Application of digital imaging and genomic predictive models for improving Fragaria vesca berry quality traits

Abstract

• The investigation of the phenotypic variation of F. vesca has provided significant insights into the factors influencing fruit traits, including shape, size, and overall quality. • Digital phenotyping has been proven to be more effective in distinguishing F. vesca fruit traits that are difficult to phenotype in the selection process. • The integration of digital phenotyping and genomic analysis have proven to be a powerful strategy for improving the selection of quality traits in F. vesca . • Genome-based breeding and predictive models facilitate the development of climate- resistant F. vesca cultivars that meet consumer preferences. The quality of Fragaria vesca berry fruits is an important factor in their marketability, and therefore, it has become a major target of breeding programs. However, berry traits are difficult to dissect due to the complex interaction of genetic and environmental factors. In this study, we evaluated phenotypic variation in commercially relevant traits, including shape, size, pH and total soluble solids (SSC) in an open-pollinated F. vesca population grown in a region characterized by high temperature fluctuations. The observed variability underscores the intricate interplay between genetic background and environmental factors on fruit morphology and quality traits. Digital imaging phenotyping proved to be a robust and objective approach for capturing morphological traits difficult to phenotype, providing quantitative data necessary for effective selection. Moreover, the ddRAD sequencing facilitated the identification of significant genetic diversity in a F. vesca population, generating approximately 4000 SNP polymorphic markers used to investigate the population structure and the potential of genomic models for selecting desirable traits, such as fruit shape and size. Several genomic selection models were tested to predict breeding values for fruit morphological traits. Prediction accuracy was substantially improved through training set optimization strategies, particularly those based on CDmean criteria. The integration of digital phenotyping, high-throughput genotyping and genomic predictive modelling has proven to be a powerful strategy for improving the selection of desirable traits in F. vesca . Overall, the findings from this study provide a foundation for further genetic improvement efforts, which will ultimately enhance the quality and marketability of strawberry cultivars.

Plant phenotyping relevance

イチゴ果実の形状・サイズを対象に、デジタル画像による表現型取得を明示的に評価しており、育種への応用だけでなく形態形質の定量的取得が研究の中心的要素である。

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