WISARD[wɪzərd] Workbench for Integrated Superfast Association study with Related Data |
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It is possible to perform analysis in either way, first is give a specific dataset and second is give required parameters to calculate expected power. In order to perform sample selection, below options are required.
It is possible to adjust an effect of covariates or changing phenotype while estimating power. In order to perform such task, --sampvar, --cname and --pname are required.
Using WISARD, it is also possible to estimate a statistical power from a multivariate analysis. In addition to above options, below options are additionally required.
NOTE! |
Tasks with sample variable file are also possible in this function! |
Instead of getting statistical power directly, it is also possible to estimate power using permutation. In this case, the power will be estimated with given number of phenotype permutations.
Although the given pedigree file contains all members in the dataset, it may possible to the number of actually sequenced or genotyped samples could be less. For this case, --nsamp option can be used.
Through power analysis, below files are generated.
power.single.res is... | A result of power calculation from single phenotype (TSV) | ||
Column | Format | Modifier | Description | sample_size | integer | NONE | Sample size | pheno_size | integer | NONE | Number of phenotypes used, always to be 1 | sig2g | real | NONE | Familial variance | sig2e | real | NONE | Phenotypic variance | power | real | NONE | Estimated power | power_gwas | real | NONE | GWAS-level estimated power |
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power.multi.res is... | A result of power calculation from multiple phenotypes (TSV) | ||
Column | Format | Modifier | Description | SAMPSIZE | integer | NONE | Sample size | PHENOSIZE | integer | --npheno | Number of phenotypes used | NONCEN_PARAM | real | w/o --nperm | Noncentrality parameter from power estimate | NUM_PERM | integer | --nperm | Number of permutations performed | POWER | real | NONE | Estimated power |
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