Genome-wide association identifies key loci controlling blackberry postharvest quality

Area of Science:

• Plant genetics
• Horticultural science
• Genomics

Background:

• Blackberry (Rubus subgenus Rubus) is a valuable specialty crop susceptible to significant economic losses during shipping due to postharvest degradation.
• Common issues include fruit leakage, decay, deformation, and discoloration, collectively known as red drupelet reversion (RDR).
• While traditional breeding has improved fruit firmness, the genetic basis for these traits remains largely unexplored.

Purpose of the Study:

• To conduct a genome-wide association study (GWAS) to identify genetic variants controlling fruit firmness and RDR in blackberries.
• To understand the genetic architecture underlying postharvest quality traits in fresh-market blackberries.
• To lay the groundwork for future genomic selection models aimed at enhancing blackberry postharvest characteristics.

Main Methods:

• A genome-wide association study was performed on 300 tetraploid fresh-market blackberry genotypes across three years (2019-2021).
• Genotyping involved 65,995 single nucleotide polymorphisms (SNPs) primarily located in genic regions of the R. argutus reference genome.
• Phenotypic data for fruit firmness and RDR were collected and analyzed in conjunction with genotypic data.

Main Results:

• Fruit firmness exhibited a heritability of 68%, while RDR had a heritability of 34%.
• Variants in homologs of polygalacturonase (PG), pectin methylesterase (PME), and glucan endo-1,3-β-glucosidase were associated with fruit firmness, explaining 27% of the variance.
• A PG homolog variant on chromosome Ra02 was linked to RDR, explaining 8% of variance, and significant negative correlation was observed between RDR and fruit firmness.

Conclusions:

• Postharvest quality traits in blackberry are complex, likely influenced by multiple quantitative trait loci (QTLs) with small effects.
• This study represents the first large-scale genetic mapping effort for quantitative traits in blackberry, establishing a foundation for future research.
• The generated phenotypic and genotypic data can be utilized to develop genomic selection models for improving postharvest quality in blackberry breeding programs.