ArticleGenetics2012
A decision rule for quantitative trait locus detection under the extended Bayesian LASSO model.
Article in Genetics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Large, regionally variable shifts in diatom and dinoflagellate biomass in the North Atlantic over six decades.PloS one · 2025Article
- Identifying the Driving Factors of Black Bloom in Lake Bay through Bayesian LASSO.International journal of environmental research and public health · 2019Article
- A fast algorithm for Bayesian multi-locus model in genome-wide association studies.Molecular genetics and genomics : MGG · 2017Article
- Article
- Next generation modeling in GWAS: comparing different genetic architectures.Human genetics · 2014Article
- Impact of prior specifications in a shrinkage-inducing Bayesian model for quantitative trait mapping and genomic prediction.Genetics, selection, evolution : GSE · 2013Article
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Authors and funding
2 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Bayesian shrinkage analysis is arguably the state-of-the-art technique for large-scale multiple quantitative trait locus (QTL) mapping. However, when the shrinkage model does not involve indicator variables for marker inclusion, QTL detection remains heavily dependent on significance thresholds derived from phenotype permutation under the null hypothesis of no phenotype-to-genotype association. This approach is computationally intensive and more importantly, the hypothetical data generation at the heart of the permutation-based method violates the Bayesian philosophy. Here we propose a fully Bayesian decision rule for QTL detection under the recently introduced extended Bayesian LASSO for QTL mapping. Our new decision rule is free of any hypothetical data generation and relies on the well-established Bayes factors for evaluating the evidence for QTL presence at any locus. Simulation results demonstrate the remarkable performance of our decision rule. An application to real-world data is considered as well.
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