Rvtests

(Updated: January 2015; latest documentation can always be found at: http://zhanxw.github.io/rvtests/)

Introduction

Rvtests, which stands for Rare Variant tests, is a flexible software package for genetic association studies. It is designed to support unrealted individual or related (family-based) individuals. Both quantitative trait and binary trait are supported. It includes a variety of association tests (e.g. single variant score test, burden test, variable threshold test, SKAT test, fast linear mixed model score test). It takes VCF format as genotype input file and takes PLINK format phenotype file and covariate file. From our practice, it is capable to analyze 8,000 related individuals using less than 400 Mb memory.

Download

Source files can be downloaded from github or github page.
Executable binary files (for Linux 64bit) can be downloaded from here.

Quick Tutorial

Here is a quick example of how to use rvtests software in typical use cases.

Single variant tests

rvtests --inVcf input.vcf --pheno phenotype.ped --out output --single wald,score

This specifies single variant Wald and score test for association
tests for every variant in the input.vcf file. The 6th column of the phenotype file, phenotype.ped, which is in PLINK format, is used. Rvtests will automatically check whether the phenotype is binary trait or quantitative trait.
For binary trait, the recommended way of coding is to code controls as 1, cases as 2, missing phenotypes as -9 or 0.

For other types of association tests, you can refer to Models

Groupwise tests

Groupwise tests includes three major kinds of tests.

• Burden tests: group variants, which are usually less than 1% or 5% rare variants, for association tests. The category includes: CMC test, Zeggini test, Madsen-Browning test, CMAT test, and rare-cover test.
  
• Variable threshold tests: group variants under different frequency thresholds.
  
• Kernel methods: suitable to tests rare variants having different directions of effects. These includes SKAT test and KBAC test.

All above tests requires to group variants into a unit. The simplist case is to use gene as grouping unit. For different grouping method, see Grouping.

To perform rare variant tests by gene, you need to use --geneFile to specify the gene range in a refFlat format. We provided different gene definitions in the Resources section. You can use --gene to specify which gene(s) to test. For example, specify --gene CFH,ARMS2 will perform association tests on CFH and ARMS2 genes. If there is no providing --gene option, all genes will be tests.

The following command line demonstrate how to use CMC method, variable threshold method(proposed by Price) and kernel based method (SKAT by Shawn Lee and KBAC by
Dajiang Liu) to test every gene listed in refFlat_hg19_uniq_gene.txt.gz.

rvtests --inVcf input.vcf --pheno phenotype.ped --out output --geneFile refFlat_hg19_uniq_gene.txt.gz --burden cmc --vt price --kernel skat,kbac

Related individual tests

To test related individuals, you will need to first create a kinship matrix:

vcf2kinship --inVcf input.vcf --bn --out output

The option --bn means calculating empirical kinship using Balding-Nicols method. You can specifiy --ibs to obtain IBS kinship or use --pedigree input.ped to calculate kinship from known pedigree information.

Then you can use linear mixed model based association tests such as Fast-LMM score test, Fast-LMM LRT test and Grammar-gamma tests. An exemplar command is shown:

rvtests --inVcf input.vcf --pheno phenotype.ped --out output --kinship output.kinship --single famScore,famLRT,famGrammarGamma

Meta-analysis tests

The meta-analysis models outputs association test results and genotype covariance matrix. These statistics can be used in rare variant association analysis.
We provide single variant score test and generate genotype covariance matrix.
You can use command:

rvtests --inVcf input.vcf --pheno phenotype.ped --meta score,cov --out output

In a more realistic scenario, you may want to adjust for covariates and want to inverse normalized residuals obtained in null model (link to our methodology paper), then this command will work:

rvtests --inVcf input.vcf --pheno phenotype.ped --covar example.covar --covar-name age,bmi --inverseNormal --useResidualAsPhenotype  --meta score,cov --out output

Here the --covar specify a covariate file, and --covar-name specify which covariates can used in the analysis. Covariate file format can be found here. --inverseNormal --useResidualAsPhenotype specifies trait transformation method. That means first fit a regression model of the phenotype on covariates (intercept automatically added), then the residuals are inverse normalized. Trait transformation details can be found here.

We support both unrelated individuals and related indivudlas (e.g. family data). You need to append --kinship input.kinship to the command line:

rvtests --inVcf input.vcf --pheno phenotype.ped --meta score,cov --out output --kinship input.kinship

The file input.kinship is calculated by vcf2kinship program, and usage to this program is described in Related individual tests.

Dominant models and recessive models

Dominant and recessive disease models are supported by appending "dominant" and/or "recessive" after "--meta" option. For example, use "--meta dominant,recessive" will
generate two sets of files. For dominant model, they are "prefix.MetaDominant.assoc" and "prefix.MetaDominantCov.assoc.gz"; for recessive model,
they are "prefix.MetaRecessive.assoc" and "prefix.MetaRecessiveCov.assoc.gz". Internally, in dominant models, genotypes 0/1/2 are coded as 0/1/1; in recessive models, genotypes 0/1/2 are
coded as 0/0/1. Missing genotypes will be imputed to the mean.

Input files

Genotype file (VCF)

Rvtests supports VCF (Variant Call Format) files. Files in both plain txt format or gzipped format are supported. To use group-based rare variant tests, indexed the VCF files using tabix are required.

Here are the commands to convert plain text format to bgzipped VCF format:

(grep ^"#" $your_old_vcf; grep -v ^"#" $your_old_vcf | sed 's:^chr::ig' | sort -k1,1n -k2,2n) | bgzip -c > $your_vcf_file 
tabix -f -p vcf $your_vcf_file

The above commands will (1) remove the chr prefix from chromosome names; (2) sort VCF files by chromosome first, then by chromosomal positions; (3) compress using bgzip; (4) create tabix index.

Rvtests support genotype dosages. Use --dosage DosageTag to specify the dosage tag. For example, if VCF format field is "GT:EC" and individual genotype fields is "0/0:0.02", you can use --dosage EC, and rvtests will use the dosage 0.02 in the regression models.

Phenotype file

You can use --mpheno $phenoypeColumnNumber or --pheno-name to specify a given phenotype.

An example phenotype file, (example.pheno), has the following format:

fid iid fatid matid sex y1 y2 y3 y4
P1 P1 0 0 0 1.7642934435605 -0.733862638327895 -0.980843608339726 2
P2 P2 0 0 0 0.457111744989746 0.623297281416372 -2.24266162284447 1
P3 P3 0 0 0 0.566689682543218 1.44136462889459 -1.6490100777089 1
P4 P4 0 0 0 0.350528353203767 -1.79533911725537 -1.11916876241804 1
P5 P5 0 0 1 2.72675074738545 -1.05487747371158 -0.33586430010589 2

Phenotype file is specified by the option --pheno example.pheno . The default phenotype column header is “y1”. If you want to use alternative columns as phenotype for association analysis (e.g the column with header y2), you may specify the phenotype by column or by name using either

• --mpheno 2
  
• --pheno-name y2

NOTE: to use “--pheno-name”, the header line must starts with “fid iid” as PLINK requires.

In phenotype file, missing values can be denoted by NA or any non-numeric values. Individuals with missing phenotypes will be automatically dropped from subsequent association analysis. For each missing phenotype value, a warning will be generated and recorded in the log file.

When the phenotype values are only 0, 1 and 2, rvtests will automatically treat it as binary traits. However, if you want to treat it as continuous trait, please use "--qtl" option.

Covariate file

You can use --covar and --covar-name to specify covariates that will be used for single variant association analysis. This is an optional parameter. If you do not have covariate in the data, this option can be ignored.

The covariate file, (e.g. example.covar) has a similar format as the phenotype file:

fid iid fatid matid sex y1 y2 y3 y4
P1 P1 0 0 0 1.911 -1.465 -0.817 1
P2 P2 0 0 0 2.146 -2.451 -0.178 2
P3 P3 0 0 0 1.086 -1.194 -0.899 1
P4 P4 0 0 0 0.704 -1.052 -0.237 1
P5 P5 0 0 1 2.512 -3.085 -2.579 1

The covariate file is specified by the --covar option (e.g. --covar example.covar). To specify covariates that will be used in the association analysis, the option --covar-name can be used. For example, when age, bmi and 3 PCs are used for association analysis, the following option can be specified for the rvtest program, i.e.
--covar example.covar --covar-name age,bmi,pc1,pc2,pc3.

Note: Missing data in the covariate file can be labeled by any non-numeric value (e.g. NA). They will be automatically imputed to the mean value in the data file.

Trait transformation

In this meta-analysis, we use inversed normal transformed residuals in the association analysis, which is achieved by using a combination of --inverseNormal and --useResidualAsPhenotype. Specifically, we first fit the null model by regressing phenotype on covariates. The residuals are then inverse normal transformed (see Appendix A more detailed formulae for transformation). Transformed residuals will be used to obtain score statistics.

In meta analysis, an exemplar command for using rvtest looks like the following:

./rvtest --inVcf $vcf --pheno $example.pheno --covar example.covar --covar-name age,bmi --inverseNormal --useResidualAsPhenotype  --meta score,cov --out $output_prefix  

Models

Rvtests support various association models.

Single variant tests

Single variant	Model(#)	Traits(##)	Covariates	Related / unrelated	Description
Score test	score	B, Q	Y	U	Only null model is used to performed the test
Wald test	wald	B, Q	Y	U	Only fit alternative model, and effect size will be estimated
Exact test	exact	B	N	U	Fisher's test
Fam LRT	famLRT	Q	Y	R, U	Fast-LMM model
Fam Score	famScore	Q	Y	R, U	Fast-LMM model style likelihood ratio test
Grammar-gamma	famGrammarGamma	Q	Y	R, U	Grammar-gamma method

(#) Model columns list the regconized names in rvtests. For example, use --single score will apply score test.

(##) In trait column, B and Q stand for binary, quantitiave trait.

Burden tests

Burden tests	Model(#)	Traits(##)	Covariates	Related / unrelated	Description
CMC	cmc	B, Q	N	U	Collapsing and combine rare variants by Bingshan Li.
Zeggini	zeggini	B, Q	N	U	Aggregate counts of rare variants by Morris Zeggini.
Madsen-Browning	mb	B	N	U	Up-weight rare variant using inverse frequency from controls by Madsen.
Fp	fp	B	N	U	Up-weight rare variant using inverse frequency from controls by Danyu Lin.
Exact CMC	exactCMC	B	N	U	Collapsing and combine rare variants, then pefore Fisher's exact test.
CMC Wald	cmcWald	B, Q	N	U	Collapsing and combine rare variants, then pefore Wald test.
RareCover	rarecover	B	N	U	Find optimal grouping unit for rare variant tests by Thomas Hoffman.
CMAT	cmat	B	N	U	Test non-coding variants by Matt Z.

(#) Model columns list the regconized names in rvtests. For example, use --burden cmc will apply CMC test.

(##) In trait column, B and Q stand for binary, quantitiave trait.

Variable threshold models

Single variant	Model(#)	Traits(##)	Covariates	Related / unrelated	Description
Variable threshold model	vt	B, Q	N	U	Every rare-variant frequency cutoffs are tests by Alkes Price.
Variable threshold CMC	cmc	B, Q	N	U	This models is natiive so that it output CMC test statistics under all possible frequency cutoffs.

(#) Model columns list the regconized names in rvtests. For example, use --vt price will apply score test.

(##) In trait column, B and Q stand for binary, quantitiave trait.

Kernel models

Kernel	Model(#)	Traits(##)	Covariates	Related / unrelated	Description
SKAT	skat	B, Q	Y	U	Sequencing kernel association test by Shawn Lee.
KBAC	kbac	B	N	U	Kernel-based adaptive clustering model by Dajiang Liu.

(#) Model columns list the regconized names in rvtests. For example, use --kernel skat will apply SKAT test.
To further customize SKAT test, you can use --kernel skat[nPerm=100:alpha=0.001:beta1=1:beta2=20] to specify permutation counts, type-1 error,
beta distribution parameters for upweighting rare variants. Rvtests will output a message showing:

[INFO]  SKAT test significance will be evaluated using 10000 permutations at alpha = 0.001 (beta1 = 1.00, beta2 = 20.00)

(##) In trait column, B and Q stand for binary, quantitiave trait.

Meta-analysis models

Type	Model(#)	Traits(##)	Covariates	Related / unrelated	Description
Score test	score	Q	Y	R, U	standard score tests
Dominant model	dominant	Q	Y	R, U	score tests and covariance matrix under dominant disease model
Recessive model	recessive	Q	Y	R, U	score tests and covariance matrix under recessive disease model
Covariance	cov	Q	Y	R, U	covariance matrix

(#) Model columns list the regconized names in rvtests. For example, use --meta score,cov will generate score statistics and covariance matrix for meta-analysis.
(##) In trait column, B and Q stand for (b)inary, (q)uantitiave trait.

Utility models

Rvtests has an usually option --outputRaw. When specify this, rvtests can output genotypes, phenotype, covariates(if any) and collapsed genotype to tabular files. These files can be imported into other software (e.g. R) for further analysis.

Association test options

Sample inclusion/exclusion

Rvtests can flexibly specify which sample(s) to include or exclude:

       --peopleIncludeID : give IDs of people that will be included in study
     --peopleIncludeFile : from given file, set IDs of people that will be included in study
       --peopleExcludeID : give IDs of people that will be included in study
     --peopleExcludeFile : from given file, set IDs of people that will be included in study

--peopleIncludeID and --peopleExcludeID are used to include/exclude samples from command line.
For example, specify --peopleIncludeID A,B,C will include A, B and C sample from the VCF files if they exists.
--peopleIncludeID and --peopleExcludeID followed by a file name will include or exclude the IDs in the file.
So to include sample A, B and C, you can provide a file, people.txt, looks like:

A
B
C

Then use --peopleIncludeFile people.txt to include them in the analysis.

Variant site filters

It is common that different frequency cutoffs are applied in rare-variant analysis.
Therefore, rvtests specify frequency cutoffs.

Frequency Cutoff

             --freqUpper : Specify upper minor allele frequency bound to be included in analysis
             --freqLower : Specify lower minor allele frequency bound to be included in analysis

If you specify --freqLower 0.01 --freqUpper 0.05, only the variants with minor allele ferquncy between 0.01 and 0.05 (boundary inclusive) will be analyzed.

Similar to sample inclusion/exclusion options, you can specify a range of variants to be included by
specifying --rangeList option. For example --rangeList 1:100-200 will include the chromosome 1 position 100bp to 200bp region.
Alternatively, use a separate file, range.txt, and --rangeFile range.txt to speicify association tests range.

             --rangeList : Specify some ranges to use, please use chr:begin-end format.
             --rangeFile : Specify the file containing ranges, please use chr:begin-end format.
              --siteFile : Specify the file containing sites to include, please use "chr pos" format.

It is supported to filter variant site by site depth, minor allele count or annotation (annotated VCF file is needed).

          --siteDepthMin : Specify minimum depth(inclusive) to be incluced in analysis
          --siteDepthMax : Specify maximum depth(inclusive) to be incluced in analysis
            --siteMACMin : Specify minimum Minor Allele Count(inclusive) to be incluced in analysis
              --annoType : Specify annotation type that is follwed by ANNO= in the VCF INFO field, regular expression is allowed

NOTE: --annoType Nonsynonymous will only analyze nonsynonymous variants where they have ANNO=Nonsynonymous in the INFO field.
VCF with annotatino information are called annotated VCF here. And to annotate
a VCF file, you can use ANNO, a fast and accurate annotation software.

Genotype filters

Genotype with low depth or low quality can be filtered out by these options:

          --indvDepthMin : Specify minimum depth(inclusive) of a sample to be incluced in analysis
          --indvDepthMax : Specify maximum depth(inclusive) of a sample to be incluced in analysis
           --indvQualMin : Specify minimum depth(inclusive) of a sample to be incluced in analysis

When genotypes are filtered, they are marked as missing genotypes.
Consequently, samples with missing genotype may or may not be included in the analysis.
That means samples with genotypes may be dropped (--impute drop)
or may still be included (--impute mean or --impute hwe).
By default, genotypes are imputed to its means.
See next section about how you like to handle missing genotypes.

Handle missing genotypes and phenotypes

When genotypes are missing (e.g. genotype = "./.") or gentoypes are filtered out,
there are three options to handle them: (1) impute to its mean(default option); (2) impute by HWE equilibrium; (3) remove from the model.
Use --impute [mean|hwe|drop] to specify which option to use.

When quantitative phenotypes are missing, for example, some samples have gneotype files, but not phenotypes,
rvtests can impute missing phenotype to its mean.

NOTE: Do not use --imputePheno for binary trait.

In summary, the following two options can be used:

           --impute : Specify either of mean, hwe, and drop
      --imputePheno : Impute phenotype to mean by those have genotypes but no
                      phenotpyes
                      
                      

Specify groups (e.g burden unit)

Rare variants association tests are usually performed in gruops of variants.
The natural grouping unit is gene. Rvtests can read gene definition file in refFlat format,
and perform association for each gene. Use --geneFile option to specify the gene file name.
For example, --geneFile refFlat_hg19.txt.gz will use refFlat_hg19.txt.gz as gene definition file,
and then perform association tests for every gene. Use --gene to specify a subset of genes to test.
For example, --gene CFH will only test CFH gene.

Alternative grouping unit can be specified as set.
These sets are treated similar to gene.
You can thus use --setFile to define sets (similar to --geneFile option),
and use --set to define a specific set (similar to --gene option).
Additionally, use --setList can speicify a set to test from command line.

The format of a set file is: (1) set names; (2) ranges (e.g. chrom:begin-end);
For example, you have a set file, example.set, like this:

set1 1:100-200,1:250-300
set2 2:500-600

You can specify --setFile example.set --set set2 to group variants
within chromosome 2, position 500 to 600bp.
If you want to test a particular region, for example, chromosome 2, position 500 to 550bp,
but do not want to make another file, you can use --setList 2:500-600.

In summary, options related to Grouping Unit are listed below:

         --geneFile : specify a gene file (for burden tests)
             --gene : specify which genes to test
          --setList : specify a list to test (for burden tests)
          --setFile : specify a list file (for burden tests, first two columns:
                      setName chr:beg-end)
              --set : specify which set to test (1st column)

Sex chromosome analysis

Rvtests suppport X chromosome analysis. In human X chromosome, there is PAR (pseudoautosomal region) and non-PAR region.
For males, there are two X allele in PAR region and one allele in non-PAR region.
While the PAR region is treated in the same way as autosomes, rvtests treate non-PAR region differently.
Below we will describe the details about how rvtests handles non-PAR region.

Prepare data. According to VCF standard, male genotype needs to coded as 0 or 1. For compatibility, rvtests also support 0/0 or 1/1 coding.
In VCF files, male genotypes can be written as "0", "1", "0|0", "0/0", "1|1", "1/1". All other genotypes will be treated as missing.

Genotype in the regression model. For consistencmaine, male genotypes are converted to 0 or 2.

MetaScore results. If specify --meta score, the output file prefix.MetaScore.assoc includes PAR-region and non-PAR region analysis.
But in the non-PAR region, the difference is that Hardy-Weinberg P-value are calculated using female samples.

Related individuals. Just append --xHemi to the vcf2kinship (more details in Kinship generation) and rvtest command lines. Rvtests can recognize non-PAR region kinship and use it in the analysis.

PAR region. PAR region is defined as two regions X:60001-2699520 and X:154931044-155270560. Use --xLabel can speicify which chromosome has PAR region (default: 23|X)
and use --xParRegion to specify PAR region (default: hg19, meaning '60001-2699520,154931044-155260560' in the UCSC build hg19, specify "hg18" will use PAR region definition in the UCSC build hg18).

Kinship generation

Analysis of related individual usually requires estimation of kinship. You can a separate tool, vcf2kinship.
vcf2kinship is usually included in rvtests binary distribution or can be built from software source codes.

vcf2kinship can calcualte pedigree kinship using a pedigree input file (PED format, see Phenotype file, use option --ped).
The output file name is specified by --prefix option. If you use --prefix output then the output files will include output.kinship.

It can also calculate empirical kinship using genotyp input file (VCF format, see Genotype file (VCF), use option --inVcf).
For empiricial kinship, you also need to specify the kinship model, either Balding-Nicols model (ue option --bn) or Identity-by-state model (use option --ibs).

In sex chromosome analysis, it is often required to generate kinship on X chromsoome regions, then you need to speicfy --xHemi. If your input VCF file has different X chromosome label (e.g. chromosome name is '23' instead of 'X'), you can use --xLabel 23.

If principal component decomposition (PCA) results are needed, you can use option --pca. Then output files with suffix '.pca' include PCA results.

When dealing with large input files, it is often preferred to use multiple CPU to speed up calculation using the option --thread N in which N is the number of CPUs.

For example, to generate pedigree-based kinship (--ped) on both autosomal region and X chromosome (--xHemi) region, the command line is:

vcf2kinship --ped input.ped --xHemi --out output

To generate empirical kinship (--inVcf) on both autosomal region and X chromosome (--xHemi) region using Balding-Nicols model, the command line is:

vcf2kinship --inVcf input.vcf.gz --ped input.ped --bn --xHemi --out output

NOTE: you need to provide a pedigree file (PED) in the above case, as vcf2kinship need the sex information of samples.

Frequently Asked Questions (FAQ)

• Does rvtests suppport binary traits of related-individuals?

Not yet. It's a complex scenario and we have not found good solutions.

• Can you provide a list of command line options?

Rvtests have build help taht can be found by executing rvtest --help.
We also put all available options in this link.

• Can you provide standard error (SE) or confidence interval (CI) for the estimated Beta in the score model?

In the output of MetaScore model (--meta score), the standard error is the inverse of SQRT_V_STAT.
For example, if SQRT_V_STAT = 2, that means the standard error of estimated beta is 1/2 = 0.5.

• Why the INFORMATIVE_ALT_AC, N_REF and N_ALT columns have zero counts for certain chromosome X regions in meta-analysis models?

These counts are calculated from female individuals. If your study only has male samples, rvtests cannot report these counts. Because if a male carries a non-reference allele, we cannot conclude that this is heterozygous (0/1) site or homozygous alternatives (1/1) site.

Feedback/Contact

Questions and requests can be sent to Xiaowei Zhan
([mailto:zhanxw@umich.edu zhanxw@umich.edu])
or Goncalo Abecasis
([mailto:goncalo@umich.edu goncalo@umich.edu])

Rvtests is a collaborative effort by Youna Hu, Bingshan Li, Dajiang Liu.