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.

[1]  S. Zacks,et al.  Ordering genes: controlling the decision-error probabilities. , 1993, American journal of human genetics.

[2]  B. Carlin,et al.  Bayesian Model Choice Via Markov Chain Monte Carlo Methods , 1995 .

[3]  M. Georges,et al.  Mapping quantitative trait loci controlling milk production in dairy cattle by exploiting progeny testing. , 1995, Genetics.

[4]  J. Besag,et al.  Bayesian Computation and Stochastic Systems , 1995 .

[5]  P. Green.  Reversible jump Markov chain Monte Carlo computation and Bayesian model determination , 1995 .

[6]  L. Andersson,et al.  Genetic mapping of quantitative trait loci for growth and fatness in pigs. , 1994, Science.

[7]  K. Otsu,et al.  Identification of a mutation in porcine ryanodine receptor associated with malignant hyperthermia. , 1991, Science.

[8]  L. Goddard,et al.  Operations Research (OR) , 2007 .

[9]  J. Ott,et al.  Strategies for multilocus linkage analysis in humans. , 1984, Proceedings of the National Academy of Sciences of the United States of America.

[10]  Philip Heidelberger,et al.  Simulation Run Length Control in the Presence of an Initial Transient , 1983, Oper. Res..

[11]  Walter R. Gilks,et al.  Bayesian model comparison via jump diffusions , 1995 .

[12]  R. Jansen.  A general Monte Carlo method for mapping multiple quantitative trait loci. , 1996, Genetics.

[13]  Bradley P. Carlin,et al.  Markov Chain Monte Carlo conver-gence diagnostics: a comparative review , 1996 .

[14]  W. K. Hastings,et al.  Monte Carlo Sampling Methods Using Markov Chains and Their Applications , 1970 .

[15]  Charles J. Geyer,et al.  Practical Markov Chain Monte Carlo , 1992 .

[16]  P. Hall,et al.  Characterizing surface smoothness via estimation of effective fractal dimension , 1994 .

[17]  P. Green,et al.  On Bayesian Analysis of Mixtures with an Unknown Number of Components (with discussion) , 1997 .

[18]  N. Metropolis,et al.  Equation of State Calculations by Fast Computing Machines , 1953, Resonance.

[19]  C. A. Smith,et al.  Estimating multipoint recombination fractions , 1995, Annals of human genetics.

[20]  J. Ott.  Analysis of Human Genetic Linkage , 1985 .

[21]  A F Smith,et al.  Bayesian inference in multipoint gene mapping , 1993, Annals of human genetics.

[22]  David B. Dunson,et al.  Bayesian Data Analysis , 2010 .

[23]  Z. Zeng.  Precision mapping of quantitative trait loci. , 1994, Genetics.

[24]  C. Haley,et al.  Mapping quantitative trait loci in crosses between outbred lines using least squares. , 1994, Genetics.

[25]  C. A. Smith.  Probabilities of orders in linkage calculations , 1990, Annals of human genetics.