| Title: | A generalized linear mixed model association tool for biobank-scale data |
| Journal: | Nature Genetics |
| Published: | 4 Nov 2021 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/34737426/ |
| DOI: | https://doi.org/10.1038/s41588-021-00954-4 |
| URL: | https://www.researchsquare.com/article/rs-128758/latest.pdf |
| Citations: | 258 (224 in last 2 years) as of 8 Aug 2024 |
Publication 3875
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Abstract
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.</p>
4 Authors
- Longda Jiang
- Zhili Zheng
- Hailing Fang
- Jian Yang
Enabling scientific discoveries that improve human health