2011
DOI: 10.1038/hdy.2011.86
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Bias correction for estimated QTL effects using the penalized maximum likelihood method

Abstract: A penalized maximum likelihood method has been proposed as an important approach to the detection of epistatic quantitative trait loci (QTL). However, this approach is not optimal in two special situations: (1) closely linked QTL with effects in opposite directions and (2) small-effect QTL, because the method produces downwardly biased estimates of QTL effects. The present study aims to correct the bias by using correction coefficients and shifting from the use of a uniform prior on the variance parameter of a… Show more

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Cited by 10 publications
(9 citation statements)
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References 35 publications
(54 reference statements)
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“…There are several methods available in the estimation of parameters in model (2), e.g., penalized maximum likelihood [ 9 , 82 ], empirical Bayesian [ 10 ], hierarchical generalized linear model [ 83 , 84 ]. Here we adopt empirical Bayesian, for technical detail the reader is referred to the original study of Xu [ 10 ].…”
Section: Methodsmentioning
confidence: 99%
“…There are several methods available in the estimation of parameters in model (2), e.g., penalized maximum likelihood [ 9 , 82 ], empirical Bayesian [ 10 ], hierarchical generalized linear model [ 83 , 84 ]. Here we adopt empirical Bayesian, for technical detail the reader is referred to the original study of Xu [ 10 ].…”
Section: Methodsmentioning
confidence: 99%
“…model selection and shrinkage estimation. The latter includes the fully Bayesian [37], the penalized maximum likelihood [38,39], least absolute shrinkage and selection operator, and empirical Bayes (E-Bayes) [32,40]. The fully Bayesian method should be optimal in theory because it captures the most information from the data, but it is computationally intensive; the penalized maximum likelihood is an approximation to the fully Bayesian approach, while its main advantages over other methods are reflected by its simplicity and fast speed; least absolute shrinkage and selection operator is a good approach but the shrinkage is too weak for spurious effects and too strong for large effects; and the E-Bayes provides the optimal estimates of variance components, because shrinkage is very selective, with large effects subject to virtually no shrinkage while small effects are shrunk to zero [40].…”
Section: Genetic Parameter Estimationmentioning
confidence: 99%
“…Although the current single variant methods of GWAS have succeeded in identifying QTNs associated with the interested traits, these approaches fail to consider the joint minor effect of multiple genetic markers on a trait ( Tamba et al, 2017 ); furthermore, they do not match the internal genetic mechanism of these quantitative traits ( Tamba et al, 2017 ; Zhang et al, 2017 ; Sun et al, 2019 ; Wen et al, 2019 ). To overcome this drawback, multi-locus methodologies have been developed, such as least absolute shrinkage and selection operator (lasso) ( Tibshirani, 1996 ; Xu, 2010 ; Zhang et al, 2012 ), Bayesian lasso ( Yi and Xu, 2008 ), adaptive mixed lasso ( Wang et al, 2011 ), and empirical Bayes ( Xu, 2007 ). All SNPs can be included in the model and can be simultaneously estimated by using multi-locus methodologies.…”
Section: Introductionmentioning
confidence: 99%