ReviewGenetics2009
Additive genetic variability and the Bayesian alphabet.
Review in Genetics, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 235 papers, 1 of them a synthesis that pooled it.
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
235 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Fast analysis of biobank-size data and meta-analysis using the BGLR R-package.G3 (Bethesda, Md.) · 2025Pooled it
- The Role of Prior-Induced Regularization in Accuracy and Stability of Genomic Prediction Across Unimodal and Multimodal Models.Plants (Basel, Switzerland) · 2026Article
- From linear models to deep learning: statistical advances in genomic selection for animal breeding.Journal of animal science and biotechnology · 2026Review
- OptimGS: a dual integrative genomic prediction framework for improving cold stress tolerance in wheat.Briefings in bioinformatics · 2026Article
- 2NPLGBM: a genomic model that merges the strengths of classical and machine learning methods in genomic prediction.Plant methods · 2026Article
- Genetic prediction with ARG-powered linear algebra.Genetics · 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
- Article
- Decoding Complex Traits in Goats Through Genome-Wide Association Studies: Progress, Challenges, and Perspectives.International journal of molecular sciences · 2026Review
- MeNet: A mixed-effect deep neural network for multi-environment genomic prediction of agronomic traits.Plant communications · 2026Article
- Sharing approaches in predictive genomics across animals, plants and humans.Nature genetics · 2026Review
- Opportunities and computational challenges in large-scale whole-genome sequencing data analysis.Journal of animal science · 2026Review
- Estimating the additive genetic variance for relative fitness from changes in allele frequency.Genetics · 2026Article
- AI-integrated digital breeding for crop improvement.Frontiers in plant science · 2026Review
- Derivation of prediction error variance for non-genotyped individuals in genomic selection.Frontiers in genetics · 2026Article
- The effect of haplotype size on genomic selection accuracy and epistasis: An empirical study in rice.The plant genome · 2025Article
- Decoding hybrid origins and genetic architecture of leaf traits variation inHorticulture research · 2025Article
- Ensemble of Bayesian alphabets via constraint weight optimization strategy improves genomic prediction accuracy.G3 (Bethesda, Md.) · 2025Article
- Semi-parametric validation of genomic predictions and polygenic risk scores with the Blupf90 software suite.G3 (Bethesda, Md.) · 2025Article
- Evaluating the effectiveness of selection indices and their genomic prediction using environmental and historical rice data.G3 (Bethesda, Md.) · 2025Article
175 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
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The use of all available molecular markers in statistical models for prediction of quantitative traits has led to what could be termed a genomic-assisted selection paradigm in animal and plant breeding. This article provides a critical review of some theoretical and statistical concepts in the context of genomic-assisted genetic evaluation of animals and crops. First, relationships between the (Bayesian) variance of marker effects in some regression models and additive genetic variance are examined under standard assumptions. Second, the connection between marker genotypes and resemblance between relatives is explored, and linkages between a marker-based model and the infinitesimal model are reviewed. Third, issues associated with the use of Bayesian models for marker-assisted selection, with a focus on the role of the priors, are examined from a theoretical angle. The sensitivity of a Bayesian specification that has been proposed (called "Bayes A") with respect to priors is illustrated with a simulation. Methods that can solve potential shortcomings of some of these Bayesian regression procedures are discussed briefly.
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.