Genomics and phenomics of root system architecture in grapevine

Abstract

Adapting viticulture to climate change is crucial, as it presents significant challenges for future grape production. One key challenge is cultivating grapevines under limited water availability, driven by irregular rainfall patterns, stricter water-use regulations, and hotter, drier conditions. Developing drought-tolerant rootstocks has, therefore, become a major objective in grapevine breeding. However, this effort is constrained by the complexity of phenotyping; evaluating drought tolerance in dry field conditions for thousands of genotypes is both time- and cost-intensive. To make drought tolerance a practical breeding target, it must be broken down into constituent traits, such as root architecture, which is closely linked to drought tolerance and the plant’s capacity to access water in soil zones with stable reserves. These traits can then be reliably assessed in small plants at high throughput. This study aimed to develop a low-cost methodology that combines minirhizotrons, AI-powered computer vision, and genetics to characterize root system architecture and root growth dynamics across commercial rootstocks and a mapping population derived from 110R (V. berlandieri × V. rupestris) and 101-14Mgt (V. riparia × V. rupestris), two rootstocks with contrasting root architectures and hydraulic characteristics. We will share how this study revealed substantial variability in root architecture and growth dynamics across the evaluated germplasm. Leveraging genotypic data from the mapping population, we identified genomic regions associated with root architecture, creating new opportunities for applying molecular breeding strategies to accelerate rootstock development. We will also discuss how the low-cost, high-throughput phenotyping methodology developed in this study holds promise for expanding our understanding of root hydraulic characteristics and morphology, which is essential for breeding efforts and the genetic mapping of other, more complex traits.