ArticleThe plant genome2020
Implementing within-cross genomic prediction to reduce oat breeding costs.
Article in The plant genome, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Improving grain yield prediction in Southern US oat germplasm using genomics information and environmental covariates.The plant genome · 2026Article
- Optimising realised genetic gain with low forward predictive ability across cycles for cooking time and correlated traits in common bean based on multivariate genomic selection.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- Accelerating genetic gain through integrated genomic selection in crop plants.Journal of applied genetics · 2026Review
- Effectiveness of low-density high-throughput marker platform and easy-to-measure traits for genomic prediction of biomass yield in oat (Avena sativa L.).The plant genome · 2026Article
- DHFS-ECM: Design of a Dual Heuristic Feature Selection-based Ensemble Classification Model for the Identification of Bamboo Species from Genomic Sequences.Current genomics · 2024Article
- Review of applications of artificial intelligence (AI) methods in crop research.Journal of applied genetics · 2024Review
- Genomic prediction of seed nutritional traits in biparental families of oat (Avena sativa).The plant genome · 2023Article
- Heuristic Analysis of Genomic Sequence Processing Models for High Efficiency Prediction: A Statistical Perspective.Current genomics · 2022Review
- Genomic prediction and training set optimization in a structured Mediterranean oat population.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2021Article
- A New Deep Learning Calibration Method Enhances Genome-Based Prediction of Continuous Crop Traits.Frontiers in genetics · 2021Article
- Implementing within-cross genomic prediction to reduce oat breeding costs.The plant genome · 2020Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
A barrier to the adoption of genomic prediction in small breeding programs is the initial cost of genotyping material. Although decreasing, marker costs are usually higher than field trial costs. In this study we demonstrate the utility of stratifying a narrow-base biparental oat population genotyped with a modest number of markers to employ genomic prediction at early and later generations. We also show that early generation genotyping data can reduce the number of lines for later phenotyping based on selections of siblings to progress. Using sets of small families selected at an early generation could enable the use of genomic prediction for adaptation to multiple target environments at an early stage in the breeding program. In addition, we demonstrate that mixed marker data can be effectively integrated to combine cheap dominant marker data (including legacy data) with more expensive but higher density codominant marker data in order to make within generation and between lineage predictions based on genotypic information. Taken together, our results indicate that small programs can test and initiate genomic predictions using sets of stratified, narrow-base populations and incorporating low density legacy genotyping data. This can then be scaled to include higher density markers and a broadened population base.
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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.