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Defining Strawberry Uniformity using 3D Imaging and Genetic Mapping

By Bo Li, Helen M. Cockerton, Abigail W Johnson, Amanda Karlström, Eleftheria Stavridou, Greg Deakin, Richard J. Harrison

Posted 03 Mar 2020
bioRxiv DOI: 10.1101/2020.03.01.972190

Strawberry uniformity is a complex trait, influenced by multiple genetic and environmental components. To complicate matters further, the phenotypic assessment of strawberry uniformity is confounded by the difficulty of quantifying geometric parameters ‘by eye’ and variation between assessors. An in-depth genetic analysis of strawberry uniformity has not been undertaken to date, due to the lack of accurate and objective data. Nonetheless, uniformity remains one of the most important fruit quality selection criteria for the development of a new variety. In this study, a 3D-imaging approach was developed to characterise berry uniformity. We show that circularity of the maximum circumference had the closest predictive relationship with the manual uniformity score. Combining five or six automated metrics provided the best predictive model, indicating that human assessment of uniformity is highly complex. Furthermore, visual assessment of strawberry fruit quality in a multi-parental QTL mapping population has allowed the identification of genetic components controlling uniformity. A “regular shape” QTL was identified and found to be associated with three uniformity metrics. The QTL was present across a wide array of germplasm, indicating a strong candidate for marker-assisted breeding. A greater understanding of berry uniformity has been achieved through the study of the relative impact of automated metrics on human perceived uniformity. Furthermore, the comprehensive definition of strawberry uniformity using 3D imaging tools has allowed precision phenotyping, which has improved the accuracy of trait quantification. This tool has allowed us to illustrate the use of advanced image analysis towards the breeding of greater uniformity in strawberry. * CIM : composite interval mapping CIR : Circularity CV_A : Coefficient of Variation of side view areas CV_C : Coefficient of Variation of curvatures CV_D : Coefficient of Variation of principal orientations i35k : Istraw35 Affymetrix chip L/W : Aspect ratio of the minimum bounding box Max\_A/Min\_A : ratio between maximum and minimum side view areas Max\_C/Min\_C : ratio between maximum and minimum curvatures MedR : Median number of roots QTL : Quantitative Trait Loci QR : Quick Response RGB : Red Green Blue SNP : Single Nucleotide Polymorphism STR : Straightness of centre axis

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