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JAMMA (Highly-Accelerated Multi-method Mixed-Model Association) is an open-source Python and C implementation of GEMMA's core linear mixed-model workflows for genome-wide association studies (GWAS). It reads PLINK binary genotypes and supports kinship estimation, Wald, likelihood-ratio and score association tests, covariates, multiple phenotypes, and leave-one-chromosome-out (LOCO) analysis. JAMMA provides a command-line interface with familiar GEMMA flags, GEMMA-compatible association output, and a Python API. Native C kernels, parallel computation and reusable eigendecompositions support large analyses. Pre-flight memory checks and streamed output help manage memory use. Numerical validation against GEMMA is documented. JAMMA runs on Linux, macOS and Windows; large-cohort analyses require sufficient RAM and a suitable 64-bit BLAS configuration. Released under GPL-3.0-or-later.

Scalable genomic analysis.

BCFtools is a set of utilities that manipulate variant calls in the Variant Call Format (VCF) and its binary counterpart BCF. All commands work transparently with both VCFs and BCFs, both uncompressed and BGZF-compressed.

Genome-wide association studies (GWAS) are widely used to investigate the genetic basis of diseases and traits, but they pose many computational challenges. We developed an R package SNPRelate to provide a binary format for single-nucleotide polymorphism (SNP) data in GWAS utilizing CoreArray Genomic Data Structure (GDS) data files. The GDS format offers the efficient operations specifically designed for integers with two bits, since a SNP could occupy only two bits. SNPRelate is also designed to accelerate two key computations on SNP data using parallel computing for multi-core symmetric multiprocessing computer architectures: Principal Component Analysis (PCA) and relatedness analysis using Identity-By-Descent measures. The SNP GDS format is also used by the GWASTools package with the support of S4 classes and generic functions. The extended GDS format is implemented in the SeqArray package to support the storage of single nucleotide variations (SNVs), insertion/deletion polymorphism (indel) and structural variation calls in whole-genome and whole-exome variant data.

jaxQTL is a flexible and efficient sc-eQTL mapping framework using count-based models, designed to analyze sparse counts in scRNA-seq data and large datasets. It has been shown to identify more eGenes compared with existing approaches, improving our ability to identify distal eQTLs.

GCAT is an association test for genome wide association studies that controls for population structure under a general class of trait models. This test conditions on the trait, which makes it immune to confounding by unmodeled environmental factors. Population structure is modeled via logistic factors, which are estimated using the `lfa` package.