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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Vydané v:	Nature genetics Ročník 55; číslo 4; s. 544 - 548
Hlavní autori:	Jurgens, Sean J., Pirruccello, James P., Choi, Seung Hoan, Morrill, Valerie N., Chaffin, Mark, Lubitz, Steven A., Lunetta, Kathryn L., Ellinor, Patrick T.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Nature Publishing Group US 01.04.2023 Nature Publishing Group
Predmet:	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
ISSN:	1061-4036, 1546-1718, 1546-1718
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Popis
Shrnutí:	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.
Bibliografia:	SourceType-Scholarly Journals-1 ObjectType-General Information-1 content type line 14 ObjectType-Article-1 ObjectType-Feature-2 content type line 23 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