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A Bayesian Approach to Ordering Gene Markers
Author(s) -
George A. W.,
Mengersen K. L.,
Davis G. P.
Publication year - 1999
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.0006-341x.1999.00419.x
Subject(s) - bayesian probability , markov chain monte carlo , computer science , markov chain , reliability (semiconductor) , focus (optics) , data mining , range (aeronautics) , population , algorithm , mathematics , machine learning , artificial intelligence , engineering , power (physics) , physics , demography , quantum mechanics , sociology , optics , aerospace engineering
Summary. 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.