Image-based Phenotyping Identifies Quantitative Trait Loci for Cluster Compactness in Grape

Abstract

Grape (Vitis vinifera) cluster compactness is an important trait due to its effect on disease susceptibility, but visual evaluation of compactness relies on human judgement and an ordinal scale that is not appropriate for all populations. We developed an image analysis pipeline and used it to quantify cluster compactness traits in a segregating hybrid wine grape (Vitis sp.) population for 2 years. Images were collected from grape clusters immediately after harvest, segmented by color, and analyzed using a custom script. Both automated and conventional phenotyping methods were used, and comparisons were made between each method. A partial least squares (PLS) model was constructed to evaluate the prediction of physical cluster compactness using image-derived measurements. Quantitative trait loci (QTL) on chromosomes 4, 9, 12, 16, and 17 were associated with both image-derived and conventionally phenotyped traits within years, which demonstrated the ability of image-derived traits to identify loci related to cluster morphology and cluster compactness. QTL for 20-berry weight were observed between years on chromosomes 11 and 17. Additionally, the automated method of cluster length measurement was highly accurate, with a deviation of less than 10 mm (r = 0.95) compared with measurements obtained with a hand caliper. A remaining challenge is the utilization of color-based image segmentation in a population that segregates for fruit color, which leads to difficulty in differentiating the stem from the fruit when the two are similarly colored in non-noir fruit. Overall, this research demonstrates the validity of image-based phenotyping for quantifying cluster compactness and for identifying QTL for the advancement of grape breeding efforts.

Candidate resistance genes to foliar phylloxera identified at Rdv3 of hybrid grape

“…Sequence variations could be compared at the candidate genes between MN1264 and MN1246 and their parents to provide further insights of inheritance of resistance. The selective phenotyping approach, using the fine mapping population we developed, will be a useful resource for identifying candidate genes for other traits previously mapped in GE1025, including color [ 41 ], trichome density [ 42 ], cluster compactness [ 43 ], and powdery mildew resistance [ 27 ].…”

Genetic mapping and selection of table grape fruit quality characteristics in a segregating population and evaluation of marker transferability

“…For cluster size, studies have identi ed QTL on chromosomes 14, 15, and 16 (Richter et al, 2019a,b). The QTL on 16 was also found in a separate study, along with a novel QTL on chromosome 9, though these QTLs were not consistent between years (Underhill et al, 2020). One study using a genome-wide association approach identi ed a novel locus on chromosome 13 for cluster weight but was unable to detect a signi cant QTL on chromosomes 9, 14, 15, or 16 (Laucou et al, 2018).…”

“…Unfortunately, many of these traits are associated with multiple small effect QTL dispersed across the grape genome. Flowering time and veraison time were associated with QTL of varying effects on chromosomes 1, 4, 8, 9, 10, 11, 13, 14, 17, 18, and 19, depending on the parentage of the cross (Fechter et al 2014; Kamal et al 2019; Underhill et al 2020). Two of those QTL on chromosomes 1 and 17 were consistently identi ed in two populations evaluated across two years (Fechter et al 2014).…”

Relieving the Phenotyping Bottleneck for Grape Bunch Architecture in Grapevine Breeding Research: Implementation of a 3D-Based Phenotyping Approach for Quantitative Trait Locus Mapping

“…This often limits the number of assessed samples per season and is recognized as the phenotypic bottleneck [18]. Several studies showed the potential of two-dimensional (2D)-based or three-dimensional (3D)-based sensor technologies to overcome the phenotyping bottleneck for grape-bunch-architecture-related traits [17,19,20]. As a result, a substantially increased number of phenotypic samples, together with a high precision of numeric data, are required to characterize single or multiple grape-bunch architecture-related traits.…”

“…To meet these challenging demands, high-throughput phenotyping methods are crucial to overcome this bottleneck by a substantial increase of the sample size and to facilitate phenomic and genomic studies. Recently, only a few studies have applied sensor-based phenotypic data for genetic analyses, which are mostly based on 2D-imaging methods for grapebunch phenotyping [20,23]. Richter et al used RGB images to quantify berry-related traits (e.g., the total number of berries and rachis-related traits) [23].…”