LDAK-KVIK steps
LDAK-KVIK uses a three-step process to test SNPs and genes for association with the phenotype:
- Compute a Leave-One-Chromosome-Out (LOCO) PRS using an Elastic Net model
- Use the PRS as offset in single-SNP analysis
- Use the summary statistics of single-SNP analysis to run a gene-based association analysis LDAK-GBAT
These steps should be run consecutively; each next step is dependent on the results of the previous step.
An example run of LDAK-KVIK is as follows:
./ldak6.linux --kvik-step1 kvik --bfile data --pheno phenofile --covar covfile --max-threads 2
./ldak6.linux --kvik-step2 kvik --bfile data --pheno phenofile --covar covfile --max-threads 2
./ldak6.linux --kvik-step3 kvik --bfile data --genefile genefile --max-threads 2
An overview of the LDAK-KVIK algorithm is included in the technical details. Here, we describe the command lines used the the main input options. Note that a more extensive list of input options is included in the the data format page.
Step 1
Please note that it is not necessary to use the full genotype data in LDAK-KVIK Step 1. Instead, we recommend using a subset of SNPs to compute LOCO PRS and estimate \(\lambda\), which substantially reduces run time with minimal impact on statistical performance. The Recommendations page provides example code for reducing the number of SNPs used in Step 1.
An example command line of Step 1 in LDAK-KVIK is given by:
./ldak6.linux --kvik-step1 kvik --bfile data --pheno phenofile --covar covfile --max-threads 2
The main input options are as follows:
| Argument | Description |
|---|---|
--kvik-step1 |
Name of the output files of the step 1 |
--bfile |
Name of the .bed file to be analyzed |
--pheno |
Name of the phenotype file |
--covar |
Name of the covariate file |
--max-threads |
Integer value, specifying the number of available threads for fitting the elastic net. By default, LDAK assumes --max-threads equals 1 |
--binary |
Indicates that the analysed trait is binary |
When analysing binary traits, the user should add --binary YES to the command. All operations in Step 1 will then be based on weighted linear regression, instead of linear regression. When analysing imputed data, it is not necessary to include all SNPs in Step 1 (see Recommendations for alternative options). A more extensive list of options can be found in data format.
Step 2
An example command line of Step 2 in LDAK-KVIK is given by:
./ldak6.linux --kvik-step2 kvik --bfile data --pheno phenofile --covar covfile --max-threads 2
The main input options are as follows:
| 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 |
--pheno |
Name of the phenotype file |
--covar |
Name of the covariate file |
Note that the input argument of --kvik-step2 (in this case, ‘kvik’) should match the argument previously used in --kvik-step1. In case the --binary YES flag is used in Step 1, this will automatically be used in Step 2, and a saddlepoint approximation will be used for association analysis. Note that Step 2 can only be run after Step1 has been run. More options for can be found in Data Filtering.
Step 3
An example command line of Step 3 in LDAK-KVIK is given by:
./ldak6.linux --kvik-step3 kvik --bfile data --genefile <annotfile> --max-threads 2
The main input options are as follows:
| 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 |
--genefile |
Name of the gene annotation file |
Note that a gene annotation file is needed to specify the locations of the genes on the genome. The gene-based testing page includes command lines for downloading gene annotation files.