ReviewTAG. Theoretical and applied genetics. Theoretische und angewandte Genetik2016
Walking through the statistical black boxes of plant breeding.
Review in TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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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
11 citing papers in PubMed.
- Models to estimate genetic gain of soybean seed yield from annual multi-environment field trials.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2023Article
- A statistical package for evaluation of hybrid performance in plant breeding via genomic selection.Scientific reports · 2023Article
- Adaptability and stability of Coffea canephora to dynamic environments using the Bayesian approach.Scientific reports · 2022Article
- Overview of Genomic Prediction Methods and the Associated Assumptions on the Variance of Marker Effect, and on the Architecture of the Target Trait.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Two decades of association mapping: Insights on disease resistance in major crops.Frontiers in plant science · 2022Review
- Additive genetic variance and covariance between relatives in synthetic wheat crosses with variable parental ploidy levels.Genetics · 2021Article
- Predicting biomass of rice with intermediate traits: Modeling method combining crop growth models and genomic prediction models.PloS one · 2020Article
- Training set determination for genomic selection.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2019Article
- Estimating variance components in population scale family trees.PLoS genetics · 2019Article
- Genome-Wide Analysis of Grain Yield Stability and Environmental Interactions in a Multiparental Soybean Population.G3 (Bethesda, Md.) · 2018Article
- Genomic prediction using subsampling.BMC bioinformatics · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
key messageThe main statistical procedures in plant breeding are based on Gaussian process and can be computed through mixed linear models. Intelligent decision making relies on our ability to extract useful information from data to help us achieve our goals more efficiently. Many plant breeders and geneticists perform statistical analyses without understanding the underlying assumptions of the methods or their strengths and pitfalls. In other words, they treat these statistical methods (software and programs) like black boxes. Black boxes represent complex pieces of machinery with contents that are not fully understood by the user. The user sees the inputs and outputs without knowing how the outputs are generated. By providing a general background on statistical methodologies, this review aims (1) to introduce basic concepts of machine learning and its applications to plant breeding; (2) to link classical selection theory to current statistical approaches; (3) to show how to solve mixed models and extend their application to pedigree-based and genomic-based prediction; and (4) to clarify how the algorithms of genome-wide association studies work, including their assumptions and limitations.
Indexed as
Identifiers
27435734What 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.