Journal
NATURE GENETICS
Volume 52, Issue 6, Pages 634-+Publisher
NATURE PORTFOLIO
DOI: 10.1038/s41588-020-0621-6
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Funding
- Stiftelsen Kristian Gerhard Jebsen
- Faculty of Medicine and Health Sciences at the Norwegian University of Science and Technology (NTNU)
- Central Norway Regional Health Authority
- National Institutes of Health [R01HG008773]
- National Human Genome Research Institute of the National Institutes of Health [T32HG010464]
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SAIGE-GENE is a scalable generalized mixed-model region-based association test that can analyze large datasets while accounting for sample relatedness and unbalanced case-control ratios for binary traits. With very large sample sizes, biobanks provide an exciting opportunity to identify genetic components of complex traits. To analyze rare variants, region-based multiple-variant aggregate tests are commonly used to increase power for association tests. However, because of the substantial computational cost, existing region-based tests cannot analyze hundreds of thousands of samples while accounting for confounders such as population stratification and sample relatedness. Here we propose a scalable generalized mixed-model region-based association test, SAIGE-GENE, that is applicable to exome-wide and genome-wide region-based analysis for hundreds of thousands of samples and can account for unbalanced case-control ratios for binary traits. Through extensive simulation studies and analysis of the HUNT study with 69,716 Norwegian samples and the UK Biobank data with 408,910 White British samples, we show that SAIGE-GENE can efficiently analyze large-sample data (N > 400,000) with type I error rates well controlled.
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