Main Arguments

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 the prefix of the output files / folder. It will usually also include one or more other arguments. For example, if you are using the Linux version of LDAK, you might use the command

./ldak5.1.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
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--cut-weights <folder> - cut genome into sections ready for calculating weights
--calc-weights <folder> - calculate weights for specified section
--join-weights <folder> - join up weights across sections
--calc-weights-all <folder> - calculate weights for all sections in one step

--cut-kins <folder> - cut genome into partitions ready for calculating kinships
--calc-kins <folder> - calculate kinship matrix for specified partition
--join-kins <folder> - join up kinship matrices across partitions
--calc-kins-direct <output> - calculate kinship matrix in one step

--cut-genes <folder> - cut genome based on annotations or into fixed length chunks
--calc-genes-kins <folder> - calculate kinships for each gene/chunk in specified partition
--calc-genes-reml <folder> - calculate association test for each gene in specified partition
--join-genes-reml <folder> - join up association test results across partitions

--reml <output> - regress phenotype on kinships and/or regions
--calc-blups <output> - use the results of REML to calculate BLUP effect size estimates
--he <output> - perform Haseman-Elston Regression (an alternative to REML)
--pcgc <output> - perform PCGC Regression (recommended instead of REML for binary traits)

--calc-traces <output> - calculate traces for use with -fast-he or -fast-pcgc
--join-traces <output> - average traces across repetitions
--fast-he <output> - perform fast (but approximate) Haseman-Elston Regression
--fast-pcgc <output> - perform fast (but approximate) PCGC Regression

--linear <output> - standard and mixed-model linear regression
--logistic <output> - logistic regression
--solve-null <output> - two-part mixed-model linear regression (for complex models)
--ridge <output> - construct a prediction model using an approximate version of BLUP
--bolt-predict <output> - construct a prediction model using a generalized version of Bolt-LMM
--bayesr <output> - construct a prediction model using a generalized version of BayesR
--elastic-net <output> - construct a prediction model using the elastic-net

--calc-tagging <output> - calculate tagging files (and check weights)
--join-tagging <output> - combine tagging files across predictors
--merge-tagging <output> - combine tagging files across categories
--reduce-tagging <output> - extract categories from a tagging file

--sum-hers <output> - estimate heritabilities from summary statistics
--sum-cors <output> - estimate genetic correlations from summary statistics
--calc-ind-hers <output> - estimate expected heritability contributed by each predictor
--calc-exps <output> - calculate expected heritability tagged by each predictor
--calc-posts <output> - calculate posterior estimate of predictor effect sizes

--thin <output> - prune predictors based on correlation squared
--thin-tops <output> - prune highly-associated predictors based on correlation squared
--find-tags <output> - search a dataset for predictors tagging those in a scorefile
--remove-tags <output> - search a dataset for tagging predictors

--add-grm <output> - combine kinship matrices
--sub-grm <output> - subtract from one kinship matrix the contributions of other kinship matrices
--convert-gz <output> - convert kinship matrix saved in gz (old GCTA) format
--convert-raw <output> - convert kinship matrix saved as a text file
--filter <output> - identify (and remove one of) pairs of individuals with high kinship
--calc-cors-grms <output> - calculate correlation between pairs of kinship matrices

--pca <output> - compute principal component axes of a kinship matrix
--calc-pca-loads <output> - calculate predictor loadings corresponding to these axes
--decompose <output> - eigen-decompose a kinship matrix
--adjust-grm <output> - adjust a kinship matrix for covariates
--gxemm-iid / --gxemm-free <output> - calculate environmental kinship matrices

--calc-stats <output> - calculate allele frequencies, call-rates, variances and infos
--calc-scores <output> - calculate one or more genetic (risk) profiles
--make-phenos <output> - simulate phenotypes
--make-snps <output> - simulate SNP data
--cut-folds <output> - divide samples into training and test sets for cross-validation
--jackknife <output> - compute the standard deviation of the correlation between two vectors

--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-cors-data <outfile> - calculate genotype concordance between two datasets