GEVALT (GEnotype Visualization and ALgorithmic Tool) is designed to simplify and expedite the process of genotype analysis and disease association tests by providing a common interface to several common tasks relating to such analyses. It is aimed for analysis of unrelated individuals as well as two-generation families.

GEVALT's functionalities:

• genotype phasing for every individual
• tag SNPs selection
• LD and haplotype block analysis
• haplotype population frequency estimation
• single SNP and haplotype association tests
• permutation testing for computing significance of disease association
• visual displays of genotypes, haplotypes, LD blocks, and more

GEVALT combines the visual (and algorithmic) abilities of Haploview [5] with three additional powerful algorithms:

1. GERBIL - an algorithm for simultaneously phasing genotypes into haplotypes and block partitioning. GERBIL was shown to be quick and accurate even when applied to many hundreds of individuals. The algorithm is described in [1] and [2].

2. STAMPA - an algorithm for tag SNPs selection. The algorithm finds a set of tag SNPs with maximal prediction accuracy. STAMPA was tested on many different genotype datasets and found tag SNPs with considerably better prediction ability than two other state-of-the-art tag SNP selection algorithms. The algorithm is described in [3].

3. RAT - an algorithm for rapid association testing. An efficient permutation test for the significance of disease association on genotype data with case-control information. The algorithm is described in [4].

GEVALT is implemented in JAVA (based on the open source code of Haploview) and the analysis algorithms are implemented in C++. Both Linux and Windows versions are available as well as the JAVA code.

Downloading GEVALT

The GEVALT software (version 2.0) can be downloaded here.
In order to use GEVALT the Java Runtime Environment (JRE) must be installed on your computer. GEVALT is designed to work with JRE v1.5 or later, but it is strongly recommended that you download and install the latest version of the JRE available for your operating system. Get the newest version of the JRE at: http://www.java.com/

The People Behind GEVALT

GEVALT is developed in Prof. Ron Shamir's Computational Genomics Laboratory at the School of Computer Science, Tel Aviv University, Israel, as part of an ongoing effort to develop mathematical models and practical tools for analyzing genotype data. The developers are Ofir Davidovich, Gad Kimmel, Eran Halperin, Oded Apel, and Ron Shamir.

We are thankful to the Haploview Team for making this software and source code available to us.

Citation

References

1. Gad Kimmel and Ron Shamir. Maximum likelihood resolution of multi-block genotypes. In Proceedings of the Eighth Annual International Conference on Research in Computational Molecular Biology (RECOMB 04). Pages 2-9, the Association for Computing Machinery, 2004.
2. Gad Kimmel and Ron Shamir. GERBIL: Genotype Resolution and Block Identification Using Likelihood. Proceedings of the National Academy of Sciences of the United States of America (PNAS) 102: 158-162, 2005.
3. Eran Halperin, Gad Kimmel and Ron Shamir. Tag SNP Selection in Genotype Data for Maximizing SNP Prediction Accuracy. Bioinformatics 21(Suppl 1): i195-i203, 2005.
4. Gad Kimmel and Ron Shamir. A Fast Method for Computing High Significance Disease Association in Large Population-Based Studies. Am. J. Hum. Genet. 2006.
5. Barrett JC, Fry B, Maller J, Daly MJ. Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics. 2005 Jan 15 [PubMed ID: 15297300]

Contact us at "gevalt AT cs.tau.ac.il"

Version Updates

Version 2.0 (25/06/07)

• Added a long phasing procedure that allows phasing thousands of SNPs.
• Improved STAMPA: allows to force include/exclude markers, and runs much faster.
• Added RAT - a rapid association permutation test.
• Added save/load status option.

Version 1.1 (10/09/06)

• Fixed bug where Gevalt crashes when loading HapMap dumps containing the JPT population.
• Added option choosing NCBI genome build when loading HapMap info track.