LDAK-KVIK options

Default parameters in LDAK-KVIK can be modified by adding options to the command line. Below, we present a list of options for each step.

Step 1

Argument	Description
--kvik-step1	Name of the output files of the step 1
--bfile	Name of the .bed file to be analyzed
--bgen	Name of the .bgen file to be analyzed
--sample	Name of the .sample file corresponding to the .bgen file
--pheno	Name of the phenotype file
--covar	Name of the quantitative covariate file
--covar-numbers	Specify a subset of covariates by number. For example, --covar-numbers 1,2,4-6,8
--covar-names	Specify a subset of covariates by names. For example, --covar-names PC1,PC3,age
--factors	Name of the categorical covariate file
--max-threads	Number of threads (used for parallel computing)
--binary YES	Indicates that the analyzed phenotype is binary
--mpheno	Specifies the phenotype number to analyze (e.g., --mpheno 2 to analyze the second phenotype in the phenotype file). When using --mpheno ALL, all phenotypes are analyzed
--num-pedigree-predictors	The number of SNPs used when testing for structure (default: 512)
–-check-pedigree NO	Indicates that there will be no check for structure. In this case, it is assumed that there is structure
-–num-MCMC	Number of random vectors used to compute the heritability estimate in randomized Haseman-Elston regression (default: ten if \(n\) < 40000, three if \(n\) > 40000)
-–num-divide	Number of partitions used to compute the heritability estimate in randomized Haseman-Elston regression (default: 40)
-–num-scans	Number of scans performed by the Variational Bayes algorithm to construct PRS
-–cv-proportion	Proportion of individuals used to determine elastic net hyperparameters
-–tolerance	This number is multiplied by \(n\), and specifies the threshold for convergence for the likelihood in the Variational Bayes algorithm (default: \(10^{-6}\))
–-num-calibration-predictors	Number of SNPs used to compute the Grammar-Gamma scaling factor (default: 20)

Step 2

Please note that several arguments must match those used in Step 1 (i.e., you must use the same output filename, provide the same data and phenotype files, and if you used covariates in Step 1, you must also use them in Step 2).

Argument	Description
--kvik-step2	Name of the output files of Step 2. Note that this should have the same name as Step 1.
--bfile	Name of the .bed file to be analyzed
--bgen	Name of the .bgen file to be analyzed
--sample	Name of the .sample file corresponding to the .bgen file
--pheno	Name of the phenotype file
--covar	Name of the quantitative covariate file
--covar-numbers	Specify a subset of covariates by number. For example, --covar-numbers 1,2,4-6,8
--covar-names	Specify a subset of covariates by names. For example, --covar-numbers PC1,PC3,age
--factors	Name of the categorical covariate file
--max-threads	Number of threads (used for parallel computing)
--mpheno	Specifies the phenotype number to analyze (e.g., --mpheno 2 to analyze the second phenotype in the phenotype file). When using --mpheno ALL, all phenotypes are analyzed
–-spa-test NO	Indicates that no saddlepoint approximation will be used when testing binary phenotypes. Conversely, it is possible to specify --spa-test YES for quantative traits to analyze using SPA

Step 3

Argument	Description
--kvik-step3	Name of the output files of Step 3. Note that this should have the same name as Step 1 and Step 2.
--bfile	Name of the .bed file to be analyzed
--bgen	Name of the .bgen file to be analyzed
--sample	Name of the .sample file corresponding to the .bgen file
--genefile	Name of the gene annotation file. Instructions for downloading this can be found on the input page
--max-threads	Number of threads (used for parallel computing)