Genetic control of sweetness and acidity in blackberry
Bibliographic record
Abstract
Introduction The global blackberry ( Rubus subgenus Rubus ) industry has experienced rapid growth during the past 20 years. Even so, many industry stakeholders report complaints from consumers and grocers stating that blackberries are often too tart or not sweet enough for their palate. Studies have shown that most consumers prefer sweet blackberries with relatively low acidity, but little research has been conducted to understand the genetic control of these traits in Rubus . Methods The objective of this study was to identify quantitative trait loci (QTL) related to sweetness and acidity in autotetraploid blackberry using a genome-wide association study (GWAS). A panel consisting of 301 commercially-available cultivars and University of Arkansas System Division of Agriculture (UA) breeding selections was phenotyped for soluble solids content (SSC), pH, and titratable acidity (TA) from 2019–2021 and genotyped with 65,995 SNPs concentrated in genic regions of the R. argutus reference genome. Results The average SSC, pH, and TA for the blackberry genotypes evaluated in this study were 10.8%, 3.61, and 0.83%, respectively. Soluble solids content had the lowest broad sense entry mean heritability at 61%, while pH and TA had heritabilities of 67% and 70%, respectively. Four SNPs on chromosome Ra02 were significantly associated with SSC, explaining 7.2% of the variance for this trait. An overlapping QTL for pH and TA, comprised of 187 SNPs significantly associated with one or both traits across a 3.7 Mb region, was discovered on chromosome Ra05. Two peak SNPs located at 4,448,123 and 4,448,155 bp on Ra05 explained 13.6% and 11.5% of observed phenotypic variance for pH and TA, respectively. A gene coding for a sucrose binding protein on chromosome Ra02 was identified as a possible candidate gene for SSC. Seven potential candidate genes for fruit acidity were proposed on chromosome Ra05, including homologs of MYB1, PEPC, malate synthase, and ALMT9. Discussion This work provides important insights on the genetic control of sweetness and acidity in fresh-market blackberries. The genotypic and phenotypic datasets reported in this study can be used to develop diagnostic molecular markers and train genomic selection models to expedite selection of new blackberry cultivars with enhanced sweetness and reduced acidity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".