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Bioinformatics. 2016 May 1;32(9):1423-6. doi: 10.1093/bioinformatics/btw079. Epub 2016 Feb 15.

RVTESTS: an efficient and comprehensive tool for rare variant association analysis using sequence data.

Author information

1
Department of Clinical Science, Quantitative Biomedical Research Center, Center for the Genetics of Host Defense, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.
2
A9.Com Inc, Palo Alto, CA 94301, USA.
3
Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN 37240, USA.
4
Center of Statistical Genetics, Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA.
5
Division of Biostatistics and Bioinformatics, Department of Public Health Sciences, Penn State College of Medicine, Hershey, PA 17033, USA and Institute for Personalized Medicine, Penn State College of Medicine, Hershey, PA 17033, USA.

Abstract

MOTIVATION:

Next-generation sequencing technologies have enabled the large-scale assessment of the impact of rare and low-frequency genetic variants for complex human diseases. Gene-level association tests are often performed to analyze rare variants, where multiple rare variants in a gene region are analyzed jointly. Applying gene-level association tests to analyze sequence data often requires integrating multiple heterogeneous sources of information (e.g. annotations, functional prediction scores, allele frequencies, genotypes and phenotypes) to determine the optimal analysis unit and prioritize causal variants. Given the complexity and scale of current sequence datasets and bioinformatics databases, there is a compelling need for more efficient software tools to facilitate these analyses. To answer this challenge, we developed RVTESTS, which implements a broad set of rare variant association statistics and supports the analysis of autosomal and X-linked variants for both unrelated and related individuals. RVTESTS also provides useful companion features for annotating sequence variants, integrating bioinformatics databases, performing data quality control and sample selection. We illustrate the advantages of RVTESTS in functionality and efficiency using the 1000 Genomes Project data.

AVAILABILITY AND IMPLEMENTATION:

RVTESTS is available on Linux, MacOS and Windows. Source code and executable files can be obtained at https://github.com/zhanxw/rvtests

CONTACT:

zhanxw@gmail.com; goncalo@umich.edu; dajiang.liu@outlook.com

SUPPLEMENTARY INFORMATION:

Supplementary data are available at Bioinformatics online.

PMID:
27153000
PMCID:
PMC4848408
DOI:
10.1093/bioinformatics/btw079
[Indexed for MEDLINE]
Free PMC Article

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