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Advances in Plant Genomics and Epigenomics in Breeding for Yield,...
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Advances in Plant Genomics and Epigenomics in Breeding for Yield, Quality, and Sustainability

A special issue of Genes (ISSN 2073-4425). This special issue belongs to the section "Plant Genetics and Genomics".

Deadline for manuscript submissions: closed (5 September 2023) | Viewed by 2867

Special Issue Editor

Dr. Aphrodite Tsaballa

E-Mail Website
Guest Editor
Institute of Plant Breeding and Genetic Resources, Hellenic Agricultural Organization–Demeter, Thermi, GR57001 Thessaloniki, Greece
Interests: plant breeding; genetics; genomics; epigenomics; fruit quality; vegetable grafting; transcriptomics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

Plant genomics is a continuously evolving and expanding area of plant science research. Assisted by advanced new technologies, DNA sequencing, and characterization of plant genomes have never been more complete. Moreover, epigenomics covers an exciting research field revolving around epigenetic changes, such as DNA methylation and small RNAs, across plant genomes. All this new knowledge is extremely valuable for modern plant breeding in dissecting complex plant characters related to yield, quality, and eventually adaptation, thus facilitating breeders’ work and efficiency. Integration of genomic information in modern breeding programs will aid the development of new cultivars that fit in sustainable agriculture. This Special Issue aims to collect original papers, reviews, and short communications that report novel findings related to the discovery, exploration, characterization, and utilization of genomics and epigenomics resources in crop breeding projects. Our goal is to collect the most recent advances in this field and compile a valuable collection of papers. 

Dr. Aphrodite Tsaballa
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Genes is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • genomics
  • epigenomics
  • plant breeding
  • DNA methylation
  • small RNAs
  • transcriptomics
  • adaptation

Published Papers (2 papers)

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Research

10 pages, 2023 KiB  
Article
Growth and DNA Methylation Alteration in Rice (Oryza sativa L.) in Response to Ozone Stress
by Hongyan Wang, Long Wang, Mengke Yang, Ning Zhang, Jiazhen Li, Yuqian Wang, Yue Wang, Xuewen Wang, Yanan Ruan and Sheng Xu
Genes 2023, 14(10), 1888; https://doi.org/10.3390/genes14101888 - 28 Sep 2023
Viewed by 932
Abstract
With the development of urban industrialization, the increasing ozone concentration (O3) at ground level stresses on the survival of plants. Plants have to adapt to ozone stress. DNA methylation is crucial for a rapid response to abiotic stress in plants. Little [...] Read more.
With the development of urban industrialization, the increasing ozone concentration (O3) at ground level stresses on the survival of plants. Plants have to adapt to ozone stress. DNA methylation is crucial for a rapid response to abiotic stress in plants. Little information is known regarding the epigenetic response of DNA methylation of plants to O3 stress. This study is designed to explore the epigenetic mechanism and identify a possible core modification of DNA methylation or genes in the plant, in response to O3 stress. We investigated the agronomic traits and genome-wide DNA methylation variations of the Japonica rice cultivar Nipponbare in response to O3 stress at three high concentrations (80, 160, and 200 nmol·mol−1), simulated using open-top chambers (OTC). The flag leaf length, panicle length, and hundred-grain weight of rice showed beneficial effects at 80 nmol·mol−1 O3 and an inhibitory effect at both 160 and 200 nmol·mol−1 O3. The methylation-sensitive amplified polymorphism results showed that the O3-induced genome-wide methylation alterations account for 14.72–15.18% at three different concentrations. Our results demonstrated that methylation and demethylation alteration sites were activated throughout the O3 stress, mainly at CNG sites. By recovering and sequencing bands with methylation alteration, ten stress-related differentially amplified sequences, widely present on different chromosomes, were obtained. Our findings show that DNA methylation may be an active and rapid epigenetic response to ozone stress. These results can provide us with a theoretical basis and a reference to look for more hereditary information about the molecular mechanism of plant resistance to O3 pollution. Full article
(This article belongs to the Special Issue Advances in Plant Genomics and Epigenomics in Breeding for Yield, Quality, and Sustainability)
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17 pages, 1169 KiB  
Article
A Comparison between Three Tuning Strategies for Gaussian Kernels in the Context of Univariate Genomic Prediction
by Osval A. Montesinos-López, Arron H. Carter, David Alejandro Bernal-Sandoval, Bernabe Cano-Paez, Abelardo Montesinos-López and José Crossa
Genes 2022, 13(12), 2282; https://doi.org/10.3390/genes13122282 - 03 Dec 2022
Cited by 1 | Viewed by 1577
Abstract
Genomic prediction is revolutionizing plant breeding since candidate genotypes can be selected without the need to measure their trait in the field. When a reference population contains both phenotypic and genotypic information, it is trained by a statistical machine learning method that is [...] Read more.
Genomic prediction is revolutionizing plant breeding since candidate genotypes can be selected without the need to measure their trait in the field. When a reference population contains both phenotypic and genotypic information, it is trained by a statistical machine learning method that is subsequently used for making predictions of breeding or phenotypic values of candidate genotypes that were only genotyped. Nevertheless, the successful implementation of the genomic selection (GS) methodology depends on many factors. One key factor is the type of statistical machine learning method used since some are unable to capture nonlinear patterns available in the data. While kernel methods are powerful statistical machine learning algorithms that capture complex nonlinear patterns in the data, their successful implementation strongly depends on the careful tuning process of the involved hyperparameters. As such, in this paper we compare three methods of tuning (manual tuning, grid search, and Bayesian optimization) for the Gaussian kernel under a Bayesian best linear unbiased predictor model. We used six real datasets of wheat (Triticum aestivum L.) to compare the three strategies of tuning. We found that if we want to obtain the major benefits of using Gaussian kernels, it is very important to perform a careful tuning process. The best prediction performance was observed when the tuning process was performed with grid search and Bayesian optimization. However, we did not observe relevant differences between the grid search and Bayesian optimization approach. The observed gains in terms of prediction performance were between 2.1% and 27.8% across the six datasets under study. Full article
(This article belongs to the Special Issue Advances in Plant Genomics and Epigenomics in Breeding for Yield, Quality, and Sustainability)
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