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Literature DB >> 10597425

Monte Carlo Markov chain methods for genome screening.

E W Daw¹, J Kumm, G L Snow, E A Thompson, E M Wijsman.

Abstract

We used Monte Carlo Markov chain (MCMC) methods to analyze a quantitative trait, MAO level, and a discrete trait, Collaborative Study on the Genetics of Alcoholism (COGA) alcoholism. Segregation, linkage, and haplotype sharing were analyzed and effects of marker map features were examined. For MAO, modest signals were found on chromosomes 1 and 17 for raw data, and 15 for covariate-adjusted data. For alcoholism, a strong signal was found on chromosome 1 with modest signals on chromosomes 4 and 10.

Entities: Disease

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Year: 1999 PMID： 10597425 DOI： 10.1002/gepi.1370170723

Source DB: PubMed Journal: Genet Epidemiol ISSN： 0741-0395 Impact factor: 2.135

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Cited

5 in total

1. Performance of Markov chain-Monte Carlo approaches for mapping genes in oligogenic models with an unknown number of loci.

Authors: J K Lee; D C Thomas
Journal: Am J Hum Genet Date: 2000-10-13 Impact factor: 11.025

Monte Carlo Markov chain methods for genome screening.

1. Performance of Markov chain-Monte Carlo approaches for mapping genes in oligogenic models with an unknown number of loci.

2. Modifier locus of the skeletal muscle involvement in Emery-Dreifuss muscular dystrophy.

3. Haplotype-sharing analysis for alcohol dependence based on quantitative traits and the Mantel statistic.

Review 4. Genetic factors modifying clinical expression of autosomal dominant RP.

5. Genome-wide mapping of modifier chromosomal loci for human hypertrophic cardiomyopathy.