Special Issue "Selection Methods in Plant Breeding: From Visual Phenotyping to NGS"

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

Deadline for manuscript submissions: 28 February 2020.

Special Issue Editors

Dr. Dan Milbourne
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Guest Editor
Teagasc - Irish Agriculture and Food Development Authority, Crops Research Centre, Carlow, Ireland
Dr. Tony Slater
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Guest Editor
AgriBio, Australia, Melbourne, Australia

Special Issue Information

Dear Colleagues

We are entering a “Golden Era” of plant breeding, where advances across a range of allied disciplines and associated technological advances are enhancing our ability to breed better varieties in a faster, more efficient, and more targeted manner. These advances are incredibly timely in the face of population expansion across valuable agricultural land, resource limitation, and climate instability. To secure food production and agricultural output against the backdrop of these challenges, the role of plant breeding has never been more important.

The sequencing of crop plant genomes is leading to a greater understanding of gene function and the underlying control of key plant processes, giving plant breeders the potential to “design” plant varieties with increased resilience to abiotic and biotic stresses, whilst increasing yield and quality characteristics. At the same time, advances in DNA sequencing and genotyping, phenotyping techniques, and predictive data analytics are being combined in approaches such as marker-assisted selection and genomic selection, radically speeding up plant breeding, which is vital if agriculture is going to feed the predicted future population. In this Special Issue of Genes, we invite you to submit papers exploring how these exciting developments are being applied to your favorite crop species.

Dr. Dan Milbourne
Dr. Tony Slater
Guest Editors

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 papers will be 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 1800 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

  • Phenotyping
  • Next-Generation Sequencing
  • Quantitative Trait Loci
  • Abiotic stress
  • Disease resistance
  • Yield
  • Quality
  • Breeding values
  • Marker-assisted selection
  • Genotyping
  • Genomic Selection
  • Speed Breeding

Published Papers (3 papers)

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Research

Open AccessArticle
A Genome-Wide Association Study Revealed Key SNPs/Genes Associated With Salinity Stress Tolerance In Upland Cotton
Genes 2019, 10(10), 829; https://doi.org/10.3390/genes10100829 - 21 Oct 2019
Abstract
Millions of hectares of land are too saline to produce economically valuable crop yields. Salt tolerance in cotton is an imperative approach for improvement in response to ever-increasing soil salinization. Little is known about the genetic basis of salt tolerance in cotton at [...] Read more.
Millions of hectares of land are too saline to produce economically valuable crop yields. Salt tolerance in cotton is an imperative approach for improvement in response to ever-increasing soil salinization. Little is known about the genetic basis of salt tolerance in cotton at the seedling stage. To address this issue, a genome-wide association study (GWAS) was conducted on a core collection of a genetically diverse population of upland cotton (Gossypium hirsutum L.) comprising of 419 accessions, representing various geographic origins, including China, USA, Pakistan, the former Soviet Union, Chad, Australia, Brazil, Mexico, Sudan, and Uganda. Phenotypic evaluation of 7 traits under control (0 mM) and treatment (150 mM) NaCl conditions depicted the presence of broad natural variation in the studied population. The association study was carried out with the efficient mixed-model association eXpedited software package. A total of 17,264 single-nucleotide polymorphisms (SNPs) associated with different salinity stress tolerance related traits were found. Twenty-three candidate SNPs related to salinity stress-related traits were selected. Final key SNPs were selected based on the r2 value with nearby SNPs in a linkage disequilibrium (LD) block. Twenty putative candidate genes surrounding SNPs, A10_95330133 and D10_61258588, associated with leaf relative water content, RWC_150, and leaf fresh weight, FW_150, were identified, respectively. We further validated the expression patterns of twelve candidate genes with qRT-PCR, which revealed different expression levels in salt-tolerant and salt-sensitive genotypes. The results of our GWAS provide useful knowledge about the genetic control of salt tolerance at the seedling stage, which could assist in elucidating the genetic and molecular mechanisms of salinity stress tolerance in cotton plants. Full article
(This article belongs to the Special Issue Selection Methods in Plant Breeding: From Visual Phenotyping to NGS)
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Open AccessArticle
Whole Genome Diversity, Population Structure, and Linkage Disequilibrium Analysis of Chickpea  (Cicer arietinum L.) Genotypes Using Genome-Wide DArTseq-Based SNP Markers
Genes 2019, 10(9), 676; https://doi.org/10.3390/genes10090676 - 04 Sep 2019
Abstract
Characterization of genetic diversity, population structure, and linkage disequilibrium is a prerequisite for proper management of breeding programs and conservation of genetic resources. In this study, 186 chickpea genotypes, including advanced “Kabuli” breeding lines and Iranian landrace “Desi” chickpea [...] Read more.
Characterization of genetic diversity, population structure, and linkage disequilibrium is a prerequisite for proper management of breeding programs and conservation of genetic resources. In this study, 186 chickpea genotypes, including advanced “Kabuli” breeding lines and Iranian landrace “Desi” chickpea genotypes, were genotyped using DArTseq-Based single nucleotide polymorphism (SNP) markers. Out of 3339 SNPs, 1152 markers with known chromosomal position were selected for genome diversity analysis. The number of mapped SNP markers varied from 52 (LG8) to 378 (LG4), with an average of 144 SNPs per linkage group. The chromosome size that was covered by SNPs varied from 16,236.36 kbp (LG8) to 67,923.99 kbp (LG5), while LG4 showed a higher number of SNPs, with an average of 6.56 SNPs per Mbp. Polymorphism information content (PIC) value of SNP markers ranged from 0.05 to 0.50, with an average of 0.32, while the markers on LG4, LG6, and LG8 showed higher mean PIC value than average. Unweighted neighbor joining cluster analysis and Bayesian-based model population structure grouped chickpea genotypes into four distinct clusters. Principal component analysis (PCoA) and discriminant analysis of principal component (DAPC) results were consistent with that of the cluster and population structure analysis. Linkage disequilibrium (LD) was extensive and LD decay in chickpea germplasm was relatively low. A few markers showed r2 ≥ 0.8, while 2961 pairs of markers showed complete LD (r2 = 1), and a huge LD block was observed on LG4. High genetic diversity and low kinship value between pairs of genotypes suggest the presence of a high genetic diversity among the studied chickpea genotypes. This study also demonstrates the efficiency of DArTseq-based SNP genotyping for large-scale genome analysis in chickpea. The genotypic markers provided in this study are useful for various association mapping studies when combined with phenotypic data of different traits, such as seed yield, abiotic, and biotic stresses, and therefore can be efficiently used in breeding programs to improve chickpea. Full article
(This article belongs to the Special Issue Selection Methods in Plant Breeding: From Visual Phenotyping to NGS)
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Open AccessArticle
Genomic Prediction and Genome-Wide Association Studies of Flour Yield and Alveograph Quality Traits Using Advanced Winter Wheat Breeding Material
Genes 2019, 10(9), 669; https://doi.org/10.3390/genes10090669 - 31 Aug 2019
Abstract
Use of genetic markers and genomic prediction might improve genetic gain for quality traits in wheat breeding programs. Here, flour yield and Alveograph quality traits were inspected in 635 F6 winter wheat breeding lines from two breeding cycles. Genome-wide association studies revealed [...] Read more.
Use of genetic markers and genomic prediction might improve genetic gain for quality traits in wheat breeding programs. Here, flour yield and Alveograph quality traits were inspected in 635 F6 winter wheat breeding lines from two breeding cycles. Genome-wide association studies revealed single nucleotide polymorphisms (SNPs) on chromosome 5D significantly associated with flour yield, Alveograph P (dough tenacity), and Alveograph W (dough strength). Additionally, SNPs on chromosome 1D were associated with Alveograph P and W, SNPs on chromosome 1B were associated with Alveograph P, and SNPs on chromosome 4A were associated with Alveograph L (dough extensibility). Predictive abilities based on genomic best linear unbiased prediction (GBLUP) models ranged from 0.50 for flour yield to 0.79 for Alveograph W based on a leave-one-out cross-validation strategy. Predictive abilities were negatively affected by smaller training set sizes, lower genetic relationship between lines in training and validation sets, and by genotype–environment (G×E) interactions. Bayesian Power Lasso models and genomic feature models resulted in similar or slightly improved predictions compared to GBLUP models. SNPs with the largest effects can be used for screening large numbers of lines in early generations in breeding programs to select lines that potentially have good quality traits. In later generations, genomic predictions might be used for a more accurate selection of high quality wheat lines. Full article
(This article belongs to the Special Issue Selection Methods in Plant Breeding: From Visual Phenotyping to NGS)
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