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- #Explantion of output in comprehensive meta analysis how to#
- #Explantion of output in comprehensive meta analysis software#
- #Explantion of output in comprehensive meta analysis code#
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 byĭajiang Liu) to test every gene listed in refFlat_. If there is no providing -gene option, all genes will be tests. For example, specify -gene CFH,ARMS2 will perform association tests on CFH and ARMS2 genes. You can use -gene to specify which gene(s) to test. We provided different gene definitions in the Resources section. To perform rare variant tests by gene, you need to use -geneFile to specify the gene range in a refFlat format. For different grouping method, see Grouping. The simplest case is to use gene as grouping unit. These includes SKAT test and KBAC test.Īll above tests requires to group variants into a unit.
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The category includes: CMC test, Zeggini test, Madsen-Browning test, CMAT test, and rare-cover test. Burden tests: group variants, which are usually less than 1% or 5% rare variants, for association tests.Groupwise tests includes three major kinds of tests.
#Explantion of output in comprehensive meta analysis code#
Rvtests will automatically check whether the phenotype is binary trait or quantitative trait.įor binary trait, the recommended way of coding is to code controls as 1, cases as 2, missing phenotypes as -9 or 0.įor other types of association tests, you can refer to Models. The 6th column of the phenotype file, phenotype.ped, which is in PLINK format, is used. Tests for every variant in the input.vcf file. This specifies single variant Wald and score test for association Rvtest -inVcf input.vcf -pheno phenotype.ped -out output -single wald,score Source codes can be downloaded from github or github page. doi:10.1093/bioinformatics/btw079 ( PDF) Download RVTESTS: An Efficient and Comprehensive Tool for Rare Variant Association Analysis Using Sequence Dataīioinformatics 2016 32: 1423-1426. Xiaowei Zhan, Youna Hu, Bingshan Li, Goncalo R. It also allows for highly effcient generation of covariance matrices between score statistics in RAREMETAL format, which can be used to support the next wave of meta-analysis that incorporates large biobank datasets.Ī (much) larger sample size can be handled using linear regression or logistic regression models. RVTESTS supports both single variant and gene-level tests.
#Explantion of output in comprehensive meta analysis software#
With new implementation of the BOLT-LMM/MINQUE algorithm as well as a series of software engineering optimizations, our software package is capable of analyzing datasets of up to 1,000,000 individuals in linear mixed models on a computer workstation, which makes our tool one of the very few options for analyzing large biobank scale datasets, such as UK Biobank. It takes VCF/BGEN/PLINK format as genotype input file and takes PLINK format phenotype file and covariate file.
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single variant score test, burden test, variable threshold test, SKAT test, fast linear mixed model score test). It includes a variety of association tests (e.g. It can analyze both unrelated individual and related (family-based) individuals for both quantitative and binary outcomes. Since its inception, rvtests was developed as a comprehensive tool to support genetic association analysis and meta-analysis. Rvtests, which stands for Rare Variant tests, is a flexible software package for genetic association analysis for sequence datasets.