LDAK includes tools for testing predictors both individually and jointly for association with a phenotype, using both classical or mixed-model regression, and for clumping the results from these analyses.
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LDAK-KVIK describes our new tool for performing linear or logistic mixed-model regression (for both single-predictor and gene-based association analysis).
Single-Predictor Analysis explains how to perform linear regression (either classical or mixed-model) and logistic regression (only classical).
Gene-Based Analysis describes the general framework for testing sets of predictors for association using either individual-level data or summary statistics; these sets typically correspond to genes, but can instead be arbitrary chunks of the genome.
LDAK-GBAT focuses on gene-based association testing using summary statistics from single-SNP analysis and a reference panel.
Clumping is used to process the results from association testing (e.g., to determine the number of approximately independent significant loci or to prioritise loci for follow up).
We also provide approximate versions of GCTA-LOCO and fastGWA, that are substantially faster than the original versions. Lastly, LDAK can perform a vQTL using DRM.
