Evidence map›Paper›PMID 39052988›Full record

ArticleG3 (Bethesda, Md.)2024

Understanding the genetic basis of blueberry postharvest traits to define better breeding strategies.

Gonzalo Casorzo, Luis Felipe Ferrão, Paul Adunola, Estefania Tavares Flores, Camila Azevedo, Rodrigo Amadeu, Patricio R Munoz

Abstract read
In one paragraph

Article in G3 (Bethesda, Md.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Gonzalo CasorzoHorticultural Sciences Department, University of Florida, Gainesville, FL 32608, USA.ORCID 0009-0001-9994-4874
Luis Felipe FerrãoHorticultural Sciences Department, University of Florida, Gainesville, FL 32608, USA.ORCID 0000-0002-9655-4838
Paul AdunolaHorticultural Sciences Department, University of Florida, Gainesville, FL 32608, USA.
Estefania Tavares FloresHorticultural Sciences Department, University of Florida, Gainesville, FL 32608, USA.
Camila AzevedoDepartment of Statistics, Federal University of Viçosa, Viçosa 36570, Brazil.
Rodrigo AmadeuHorticultural Sciences Department, University of Florida, Gainesville, FL 32608, USA.
Patricio R MunozHorticultural Sciences Department, University of Florida, Gainesville, FL 32608, USA.ORCID 0000-0001-8973-9351

Funding

Blueberry Breeding Program
6 · The paper itself

Abstract

Blueberry (Vaccinium spp.) is among the most-consumed soft fruit and has been recognized as an important source of health-promoting compounds. Highly perishable and susceptible to rapid spoilage due to fruit softening and decay during postharvest storage, modern breeding programs are looking to maximize the quality and extend the market life of fresh blueberries. However, it is uncertain how genetically controlled postharvest quality traits are in blueberries. This study aimed to investigate the prediction ability and the genetic basis of the main fruit quality traits affected during blueberry postharvest to create breeding strategies for developing cultivars with an extended shelf life. To achieve this goal, we carried out target genotyping in a breeding population of 588 individuals and evaluated several fruit quality traits after 1 day, 1 week, 3 weeks, and 7 weeks of postharvest storage at 1°C. Using longitudinal genome-based methods, we estimated genetic parameters and predicted unobserved phenotypes. Our results showed large diversity, moderate heritability, and consistent predictive accuracies along the postharvest storage for most of the traits. Regarding the fruit quality, firmness showed the largest variation during postharvest storage, with a surprising number of genotypes maintaining or increasing their firmness, even after 7 weeks of cold storage. Our results suggest that we can effectively improve the blueberry postharvest quality through breeding and use genomic prediction to maximize the genetic gains in the long term. We also emphasize the potential of using longitudinal genomic prediction models to predict the fruit quality at extended postharvest periods by integrating known phenotypic data from harvest.

Indexed as

Blueberry PlantsFruitPhenotypePlant BreedingQuantitative Trait, HeritableGenotypePolymorphism, Single NucleotideQuantitative Trait Lociblueberryfirmnessfruit qualityGenomic PredictionGenPredheritabilitymixed modelsplant breedingpostharvestquantitative geneticsrepeated measuresShared Data Resourcessoftening

Identifiers

PMID39052988
PMCPMC11373639

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.