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Agriculture
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QTL Mapping for Yield and Quality Traits in Crops
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QTL Mapping for Yield and Quality Traits in Crops

A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Crop Genetics, Genomics and Breeding".

Deadline for manuscript submissions: closed (15 May 2022) | Viewed by 5823

Special Issue Editor

Prof. Dr. Linhai Wang

E-Mail Website
Guest Editor
Oil Crops Research Institute, Chinese Academy of Agriculture Science, Wuhan 430062, China
Interests: sesamum indicum; oil; lignan; plant architecture; abiotic stress; quality; yield; genome; gene; metabolite

Special Issue Information

Dear Colleagues,

Increasing crop yields and improving quality are eternal topics in agricultural research. The supply of sufficient and high-quality agricultural products is key to ensuring the sustainable development of humankind in the future. Yield and quality are often difficult to achieve to a high level in synergy, but that does not mean they are impossible. Reasonable gene or locus combinations are the basis for the improvement of yield and quality. Presently, many crops are still extremely weak in QTL mapping and gene identification, and the improvement of yield or quality through genetic manipulation cannot be carried out widely and efficiently. Discovering QTL and genes is not only the first step toward understanding the molecular basis of yield and quality, but also a prerequisite for establishing efficient marker-assisted breeding technology and conducting gene identification and editing.

This Special Issue will focus on mapping QTL, identifying key genes, establishing and applying marker-assisted breeding technology, and studying the molecular basis of yield and quality, with the purpose of promoting the improvement of crop yield and nutritional quality.

Prof. Dr. Linhai Wang
Guest Editor

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Keywords

  • crop
  • genetics
  • QTL mapping
  • gene identification
  • marker-assisted breeding
  • yield
  • quality

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Published Papers (3 papers)

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Research

13 pages, 4010 KiB  
Article
DNA Barcoding of Endangered and Rarely Occurring Plants in Faifa Mountains (Jazan, Saudi Arabia)
by Fatmah Ahmed Safhi, Salha Mesfer Alshamrani, Yosur Gamal Fiteha and Diaa Abd El-Moneim
Agriculture 2022, 12(11), 1931; https://doi.org/10.3390/agriculture12111931 - 17 Nov 2022
Cited by 7 | Viewed by 2743
Abstract
Conservation of plant genetic resources, especially threatened species, is an important topic in biodiversity. It is a field that requires prior knowledge of the target species, in addition to correct identification and taxonomic description. In botany, the identification of plant species traditionally relies [...] Read more.
Conservation of plant genetic resources, especially threatened species, is an important topic in biodiversity. It is a field that requires prior knowledge of the target species, in addition to correct identification and taxonomic description. In botany, the identification of plant species traditionally relies on key morphological descriptions and anatomical features. However, in complex species and tree plants, molecular identification can facilitate identification and increase species delimitation accuracy. In the Faifa mountains of Jazan province in Saudi Arabia, 12 rarely occurring plants were recorded and identified using two DNA barcoding regions (i.e., rbcL and ITS). All the samples were successfully amplified, sequenced, and analyzed using the standard DNA barcode protocol, and this resulted in the clear and accurate identification of 11 out of the 12 sampled species. A total of five species were in agreement in terms of both morpho- and molecular-based identification. Four and two species were identified based solely on ITS and rbcL phylogenetics, respectively. The geographic distribution records of the identified species showed that some species were distributed at a distance far from their usual region, while others were reported in proximate regions and localities. Some species were found to be medicinally important and required additional conservation plans. Full article
(This article belongs to the Special Issue QTL Mapping for Yield and Quality Traits in Crops)
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19 pages, 2277 KiB  
Article
QTL Mapping of Leaf Area Index and Chlorophyll Content Based on UAV Remote Sensing in Wheat
by Wei Wang, Xue Gao, Yukun Cheng, Yi Ren, Zhihui Zhang, Rui Wang, Junmei Cao and Hongwei Geng
Agriculture 2022, 12(5), 595; https://doi.org/10.3390/agriculture12050595 - 23 Apr 2022
Cited by 16 | Viewed by 2837
Abstract
High-throughput phenotypic identification is a prerequisite for large-scale identification and gene mining of important traits. However, existing work has rarely leveraged high-throughput phenotypic identification into quantitative trait locus (QTL) acquisition in wheat crops. Clarifying the feasibility and effectiveness of high-throughput phenotypic data obtained [...] Read more.
High-throughput phenotypic identification is a prerequisite for large-scale identification and gene mining of important traits. However, existing work has rarely leveraged high-throughput phenotypic identification into quantitative trait locus (QTL) acquisition in wheat crops. Clarifying the feasibility and effectiveness of high-throughput phenotypic data obtained from UAV multispectral images in gene mining of important traits is an urgent problem to be solved in wheat. In this paper, 309 lines of the spring wheat Worrakatta × Berkut recombinant inbred line (RIL) were taken as materials. First, we obtained the leaf area index (LAI) including flowering, filling, and mature stages, as well as the flag leaf chlorophyll content (CC) including heading, flowering, and filling stages, from multispectral images under normal irrigation and drought stress, respectively. Then, on the basis of the normalized difference vegetation index (NDVI) and green normalized difference vegetation index (GNDVI), which were determined by multispectral imagery, the LAI and CC were comprehensively estimated through the classification and regression tree (CART) and cross-validation algorithms. Finally, we identified the QTLs by analyzing the predicted and measured values. The results show that the predicted values of determination coefficient (R2) ranged from 0.79 to 0.93, the root-mean-square error (RMSE) ranged from 0.30 to 1.05, and the relative error (RE) ranged from 0.01 to 0.18. Furthermore, the correlation coefficients of predicted and measured values ranged from 0.93 to 0.94 for CC and from 0.80 to 0.92 for LAI at different wheat growth stages under normal irrigation and drought stress. Additionally, a linkage map of this RIL population was constructed by 11,375 SNPs; eight QTLs were detected for LAI on wheat chromosomes 1BL, 2BL (four QTLs), 3BL, 5BS, and 5DL, and three QTLs were detected for CC on chromosomes 1DS (two QTLs) and 3AL. The closely linked QTLs formed two regions on chromosome 2BL (from 54 to 56 cM and from 96 to 101 cM, respectively) and one region on 1DS (from 26 to 27 cM). Each QTL explained phenotypic variation for LAI from 2.5% to 13.8% and for CC from 2.5% to 5.8%. For LAI, two QTLs were identified at the flowering stage, two QTLs were identified at the filling stage, and three QTLs were identified at the maturity stage, among which QLAI.xjau-5DL-pre was detected at both filling and maturity stages. For CC, two QTLs were detected at the heading stage and one QTL was identified at the flowering stage, among which QCC.xjau-1DS was detected at both stages. Three QTLs (QLAI.xjau-2BL-pre.2, QLAI.xjau-2BL.2, and QLAI.xjau-3BL-pre) for LAI were identified under drought stress conditions. Five QTLs for LAI and two QTLs for CC were detected by imagery-predicted values, while four QTLs for LAI and two QTLs for CC were identified by manual measurement values. Lastly, investigations of these QTLs on the wheat reference genome identified 10 candidate genes associated with LAI and three genes associated with CC, belonging to F-box family proteins, peroxidase, GATA transcription factor, C2H2 zinc finger structural protein, etc., which are involved in the regulation of crop growth and development, signal transduction, and response to drought stress. These findings reveal that UAV sensing technology has relatively high reliability for phenotyping wheat LAI and CC, which can play an important role in crop genetic improvement. Full article
(This article belongs to the Special Issue QTL Mapping for Yield and Quality Traits in Crops)
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13 pages, 1714 KiB  
Article
Genome-Wide Association Study Uncovers Loci and Candidate Genes Underlying Phytosterol Variation in Sesame (Sesamum indicum L.)
by Zhijian Wang, Qi Zhou, Senouwa Segla Koffi Dossou, Rong Zhou, Yingzhong Zhao, Wangyi Zhou, Yanxin Zhang, Donghua Li, Jun You and Linhai Wang
Agriculture 2022, 12(3), 392; https://doi.org/10.3390/agriculture12030392 - 11 Mar 2022
Cited by 7 | Viewed by 2870
Abstract
Sesame is one of the most important oilseed crops grown worldwide. It provides diverse nutraceuticals—including lignans, unsaturated fatty acids (UFA), phytosterols, etc.—to humans. Among sesame’s nutraceuticals, phytosterols have received less attention from sesame breeders, although their biological and pharmacological functions have been recorded. [...] Read more.
Sesame is one of the most important oilseed crops grown worldwide. It provides diverse nutraceuticals—including lignans, unsaturated fatty acids (UFA), phytosterols, etc.—to humans. Among sesame’s nutraceuticals, phytosterols have received less attention from sesame breeders, although their biological and pharmacological functions have been recorded. Therefore, in the present study, we evaluated the variation of phytosterol contents in 402 sesame accessions grown in two environments and revealed their associated loci and candidate genes. Gas chromatography (GC) analysis unveiled that sesame mainly contains four phytosterols: campesterol, stigmasterol, β-sitosterol, and Δ5-avenasterol. β-sitosterol (1.6–4.656 mg/g) was the major phytosterol, followed by campesterol (0–2.847 mg/g), stigmasterol (0.356–1.826 mg/g), and Δ5-avenasterol (0–1.307 mg/g). The total phytosterol content varied from 2.694 to 8.388 mg/g. Genome-wide association study identified 33 significant associated single nucleotide polymorphism (SNP) loci for the four traits, of which Ch6-39270 and Ch11-142842 were environmentally stable and simultaneously linked with campesterol and stigmasterol content variation. Candidate genes screening indicated that SINPZ1100015 encoding a NAC domain-containing protein 43 is likely the major candidate effect gene of phytosterol variation in sesame. The results of this study extend knowledge of phytosterol variation in sesame and provide important resources for markers-assisted breeding of high-phytosterol content varieties. Full article
(This article belongs to the Special Issue QTL Mapping for Yield and Quality Traits in Crops)
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