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IVES 9 IVES Conference Series 9 Grapevine Breeding and Genetics 9 Grapevine Breeding and Genetics 2026 9 GBG 2026 – Session 2: Genomics and functional genetics 9 A curation framework for a genome-anchored grapevine QTL browser: insights from phenology

A curation framework for a genome-anchored grapevine QTL browser: insights from phenology

Abstract

Over the past few decades, Quantitative Trait Loci (QTL) mapping has been widely applied in grapevine to investigate the genetic architecture of complex traits. However, this extensive body of information has been only partially exploited in genetic research and breeding programs, mainly due to poor data accessibility, heterogeneous reporting formats, limited QTL mapping resolution, and variable robustness of QTL detection. To address these limitations and promote FAIR (Findable, Accessible, Interoperable and Reusable) use of existing data, QTL experts from around the world have joined forces, building on the CIG GRAPEDIAinitiative, to lay the foundation for a community-accessible Grapevine QTL Browser (https://grapedia.org/qtl-browser/).

QTL data from more than 150 peer-reviewed studies published between 2000 and 2025 were manually surveyed and curated, comprising several thousand QTLs spanning seven major trait classes (abiotic stress, agronomical, biochemical, biotic stress, morphological, phenological, and technological traits). To this end, a structured annotation scheme comprising approximately 70 standardized descriptors was defined, covering phenotypic variables and protocols, genetic materials, and QTL mapping parameters and outputs. Annotations were harmonized, whenever possible, with existing ontologies and public databases. Adedicated web-based frontend was developed to support structured data upload and expert curation.

As a proof of concept, phenology-related QTLs were analyzed in a pilot study. The dataset comprised approximately 500 QTLs associated with five major developmental stages (budbreak, flowering, veraison, ripening, and end of the vegetative season), derived from 13 biparental populations and three association panels. This case study highlights the potential of the Grapevine QTL Browser for trait-specific QTLome compilation, cross-study QTL validation, and refinement of genomic intervals for candidate gene prioritization. Additional key developments enabled by this framework include the extension of the Vitis Ontology and the integration of the Grapevine QTL Browser with the Grapevine Gene Catalogue.

Overall, the Grapevine QTL Browser represents a scalable resource for systematic QTL integration and provides a foundation for linking bi-parental population studies, GWAS (Genome-Wide Association Studies), and functional genomics resources to support grapevine research and breeding.

Publication date: June 22, 2026

Issue: GBG 2026

Type: Poster

Authors

Laura Costantini1,*, Diana Bellin2, Paola Bettinelli1, Amandine Velt3, Éric Duchêne3, Franco Röckel4, Ludger Hausmann4, Javier Tello5, Pablo Carbonell-Bejerano5, Elsa Chedid6, Marina de Miguel6, Elisa Marguerit6, Agnès Doligez7, Varoostha Govender8, Justin Lashbrooke8, Patricio Hinrichsen9, Marco Meneses9, Tom Heinekamp4, Anna Werner4, Marko Medic4, Leonie Chretien3, Daniela Holtgräwe10, Noam Reshef11, Nadav Vulkan11, Chin Feng Hwang12, Ira Herniter13, Dustin Guy Wilkerson13, Aliyah Brewer13, Silvas Kirubakaran13, Lance Cadle-Davidson13, Patricia Weber14, Anne Fennell15, Claudia Muñoz-Espinoza16, Martina Marini2, Alessandra Amato2, Marianna Fasoli2, Tomas Matus17, Lakshay Anand18, Camille Rustenholz3, Marco Moretto1, Silvia Vezzulli1

1 Research and Innovation Centre, Fondazione Edmund Mach, San Michele all’Adige (Trento), Italy

2 Department of Biotechnology, University of Verona, Verona, Italy

3 SVQV, INRAE-University of Strasbourg, Colmar, France

4 Julius Kühn Institute (JKI) – Institute for Grapevine Breeding Geilweilerhof, Siebeldingen, Germany

5 Instituto de Ciencias de la Vid y del Vino (CSIC, UR, Gobierno de La Rioja), Logroño, Spain

6 EGFV, Univ. Bordeaux, Bordeaux Sciences Agro, INRAE, ISVV, F-33882, Villenave d’Ornon, France

7 AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France

8 Department of Genetics, Stellenbosch University, Matieland, South Africa

9 Instituto de Investigaciones Agropecuarias, INIA La Platina, Santiago, Chile

10 Genetics and Genomics of Plants, CeBiTec & Faculty of Biology, Bielefeld University, Bielefeld, Germany

11 Department of Postharvest Science, Agricultural Research Organization, The Volcani Center, Bet Dagan, Israel

12 State Fruit Experiment Station at Mountain Grove Campus, Missouri State University, Springfield, MO, USA

13 USDA-ARS Grape Genetics Research Unit, Geneva, NY, USA

14 State Education and Research Center of Viticulture, Horticulture and Rural Development, Institute of Plant Protection, 67435 Neustadt /Weinstraße, Germany

15 Agronomy, Horticulture, and Plant Science Department, South Dakota State University, Brookings, SD, United States

16 Agronomy Faculty, Universidad de Concepción, Chillán, Chile

17 Institute for Integrative Systems Biology (I2SysBio), Universitat de Valencia-CSIC, Paterna, Valencia, Spain

18 Environmental Epigenetics and Genetics Group, Department of Horticulture, University of Kentucky, Lexington, KY, USA

Contact the author*

Keywords

complex trait genetics, data integration, FAIR data principles, QTL database and browser, phenology

Tags

GBG | GBG 2026 | IVES Conference Series

Citation

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