Scientific Research and Essays

  • Abbreviation: Sci. Res. Essays
  • Language: English
  • ISSN: 1992-2248
  • DOI: 10.5897/SRE
  • Start Year: 2006
  • Published Articles: 2755

Full Length Research Paper

A new algorithm for setting initial values for Markov Chain Monte Carlo in genetic linkage analysis via Gibbs sampling

Gholamreza Jandaghi
University of Tehran, Qom College, Iran.
Email: [email protected]

  •  Accepted: 05 October 2010
  •  Published: 18 November 2010



In recent decades the Markov Chain Monte Carlo (MCMC) method has received a considerable attention in the area of genetic linkage analysis. Pedigree Gibbs sampler as a MCMC method like the other Markov Chain-based methods of resampling faces some difficulties in application. One of the difficulties is setting the initial genotypes consistent with observed phenotypes in order to make the chain start moving to different states (sets of consistent genotypes). In this paper a new algorithm for setting the initial genotypes for the pedigree under analysis is proposed. The proposed algorithm showed faster convergence than the existing algorithm. The efficiency of the new algorithm will be shown through examples. This algorithm is faster and more efficient than the existing algorithms.


Key words: Markov Chain Monte Carlo, pedigree, linkage analysis, genotype,phenotype.