received:
25
07
2021
accepted:
02
11
2021
pubmed:
26
11
2021
medline:
29
12
2021
entrez:
25
11
2021
Statut:
ppublish
Résumé
Whole-genome sequencing studies applied to large populations or biobanks with extensive phenotyping raise new analytic challenges. The need to consider many variants at a locus or group of genes simultaneously and the potential to study many correlated phenotypes with shared genetic architecture provide opportunities for discovery not addressed by the traditional one variant, one phenotype association study. Here, we introduce a Bayesian model comparison approach called MRP (multiple rare variants and phenotypes) for rare-variant association studies that considers correlation, scale, and direction of genetic effects across a group of genetic variants, phenotypes, and studies, requiring only summary statistic data. We apply our method to exome sequencing data (n = 184,698) across 2,019 traits from the UK Biobank, aggregating signals in genes. MRP demonstrates an ability to recover signals such as associations between PCSK9 and LDL cholesterol levels. We additionally find MRP effective in conducting meta-analyses in exome data. Non-biomarker findings include associations between MC1R and red hair color and skin color, IL17RA and monocyte count, and IQGAP2 and mean platelet volume. Finally, we apply MRP in a multi-phenotype setting; after clustering the 35 biomarker phenotypes based on genetic correlation estimates, we find that joint analysis of these phenotypes results in substantial power gains for gene-trait associations, such as in TNFRSF13B in one of the clusters containing diabetes- and lipid-related traits. Overall, we show that the MRP model comparison approach improves upon useful features from widely used meta-analysis approaches for rare-variant association analyses and prioritizes protective modifiers of disease risk.
Identifiants
pubmed: 34822764
pii: S0002-9297(21)00417-1
doi: 10.1016/j.ajhg.2021.11.005
pmc: PMC8715195
pii:
doi:
Types de publication
Comparative Study
Journal Article
Research Support, N.I.H., Extramural
Langues
eng
Sous-ensembles de citation
IM
Pagination
2354-2367Subventions
Organisme : Medical Research Council
ID : MC_PC_17228
Pays : United Kingdom
Organisme : Medical Research Council
ID : MC_QA137853
Pays : United Kingdom
Organisme : NHGRI NIH HHS
ID : R01 HG010140
Pays : United States
Organisme : NHGRI NIH HHS
ID : U01 HG009080
Pays : United States
Informations de copyright
Copyright © 2021 American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.
Déclaration de conflit d'intérêts
Declaration of interests M.A.R. is on the SAB of 54Gene and Related Sciences; is scientific founder of Broadwing Bio; and has advised BioMarin, Third Rock Ventures, and MazeTx. C.D.B. is the owner and president of C.D.B. Consulting, LTD, and also a director at EdenRoc Sciences, LLC and BigData Bio LLC (LLC) and Etalon DX; founder of Arc Bio LLC (formerly IdentifyGenomics LLC and BigData Bio LLC); and an SAB member of Imprimed, FaunaBio, Columbia Care, and Digitalis Ventures. He is also a venture partner at F-Prime Capital Partners. M.J.D. is a founder of MazeTx.
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