LDAK includes an approximate version of fastGWA that can be substantially faster than the original version (which is available within the GCTA software). For example, we found that to analyse human data for 368k individuals and 690k SNPs, our version of fastGWA took 16 CPU hours, whereas the original version of fastGWA took 764 CPU hours.
Here we explain how to run the our version of fastGWA. Note that this is a two-step process: the first step constructs the PRS, while the second performs the association analysis.
Always read the screen output, which suggests arguments and estimates memory usage.
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fastGWA Step 1:
The main argument is --fastGWA-step1 <outfile>.
This requires the options
--bfile/--speed <datastem> or --bgen <datafile> - to specify the genetic data files (see File Formats).
--pheno <phenofile> - to specify phenotypes (in PLINK format). If <phenofile> contains more than one phenotype, specify which should be used with --mpheno <integer> or --pheno-name <string> (the latter requires that <phenofile> has a header row), or use --mpheno ALL to analyse all phenotypes.
You can use --keep <keepfile> and/or --remove <removefile> to restrict to a subset of samples, and --extract <extractfile> and/or --exclude <excludefile> to restrict to a subset of predictors (for more details, see Data Filtering).
You can use --covar <covarfile> or --factors <factorfile> to provide quantitative or categorical covariates (in PLINK format) as fixed effects in the regression (if <covarfile> contains multiple covariates, you can specify a subset using either --covar-names <string> or --covar-numbers <string>).
If the phenotype is binary, you must tell LDAK whether to perform linear regression (--binary NO) or logistic regression (--binary YES).
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fastGWA Step 2:
The main argument is --fastGWA-step2 <outfile>.
This requires the options
--bfile/--speed <datastem> or --bgen <datafile> - to specify the genetic data files (see File Formats).
--pheno <phenofile> - to specify phenotypes (in PLINK format). Samples without a phenotype will be excluded. If <phenofile> contains more than one phenotype, specify which should be used with --mpheno <integer>, or use --mpheno ALL to analyse all phenotypes.
You can use --keep <keepfile> and/or --remove <removefile> to restrict to a subset of samples, and --extract <extractfile> and/or --exclude <excludefile> to restrict to a subset of predictors (for more details, see Data Filtering).
You can use --covar <covarfile> or --factors <factorfile> to provide quantitative or categorical covariates (in PLINK format) as fixed effects in the regression (if <covarfile> contains multiple covariates, you can specify a subset using either --covar-names <string> or --covar-numbers <string>).
Please note that the options used in Step 2 must be consistent with those used in Step 1 (e.g., if you used covariates in Step 1, you must also use them in Step 2).
The main output file is <outfile>.step2.assoc, while <outfile>.step2.summaries contains summary statistics (in the format required for use with SumHer and MegaPRS).
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Example:
Here we use the binary PLINK files human.bed, human.bim and human.fam, the phenotypes quant.pheno and binary.pheno, and the covariates human.covar from the Test Datasets.
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1 - Linear fastGWA
We analyse the quantitative phenotype quant.pheno, including the covariates human.covar, by running
./ldak.out --fastGWA-step1 fastGWA --bfile human --pheno quant.pheno --covar human.covar
followed by
./ldak.out --fastGWA-step2 fastGWA --bfile human --pheno quant.pheno --covar human.covar
The main results are saved in fastGWA.step2.assoc.
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2 - Logistic fastGWA
We analyse the binary phenotype binary.pheno, including the covariates human.covar, by running
./ldak.out --fastGWA-step1 fastGWA2 --bfile human --pheno binary.pheno --covar human.covar --binary YES
followed by
./ldak.out --fastGWA-step2 fastGWA2 --bfile human --pheno binary.pheno --covar human.covar
The main results are saved in fastGWA2.step2.assoc.
