| Price: | This software is freely available for non-commercial scientific use |
| Download: | DOWNLOAD – Please register your details before downloading this software |
| Product author: | Molecular Fisheries Laboratory |
| Release date: | July 2011 |
| Version: | 2.00 |
| Version changes: | Version 2.00 uses an improved method of detecting pairwise matches with errors compared to version 1.00. |
| Download format: | .zip packages of files for Mac, PC and Linux operating systems |
Product information
SHAZA is a statistical package suitable for the analysis of matching genotypes. It is based on ranking the likelihoods of individual matches against the cumulative estimation of matches occurring by chance. SHAZA was specifically designed to detect low frequency genotype matches from a large number of samples.
The target users of this program would typically be researchers in wildlife studies who use genotype data (microsatellites or SNPs) to identify animals. SHAZA is suitable for estimating recaptures in a range of applications, including mark-release-recapture studies where animals are tagged using genotypes in:
- data that is low in statistical power (i.e. ‘shadows’ prevalent)
- data that is high in statistical power with genotype errors prevalent
- data that has loci missing in many samples (e.g. through degraded or non-invasive sampling).
The unique ability of SHAZA to correct for both Type I and Type II errors leads to more accurate counts of matches, which in turn would produce more accurate estimates of population parameters from a mark-recapture study (e.g. Population size).
In forensic applications SHAZA is suitable for finding genotype matches and suspect matches from poor quality data containing missing loci and genotyping errors. This may be typical in crime scenes where DNA may be in small quantities and partially degraded.
The built in simulation options of SHAZA make it a powerful statistical tool for testing experimental design and robustness of model assumptions.
Preferred Citation and Further Information
Macbeth, G. M., Broderick, D., Ovenden, J. R. & Buckworth, R. C. (2011). Likelihood-based genetic mark-recapture estimates when genotype samples are incomplete and contain typing errors. Theoretical Population Biology 80, 185-196. doi:10.1016/j.tpb.2011.06.006
Computing requirements
Binary executable files are available for Windows XP, Linux kernel 2.6 and MacIntosh (OS X). It is assumed that if you are able to run one of these operating systems, you have enough computing power to run the SHAZA software. ANSI C source code is available for other computer platforms.
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