Rare variant association is tested with a set of rare variants simultaneously because of large false negative rate, and thus a set of rare variants should be defined. WISARD supports four types of set file format, and it can be selected by using --set option.

Type-I file format

For type-I format, each line consists of two columnes for gene set name (e.g. SET_A) and variant name respectively, and they should be separated with whitespace (space or tab). Gene set name might be a gene name.

SET_A	rs172
SET_A	rs29445
SET_A	SNP_A-1924825
SET_B	rs2851
SET_B	SNP_A-124
SET_B	rs38985

Type-II file format

Type-II file format is equal to t he set definition used in PLINK(see here for plink). Each set must start with a set name which can not have any spaces in it. The name is followed by a list of variants in that gene set, and the keyword END specifies the end of that particular set. You also can refer below example:

SET_A
rs172
rs29445
SNP_A-1924825
END

SET_B
rs2851
SNP_A-124
rs38985
END

Type-III file format

Type-III file format is similar to the type-I definition, but all variants for each set should be enumerated in a single line. Type-III file format is equal to the set definition used in EPACTS.

SET_A	rs172	rs29445	SNP_A-1924825
SET_B	rs2851	SNP_A-124	rs38985

Type-IV file format

Type-IV file format is different with the other three types of set. It defines a set of multiple variants by allocating specific region to each set. Each set can be overlapped among other sets, and a variant which is placed on overlapped region will be assigned to every sets that occupies that region.

Type-V file format (refGenes format)

In many analysis toolsets such as Rvtests uses an existing format for representing gene information.

Weighting variants with a prior information [top]

Rarer variants are often assumed to be functionally more important for phenotypes, and thus WISARD provides several ways to weight each variant by using MAF.

• If we let $\small{p_k}$ be MAF for variant $\small{k}$, $\small{1/\sqrt{p_k(1-p_k)}}$, is used as a weight by default.
• --noweight : it disables the default weight.
• --betaweight: MAF is transformed by using Beta-distribution based and they are used as set file generated from Beta(1,25).
• --weight filename: it makes WISARD use the user-defined weight.

User-defined weight can be loaded by using --weight option, and this option is often used to weight each varaint by measuring the importance of variant in terms of protein structure. Some softwares such as SIFT or PolyPhen score can calculate this information. In this file, each line should have two columns where the first column is a variant name and the second column is a weight. Two columns should be separated by whitespace (space or tab)

Example 4 : An example of user-defined weight file

rs8238523	0.3833
rs92484829	0.9217
var_3_32883571	0.0042

NOTE!

The user-defined weight file should contain weights for all variants within a dataset to be analyzed!

CMC test [top]

Combined Multivariate and Collapsing(CMC) test was suggested by Li and Leal (Am J Hum Genet 2008), and it can be applied to dichotomous phenotypes. This approach is useful for case-control design and it assumes that the presence of rare alleles increases or decreases the disease risk, and the number of rare alleles is not important.

Example code

• Calculating CMC test with --genetest option
CMC test
C:\Users\WISARD> wisard --bed test_miss0.bed --set test_gene.txt --indep --genoctrl --genetest --out res_collapsing
It is recommended to perform CMC test with --genoctrl option, to adjust its p-value by genomic control method.
• Obtaining the detailed information with --verbose option
CMC test
C:\Users\WISARD> wisard --bed test_miss0.bed --set test_gene.txt --indep --genoctrl --genetest --verbose --out res_collapsing
If there are too many variants within a gene or MAFs of some variants are large, the probability that at least a single variant has rare allele become large and then CMC test does not follow the chi-square distribution under the null hypothesis. In such a case, CMC test cannot be utilized and Fisher's exact test must be applied.

Weighted-sum test [top]

Weighted-sum test (WST) was suggested by Madsen and Browning (Plos Genetics 2009) and can be applied to dichotomous phenotypes. WST tests whether weighted rare allele counts ares associated with phenotypes and is efficient if the effects of all rare variants on phenotypes are in the same direction. Significance is calculated by comparing the weighted rare allele counts between cases and controls.

Example code

• Calculating WST by using --genetest and --wsum options
Weighted-sum test
C:\Users\WISARD> wisard --bed test_miss0.bed --set test_gene.txt --genetest --wsum --out res_wsum

KBAC test [top]

The kernel-based adaptive cluster (KBAC) test was suggested by Liu and Leal, and can be applied for dichotomous phenotypes. KBAC test categorizes set of rare variants depending on the pattern of rare alleles, and it may be efficient if there is a joint interaction between rare variants

Adjusting alpha level for KBAC test

Example code

• Calculating KBAC test by using

Collapsing-based test [top]

Collapsing-based test was suggested by Morris and Zeggini (Genet Epi 2010) and can be applied to dichotomous and quantitative phenotypes. Collapsing-based tests check whether weighted rare allele counts ares associated with phenotypes and is efficient if the effects of all rare variants on phenotypes are in the same direction.

Collapsing-based test incorporates the weighted rare allele counts as a covariate, and for dichotomous and quantitative phenotypes, logistic and linear regressions are respectively used.

Example code

• Collapsing test can be performed by WISARD in default of --genetest option.
Collapsing test
C:\Users\WISARD> wisard --bed test_miss0.bed --set test_gene.txt --indep --genetest --out res_collapsing

Variable-Threshold test [top]

Variable-threshold(VT) test was suggested by Price et al (Am J Hum Genet 2010). For rare variant analysis the definition of rare variants is unclear and different MAF thresholds are used. In this reason, VT test selects the MAF threshold which maximize the significance. VT method can be applied for dichotomous and quantitative phenotypes, and it may be useful if rarer variants have strong effect on phenotype.

Final p-values for VT test is calcualted with permutation and the number of iteration should be decided with the significance level. For instance if you are interested in the 0.05 significance level, then we suggest to iterate at least 1/0.05 *10 times.

Example codes

• Calculating VT test by using --genetest and --vt options
  Performing VT test

  C:\Users\WISARD> wisard --ped test_miss0.ped --genetest --indep --vt --out result_vt --set test_gene.txt --out res_vt
• Calculating VT test with 100,000 iteration for permutation
  Performing VT test with specified number (100,000) of permutations

  C:\Users\WISARD> wisard --ped test_miss0.ped --genetest --indep --vt --out result_vt --set test_gene.txt --nperm 100000 --out res_vt_100Kperm
Default number of interation is 1,000 and the maximum number of iteration is 2^32 - 1.

SKAT method [top]

SNP-set/Sequence kernel association test (SKAT) was suggested by Wu et al (Am J Hum Genet 2011) and can be applied for dichotomous and quantitative phenotypes. SKAT is efficient if rare variants with positive and negative effect on phenotype are grouped as a set and results from SKAT are usually similar with C-alpha test.

SKAT approximately follows the mixture of chi-square distribution if sample size is sufficiently large, and p-values for SKAT are calculated with numerical algorithm by Liu et al (Com Stat Data Anal 2009)

Example codes

• Calculating SKAT method by using --genetest and --skat options
SKAT method
C:\Users\WISARD> wisard --bed test_miss0.bed --sampvar test_miss0_phen.txt --pname sbp --set test_gene.txt --indep --genetest --skat --out res_skat

SKAT-o method [top]

SNP-set/Sequence Kernel Association Test-optimal(SKAT-o) is an extension of SKAT and was suggested by Lee et al (Biostatistics 2012). It is a mixture of burden-type test and SKAT, and can be applied to dichotomous and quantitative phenotypes. comes from an integration of mixture probability distribution function of that statistics. SKAT-o is a method robust to the direction of effects of rare variants on phenotype.

Custom optimal weights

WISARD borrows an idea of optimal selection weights from SKAT package of R. In default, the weights for optimal selection is $0$, $0.1^2$, $0.2^2$, $0.3^2$, $0.4^2$, $0.5^2$, 0.5 and 1. The weights can be altered in two ways: Dividing the range from 0 to 1 with given number of equal segments(--skatondiv) and assigning user-defined weights(--skatodivs).

Example codes

• Calculating SKAT-o with --genetest and --skato options
SKAT-o test
C:\Users\WISARD> wisard --bed test_miss0.bed --set test_gene.txt --sampvar test_miss0_phen.txt --pname sbp --indep --genetest --skato --out res_skato

NOTE!

SKAT-o method will not be performed on the gene has single variant.

Q-test [top]

Q-test was suggested by Lee et al. and is a test combining collasping-based test and SKAT test. Q-test can be applied to quantitative phenotype. Q-test has a similar property as SKAT-o test, and Wald-type test while SKAT-o is a score-type test. Q-test may be more efficient if the number of rare variants are not large.

Example codes

• Calculating Q-test with --qtest
Perform Q-test
C:\Users\WISARD> wisard --qtest --bed test_miss0.bed --set test_gene.txt --sampvar test_miss0_phen.txt --pname height --out res_qtst
• Q-test different MAF cutoff
Using rare variants of which MAFs are between 0 and 0.1
C:\Users\WISARD> wisard --qtest --bed test_miss0.bed --set test_gene.txt --qtestrange "(0,0.1)" --sampvar test_miss0_phen.txt --pname height --out res_qtst_10per
--qtestrange option makes WISARD use rare variants of which MAFs are in a certain range. By default, Q-test implemented in WISARD uses variants of which MAFs are less than 0.05.
• Calculating Q-test with covariate effect adjustment
Perform Q-test with covariate adjustment
C:\Users\WISARD> wisard --qtest --sampvar test_miss0_phen.txt --pname height --cname age,weight --bed test_miss0.bed --set test_gene.txt --out res_qtst_cov
Infomration for the covariates is in cov.txt file, and AGE and SEX are included as covariates.
• Gene set Q-test
Perform set level Q-test
C:\Users\WISARD> wisard --qtest --bed test_miss0.bed --set test_gene.txt --geneset gs_def.txt --out res_qtst_geneset
Gene sets are sets of genes that have something in common, and Q-test is extended to test the assocation between gene sets and phenotype. For gene set test, genes which belong to the same pathway are grouped. Q-test can be performed with set level. Gene set Q-test requires to specify the set file by using --geneset option. The file for gene-set definition is specified by using --geneset option. Each line in gene-set file has two column, and first element is for gene-set name, followed by lists of genes belonging to the gene set.
Example 5 : Example of gene-set definition file

GENE_SET1	GENE1 GENE2 GENE3
GENE_SET2	GENE2 GENE4
GENE_SET3	GENE5 GENE6 GENE7 GENE8

Gene-level test with longitudinal data [top]

WISARD can conduct the gene-level with a longitudinal data and this analysis can be applied only for SKAT and SKAT-o. For longitudinal data analysis, there exists the correlation between repeated measurements. Estimation of correlation matrix can be conducted with the statistical software such as R and SAS under the assumption that genotypes do not have any effect on phenotype. Then WISARD loads the correlation matrix and the score-type test for gene-level test can be calculated.

Example codes

• Specifying correlation matrix between repeated measurements by using --longitudinal option
Gene-level test with longitudinal data
C:\Users\WISARD> WISARD --ped test_miss0.ped --sampvar test_miss0_phen2.txt --pname bmi,bmi2,bmi3 --longitudinal test_varcov.txt --genetest --skato --set test_gene.txt
The file format for correlation matrix is
Example 6 : Variance-covariance matrix file (namely test_varcov.txt)

1 0.5 0.25
0.5 1 0.5
0.25 0.5 1

NOTE!

Correlation matrix should be symmetric!

• Phenotype file is specified by using --sampvar option.
• Phenotype names for repeated measurement is specified by using --pname option.