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• W3149385462 abstract "Recently, the Bayesian least absolute shrinkage and selection operator (LASSO) has been successfully applied to multiple quantitative trait loci (QTL) mapping, which assigns the double-exponential prior and the Stu- dent's t prior to QTL effect that lead to the shrinkage estimate of QTL effect. However, as reported by many researchers, the Bayesian LASSO usually cannot effec- tively shrink the effects of zero-effect QTL very close to zero. In this study, the double-exponential prior and Stu- dent's t prior are modified so that the estimate of the effect for zero-effect QTL can be effectively shrunk toward zero. It is also found that the Student's t prior is virtually the same as the Jeffreys' prior, since both the shape and scale parameters of the scaled inverse Chi-square prior involved in the Student's t prior are estimated very close to zero. Besides the two modified Bayesian Markov chain Monte Carlo (MCMC) algorithms, an expectation-maximization (EM) algorithm with use of the modified double-expo- nential prior is also adapted. The results shows that the three new methods perform similarly on true positive rate and false positive rate for QTL detection, and all of them outperform the Bayesian LASSO." @default.
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• W3149385462 date "2012-01-01" @default.
• W3149385462 modified "2023-09-27" @default.
• W3149385462 title "Improved LASSO priors for shrinkage quantitative trait loci mapping" @default.
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