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Applications of Advanced Genomic and Phenomic Technologies for Plant Improvement II

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Agricultural Science and Technology".

Deadline for manuscript submissions: closed (31 March 2023) | Viewed by 13927

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Special Issue Editor

Prof. Dr. Alexios Polidoros

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Guest Editor

Laboratory of Genetics and Plant Breeding, Faculty of Agriculture, Forestry and Natural Environment, Aristotle University of Thessaloniki, 541 24 Thessaloniki, Greece
Interests: plant’s ability to cope with stress, including genetic and epigenetic responses, establishment of principles and criteria for breeding stress-tolerant crop plants; integration of genomic, transcriptomic, and phenomic analysis in modern breeding
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Special Issue Information

Dear Colleagues,

Global agriculture is facing three colossal challenges: an increasing world population, the adverse effects of climate change, and the diminishing genetic resources for cultivated species. Thus, there is an urgent need to find innovative solutions to rise agricultural production and maintain food security.

Plant breeding has always been the only resource of suitable plant types for cultivation. Conventional breeding has been an art and science performed over the last century by specialists and trained scientists reliant on plant phenotype to select the best individuals for cultivar improvement. However, because of the intervention of unstable environmental conditions, plant phenotype is not always a good representation of a superior genotype. Modern plant breeding is minimizing its “art” component, taking advantage of the scientific progress and novel analytical methods to enable the prediction of plant performance from biological entities. In other words, it seeks to increase the accuracy of prediction of plant performance by accurately associating an individual genotype to a specific phenotype.

Genomic tools based on next-generation sequencing (NGS) technologies, such as whole-genome sequencing, de novo sequencing and re-sequencing, transcriptome sequencing (mRNA-seq and/or miRNA-seq), target re-sequencing, exome and reduced-representation sequencing, gene panel and amplicon sequencing, generate a wealth of genomic information for virtually any crop. Combined with precise high-throughput phenotyping systems referred to as phenomics, these innovative technologies provide analytical tools for predicting plant performance from genetic data.

Genomics and phenomics, with the support of powerful bioinformatic and image analysis software, can revolutionize crop improvement by identifying the genetic basis of agriculturally important traits and increase the genetic gain by direct genotypic selection of high-breeding-value individuals in a plant breeding population.

This Special Issue will provide a platform to present research results in all applications of advanced genomics and phenomics tools related to crop improvement and to discuss current trends and future prospects of progress in these fields for modern plant breeding.

Prof. Dr. Alexios Polidoros
Guest Editor

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Keywords

plant breeding
high-throughput technologies
next-generation sequencing
genomics
transcriptomics
genomic selection
genotyping
phenomics
image analysis
phenotyping
molecular markers
marker-assisted selection
SNPs
bioinformatics

Published Papers (5 papers)

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Research

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14 pages, 6535 KiB

Open AccessArticle

Linear Mixed Model for Genotype Selection of Sorghum Yield

by Mulugeta Tesfa, Temesgen Zewotir, Solomon Assefa Derese, Denekew Bitew Belay and Hussein Shimelis

Appl. Sci. 2023, 13(5), 2784; https://0-doi-org.brum.beds.ac.uk/10.3390/app13052784 - 21 Feb 2023

Viewed by 1045

Abstract

Data analysis using the General linear model assumes the factors to be fixed effects, and the BLUE method, which is based on their mean performance, is appropriate to select the best performing genotypes. The linear mixed model incorporates fixed and random effects that are very important to compare a genotype’s performance through BLUP. The purpose of this study was to identify the best performing genotypes that provided a high grain yield using a mixed model, compare the mean performance of genotypes on grain yield using BLUP and BLUE, and determine the impact of drought on sorghum production in Ethiopia. The experiment used water availability as a treatment, and each replication within the treatment levels used a lattice square design for data collection. The design consisted of 14 × 14 square experimental units (plots) comprising 196 genotypes, where each row of the square was represented as a block receiving 14 genotypes. The phenotypic characteristics were measured for the study. The statistical methods used for the study were ANOVA and the linear mixed model to identify the best performing genotypes of sorghum. The study found that sorghum production was influenced by drought, which restricted sorghum growth due to a shortage of water. The implementation of irrigation increased the grain yield from 2.48 to 3.17 t/ha, indicating that the difference in grain yield between treatments (with and without irrigation) was 0.69 t/ha. The study compared the general linear model and linear mixed model, and the investigation revealed that the mixed model was more accurate than the general linear model. The linear mixed model selected the best performing genotypes in grain yield with better accuracy. It is recommended to use the linear mixed model to select the best performing genotypes in grain yield. Full article

(This article belongs to the Special Issue Applications of Advanced Genomic and Phenomic Technologies for Plant Improvement II)

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17 pages, 1800 KiB

Open AccessArticle

Comparative Analysis of Grapevine Epiphytic Microbiomes among Different Varieties, Tissues, and Developmental Stages in the Same Terroir

by Murad Awad, Georgios Giannopoulos, Photini V. Mylona and Alexios N. Polidoros

Appl. Sci. 2023, 13(1), 102; https://0-doi-org.brum.beds.ac.uk/10.3390/app13010102 - 21 Dec 2022

Cited by 5 | Viewed by 1566

Abstract

There is limited knowledge about the relationships of epiphytic microbiomes associated with the phyllosphere of different Vitis vinifera cultivars in the same vineyard and terroir. To address this research gap, we investigated the microbiome compositionof 36 grapevine genotypes grown in the same vineyard in different plant sections during the growing season. Using high-throughput NGS-based metagenomic analysis targeting the ITS2 and the V4 regions of the 16S ribosomal gene of fungal and bacterial communities, respectively, weassessed the impact of grapevine genotypes on microbial assemblages in various parts of the phyllosphere. The results indicated that different phyllosphere tissues display high microbial diversity regardless of the cultivars’ identity and use. The selected three phyllosphere parts representing three distinct phenological stages, namely bark and bud, berry set, and fruit harvest, had almost a similar number of fungal OTUs, while a difference was recorded for the bacterial species. The fruit harvest stage hosted the highest number of bacterial OTUs, whereas the bark and bud stage contained the lower. Bacterial dominant phyla were Proteobacteria, Bacteroidetes, Actinobacteria, and Firmicutes, and the genera were Gluconacetobacter, Erwinia, Gluconobacter, Zymobacter, Buchnera, Pseudomonas, Pantoea, Hymenobacter, Pedobacter, Frigoribacterium, Sphingomonas, and Massilia. For fungi, the dominant phyla were Ascomycota and Basidiomycota, and the genera were Aureobasidium, Cladosporium, Alternaria, Aspergillus, Davidiella, Phoma, Epicoccum, Rhodosporidium, Glomerella, Botryosphaeria, Metschnikowia, Issatchenkia, and Lewia. Both the genotype of the cultivar and the phenological stage appeared to considerably impact the shape of microbial diversity and structure within the same terroir. Taken together, these results indicate that microbiome analysis could be proved to be an important molecular fingerprint of cultivars and provide an efficient management tool for the traceability of wine and grape end products. Moreover, the unique identity of cultivars’ microbial signatures highlights the need for further development of precision management to support viticulture sustainability in the face of climate change. Full article

(This article belongs to the Special Issue Applications of Advanced Genomic and Phenomic Technologies for Plant Improvement II)

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11 pages, 883 KiB

Open AccessArticle

Genome-Wide Association Study Identifies Two Loci for Stripe Rust Resistance in a Durum Wheat Panel from Iran

by Ali Ashraf Mehrabi, Brian J. Steffenson, Alireza Pour-Aboughadareh, Oadi Matny and Mahbubjon Rahmatov

Appl. Sci. 2022, 12(10), 4963; https://0-doi-org.brum.beds.ac.uk/10.3390/app12104963 - 13 May 2022

Cited by 4 | Viewed by 6501

Abstract

Stripe rust (Puccinia striiformis f. sp. tritici (Pst)) is one of the most devastating fungal diseases of durum wheat (Triticum turgidum L. var. durum Desf.). Races of Pst with new virulence combinations are emerging more regularly on wheat-growing continents, which challenges wheat breeding for resistance. This study aimed to identify and characterize resistance to Pst races based on a genome-wide association study. GWAS is an approach to analyze the associations between a genome-wide set of single-nucleotide polymorphisms (SNPs) and target phenotypic traits. A total of 139 durum wheat accessions from Iran were evaluated at the seedling stage against isolates Pstv-37 and Pstv-40 of Pst and then genotyped using a 15K SNP chip. In total, 230 significant associations were identified across 14 chromosomes, of which 30 were associated with resistance to both isolates. Furthermore, 17 durum wheat landraces showed an immune response against both Pst isolates. The SNP markers and resistant accessions identified in this study may be useful in programs breeding durum wheat for stripe rust resistance. Full article

(This article belongs to the Special Issue Applications of Advanced Genomic and Phenomic Technologies for Plant Improvement II)

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14 pages, 2016 KiB

Open AccessArticle

Genetic Variation of a Lentil (Lens culinaris) Landrace during Three Generations of Breeding

by Anthoula Gleridou, Ioannis Tokatlidis and Alexios Polidoros

Appl. Sci. 2022, 12(1), 450; https://0-doi-org.brum.beds.ac.uk/10.3390/app12010450 - 04 Jan 2022

Cited by 4 | Viewed by 1572

Abstract

Genetic differentiation between 40 lentil genotypes was tested using molecular markers. The genotypes were produced from a Greek landrace of commercial interest via the honeycomb breeding methodology, i.e., single-plant selection in the absence of competition, across three successive pedigree generations. The selected genotypes from each generation were examined for genetic relationships using 15 SSR molecular markers with HRM analysis. As expected, low variation among consecutive generations at the level of 2.5–7.7% was detected. Analysis of molecular variance (AMOVA) revealed that partitioning of this variation was at higher percentage within each generation’s population than between them. Population structure analysis indicated that ongoing selection could effectively shift the allelic composition in each generation. The applied honeycomb breeding methodology that effectively improved progeny yield and seed quality increased the percentage of favorable alleles altering allelic composition but not eliminating genetic variation of the breeding population. Full article

(This article belongs to the Special Issue Applications of Advanced Genomic and Phenomic Technologies for Plant Improvement II)

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Review

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31 pages, 2650 KiB

Open AccessReview

Overview of Identified Genomic Regions Associated with Various Agronomic and Physiological Traits in Barley under Abiotic Stresses

by Farzaneh Fatemi, Farzad Kianersi, Alireza Pour-Aboughadareh, Peter Poczai and Omid Jadidi

Appl. Sci. 2022, 12(10), 5189; https://0-doi-org.brum.beds.ac.uk/10.3390/app12105189 - 20 May 2022

Cited by 13 | Viewed by 2446

Abstract

Climate change has caused breeders to focus on varieties that are able to grow under unfavorable conditions, such as drought, high and low temperatures, salinity, and other stressors. In recent decades, progress in biotechnology and its related tools has provided opportunities to dissect and decipher the genetic basis of tolerance to various stress conditions. One such approach is the identification of genomic regions that are linked with specific or multiple characteristics. Cereal crops have a key role in supplying the energy required for human and animal populations. However, crop products are dramatically affected by various environmental stresses. Barley (Hordeum vulgare L.) is one of the oldest domesticated crops that is cultivated globally. Research has shown that, compared with other cereals, barley is well adapted to various harsh environmental conditions. There is ample literature regarding these responses to abiotic stressors, as well as the genomic regions associated with the various morpho-physiological and biochemical traits of stress tolerance. This review focuses on (i) identifying the tolerance mechanisms that are important for stable growth and development, and (ii) the applicability of QTL mapping and association analysis in identifying genomic regions linked with stress-tolerance traits, in order to help breeders in marker-assisted selection (MAS) to quickly screen tolerant germplasms in their breeding cycles. Overall, the information presented here will inform and assist future barley breeding programs. Full article

(This article belongs to the Special Issue Applications of Advanced Genomic and Phenomic Technologies for Plant Improvement II)

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