Genometric Analysis Simulation Program (GASP)
Development of computer software for statistical genetic analysis can be facilitated by the availability of software tools that can be used to:
- verify the algorithms underlying a particular method of analysis (i.e. statistical test)
- determine empirical type I error rates for a statistical test
- determine the power of a test
- determine the robustness of a test with respect to failures of underlying assumptions
The Genometric Analysis Simulation Program (G.A.S.P.) is a software tool that can generate samples of family data based on user specified genetic models. Data generated can be as simple as a single sample of random individuals with a single normally distributed trait or as complex as thousands of samples of extended families with multiple traits based on a linear combination of major locus, polygenic, common sibship environment and covariate components. Traits can be generated based on a number of user specified components, and components can be unique to a single trait or shared by multiple traits. The user first specifies a list of all desired components and then creates each trait by specifying the desired component weighted by its contribution to the phenotypic variance.
G.A.S.P. can be used in two ways. First, data can be generated in a standalone fashion. The resulting family data can be saved and then used as sample data for demonstrating applications and methods of genetic analysis or for testing and verifying newly developed algorithms in statistical genetics. A simple driver ("dataonly") is provided for this application. Second, data can be generated and analyzed immediately using an existing statistical package. A driver can be designed to call subroutine versions of widely available genetic analysis programs.
What can G.A.S.P. be Used For?
- Verify analysis algorithms with respect to the underlying theory
- Test the statistical validity of newly developed methods of genetic segregation and linkage analysis and investigate the statistical properties of the test statistics
- Determine the power and robustness of these methods
- Apply insights gained from these simulation experiments to ongoing collaborative genetic analyses
Software Version: V3.33 (7/30/03)
Last Modified: Monday, 01-May-2017 15:31:39 EDT
