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Linkage analyses in type I diabetes mellitus using CASPAR, a software and statistical program for conditional
J Buhler1, D Owerbach, A A Schäffer
1Department of Computer Science, Rice University, Houston, Tex., USA.
Human Heredity
|July 1, 1997
Summary
New software, CASPAR, aids in analyzing polygenic diseases like type I diabetes. It efficiently tests genetic linkage using multiple DNA markers, improving the identification of disease susceptibility loci.
Area of Science:
- Genetics
- Computational Biology
- Medical Statistics
Background:
- Polygenic diseases, such as type I diabetes mellitus (insulin-dependent diabetes mellitus, IDDM), are influenced by multiple genetic loci.
- Established susceptibility loci for IDDM include IDDM1 and IDDM2, with ongoing research suggesting additional loci.
- Accurate linkage analysis is crucial for identifying and characterizing these genetic factors.
Purpose of the Study:
- To develop and validate novel software and statistical tools for efficient linkage analysis of polygenic diseases.
- To implement CASPAR (Computer Analysis of Sibpair Linkage) for rapid and simultaneous testing of multiple DNA markers in nuclear families.
- To assess the utility of CASPAR in identifying and fine-mapping IDDM susceptibility loci, specifically IDDM5 and IDDM7.
Main Methods:
- Development of CASPAR software for linkage analysis using nuclear families with two unaffected parents and a pair of affected siblings (ASP).
- Utilization of a simulation-based method to determine the statistical significance of lod scores from ASP tests.
- Application of CASPAR to analyze linkage of IDDM5 and IDDM7, conditioned on the presence of other known unlinked IDDM susceptibility loci (IDDM1, IDDM2, IDDM4).
Main Results:
- CASPAR enables quick and efficient linkage testing using multiple polymorphic DNA markers simultaneously.
- Simulation-based methods provide a robust approach to assess the significance of lod scores.
- Conditioning the analysis of IDDM5 on IDDM1 and IDDM4, and IDDM7 on IDDM1 and IDDM2, significantly improved the genetic analysis of these polygenic loci.
Conclusions:
- The developed software and statistical tools, particularly CASPAR, offer significant benefits for the genetic analysis of polygenic loci.
- Conditioning linkage analyses on known susceptibility loci enhances the power to detect and characterize novel disease-associated genes.
- This approach facilitates a more precise understanding of the genetic architecture underlying complex diseases like type I diabetes.
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