A generalized linear mixed model association tool for biobank-scale data

Nat Genet. 2021 Nov;53(11):1616-1621. doi: 10.1038/s41588-021-00954-4. Epub 2021 Nov 4.


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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Aged
  • Algorithms*
  • Biological Specimen Banks* / statistics & numerical data
  • Case-Control Studies
  • Genetic Variation
  • Genome-Wide Association Study / statistics & numerical data
  • Genotype
  • Humans
  • Linear Models*
  • Middle Aged
  • Models, Genetic*
  • Phenotype
  • United Kingdom