Literature DB >> 11318195

A Bayesian approach to ordering gene markers.

A W George1, K L Mengersen, G P Davis.   

Abstract

A technique is presented whereby a marker map can be constructed using resource family data with an entire class of missing data. The focus is on a half-sib design where there is only information on a single parent and its progeny. A Bayesian approach is utilised with solutions obtained via a Markov chain Monte Carlo algorithm. Features of the approach include the capacity to determine parameters for the ungenotyped dam population, the ability to incorporate published information and its reliability, and the production of posterior densities and the consequent deduction of a wide range of inferences. These features are demonstrated through the analysis of simulated and experimental data.

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Year:  1999        PMID: 11318195     DOI: 10.1111/j.0006-341x.1999.00419.x

Source DB:  PubMed          Journal:  Biometrics        ISSN: 0006-341X            Impact factor:   2.571


  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

2.  Constructing the parental linkage phase and the genetic map over distances <1 cM using pooled haploid DNA.

Authors:  Dario Gasbarra; Mikko J Sillanpää
Journal:  Genetics       Date:  2005-11-19       Impact factor: 4.562

3.  Construction of linkage maps in full-sib families of diploid outbreeding species by minimizing the number of recombinations in hidden inheritance vectors.

Authors:  J Jansen
Journal:  Genetics       Date:  2005-06-08       Impact factor: 4.562

4.  A novel Markov chain monte carlo approach for constructing accurate meiotic maps.

Authors:  Andrew W George
Journal:  Genetics       Date:  2005-06-18       Impact factor: 4.562

5.  Three-point appraisal of genetic linkage maps.

Authors:  W R Gilks; S J Welham; J Wang; S J Clark; G J King
Journal:  Theor Appl Genet       Date:  2012-06-29       Impact factor: 5.699

  5 in total

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