Bayesian analysis of genetic change due to selection using Gibbs sampling

Sorensen, D.; Cs, Wang; Jensen, Just; Gianola, Daniel

doi:10.1186/1297-9686-26-4-333

Cited by 97 publications

(34 citation statements)

References 6 publications

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“…recently been often used for estimating VCs in animal breeding researches and applications (Sorensen et al 1994;Wang et al 1994;van Tassell et al 1995;Magnabosco et al 2000;Schenkel & Schaeffer 2000;Luo et al 2001;López-Romero et al 2003;Jamrozik 2004). The GS has some advantages over REML (Gianola & Foulley 1990;Wang et al 1993;van Tassell et al 1995).…”

Section: Introductionmentioning

confidence: 99%

Investigation of Gibbs sampling conditions to estimate variance components from Japanese Black carcass field data

Arakawa

Iwaisaki

Anada³

2009

Animal Science Journal

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The genetic evaluation using the carcass field data in Japanese Black cattle has been carried out employing an animal model, implementing the restricted maximum likelihood (REML) estimation of additive genetic and residual variances. Because of rapidly increasing volumes of the official data sets and therefore larger memory spaces required, an alternative approach like the REML estimation could be useful. The purpose of this study was to investigate Gibbs sampling conditions for the single-trait variance component estimation using the carcass field data. As prior distributions, uniform and normal distributions and independent scaled inverted chi-square distributions were used for macro-environmental effects, breeding values, and the variance components, respectively. Using the data sets of different sizes, the influences of Gibbs chain length and thinning interval were investigated, after the burn-in period was determined using the coupling method. As would be expected, the chain lengths had obviously larger effects on the posterior means than those of thinning intervals. The posterior means calculated using every 10th sample from 90 000 of samples after 10 000 samples discarded as burn-in period were all considered to be reasonably comparable to the corresponding estimates by REML.

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Section: Introductionmentioning

confidence: 99%

Investigation of Gibbs sampling conditions to estimate variance components from Japanese Black carcass field data

Arakawa

Iwaisaki

Anada³

2009

Animal Science Journal

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“…MCMC) in the late 1980s and the continued increase in computer power meant that Bayesian methods could more readily be applied to quantitative genetics. As in the early 1990s, applications have been developing at a steady pace (Wang et al, 1993Sorensen et al, 1994), covering an impressive array of topics and methods of paramount interest for evolutionary biologists. Estimation techniques and associated software have been developed to fit multivariate normal animal models with an arbitrary number of causal components Van Tassel & Van Vleck, 1996;Ovaskainen et al, 2008), and extended to models with binary traits (Sorensen & Gianola, 2002;Kovač & Groeneveld, 2003;Bennewitz et al, 2007).…”

Section: Methodsmentioning

confidence: 99%

Bayesian approaches in evolutionary quantitative genetics

O’Hara

Cano

Ovaskainen

et al. 2008

J of Evolutionary Biology

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The study of evolutionary quantitative genetics has been advanced by the use of methods developed in animal and plant breeding. These methods have proved to be very useful, but they have some shortcomings when used in the study of wild populations and evolutionary questions. Problems arise from the small size of data sets typical of evolutionary studies, and the additional complexity of the questions asked by evolutionary biologists. Here, we advocate the use of Bayesian methods to overcome these and related problems. Bayesian methods naturally allow errors in parameter estimates to propagate through a model and can also be written as a graphical model, giving them an inherent flexibility. As packages for fitting Bayesian animal models are developed, we expect the application of Bayesian methods to evolutionary quantitative genetics to grow, particularly as genomic information becomes more and more associated with environmental data.

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“…There is no unifying theory within the Bayesian paradigm for choosing proper prior distributions. HOBERT and CASELLA (1996) point out that¯at priors commonly used in Gibbs sampling applications in animal breeding (WANG et al 1993(WANG et al , 1994bJENSEN et al 1994;SORENSEN et al 1994) can give an improper joint posterior distribution. In general, however, use of proper priors is one way to avoid improper posteriors.…”

Section: Methodsmentioning

confidence: 99%

Comparison of (co)variance component estimates in control populations of red flour beetle (Tribolium castaneum) using restricted maximum likelihood and Gibbs sampling

Lin¹,

Berger²

2001

J Anim Breed Genet

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Bayes Analyse in der Implementierung mit Gibbs sampling ermo È glicht. Generell konnte festgestellt werden, dass das 95%ige Zentralintervall der Scha Ètzungen unter den drei Gewichtungen in den Populationen denen der Kontrolllinien a Èhnlich war. Die meisten REML Scha Ètzer in den Basispopulationen unterscheiden sich von den REML Scha Ètzern in den Kontrolllinien. Unzureichende Informationen aus den Daten und confounding der zufa Èlligen Effekte trugen zur Variabilita Èt der REML Scha Ètzungen in den Basispopulationen bei. Dies zeigt sich darin, dass einige (Ko)varianz Komponenten mit geringerer Genauigkeit gescha Ètzt wurden als andere. Die Ergebnisse unterstu È tzen auch die Hypothese, dass REML Scha Ètzungen dem Zentralwert der a posteriori Verteilung entspricht, die man aus der Bayes Analyse mit¯at priors erha Èlt. Das aber nur dann, wenn ausreichende Informationen aus den Daten vorliegen und kein confounding zwischen zufa Èlligen Effekten vorliegt. 22E

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Bayesian analysis of genetic change due to selection using Gibbs sampling

Abstract: Summary -A method of analysing response to selection using a Bayesian perspective is presented. The

Cited by 97 publications

References 6 publications

Investigation of Gibbs sampling conditions to estimate variance components from Japanese Black carcass field data

Investigation of Gibbs sampling conditions to estimate variance components from Japanese Black carcass field data

Bayesian approaches in evolutionary quantitative genetics

Comparison of (co)variance component estimates in control populations of red flour beetle (Tribolium castaneum) using restricted maximum likelihood and Gibbs sampling

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