ArticleTAG. Theoretical and applied genetics. Theoretische und angewandte Genetik2012
Swift block-updating EM and pseudo-EM procedures for Bayesian shrinkage analysis of quantitative trait loci.
Article in TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- Local and Bayesian Survival FDR Estimations to Identify Reliable Associations in Whole Genome of Bread Wheat.International journal of molecular sciences · 2023Article
- Data-adaptive Shrinkage via the Hyperpenalized EM Algorithm.Statistics in biosciences · 2015Article
- Prediction of complex human diseases from pathway-focused candidate markers by joint estimation of marker effects: case of chronic fatigue syndrome.Human genomics · 2015Article
- Article
- A decision rule for quantitative trait locus detection under the extended Bayesian LASSO model.Genetics · 2012Article
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Authors and funding
2 authors.
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Abstract
introductionVirtually all existing expectation-maximization (EM) algorithms for quantitative trait locus (QTL) mapping overlook the covariance structure of genetic effects, even though this information can help enhance the robustness of model-based inferences.
resultsHere, we propose fast EM and pseudo-EM-based procedures for Bayesian shrinkage analysis of QTLs, designed to accommodate the posterior covariance structure of genetic effects through a block-updating scheme. That is, updating all genetic effects simultaneously through many cycles of iterations.
conclusionSimulation results based on computer-generated and real-world marker data demonstrated the ability of our method to swiftly produce sensible results regarding the phenotype-to-genotype association. Our new method provides a robust and remarkably fast alternative to full Bayesian estimation in high-dimensional models where the computational burden associated with Markov chain Monte Carlo simulation is often unwieldy. The R code used to fit the model to the data is provided in the online supplementary material.
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