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...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	Nature genetics Ročník 53; číslo 11; s. 1616 - 1621
Hlavní autoři:	Jiang, Longda, Zheng, Zhili, Fang, Hailing, Yang, Jian
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York Nature Publishing Group US 01.11.2021 Nature Publishing Group
Témata:	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
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Popis
Shrnutí:	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.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	1061-4036 1546-1718 1546-1718
DOI:	10.1038/s41588-021-00954-4