A generalized linear mixed model association tool for biobank-scale data.

Szczegóły
Abstrakt

Tytuł:: A generalized linear mixed model association tool for biobank-scale data.
Autorzy:: Jiang L; Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia.; School of Life Sciences, Westlake University, Hangzhou, China.
Zheng Z; Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia.
Fang H; School of Life Sciences, Westlake University, Hangzhou, China.; Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China.
Yang J; Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia. .; School of Life Sciences, Westlake University, Hangzhou, China. .; Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China. .
Źródło:: Nature genetics [Nat Genet] 2021 Nov; Vol. 53 (11), pp. 1616-1621. Date of Electronic Publication: 2021 Nov 04.
Typ publikacji:: Journal Article; Research Support, Non-U.S. Gov't
Język:: English
Imprint Name(s):: Original Publication: New York, NY : Nature Pub. Co., c1992-
MeSH Terms:: Algorithms*
Biological Specimen Banks*/statistics & numerical data
Linear Models*
Models, Genetic*
Adult ; Aged ; Case-Control Studies ; Genetic Variation ; Genome-Wide Association Study/statistics & numerical data ; Genotype ; Humans ; Middle Aged ; Phenotype ; United Kingdom
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Grant Information:: MC_PC_17228 United Kingdom MRC_ Medical Research Council; MC_QA137853 United Kingdom MRC_ Medical Research Council
Entry Date(s):: Date Created: 20211105 Date Completed: 20211227 Latest Revision: 20230207
Update Code:: 20240105
DOI:: 10.1038/s41588-021-00954-4
PMID:: 34737426
: Czasopismo naukowe

Compared with linear mixed model-based genome-wide association (GWA) methods, generalized linear mixed model (GLMM)-based methods have better statistical properties when applied to binary traits but are computationally much slower. In the present study, leveraging efficient sparse matrix-based algorithms, we developed a GLMM-based GWA tool, fastGWA-GLMM, that is severalfold to orders of magnitude faster than the state-of-the-art tools when applied to the UK Biobank (UKB) data and scalable to cohorts with millions of individuals. We show by simulation that the fastGWA-GLMM test statistics of both common and rare variants are well calibrated under the null, even for traits with extreme case-control ratios. We applied fastGWA-GLMM to the UKB data of 456,348 individuals, 11,842,647 variants and 2,989 binary traits (full summary statistics available at http://fastgwa.info/ukbimpbin ), and identified 259 rare variants associated with 75 traits, demonstrating the use of imputed genotype data in a large cohort to discover rare variants for binary complex traits.
(© 2021. The Author(s), under exclusive licence to Springer Nature America, Inc.)

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