Efficiently controlling for case-control imbalance and sample relatedness in large-scale genetic association studies

In genome-wide association studies (GWAS) for thousands of phenotypes in large biobanks, most binary traits have substantially fewer cases than controls. Both of the widely used approaches, the linear mixed model and the recently proposed logistic mixed model, perform poorly; they produce large type...

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Bibliographic Details
Published in:Nature genetics Vol. 50; no. 9; pp. 1335 - 1341
Main Authors: Zhou, Wei, Nielsen, Jonas B., Fritsche, Lars G., Dey, Rounak, Gabrielsen, Maiken E., Wolford, Brooke N., LeFaive, Jonathon, VandeHaar, Peter, Gagliano, Sarah A., Gifford, Aliya, Bastarache, Lisa A., Wei, Wei-Qi, Denny, Joshua C., Lin, Maoxuan, Hveem, Kristian, Kang, Hyun Min, Abecasis, Goncalo R., Willer, Cristen J., Lee, Seunggeun
Format: Journal Article
Language:English
Published: New York Nature Publishing Group US 01.09.2018
Nature Publishing Group
Subjects:
45
45/43
631/208/205/2138
639/705/531
Agriculture
Analysis
Animal Genetics and Genomics
Biomedical and Life Sciences
Biomedicine
Cancer Research
Case-Control Studies
Computer applications
Computer Simulation
Costs
Data processing
Electronic health records
Gene Function
Genome-wide association studies
Genome-Wide Association Study - methods
Genomes
Human Genetics
Humans
Linear Models
Logistic Models
Methods
Model testing
Models, Genetic
Normal distribution
Parameter estimation
Phenotype
Phenotypes
Polymorphism, Single Nucleotide
Quantitative genetics
Statistical analysis
Statistical methods
Statistical tests
United Kingdom > UK
ISSN:1061-4036, 1546-1718, 1546-1718
Online Access:Get full text
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Summary:In genome-wide association studies (GWAS) for thousands of phenotypes in large biobanks, most binary traits have substantially fewer cases than controls. Both of the widely used approaches, the linear mixed model and the recently proposed logistic mixed model, perform poorly; they produce large type I error rates when used to analyze unbalanced case-control phenotypes. Here we propose a scalable and accurate generalized mixed model association test that uses the saddlepoint approximation to calibrate the distribution of score test statistics. This method, SAIGE (Scalable and Accurate Implementation of GEneralized mixed model), provides accurate P values even when case-control ratios are extremely unbalanced. SAIGE uses state-of-art optimization strategies to reduce computational costs; hence, it is applicable to GWAS for thousands of phenotypes by large biobanks. Through the analysis of UK Biobank data of 408,961 samples from white British participants with European ancestry for > 1,400 binary phenotypes, we show that SAIGE can efficiently analyze large sample data, controlling for unbalanced case-control ratios and sample relatedness. SAIGE (Scalable and Accurate Implementation of GEneralized mixed model) is a generalized mixed model association test that can efficiently analyze large data sets while controlling for unbalanced case-control ratios and sample relatedness, as shown by applying SAIGE to the UK Biobank data for > 1,400 binary phenotypes.
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These authors contributed equally to this work
ISSN:1061-4036
1546-1718
1546-1718
DOI:10.1038/s41588-018-0184-y