2012
DOI: 10.1534/genetics.112.139014
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Back to Basics for Bayesian Model Building in Genomic Selection

Abstract: Numerous Bayesian methods of phenotype prediction and genomic breeding value estimation based on multilocus association models have been proposed. Computationally the methods have been based either on Markov chain Monte Carlo or on faster maximum a posteriori estimation. The demand for more accurate and more efficient estimation has led to the rapid emergence of workable methods, unfortunately at the expense of well-defined principles for Bayesian model building. In this article we go back to the basics and bu… Show more

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Cited by 69 publications
(90 citation statements)
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References 56 publications
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“…Both GY and GM are considered to be highly polygenic traits, based on QTL mapping results (Schön et al 2004;Huang et al 2010). Several authors found in simulation studies and for real data sets that GBLUP models were superior to or equally well performing as Bayesian wholegenome regression methods for such traits (Zhong et al 2009;Hayes et al 2010;Clark et al 2011;Kärkkäinen and Sillanpää 2012;Wimmer et al 2013). Daetwyler et al (2010) arrived at the same conclusion based on theoretical results.…”
Section: Comparison Of Prediction Methodsmentioning
confidence: 87%
“…Both GY and GM are considered to be highly polygenic traits, based on QTL mapping results (Schön et al 2004;Huang et al 2010). Several authors found in simulation studies and for real data sets that GBLUP models were superior to or equally well performing as Bayesian wholegenome regression methods for such traits (Zhong et al 2009;Hayes et al 2010;Clark et al 2011;Kärkkäinen and Sillanpää 2012;Wimmer et al 2013). Daetwyler et al (2010) arrived at the same conclusion based on theoretical results.…”
Section: Comparison Of Prediction Methodsmentioning
confidence: 87%
“…We used r = 1000, which is a good choice when the mean count is less than 100. Also, Model Normal and Model log-normal were implemented under a Bayesian framework following Kärkkäinen and Sillanpää (2012).…”
Section: Full Conditional Distributionsmentioning
confidence: 99%
“…The number of variables selected for the multilocus analysis depends on the number of individuals in the data set. Generally, the maximum number is assumed to be 10 times the number of individuals (Hoti and Sillanpää 2006), but in our experience, an even smaller proportion may be optimal (Kärkkäinen and Sillanpää 2012a).…”
Section: The Multilocus Methodsmentioning
confidence: 99%
“…Computation times required to estimate parameters in these models are generally much higher than when single-locus models are used. Therefore, one may prefer to apply faster maximum a posteriori probability (MAP) estimation tools rather than Markov chain Monte Carlo (MCMC) techniques (Kärkkäinen and Sillanpää 2012a;Knürr et al 2013).…”
mentioning
confidence: 99%
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