terclim by ICS banner
IVES 9 IVES Conference Series 9 Exploring high throughput secondary trait phenomics to improve grapevine breeding

Exploring high throughput secondary trait phenomics to improve grapevine breeding

Abstract

Modern grapevine breeding programs have overcome many challenges using genomic selection, which has allowed breeders to make targeted selections at earlier stages in the breeding process. However, the cost of genetic testing may present a burden for some programs, and markers often struggle to accurately predict quantitative traits. Recent advances in high throughput, high-dimensional data have provoked investigation into the use of high-dimensional phenomics as a low-cost addition to the grape breeder’s toolkit that may offer advantages in predicting quantitative traits. High-dimensional secondary trait (HDST) data has been employed in annual crops for prediction of agriculturally important traits such as yield. To explore the potential of HDST data in grapes, 1618 grapevine seeds and seedlings from six populations were evaluated using hyperspectral and high-dimensional HSV color data.  We show that HDST data are variable within seed populations. To start, we explore correlations of HDST data with early life stage traits, demonstrating potential to develop predictive models. Our work utilizes low-cost, high throughput data which has the potential to supplement genomic selection, allowing breeders to make decisions at the earliest stage in the breeding cycle. This work lays a foundation for the use of HDST data from seeds to predict traits in grapevine.

DOI:

Publication date: June 14, 2024

Issue: Open GPB 2024

Type: Poster

Authors

Danielle Hopkins1*, Matthew Rubin2, Allison Miller1,2

1 Department of Biology, Saint Louis University, St. Louis, MO
2 Donald Danforth Plant Science Center, St. Louis, MO

Contact the author*

Keywords

phenomic selection, high throughput phenotyping, high-dimensional data

Tags

IVES Conference Series | Open GPB | Open GPB 2024

Citation

Related articles…

Model comparison and parametrization strategies for accurate RGB‑Based grapevine phenology classification

Accurate monitoring of grapevine phenology is essential for vineyard management and for understanding seasonal responses to climate variability (Reis et al., 2020).

Reviving grapevine massal selection in the context of climate change: A strategy for resilient vineyards in the 21st century

Modern viticulture faces a critical paradox. The widespread adoption of clonal selection in the mid-20th century successfully standardized production and reduced viral incidence through sanitization and certification, but unintentionally created a genetically simplified vineyard landscape.

Diversity in grape composition for sugars and acidity opens options to mitigate the effect of warming during ripening

The marked climate change impact on vine and grape development (phenology, sugar content, acidity …) is one of the manifestations of Genotype X Environment X Management interactions importance in viticulture. Some practices, such as irrigation, can mitigate the effect of water deficit on grape development, but warming is much more difficult to challenge. High temperatures tend to alter the acid balance of the fruit with a parallel increase in sugar concentration. In the long term, genetic improvement to select varieties better coping with temperature elevation appear as a good option to support sustainable viticulture. Nevertheless, the existing phenotypic diversity for grape quality components that are influenced by temperature is poorly understood, which jeopardizes breeding strategies.

Implementation of a deep learning-based approach for detecting and localising automatically grapevine leaves with downy mildew symptoms

Grapevine downy mildew is a disease of foliage caused by Oomycete Plasmopara viticola an endoparasite that develops inside grapevine organs and can infect virtually every green organ. Downy mildew is one of the most destructive diseases in wine-growing regions, drastically reducing yield and fruit quality. Traditional manual disease detection relies on farm experts. Human field scouting has been widely used for monitoring the disease progress, however, is costly, laborious, subjective, and often imprecise.

Influence of polysaccharide extracts from wine by-products on the volatile composition of sparkling white wines

In the production of sparkling wines, during the second fermentation, mannoproteins are released by yeast autolysis, which affect the quality of the wines. The effect of mannoproteins has been extensively studied, and may affect aroma and foam quality. However, there are no studies on the effect of other polysaccharides such as those from grapes. Considering the large production of waste from the wine industry, it was proposed to obtain polysaccharide-rich extracts from some of these by-products[1].