A comparison of strategies for Markov chain Monte Carlo computation in quantitative genetics

Genetics Selection Evolution

Volumn 40, Issue 2, 2008, Pages 161-176

  Waagepetersen, Rasmus ^a   Ibánẽz Escriche, Noelia ^b   Sorensen, Daniel ^c  

^a AALBORG UNIVERSITY   (Denmark)

^b IRTA   (Spain)

^c DANISH INSTITUTE OF AGRICULTURAL SCIENCES   (Denmark)

Author keywords

Langevin Hastings; Markov chain Monte Carlo; Normal approximation; Proposal distributions; Reparameterization

Indexed keywords

BAYESIAN ANALYSIS; COMPARATIVE STUDY; COVARIANCE ANALYSIS; DATA SET; GENETIC STRUCTURE; GENETICS; HERITABILITY; HETEROGENEITY; MARKOV CHAIN; MOLECULAR ANALYSIS; MONTE CARLO ANALYSIS; NUMERICAL MODEL; PARAMETERIZATION; QUANTITATIVE ANALYSIS; VARIANCE ANALYSIS;

ALGORITHM; ANIMAL; ARTICLE; COMPARATIVE STUDY; GENETIC VARIABILITY; MONTE CARLO METHOD; PROBABILITY; RABBIT; SNAIL; SWINE;

ALGORITHMS; ANIMALS; MARKOV CHAINS; MONTE CARLO METHOD; RABBITS; SNAILS; SWINE; VARIATION (GENETICS);

EID: 39849086637     PISSN: 0999193X     EISSN: 12979686     Source Type: Journal    
DOI: 10.1051/gse:2007042     Document Type: Article

References (19)

1. Besag J., Discussion on the paper by Grenander and Miller, J. Roy. Statist. Soc. Ser. B 56 (1994) 591-592.
2. Christensen O.F., Waagepetersen R.P., Bayesian prediction of spatial count data using generalized linear mixed models, Biometrics 58 (2002) 280-286.
3. Damgaard L.H., Joint quantitative genetic analysis of survival, linear Gaussian and ordered categorical traits, in Proceedings of the 8th World Congress of Genetics Applied to Livestock Production, Belo Horizonte, Minas Gerais, Brazil, August 13-18 2006.
4. García-Cortés L.A., Sorensen D., On a multivariate implementation of the Gibbs sampler, Genet. Sel. Evol. 28 (1996) 121-126.
5. Geyer C.J., Practical Markov chainMonte Carlo, Statist. Sci. 7 (1992) 473-511.
6. Geyer C.J., Thompson E.A., Constrained Monte Carlo maximum likelihood for dependent data, J. Roy. Soc. Statist. Ser. B 54 (1992) 657-699.
7. Gustafson P., MacNab Y.C., Wen S., On the value of derivative evaluations and random walk suppression in Markov chain Monte Carlo algorithms, Stat. Comput. 14 (2004) 23-38.
8. Henderson C.R., A simple method for the inverse of a numerator relationship matrix used in prediction of breeding values, Biometrics 32 (1976) 69-83.
9. Ibáñez-Escriche N., Models for residual variances in quantitative genetics. An application to rabbit uterine capacity, Ph.D. thesis, Departamento de Ciencia Animal, Universidad Politécnica de Valencia, Valencia, Spain, 2006.
10. Knorr-Held L., Rue H., On block updating in Markov random field models for disease mapping, Scand. J. Statist. 29 (2002) 597-614.
11. Roberts G.O., Tweedie R.L., Exponential convergence of Langevin diffusions and their discrete approximations, Bernoulli 2 (1996) 341-363.
12. Ros M., Sorensen D., Waagepetersen R., Dupont-Nivet M., San Cristobal M., Bonnet J.-C., Mallard J., Evidence for genetic control of adult weight plasticity in the snail helix aspersa, Genetics 168 (2004) 2089-2097.
13. Rue H., Fast sampling of Gaussian Markov random fields, J. Roy. Statist. Soc. Ser. B 63 (2001) 325-338.
14. Rue H., Knorr-Held L., Gaussian Markov random fields - theory and applications, Chapman & Hall/CRC, 2005.
15. San Cristobal-Gaudy M., Elsen J.M., Bodin L., Chevalet C., Prediction of the response to a selection for canalisation of a continuous trait in animal breeding, Genet. Sel. Evol. 30 (1998) 423-451.
16. Sorensen D., Gianola D., Likelihood, Bayesian, and Markov chain Monte Carlo methods in quantitative genetics, Springer-Verlag, 2002.
17. Sorensen D., Waagepetersen R., Normal linear models with genetically structured variance heterogeneity: a case study, Genet. Res. 82 (2003) 207-222.
18. Steinsland I., Jensen H., Making inference from Bayesian animal models utilising Gaussian Markov random field properties, Statistics Preprint 10, Norwegian University of Science and Technology, 2005.
19. Waagepetersen R., Sorensen D., A tutorial on reversible jump MCMC with a view towards applications in QTL-mapping, Int. Stat. Rev. 69 (2001) 49-61.