Adjusting for common variant polygenic scores improves yield in rare variant association analyses

With the emergence of large-scale sequencing data, methods for improving power in rare variant association tests are needed. Here we show that adjusting for common variant polygenic scores improves yield in gene-based rare variant association tests across 65 quantitative traits in the UK Biobank (up...

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Bibliographic Details
Published inNature genetics Vol. 55; no. 4; pp. 544 - 548
Main Authors Jurgens, Sean J., Pirruccello, James P., Choi, Seung Hoan, Morrill, Valerie N., Chaffin, Mark, Lubitz, Steven A., Lunetta, Kathryn L., Ellinor, Patrick T.
Format Journal Article
LanguageEnglish
Published New York Nature Publishing Group US 01.04.2023
Nature Publishing Group
Subjects
45/43
631/114/2785
631/208/205/2138
631/208/514/1948
Agriculture
Animal Genetics and Genomics
Biobanks
Biomedical and Life Sciences
Biomedicine
Brief Communication
Cancer Research
Gene Function
Genome-Wide Association Study
Genomes
Genotype & phenotype
Human Genetics
Models, Genetic
Multifactorial Inheritance - genetics
Phenotype
Polygenic inheritance
Polymorphism, Single Nucleotide - genetics
Power
Quantitative Trait Loci
United Kingdom > UK
Online AccessGet full text
ISSN1061-4036
1546-1718
1546-1718
DOI10.1038/s41588-023-01342-w

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Summary:With the emergence of large-scale sequencing data, methods for improving power in rare variant association tests are needed. Here we show that adjusting for common variant polygenic scores improves yield in gene-based rare variant association tests across 65 quantitative traits in the UK Biobank (up to 20% increase at α  = 2.6 × 10 −6 ), without marked increases in false-positive rates or genomic inflation. Benefits were seen for various models, with the largest improvements seen for efficient sparse mixed-effects models. Our results illustrate how polygenic score adjustment can efficiently improve power in rare variant association discovery. Adjusting for common variant polygenic scores improves yield in gene-based rare variant association tests for quantitative traits, particularly when using sparse mixed models or simple linear models as an alternative to dense mixed-model approaches.
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Author Contributions Statement
S.J.J., J.P.P. and P.T.E. conceived and designed the study. S.J.J., J.P.P., S.H.C. and V.N.M. performed data curation and data processing. S.J.J. and J.P.P. performed statistical and bioinformatic analyses. S.A.L., K.L.L., and P.T.E. supervised the overall study. S.J.J., J.P.P. and P.T.E. wrote the manuscript. M.C. contributed critically to the analysis plan. All authors critically revised and approved the manuscript.
These authors contributed equally
ISSN:1061-4036
1546-1718
1546-1718
DOI:10.1038/s41588-023-01342-w