ReviewHeredity2020
Reinventing quantitative genetics for plant breeding: something old, something new, something borrowed, something BLUE.
Review in Heredity, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 84 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
84 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- Elucidating the genetic architecture of yield components in pyramidale wheat (Molecular breeding : new strategies in plant improvement · 2026Article
- Methodology for Selecting Stable UAV-Based Vegetation Indices for Prediction of Agronomic Variables in Maize Using a Multispectral Sensor.Plants (Basel, Switzerland) · 2026Article
- Genetic architecture of angular leaf spot resistance in cultivated strawberry shaped by epistasis and genotype-by-environment interactions.The plant genome · 2026Article
- A guide to plant breeding for animal breeders.G3 (Bethesda, Md.) · 2026Review
- Dissecting heterosis and combining ability for yield and TYLCV resistance in tomato under field conditions.BMC plant biology · 2026Article
- Spider Mite Response, Agronomic Performance, and Stability of aPlants (Basel, Switzerland) · 2026Article
- Genome-wide association study of resistance to taro leaf blight and yield traits in taro (Colocasia esculenta (L.) Schott).Scientific reports · 2026Article
- A bacterial ecocline in Klebsiella pneumoniae may explain its backboned phylogeny.PLoS biology · 2026Article
- Optimizing phenotypic data analysis strategies to enhance genomic prediction in winter wheat breeding.Frontiers in plant science · 2026Article
- Multiyear evaluation of agronomic traits, nutritional quality, macro and microelement profiles of white maize genotypes (Frontiers in plant science · 2026Article
- Article
- Dissecting the yield architecture and selecting superior seedling-derived half-sib progenies of turmeric (Frontiers in plant science · 2026Article
- MAGIC populations: a next-generation framework for dissecting complex quantitative traits and accelerating molecular breeding in crops.Frontiers in plant science · 2026Review
- Accelerated breeding modernization: a global blueprint for driving genetic gains, climate resilience, and food security in rice.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Review
- UAV-based multispectral image analysis revealed stay-green haplotypes in wheat specific for different soil nitrogen levels.BMC plant biology · 2025Article
- Order from entropy: big data from FAIR data cohorts in the digital age of plant breeding.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Review
- Genome-wide association analysis of winter survival in a diverse Canadian winter wheat population.The plant genome · 2025Article
- Harnessing big data for enhanced genome-wide prediction in winter wheat breeding.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- Breeding perspectives on tackling trait genome-to-phenome (G2P) dimensionality using ensemble-based genomic prediction.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Review
24 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
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The goals of quantitative genetics differ according to its field of application. In plant breeding, the main focus of quantitative genetics is on identifying candidates with the best genotypic value for a target population of environments. Keeping quantitative genetics current requires keeping old concepts that remain useful, letting go of what has become archaic, and introducing new concepts and methods that support contemporary breeding. The core concept of continuous variation being due to multiple Mendelian loci remains unchanged. Because the entirety of germplasm available in a breeding program is not in Hardy-Weinberg equilibrium, classical concepts that assume random mating, such as the average effect of an allele and additive variance, need to be retired in plant breeding. Doing so is feasible because with molecular markers, mixed-model approaches that require minimal genetic assumptions can be used for best linear unbiased estimation (BLUE) and prediction. Plant breeding would benefit from borrowing approaches found useful in other disciplines. Examples include reliability as a new measure of the influence of genetic versus nongenetic effects, and operations research and simulation approaches for designing breeding programs. The genetic entities in such simulations should not be generic but should be represented by the pedigrees, marker data, and phenotypic data for the actual germplasm in a breeding program. Over the years, quantitative genetics in plant breeding has become increasingly empirical and computational and less grounded in theory. This trend will continue as the amount and types of data available in a breeding program increase.
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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.