ArticleGenetics2008
Bayesian LASSO for quantitative trait loci mapping.
Article in Genetics, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 149 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
149 citing papers in PubMed.
- Functional genomic dissection and prediction of body size traits in pigs.Genetics, selection, evolution : GSE · 2026Article
- OptimGS: a dual integrative genomic prediction framework for improving cold stress tolerance in wheat.Briefings in bioinformatics · 2026Article
- Impact of scale parameter for marker variance prior in some Bayesian whole-genome regression methods.Mammalian genome : official journal of the International Mammalian Genome Society · 2026Article
- Integrating gene expression data via weighted multiple kernel ridge regression improved accuracy of genomic prediction.Genetics, selection, evolution : GSE · 2025Article
- Ensemble of Bayesian alphabets via constraint weight optimization strategy improves genomic prediction accuracy.G3 (Bethesda, Md.) · 2025Article
- Investigating the Performance of Frequentist and Bayesian Techniques in Genomic Evaluation.Biochemical genetics · 2025Article
- Development and Validation of Multi-Locus GWAS-Based KASP Markers for MaizePlants (Basel, Switzerland) · 2025Article
- Prioritized SNP Selection from Whole-Genome Sequencing Improves Genomic Prediction Accuracy in Sturgeons Using Linear and Machine Learning Models.International journal of molecular sciences · 2025Article
- Low density marker-based effectiveness and efficiency of early-generation genomic selection relative to phenotype-based selection in dolichos bean (Lablab purpureus L. Sweet).The plant genome · 2025Article
- ExAutoGP: Enhancing Genomic Prediction Stability and Interpretability with Automated Machine Learning and SHAP.Animals : an open access journal from MDPI · 2025Article
- Mutual information-based best linear unbiased prediction for enhanced genomic prediction accuracy.Journal of animal science · 2025Article
- Genome-Wide Association Study and Phenotype Prediction of Reproductive Traits in Large White Pigs.Animals : an open access journal from MDPI · 2024Article
- Dealing with adverse drug reactions in the context of polypharmacy using regression models.Scientific reports · 2024Article
- Ensemble learning for integrative prediction of genetic values with genomic variants.BMC bioinformatics · 2024Article
- Evaluation of eight Bayesian genomic prediction models for three micronutrient traits in bread wheat (Triticum aestivum L.).The plant genome · 2023Article
- Genomic prediction based on selective linkage disequilibrium pruning of low-coverage whole-genome sequence variants in a pure Duroc population.Genetics, selection, evolution : GSE · 2023Article
- Expanding the application of haplotype-based genomic predictions to the wild: A case of antibody response against Teladorsagia circumcincta in Soay sheep.BMC genomics · 2023Article
- Article
- ordinalbayes: Fitting Ordinal Bayesian Regression Models to High-Dimensional Data Using R.Stats · 2022Article
- Performance of Bayesian and BLUP alphabets for genomic prediction: analysis, comparison and results.Heredity · 2022Article
89 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
The mapping of quantitative trait loci (QTL) is to identify molecular markers or genomic loci that influence the variation of complex traits. The problem is complicated by the facts that QTL data usually contain a large number of markers across the entire genome and most of them have little or no effect on the phenotype. In this article, we propose several Bayesian hierarchical models for mapping multiple QTL that simultaneously fit and estimate all possible genetic effects associated with all markers. The proposed models use prior distributions for the genetic effects that are scale mixtures of normal distributions with mean zero and variances distributed to give each effect a high probability of being near zero. We consider two types of priors for the variances, exponential and scaled inverse-chi(2) distributions, which result in a Bayesian version of the popular least absolute shrinkage and selection operator (LASSO) model and the well-known Student's t model, respectively. Unlike most applications where fixed values are preset for hyperparameters in the priors, we treat all hyperparameters as unknowns and estimate them along with other parameters. Markov chain Monte Carlo (MCMC) algorithms are developed to simulate the parameters from the posteriors. The methods are illustrated using well-known barley data.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.