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

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 algor...

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Veröffentlicht in:Nature genetics Jg. 53; H. 11; S. 1616 - 1621
Hauptverfasser: Jiang, Longda, Zheng, Zhili, Fang, Hailing, Yang, Jian
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Nature Publishing Group US 01.11.2021
Nature Publishing Group
Schlagworte:
631/114/794
631/208/205/2138
Adult
Aged
Agriculture
Algorithms
Animal Genetics and Genomics
Approximation
Biobanks
Biological Specimen Banks - statistics & numerical data
Biomedical and Life Sciences
Biomedicine
Cancer Research
Case-Control Studies
Gene Function
Genetic research
Genetic Variation
Genome-wide association studies
Genome-Wide Association Study - statistics & numerical data
Genomes
Genotype
Genotype & phenotype
Genotypes
Human Genetics
Humans
Identification and classification
Linear Models
Middle Aged
Models, Genetic
Parameter estimation
Phenotype
Regression analysis
Simulation
Sparse matrices
Sparsity
Statistical methods
Statistical models
Statistical tests
Statistics
technical-report
Technology application
United Kingdom
United Kingdom
Australia
ISSN:1061-4036, 1546-1718, 1546-1718
Online-Zugang:Volltext
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Zusammenfassung: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. FastGWA-GLMM is a fast, scalable generalized linear mixed model method for genetic association testing for binary traits in large cohorts that is robust to variant frequency and case–control imbalance.
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ISSN:1061-4036
1546-1718
1546-1718
DOI:10.1038/s41588-021-00954-4