Genome-Wide Robust Analysis for Biobank Data (GRAB)
Cauchy Combination Test for p-value aggregation
Fit POLMM null model for ordinal outcomes
Fit SPACox null model from survival outcomes or residuals
Fit a SPAmix null model from a survival response (Surv) with covaria...
Fit weighted Cox null model with outlier handling and batch-effect QC
Calculate Pairwise IBD (Identity By Descent)
Make a SparseGRMFile for GRAB.NullModel.
Make temporary files to be passed to function getSparseGRM.
Get allele frequency and missing rate information from genotype data
Convert genotype matrix to PLINK format files
Perform single-marker association tests using a fitted null model
Top-level API for generating a null model object used by GRAB.Marker a...
Instruction of POLMM method
Instruction of POLMM-GENE method
Read genotype data from multiple file formats
Perform region-based association tests
SAGELD method in GRAB package
Simulate random effects based on family structure
Simulate genotype data matrix for related and unrelated subjects
Simulate genotype matrix from external genotype file
Simulate phenotypes from linear predictors
Instruction of SPACox method
Instruction of SPAGRM method
Instruction of SPAmix method
Instruction of WtCoxG method
Construct SAGELD/GALLOP null model from a mixed-effects fit
Fit SPAGRM null model from residuals and relatedness inputs
Quality control to check batch effect between study cohort and referen...
Provides a comprehensive suite of genome-wide association study (GWAS) methods specifically designed for biobank-scale data, including but not limited to, robust approaches for time-to-event traits (Li et al., 2025 <doi:10.1038/s43588-025-00864-z>) and ordinal categorical traits (Bi et al., 2021 <doi:10.1016/j.ajhg.2021.03.019>). The package also offers general frameworks for GWAS of any trait type (Bi et al., 2020 <doi:10.1016/j.ajhg.2020.06.003>), while accounting for sample relatedness (Xu et al., 2025 <doi:10.1038/s41467-025-56669-1>) or population structure (Ma et al., 2025 <doi:10.1186/s13059-025-03827-9>). By accurately approximating score statistic distributions using saddlepoint approximation (SPA), these methods can effectively control type I error rates for rare variants and in the presence of unbalanced phenotype distributions. Additionally, the package includes functions for simulating genotype and phenotype data to support research and method development.