ArticleHeredity2020
Optimizing whole-genomic prediction for autotetraploid blueberry breeding.
Article in Heredity, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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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.
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Who cites it
28 citing papers in PubMed, 51 citations in OpenAlex.
- Genetic control underlying dynamics of blueberry bioactive compounds during postharvest can drive breeding for functional fruits.The plant genome · 2026Article
- Genetic insights into bacterial wilt resistance using genomic prediction and association mapping in blueberry.The plant genome · 2026Article
- Imputef: imputation of polyploid genotype classes and allele frequencies.BMC genomics · 2025Article
- Transferability of genomic prediction models across market segments in potato and the effect of selection.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- Genomic prediction and association analyses for breeding parthenocarpic blueberries.Horticulture research · 2025Article
- Genetic architecture and genomic prediction for yield, winter damage, and digestibility traits in timothy (Phleum pratense L.) using genotyping-by-sequencing data.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- Exploiting historical agronomic data to develop genomic prediction strategies for early clonal selection in the Louisiana sugarcane variety development program.The plant genome · 2025Article
- Within-family genomic selection in strawberry: Optimization of marker density, trial design, and training set composition.The plant genome · 2025Article
- Optimization of high-throughput marker systems for genomic prediction in alfalfa family bulks.The plant genome · 2025Article
- Genomic selection optimization in blueberry: Data-driven methods for marker and training population design.The plant genome · 2024Article
- A marker weighting approach for enhancing within-family accuracy in genomic prediction.G3 (Bethesda, Md.) · 2024Article
- polyGBLUP: a modified genomic best linear unbiased prediction improved the genomic prediction efficiency for autopolyploid species.Briefings in bioinformatics · 2024Article
- To be or not to be tetraploid-the impact of marker ploidy on genomic prediction and GWAS of potato.Frontiers in plant science · 2024Article
- Impact of genotype-calling methodologies on genome-wide association and genomic prediction in polyploids.The plant genome · 2023Article
- An Efficient Transformation System for Fast Production ofPlants (Basel, Switzerland) · 2023Article
- Genomic prediction for complex traits across multiples harvests in alfalfa (Medicago sativa L.) is enhanced by enviromics.The plant genome · 2023Article
- Genome-wide family prediction unveils molecular mechanisms underlying the regulation of agronomic traits inFrontiers in plant science · 2023Article
- Boosting grapevine breeding for climate-smart viticulture: from genetic resources to predictive genomics.Frontiers in plant science · 2023Review
- Genomic prediction for canopy height and dry matter yield in alfalfa using family bulks.The plant genome · 2022Article
- There and back again; historical perspective and future directions forHorticulture research · 2022Article
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 at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Blueberry (Vaccinium spp.) is an important autopolyploid crop with significant benefits for human health. Apart from its genetic complexity, the feasibility of genomic prediction has been proven for blueberry, enabling a reduction in the breeding cycle time and increasing genetic gain. However, as for other polyploid crops, sequencing costs still hinder the implementation of genome-based breeding methods for blueberry. This motivated us to evaluate the effect of training population sizes and composition, as well as the impact of marker density and sequencing depth on phenotype prediction for the species. For this, data from a large real breeding population of 1804 individuals were used. Genotypic data from 86,930 markers and three traits with different genetic architecture (fruit firmness, fruit weight, and total yield) were evaluated. Herein, we suggested that marker density, sequencing depth, and training population size can be substantially reduced with no significant impact on model accuracy. Our results can help guide decisions toward resource allocation (e.g., genotyping and phenotyping) in order to maximize prediction accuracy. These findings have the potential to allow for a faster and more accurate release of varieties with a substantial reduction of resources for the application of genomic prediction in blueberry. We anticipate that the benefits and pipeline described in our study can be applied to optimize genomic prediction for other diploid and polyploid species.
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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.