SAIGE is an R package with Scalable and Accurate Implementation of Generalized mixed model (Chen, H. et al. 2016). It accounts for sample relatedness and is feasible for genetic association tests in large cohorts and biobanks (N > 400,000). SAIGE performs single-variant association tests for binary traits and quantitative taits. For binary traits, SAIGE uses the saddlepoint approximation (SPA)(mhof, J. P. , 1961; Kuonen, D. 1999; Dey, R. 2017) to account for case-control imbalance. SAIGE-GENE (implemented in the SAIGE R package) performs gene- or region-based association tests (Burde, SKAT, SKAT-O) for binary traits and quantitative traits. Note: SAIGE-GENE accounts for case-control imbalance in gene-based tests (>=

Accessing SAIGE

To load the module for SAIGE please use this command on the BEAR systems (BlueBEAR, BEARCloud VMs, and CaStLeS VMs):

module load SAIGE/

BEAR Apps Version




The listed architectures consist of two part: OS-CPU.

  • BlueBEAR: The OS used on BlueBEAR is represented by EL and there are several different processor (CPU) types available on BlueBEAR. More information about the processor types on BlueBEAR is available on the BlueBEAR Job Submission page.
  • BEAR and CaStLeS Cloud VMs: These VMs can have one of two OSes. Those with access to a BEAR Cloud or CaStLeS VM should check that the listed architectures for an application include the OS of VM being used. The VMs, irrespective of OS, will use the haswell CPU type.


  • MetaSKAT 0.71
  • RcppParallel 4.4.4
  • SPAtest 3.0.0

More Information

For more information visit the SAIGE website.


This version of SAIGE has a direct dependency on: Boost/1.70.0-gompi-2019a bzip2/1.0.6-GCCcore-8.2.0 foss/2019a R/3.6.0-foss-2019a savvy/1.3.0-GCC-8.2.0-2.31.1 SuperLU/5.2.1-foss-2019a

Last modified on 28th October 2019