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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Bibliographic Details
Published in:	Nature genetics Vol. 53; no. 11; pp. 1616 - 1621
Main Authors:	Jiang, Longda, Zheng, Zhili, Fang, Hailing, Yang, Jian
Format:	Journal Article
Language:	English
Published:	New York Nature Publishing Group US 01.11.2021 Nature Publishing Group
Subjects:	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 Access:	Get full text
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