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Vegetable Breeding, Genetics and Genomics, 2nd Volume
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Gus R. Douglass Institute and Department of Biology, West Virginia State University, Institute, WV 25112-1000, USA
Department of Biology, West Virginia State University, Institute, WV 25112-1000, USA
Laboratory of Vegetable Production, Department of Crop Science, Agricultural University of Athens, Iera Odos 75, 11855 Athens, Greece
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Vegetable Breeding, Genetics and Genomics, 2nd Volume

Abstract submission deadline
31 August 2025
Manuscript submission deadline
31 October 2025
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1781

Topic Information

Dear Colleagues,

In recent years, vegetable breeding has been driven by genomics and next-generation sequencing tools. NGS has advanced breeding to the next level, as orphan crops, or understudied vegetable and fruit crops, are currently being sequenced to generate gold-standard genome sequences, deep sequencing of germplasm collections and breeding populations. These resources were used to perform genomic-assisted selections, to identify deleterious alleles and targets for genome editing, and, most importantly, to identify lines with the highest breeding value based on genomic predictions. For this Topic, we invite papers addressing the development and use of whole-genome sequencing, SNP, or structural variants, genome-wide association studies, genomic predictions, QTL analysis, methods for analysis, review articles related to this topic, and methods to purge deleterious alleles from the crops, with special reference being made to vegetable crops.

Prof. Dr. Umesh K. Reddy
Prof. Dr. Padma Nimmakayala
Dr. Georgia Ntatsi
Topic Editors

Keywords

  • vegetable
  • breeding
  • quantitative trait locus (QTL) analysis
  • genetics and genomics
  • single nucleotide polymorphism (SNP)

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Agriculture
agriculture 		3.3 4.9 2011 19.2 Days CHF 2600 Submit
Agronomy
agronomy 		3.3 6.2 2011 17.6 Days CHF 2600 Submit
Crops
crops 		- - 2021 22.1 Days CHF 1000 Submit
Genes
genes 		2.8 5.2 2010 14.9 Days CHF 2600 Submit
Plants
plants 		4.0 6.5 2012 18.9 Days CHF 2700 Submit
DNA
dna 		- - 2021 23.3 Days CHF 1000 Submit

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Published Papers (1 paper)

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Article
Genotype-Driven Phenotype Prediction in Onion Breeding: Machine Learning Models for Enhanced Bulb Weight Selection
by Junhwa Choi, Sunghyun Cho, Subin Choi, Myunghee Jung, Yu-jin Lim, Eunchae Lee, Jaewon Lim, Han Yong Park and Younhee Shin
Agriculture 2024, 14(12), 2239; https://doi.org/10.3390/agriculture14122239 - 6 Dec 2024
Viewed by 756
Abstract
Onions (Allium cepa L.) are a globally significant horticultural crop, ranking second only to tomatoes in terms of cultivation and consumption. However, due to the crop’s complex genome structure, lengthy growth cycle, self-incompatibility, and susceptibility to disease, onion breeding is challenging. To [...] Read more.
Onions (Allium cepa L.) are a globally significant horticultural crop, ranking second only to tomatoes in terms of cultivation and consumption. However, due to the crop’s complex genome structure, lengthy growth cycle, self-incompatibility, and susceptibility to disease, onion breeding is challenging. To address these issues, we implemented digital breeding techniques utilizing genomic data from 98 elite onion lines. We identified 51,499 high-quality variants and employed these data to construct a genomic estimated breeding value (GEBV) model and apply machine learning methods for bulb weight prediction. Validation with 260 new individuals revealed that the machine learning model achieved an accuracy of 83.2% and required only thirty-nine SNPs. Subsequent in silico crossbreeding simulations indicated that offspring from the top 5% of elite lines exhibited the highest bulb weights, aligning with traditional phenotypic selection methods. This approach demonstrates that early-stage selection based on genotypic information followed by crossbreeding can achieve economically viable breeding results. This methodology is not restricted to bulb weight and can be applied to various horticultural traits, significantly improving the efficiency of onion breeding through advanced digital technologies. The integration of genomic data, machine learning, and computer simulations provides a powerful framework for data-driven breeding strategies, accelerating the development of superior onion varieties to meet global demand. Full article
(This article belongs to the Topic Vegetable Breeding, Genetics and Genomics, 2nd Volume)
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