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The Contribution of Genetics and Genomics to Crop Breeding for Quantitative/Qualitative Traits

A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Plant Sciences".

Deadline for manuscript submissions: closed (20 November 2024) | Viewed by 5731

Special Issue Editors


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Guest Editor
Research Centre for Cereal and Industrial Crops, Council for Agricultural Research and Economics, 24126 Bergamo, Italy
Interests: transcriptional and post-transcriptional regulation mechanisms; genetic analysis of quantitative traits related to grain yield and quality of cereals; functional analysis of abiotic stress regulated genes
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Guest Editor
Research Centre for Cereal and Industrial Crops, Council for Agricultural Research and Economics, 71122 Foggia, Italy
Interests: phenotypic and genetic analysis of roots and quantitative traits related to grain yield and quality of cereals

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Guest Editor
Research Centre for Cereal and Industrial Crops, Council for Agricultural Research and Economics, 71122 Foggia, Italy
Interests: durum wheat; disease resistance; genetic mapping; quantitative traits; cisgenesis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the last several decades, next-generation sequencing approaches underwent rapid progress and became more affordable. This promoted a revolution in breeding, from the use of molecular markers to map genetic determinants of agronomic traits and assist in their selection, to the employment of new advanced breeding techniques based on cisgenesis and genome editing.

The purpose of this Special Issue is to report the recent progress achieved in genomics studies in crops. This includes, but is not limited to, mapping, including through genome-wide association studies, studies on sequencing, structure and organization of crop genomes, gene functional studies, and advanced methods in breeding, including genome editing and genomic selection.

Dr. Anna M. Mastrangelo
Dr. Maria Anna Russo
Dr. Daniela Marone
Guest Editors

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Published Papers (3 papers)

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Research

15 pages, 6516 KiB  
Article
Transcriptomic and Metabolomic Analyses Reveal the Key Genes Related to Shade Tolerance in Soybean
by Aohua Jiang, Jiaqi Liu, Weiran Gao, Ronghan Ma, Jijun Zhang, Xiaochun Zhang, Chengzhang Du, Zelin Yi, Xiaomei Fang and Jian Zhang
Int. J. Mol. Sci. 2023, 24(18), 14230; https://doi.org/10.3390/ijms241814230 - 18 Sep 2023
Cited by 4 | Viewed by 1673
Abstract
Soybean (Glycine max) is an important crop, rich in proteins, vegetable oils and several other phytochemicals, which is often affected by light during growth. However, the specific regulatory mechanisms of leaf development under shade conditions have yet to be understood. In [...] Read more.
Soybean (Glycine max) is an important crop, rich in proteins, vegetable oils and several other phytochemicals, which is often affected by light during growth. However, the specific regulatory mechanisms of leaf development under shade conditions have yet to be understood. In this study, the transcriptome and metabolome sequencing of leaves from the shade-tolerant soybean ‘Nanxiadou 25′ under natural light (ND1) and 50% shade rate (SHND1) were carried out, respectively. A total of 265 differentially expressed genes (DEGs) were identified, including 144 down-regulated and 121 up-regulated genes. Meanwhile, KEGG enrichment analysis of DEGs was performed and 22 DEGs were significantly enriched in the top five pathways, including histidine metabolism, riboflavin metabolism, vitamin B6 metabolism, glycerolipid metabolism and cutin, suberine and wax biosynthesis. Among all the enrichment pathways, the most DEGs were enriched in plant hormone signaling pathways with 19 DEGs being enriched. Transcription factors were screened out and 34 differentially expressed TFs (DETFs) were identified. Weighted gene co-expression network analysis (WGCNA) was performed and identified 10 core hub genes. Combined analysis of transcriptome and metabolome screened out 36 DEGs, and 12 potential candidate genes were screened out and validated by quantitative real-time polymerase chain reaction (qRT-PCR) assay, which may be related to the mechanism of shade tolerance in soybean, such as ATP phosphoribosyl transferase (ATP-PRT2), phosphocholine phosphatase (PEPC), AUXIN-RESPONSIVE PROTEIN (IAA17), PURPLE ACID PHOSPHATASE (PAP), etc. Our results provide new knowledge for the identification and function of candidate genes regulating soybean shade tolerance and provide valuable resources for the genetic dissection of soybean shade tolerance molecular breeding. Full article
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14 pages, 9053 KiB  
Article
Comprehensive Identification of the Pum Gene Family and Its Involvement in Kernel Development in Maize
by Wenqi Feng, Hongwanjun Zhang, Yang Cao, Cheng Yang, Muhammad Hayder Bin Khalid, Qingqing Yang, Wanchen Li, Yingge Wang, Fengling Fu and Haoqiang Yu
Int. J. Mol. Sci. 2023, 24(18), 14036; https://doi.org/10.3390/ijms241814036 - 13 Sep 2023
Cited by 1 | Viewed by 1417
Abstract
The Pumilio (Pum) RNA-binding protein family regulates post-transcription and plays crucial roles in stress response and growth. However, little is known about Pum in plants. In this study, a total of 19 ZmPum genes were identified and classified into two groups in maize. [...] Read more.
The Pumilio (Pum) RNA-binding protein family regulates post-transcription and plays crucial roles in stress response and growth. However, little is known about Pum in plants. In this study, a total of 19 ZmPum genes were identified and classified into two groups in maize. Although each ZmPum contains the conserved Pum domain, the ZmPum members show diversity in the gene and protein architectures, physicochemical properties, chromosomal location, collinearity, cis-elements, and expression patterns. The typical ZmPum proteins have eight α-helices repeats, except for ZmPum2, 3, 5, 7, and 14, which have fewer α-helices. Moreover, we examined the expression profiles of ZmPum genes and found their involvement in kernel development. Except for ZmPum2, ZmPum genes are expressed in maize embryos, endosperms, or whole seeds. Notably, ZmPum4, 7, and 13 exhibited dramatically high expression levels during seed development. The study not only contributes valuable information for further validating the functions of ZmPum genes but also provides insights for improvement and enhancing maize yield. Full article
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11 pages, 1462 KiB  
Article
Genomic Prediction of Resistance to Tan Spot, Spot Blotch and Septoria Nodorum Blotch in Synthetic Hexaploid Wheat
by Guillermo García-Barrios, José Crossa, Serafín Cruz-Izquierdo, Víctor Heber Aguilar-Rincón, J. Sergio Sandoval-Islas, Tarsicio Corona-Torres, Nerida Lozano-Ramírez, Susanne Dreisigacker, Xinyao He, Pawan Kumar Singh and Rosa Angela Pacheco-Gil
Int. J. Mol. Sci. 2023, 24(13), 10506; https://doi.org/10.3390/ijms241310506 - 22 Jun 2023
Cited by 2 | Viewed by 1696
Abstract
Genomic prediction combines molecular and phenotypic data in a training population to predict the breeding values of individuals that have only been genotyped. The use of genomic information in breeding programs helps to increase the frequency of favorable alleles in the populations of [...] Read more.
Genomic prediction combines molecular and phenotypic data in a training population to predict the breeding values of individuals that have only been genotyped. The use of genomic information in breeding programs helps to increase the frequency of favorable alleles in the populations of interest. This study evaluated the performance of BLUP (Best Linear Unbiased Prediction) in predicting resistance to tan spot, spot blotch and Septoria nodorum blotch in synthetic hexaploid wheat. BLUP was implemented in single-trait and multi-trait models with three variations: (1) the pedigree relationship matrix (A-BLUP), (2) the genomic relationship matrix (G-BLUP), and (3) a combination of the two matrices (A+G BLUP). In all three diseases, the A-BLUP model had a lower performance, and the G-BLUP and A+G BLUP were statistically similar (p ≥ 0.05). The prediction accuracy with the single trait was statistically similar (p ≥ 0.05) to the multi-trait accuracy, possibly due to the low correlation of severity between the diseases. Full article
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