gwas

Using real genotype data, simulate a complex trait as a function of latent expression, fit eQTL weights in independent data, and perform GWAS/TWAS on complex trait.

Convert GWAS sumstat files into a common format with a common reference for positions, rsids and effect alleles.

SCALLOP-INF analysis

A collection of scripts to run GWAS, regional, gene-oriented, or per-variant analyses.

A ToolKit to perform a Meta-analysis of Genome-Wide Association Studies

Format of GWAS summary statistics.

SpecVar is a convenient tool for estimating interpretable genetic correlation of human complex traits and annotating the SNPs with context specific regulatory networks

SomaLogic data analysis

gwas workflow from raw intensity data to in-silico functional mapping

A ToolKit to perform gene-based association studies using VEGAS or MAGMA

Holstein pangenome construction and structural variant calling: HiFi assembly, Minigraph-Cactus graphs, SV/SNV calling, benchmarking, SV-GWAS (Nat Commun 2026)

End-to-end GWAS pipeline for type 2 diabetes: PLINK quality control, PCA population structure correction, and Firth logistic regression run as SLURM jobs on HPC. 1.05M variants across 402 samples, 41 genome-wide significant hits, lambda_GC 0.986.

Code and variants to generate results published in Waller RG et al, Hum Mol Genet, 2021.

Pangenome structural variant imputation for cattle: SV+SNP reference panels, imputation accuracy across marker densities, GWAS on imputed genotypes (Nat Commun 2026)

LABRS: an ancestry-based algorithm that predicts Parkinson's risk by analyzing local genetic variations.

Pipeline to calculate local ancestries using RFMix 1.5 or RFMix 2.0 and Beagle5 or ShapeIT

End-to-end GWAS pipeline for rheumatoid arthritis (NARAC): QC, PCA-based stratification control, sex-stratified mixed-model association testing, PRS, and LD score regression.

This repository presents joint PCA of an Indian COVID-19 GWAS cohort with the 1000 Genomes Project to evaluate population structure and ancestry alignment.

client to the cimr-d database for continuous integration and analysis using cimr

Detailed GWAS using NARAC data to investigate genetic risk factors for RA. Key steps include genetic data cleaning, PCA, PRS and sex-specific GWAS.