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A Block-Free Hidden Markov Model for Genotypes and Its Application to Disease Association (2005)
| Content Provider | CiteSeerX |
|---|---|
| Author | Kimmel, Gad Shamir, Ron |
| Abstract | We present a new stochastic model for genotype generation. The model offers a compromise between rigid block structure and no structure altogether: It reflects a general blocky structure of haplotypes, but also allows for “exchange ” of haplotypes at nonboundary SNP sites; it also accommodates rare haplotypes and mutations. We use a hidden Markov model and infer its parameters by an expectation-maximization algorithm. The algorithm was implemented in a software package called HINT (haplotype inference tool) and tested on 58 datasets of genotypes. To evaluate the utility of the model in association studies, we used biological human data to create a simple disease association search scenario. When comparing HINT to three other models, HINT predicted association most accurately. |
| File Format | |
| Volume Number | 12 |
| Journal | J. of Computational Biology |
| Language | English |
| Publisher Date | 2005-01-01 |
| Access Restriction | Open |
| Subject Keyword | Disease Association Block-free Hidden Markov Model Rigid Block Structure Software Package Rare Haplotype General Blocky Structure Simple Disease Association Search Scenario New Stochastic Model Hidden Markov Model Haplotype Inference Tool Expectation-maximization Algorithm Nonboundary Snp Biological Human Data Genotype Generation Association Study |
| Content Type | Text |
| Resource Type | Article |
