LDAK is a command-line software. Each LDAK command starts with the name of the executable file, followed by arguments. The command must include one main argument, which tells LDAK which feature to use and either the prefix of the output files or the name of the output folder. It will usually also include one or more other arguments. For example, consider the following command:
./ldak6.3.linux --calc-stats results --bfile data
The main argument is --calc-stats results, which tells LDAK to calculate predictor statistics and save the output files with prefix results. Meanwhile, the other argument --bfile data tells LDAK that the data are stored in Binary PLINK format in the files data.bed, data.bim and data.fam.
Below is a list of the possible main arguments (this list appears whenever you run LDAK without specifying a main argument).
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Classical association testing:
--linear <outfile> - perform standard linear regression
--logistic <outfile> - perform standard logistic regression
--cut-genes <folder> - cut predictors into genes (or fixed-length chunks)
--calc-genes-reml <folder> - test the genes (or chunks) for association
--join-genes-reml <folder> - compute final p-values
Mixed-model association testing:
--kvik-step1 <output> - perform LDAK-KVIK Step 1 (construct LOCO PRS)
--kvik-step2 <output> - perform LDAK-KVIK Step 2 (single-SNP testing)
--kvik-step3 <output> - perform LDAK-KVIK Step 3 (gene-based testing)
Heritability analysis using individual-level data:
--calc-kins-direct <output> - construct a kinship matrix (GRM)
--reml <output> - regress phenotype on one or more kinship matrices
--calc-blups <output> - get BLUP effect size estimates (using results from --reml)
--he <output> - perform Haseman-Elston Regression (an alternative to --reml)
--pcgc <output> - perform PCGC Regression (alternative to --he for binary traits)
--fast-he <output> - perform fast (but approximate) Haseman-Elston Regression
--fast-pcgc <output> - perform fast (but approximate) PCGC Regression
--quant-her <output> - estimate total heritability for quantitative phenotypes
--tetra-her <output> - estimate total heritability for binary phenotypes
Heritability analysis using summary statistics:
--calc-tagging <output> - calculate tagging file for use with --sum-hers or --sum-cors
--sum-hers <output> - estimate heritabilities from summary statistics
--sum-cors <output> - estimate genetic correlations from summary statistics
--find-gaussian <output> - estimate the parameter alpha (using results from --sum-hers)
Prediction:
--ridge <outfile> - construct a ridge regression prediction model
--bolt <outfile> - construct a Bolt prediction model
--bayesr <outfile> - construct a BayesR prediction model
--elastic <outfile> - construct an elastic net prediction model
--calc-cors <outfile> - calculate predictor-predictor correlations for use with --mega-prs
--mega-prs <output> - construct a PRS using summary statistics and a reference panel
--quick-prs <output> - construct a PRS using summary statistics (human SNP data only)
Other features:
--thin <outfile> - prune predictors based on pairwise correlations
--thin-tops <outfile> - prune highly-associated predictors based on pairwise correlations
--filter <outfile> - filter samples based on relatedness
--add-grm <outfile> - combine kinship matrices
--sub-grm <outfile> - compute the differences between two or more kinship matrices
--pca <outfile> - compute the principal component axes of a kinship matrix
--calc-pca-loads <outfile> - calculate predictor loadings (using results from --pca)
--decompose <outfile> - eigen-decompose a kinship matrix
--adjust-grm <outfile> - adjust a kinship matrix for covariates
--gxemm-iid / --gxemm-free <outfile> - calculate environmental kinship matrices
--calc-stats <outfile> - calculate allele frequencies, call-rates and info scores
--calc-scores <outfile> - calculate one or more polygenic risk scores
--make-phenos <outfile> - simulate phenotypes
--make-snps <outfile> - simulate SNP data
--jackknife <output> - measure prediction accuracy, obtaining SDs via block jackknifing
--make-bed / --make-sp / --make-sped / --make-speed / --make-gen <outfile> - convert to bed / sp / sped / speed / gen format
--condense-bed / condense-sp / condense-sped / condense-speed <outfile> - condense to bed / sp / sped / speed format
--calc-sim-data <outfile> - calculate concordance between two datasets
