A Bayesian approach to ordering gene markers.
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.
George
A W
AW
School of Mathematics, Queensland University of Technology, Brisbane, Australia. a.george@fsc.qut.edu.au
Mengersen
K L
KL
Davis
G P
GP
D000465:Algorithms; D000818:Animals; D001499:Bayes Theorem; D002417:Cattle; D002874:Chromosome Mapping / Q000379:methods / Q000706:statistics & numerical data; D005260:Female; D005787:Gene Frequency; D005819:Genetic Markers; D005838:Genotype; D016013:Likelihood Functions; D008297:Male; D008390:Markov Chains; D008957:Models, Genetic; D015233:Models, Statistical; D009010:Monte Carlo Method; D019655:Quantitative Trait, Heritable
D005819:Genetic Markers