2015
DOI: 10.1007/s13253-015-0224-3
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An Integrated Approach to Empirical Bayesian Whole Genome Prediction Modeling

Abstract: Computational efficiency is an increasing concern for whole genome prediction (WGP) based on denser genetic marker panels such that algorithms other than Markov Chain Monte Carlo (MCMC) warrant greater consideration, particularly for hierarchical models that flexibly confer either heavy-tailed (e.g., BayesA) or stochastic search and variable selection (SSVS) instead of Gaussian specifications on marker effect distributions. The expectation maximization (EM) algorithm is one attractive alternative; however, rec… Show more

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Cited by 6 publications
(16 citation statements)
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“…] can be determined using the REML or marginal maximum likelihood (MML) 222 estimation strategies as described byChen and Tempelman (2015) noting that we choose to fix223 …”
mentioning
confidence: 99%
“…] can be determined using the REML or marginal maximum likelihood (MML) 222 estimation strategies as described byChen and Tempelman (2015) noting that we choose to fix223 …”
mentioning
confidence: 99%
“…However, perhaps an even more serious concern with these EM schemes is that they are known to converge or get trapped at local modes that can be very different from global maxima (de los Campos et al 2013;Gianola 2013). This is a particular concern as much previous WGP EM-based research has been based on using g = 0 as starting values as noted by Chen and Tempelman (2015).…”
Section: Approximate Analytical Approachesmentioning
confidence: 99%
“…Kärkkäinen and Sillanpää (2012) deemed such a process to be nearly impossible. Chen and Tempelman (2015) propose a REML like strategy to estimate all elements of ϑ in a SSVS or BayesA WGP within an EM inference framework. An obvious limitation using EM is that uncertainty in the hyperparameters may not be adequately accounted for as it would be in a fully Bayesian analysis.…”
Section: Approximate Analytical Approachesmentioning
confidence: 99%
“…Nowadays, several statistical methods were used to develop genomic selection approach for numerous beef cattle breeds (SAATCHI et al, 2011;NEVES et al, 2014;CHEN et al, 2015;MEHRBAN et al, 2017;FARAH et al, 2016;WANG et al, 2019;MRODE et al, 2019).…”
Section: Introductionmentioning
confidence: 99%