WISARD[wɪzərd] Workbench for Integrated Superfast Association study with Related Data |
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WISARD provides several helpful measures for variants in each chromosome. This section describes about
Deviations from Hardy-Weinberg equilibrium (HWE) can indicate inbreeding,
population stratification, and genotyping errors.
Hardy-Weinberg Equilibrium test is often used for quality control (QC) of genetic variant.
For case-control sample, HWE test with controls is recommended for QC
and for cohort-based sample, HWE test with all samples is recommended.
In particular, HWE test only with cases is sometimes useful for association analysis.
WISARD provides HWE test and variant filtering based on the result.
Small p-value indicates some evidence of Hardy-Weinberg disequilibrium.
WISARD provides two statistics for HWE: exact method (Wigginton et al AJHG 2005) and chi-sqaure method.
While p-value from the exact method is always stable, the p-value from chi-square method can be unstable if sample size is relatively small.
For family-based samples, WISARD calculates HWE for two different sets of samples: founder-only and all-individuals.
It should be noted that estimates from both strategies are same for population-based samples (such as case-control design),
but for family-based samples, their estimates can be substantially different.
Founder-only indicates that HWE for each variant is calculated by using only founders and it can be calculated with WISARD by using "--hwe" or "--hwe founder" option. This approach is computationally fast and easy to compute. However, if there are many founders with missing genotype, this approach is not efficient anymore. WISARD calculates HWE in this way by default and the output file extension is ".hwe".
Above code produces the following [prefix].founders.hwe.
founders.hwe is... | A computed HWE using only founder samples (TSV) | ||
Column | Format | Modifier | Description | CHR | integer | NONE | Incorporated number of individuals for the test | VARIANT | integer | NONE | Incorporated number of individuals for the test | POS | integer | NONE | Incorporated number of individuals for the test | ALT | integer | NONE | Incorporated number of individuals for the test | ANNOT | integer | NONE | Incorporated number of individuals for the test | GENO | integer/integer/integer | NONE | Number of alleles for each of aa/Aa/AA, respectively | O(HET) | integer | NONE | Observed number of heterozygotes | E(HET) | real | NONE | Expected number of heterozygotes | STAT_HWE | real | NONE | Statistic of Hardy-Weinberg Equilibrium test | P_HWE | real | NONE | Computed exact p-value of HWE test | NIND | integer | NONE | Incorporated number of individuals for the test |
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This approach is computationally fast and easy to compute. However nonfounders' genotype is not informative for HWE if founders' genotype is known. If family sizes are heterogeneous, the estimaed HWE using all individuals can be inefficient. In order to calculate HWE using all individuals, use option "--hwe all".
Above code produces the following [prefix].all.hwe.
all.hwe is... | A computed HWE using all samples (TSV) | ||
Column | Format | Modifier | Description | CHR | Positive integer | NONE | The rank of least conditional variance | VARIANT | Positive integer | NONE | The rank of least conditional variance | POS | Positive integer | NONE | The rank of least conditional variance | ALT | Positive integer | NONE | The rank of least conditional variance | ANNOT | Positive integer | NONE | The rank of least conditional variance | GENO | integer/integer/integer | NONE | Number of alleles for each of aa/Aa/AA, respectively | O(HET) | integer | NONE | Observed number of heterozygotes | E(HET) | real | NONE | Expected number of heterozygotes | STAT_HWE | real | NONE | Statistic of Hardy-Weinberg Equilibrium test | P_HWE | real | NONE | Exact p-value of HWE test | NIND | integer | NONE | Incorporated number of individuals for the test |
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NOTE! |
The parameter of this option supports range type parameter |