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Regression-based association analysis with clustered haplotypes through use of genotypes
Jung-Ying Tzeng1, Chih-Hao Wang, Jau-Tsuen Kao
1Department of Statistics and Bioinformatics Research Center, North Carolina State University, Raleigh, NC 27695, USA. jytzeng@stat.ncsu.edu
American Journal of Human Genetics
|December 21, 2005
Summary
This study introduces a novel regression-based method for haplotype-phenotype association analysis, improving statistical power by clustering common and rare haplotypes. The approach enhances the detection of modest genetic effects, crucial for understanding complex diseases.
Area of Science:
- Genetics
- Statistical Genetics
- Bioinformatics
Background:
- Haplotype-based association analysis offers high resolution for detecting modest genetic effects.
- Current methods often lack power due to extensive parameterization and insufficient handling of rare haplotypes.
Purpose of the Study:
- To develop a regression-based approach using clustered haplotypes for robust haplotype-phenotype association analysis.
- To improve statistical power by focusing on relevant haplotype inferences.
Main Methods:
- Generalized the probabilistic clustering methods of Tzeng to the generalized linear model (GLM) framework.
- Incorporated phase and clustering uncertainty using unphased genotypes.
- Applied the method to study hypertriglyceridemia and the apolipoprotein A5 gene.
Main Results:
- The proposed method demonstrates validity and power in testing for haplotype-trait association through simulation studies.
- The regression-based clustered haplotype approach effectively concentrates statistical power.
Conclusions:
- The developed method provides a powerful and flexible tool for genetic association studies.
- This approach enhances the ability to identify genetic factors contributing to complex traits like hypertriglyceridemia.