- 1 Useful Wiki Pages
- 2 Brief Description
- 3 Download and Installation
- 4 Command Reference
- 5 Approach
- 6 Input Files
- 7 Example Command Line
- 8 Change Log
Useful Wiki Pages
There are a few pages in this Wiki that may be useful to famRvTest users. Here are links to key pages:
famrvtest is a computationally efficient tool for family-based rare variant association analyses using genotyping array or sequencing data. famrvtest supports both single variant and gene-level associations.
Download and Installation
- University of Michigan CSG users can go to the following:
Where to Download
- Source code can be downloaded in the following
- Executable can be downloaded in the following:
How to Compile
- Save it to your local path and decompress using the following command:
tar xvzf LINUX_famrvtest.2.4.tgz
- Go to promp>famrvtest and type the following command to compile:
How to Execute
- Go to famrvtest/bin and use the following:
Please go to Command Reference Page for details.
famrvtest uses linear mixed model approach, incorporating efficient optimization algorithm, to account for familial relationship, where kinship is either quantified based upon pedigree structures or estimated from genotypes of markers from genome-wide. Single marker associations including score, likelihood ratio and ward tests and gene-level associations methods (weighted and un-weighted burden, SKAT and variable threshold tests) have been implemented. Manuscript is under preparation.
famrvtest needs the following files as input: PED and DAT file in Merlin format, AND/OR a VCF file. When genotypes are stored in PED and DAT file, the VCF file is not needed. However, even if genotypes are saved in a VCF file, PED and DAT files are still needed for carrying covariate and trait information.
PED and DAT Files
- When PED file has genotypes saved, there is no need for a VCF file as input.
- famrvtest takes PED/DAT file in Merlin format. Please refer to PED/DAT format description for details.
- An example PED file is in the following:
1 1 0 0 1 1.5 1 23 A A A A A A A A A A 2 1 0 0 1 1.0 1 34 A C A C A C A C A C 3 1 0 0 2 0.4 1 43 A A A A A A A A A A 4 1 0 0 2 0.9 1 13 A C A C A C A C A C
- The matching DAT file is in the following:
T YourTraitName C SEX C AGE M 1:123456a M 1:234567 M 2:111111 M 2:222222 M X:12345
- DAT file must have variant names in the following format "M chr:pos".
- Orders of labels in DAT file have to match the order of fields in PED file.
- Markers in PED and DAT file must be sorted by chromosome and position.
- Covariate and trait values are saved in PED file. Covariate and trait descriptions are saved in DAT file.
- Another option is to use VCF as input. Please refer to the following link for VCF file specification: 1000 genome wiki VCF specs
- VCF file should be compressed by bgzip and indexed by tabix, using the following command:
bgzip input.vcf ## this command will generate input.vcf.gz tabix -p vcf -f input.vcf.gz ## this command will generate input.vcf.gz.tbi
- Even with the presence of VCF file, PED/DAT files are still needed for covariates and phenotypes.
Group File for Gene-level Tests
- Grouping methods are only necessary for gene-level tests.
- With --groupFile option, you can specify particular set of variants to be grouped for burden tests.
- The group file must be a tab or space delimited file in the following format:
GROUP_ID MARKER1_ID MARKER2_ID MARKER3_ID ...
- MARKER_ID must be in the following format:
- An example group file is:
PLEKHN1 1:901922:G:A 1:901923:C:A 1:902088:G:A 1:902128:C:T 1:902133:C:G 1:902176:C:T 1:905669:C:G HES4 1:934735:A:C 1:934770:G:A 1:934801:C:T 1:935085:G:A 1:935089:C:G
- Version 2.4 and later allow variants from different chromosomes to be grouped for testing. This might be useful for pathway analysis.
- Note: any variants that have different alleles from listed in group file will be excluded from gene-level tests.
Example Command Line
Single Variant Analysis
The following command lines let you run single variant association analysis of trait "LDL" using score test, after inverse normalization of the quantitative trait and adjusting covariates. --traitName specifies the single trait or traits you want to analyze in this batch. If this option is not used, then all traits coded in data file will be analyzed accordingly. --SingleVarLRT provides essentially the same test as in merlin --fastAssoc option.
./famrvtest --ped your.ped --dat your.dat --vcf your.vcf.gz --SingleVarScore --inverseNormal --useCovariates --traitName LDL
All the above commands will let you do family-based association analysis using kinship matrices generated using pedigree structure coded in pedigree file. The following command lines show examples of using genotype to estimate empirical relationship matrix to do the work.
./famrvtest --ped your.ped --dat your.dat --SingleVarScore --inverseNormal --useCovariates --traitName LDL --kinPedigree
The following command lines let you run gene-level association analysis of genes listed in "your.genes.groupfile" for trait "LDL" using SKAT, Madsen-Browning weighted burden, rare allele counts un-weighted burden and collapsing burden and variable threshold tests, after inverse normalization of the quantitative trait and adjusting covariates. Only rare variants with maf less than or equal to 0.05 and minor allele count greater than or equal to 3 are grouped.
./famrvtest -ped your.ped -dat your.dat --SKAT_BETA --MB --burden --VT --inverseNormal --useCovariates --traitName LDL --groupFile your.genes.groupfile --maf 0.05
- Released version 0.0.9 with a bug fixed for potential compiling error. (10/10/2013)
- Released version 2.0, a faster version and added family-based single variant permutation test. (7/14/2014)
- Released version 2.2, a bug fixed which causes single variant test can not be run alone. (7/15/2014)
- Uploaded new source code package for version2.2, with updated makefiles. (8/4/14)
- Released version 2.3. Fixed a bug which causes compiling error (not finding the correct makefile). (8/20/14)
- Released version 2.4. Enable analyzing pathways where variants from different chromosomes can be grouped. (9/27/2014)