GEVALT (GEnotype Visualization and ALgorithmic Tool) is
designed to simplify and
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
- 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
- visual displays of genotypes, haplotypes, LD blocks, and
GEVALT combines the visual (and algorithmic) abilities of Haploview
 with three additional powerful algorithms:
GERBIL - an algorithm for simultaneously
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
 and .
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 .
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 .
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.
The GEVALT software (version 2.0) can be downloaded
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
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,
Halperin, Oded Apel, and Ron Shamir.
We are thankful to the Haploview Team
for making this software and source code available to us.
GEVALT can be cited with the following paper:
Ofir Davidovich, Gad Kimmel and Ron Shamir. GEVALT:
An integrated software tool for genotype analysis. BMC
Bioinformatics 2007, 8:36.
- Gad Kimmel and Ron Shamir. Maximum likelihood
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.
- 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,
- Eran Halperin, Gad Kimmel and Ron Shamir. Tag SNP
Selection in Genotype Data for Maximizing SNP Prediction Accuracy. Bioinformatics 21(Suppl 1):
- Gad Kimmel and Ron
Shamir. A Fast Method for Computing High Significance Disease
in Large Population-Based Studies. Am.
J. Hum. Genet. 2006.
- Barrett JC, Fry B, Maller J, Daly MJ. Haploview:
visualization of LD and haplotype maps. Bioinformatics. 2005
Jan 15 [PubMed ID: 15297300]
Contact us at "gevalt AT cs.tau.ac.il"
Version 2.0 (25/06/07)
- Added a long phasing procedure that allows phasing
- Improved STAMPA: allows to force include/exclude markers,
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