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Genetic Analysis of Quantitative Traits in Plants
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Genetic Analysis of Quantitative Traits in Plants

A special issue of Plants (ISSN 2223-7747). This special issue belongs to the section "Plant Genetics, Genomics and Biotechnology".

Deadline for manuscript submissions: closed (31 March 2024) | Viewed by 4867

Special Issue Editors

Prof. Dr. Jiankang Wang

E-Mail Website
Guest Editor
Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, No. 12 Zhongguancun South Street, Beijing 100081, China
Interests: genetic analysis and tool development of quantitative traits; applied quantitative genetics; modelling; simulation and prediction in plant breeding
Dr. Luyan Zhang

E-Mail Website
Guest Editor
Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, No. 12 Zhongguancun South Street, Beijing 100081, China
Interests: genetic analysis methods; integrated genetic analysis tools; genotype to phenotype prediction; biometrics and quantitative genetics

Special Issue Information

Dear Colleagues,

Quantitative traits have been extensively investigated and studied in evolutionary and genetic studies and plant and animal breeding. Most quantitative traits are controlled by multiple genes with various genetic effects, and phenotypes can be readily modified via environmental variation. Rapid progress in molecular biology, genomics and information technology has fundamentally impacted theoretical and applied studies of quantitative traits. In the era of omics, the availability of fine-scale genetic linkage maps and genotyping technologies has led to significant progress in quantitative genetics theory, together with the intensive use of gene mapping and map-based cloning in genetic studies of quantitative traits. In addition, novel mating designs and genetic populations have been proposed and developed to allow better dissection of the genetic architectures of quantitative traits. Consequently, novel breeding methods and strategies have been developed, including marker-assisted selection, design breeding, genomic selection and intelligent breeding. This Special Issue of Plants will highlight quantitative trait analysis and its applications in genetic studies and breeding in plants, such as field crops, forest trees, vegetables, etc. We welcome papers on novel genetic designs and populations, novel statistical methods and models in analysis and prediction, functional and network analysis of quantitative trait genes, novel analysis tools and breeding applications.

Prof. Dr. Jiankang Wang
Dr. Luyan Zhang
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 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. Plants is an international peer-reviewed open access semimonthly 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 2700 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

  • quantitative traits
  • genetic architecture
  • genetic population
  • gene mapping
  • functional analysis
  • plant breeding

Published Papers (4 papers)

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Research

18 pages, 1806 KiB  
Article
The Combining Ability and Heterosis Analysis of Sweet–Waxy Corn Hybrids for Yield-Related Traits and Carotenoids
by Kanyarat Prai-anun, Yaowapha Jirakiattikul, Khundej Suriharn and Bhornchai Harakotr
Plants 2024, 13(2), 296; https://doi.org/10.3390/plants13020296 - 18 Jan 2024
Viewed by 826
Abstract
Improving sweet–waxy corn hybrids enriched in carotenoids via a hybrid breeding approach may provide an alternative cash crop for growers and provide health benefits for consumers. This study estimates the combining ability and heterosis of sweet–waxy corn hybrids for yield-related traits and carotenoids. [...] Read more.
Improving sweet–waxy corn hybrids enriched in carotenoids via a hybrid breeding approach may provide an alternative cash crop for growers and provide health benefits for consumers. This study estimates the combining ability and heterosis of sweet–waxy corn hybrids for yield-related traits and carotenoids. Eight super sweet corn and three waxy corn lines were crossed to generate 24 F1 hybrids according to the North Carolina Design II scheme, and these hybrids were evaluated across two seasons of 2021/22. The results showed that both additive and non-additive genetic effects were involved in expressing the traits, but the additive genetic effect was more predominant. Most observed traits exhibited moderate to high narrow-sense heritability. Three parental lines, namely the ILS2 and ILS7 females and the ILW1 male, showed the highest positive GCA effects on yield-related traits, making them desirable for developing high-yielding hybrids. Meanwhile, five parental lines, namely the ILS3, ILS5, and ILS7 females and the ILW1 and ILW2 males, were favorable general combiners for high carotenoids. A tested hybrid, ILS2 × ILW1, was a candidate biofortified sweet–waxy corn hybrid possessing high yields and carotenoids. Heterosis and per se performance were more positively correlated with GCAsum than SCA, indicating that GCAsum can predict heterosis for improving biofortified sweet–waxy corn hybrid enriched in carotenoids. The breeding strategies of biofortified sweet–waxy corn hybrids with high yield and carotenoid content are discussed. Full article
(This article belongs to the Special Issue Genetic Analysis of Quantitative Traits in Plants)
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18 pages, 15475 KiB  
Article
Identification of Quantitative Trait Nucleotides and Development of Diagnostic Markers for Nine Fatty Acids in the Peanut
by Juan Wang, Haoning Chen, Yuan Li, Dachuan Shi, Wenjiao Wang, Caixia Yan, Mei Yuan, Quanxi Sun, Jing Chen, Yifei Mou, Chunjuan Qu and Shihua Shan
Plants 2024, 13(1), 16; https://doi.org/10.3390/plants13010016 - 20 Dec 2023
Viewed by 812
Abstract
The cultivated peanut (Arachis hypogaea L.) is an important oilseed crop worldwide, and fatty acid composition is a major determinant of peanut oil quality. In the present study, we conducted a genome-wide association study (GWAS) for nine fatty acid traits using the [...] Read more.
The cultivated peanut (Arachis hypogaea L.) is an important oilseed crop worldwide, and fatty acid composition is a major determinant of peanut oil quality. In the present study, we conducted a genome-wide association study (GWAS) for nine fatty acid traits using the whole genome sequences of 160 representative Chinese peanut landraces and identified 6-1195 significant SNPs for different fatty acid contents. Particularly for oleic acid and linoleic acid, two peak SNP clusters on Arahy.09 and Arahy.19 were found to contain the majority of the significant SNPs associated with these two fatty acids. Additionally, a significant proportion of the candidate genes identified on Arahy.09 overlap with those identified in early studies, among which three candidate genes are of special interest. One possesses a significant missense SNP and encodes a known candidate gene FAD2A. The second gene is the gene closest to the most significant SNP for linoleic acid. It codes for an MYB protein that has been demonstrated to impact fatty acid biosynthesis in Arabidopsis. The third gene harbors a missense SNP and encodes a JmjC domain-containing protein. The significant phenotypic difference in the oleic acid/linoleic acid between the genotypes at the first and third candidate genes was further confirmed with PARMS analysis. In addition, we have also identified different candidate genes (i.e., Arahy.ZV39IJ, Arahy.F9E3EA, Arahy.X9ZZC1, and Arahy.Z0ELT9) for the remaining fatty acids. Our findings can help us gain a better understanding of the genetic foundation of peanut fatty acid contents and may hold great potential for enhancing peanut quality in the future. Full article
(This article belongs to the Special Issue Genetic Analysis of Quantitative Traits in Plants)
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21 pages, 25499 KiB  
Article
GWAS and Meta-QTL Analysis of Yield-Related Ear Traits in Maize
by Fu Qian, Jianguo Jing, Zhanqin Zhang, Shubin Chen, Zhiqin Sang and Weihua Li
Plants 2023, 12(22), 3806; https://doi.org/10.3390/plants12223806 - 08 Nov 2023
Cited by 1 | Viewed by 1181
Abstract
Maize ear traits are an important component of yield, and the genetic basis of ear traits facilitates further yield improvement. In this study, a panel of 580 maize inbred lines were used as the study material, eight ear-related traits were measured through three [...] Read more.
Maize ear traits are an important component of yield, and the genetic basis of ear traits facilitates further yield improvement. In this study, a panel of 580 maize inbred lines were used as the study material, eight ear-related traits were measured through three years of planting, and whole genome sequencing was performed using the maize 40 K breeding chip based on genotyping by targeted sequencing (GBTS) technology. Five models were used to conduct a genome-wide association study (GWAS) on best linear unbiased estimate (BLUE) of ear traits to find the best model. The FarmCPU (Fixed and random model Circulating Probability Unification) model was the best model for this study; a total of 104 significant single nucleotide polymorphisms (SNPs) were detected, and 10 co-location SNPs were detected simultaneously in more than two environments. Through gene function annotation and prediction, a total of nine genes were identified as potentially associated with ear traits. Moreover, a total of 760 quantitative trait loci (QTL) associated with yield-related traits reported in 37 different articles were collected. Using the collected 760 QTL for meta-QTL analysis, a total of 41 MQTL (meta-QTL) associated with yield-related traits were identified, and 19 MQTL detected yield-related ear trait functional genes and candidate genes that have been reported in maize. Five significant SNPs detected by GWAS were located within these MQTL intervals, and another three significant SNPs were close to MQTL (less than 1 Mb). The results provide a theoretical reference for the analysis of the genetic basis of ear-related traits and the improvement of maize yield. Full article
(This article belongs to the Special Issue Genetic Analysis of Quantitative Traits in Plants)
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12 pages, 1977 KiB  
Article
Fine Mapping of fw6.3, a Major-Effect Quantitative Trait Locus That Controls Fruit Weight in Tomato
by Yu Ning, Kai Wei, Shanshan Li, Li Zhang, Ziyue Chen, Feifei Lu, Pei Yang, Mengxia Yang, Xiaolin Liu, Xiaoyan Liu, Xiaotian Wang, Xue Cao, Xiaoxuan Wang, Yanmei Guo, Lei Liu, Xin Li, Yongchen Du, Junming Li and Zejun Huang
Plants 2023, 12(11), 2065; https://doi.org/10.3390/plants12112065 - 23 May 2023
Cited by 1 | Viewed by 1217
Abstract
Tomato (Solanum lycopersicum) is a widely consumed vegetable, and the tomato fruit weight is a key yield component. Many quantitative trait loci (QTLs) controlling tomato fruit weight have been identified, and six of them have been fine-mapped and cloned. Here, four [...] Read more.
Tomato (Solanum lycopersicum) is a widely consumed vegetable, and the tomato fruit weight is a key yield component. Many quantitative trait loci (QTLs) controlling tomato fruit weight have been identified, and six of them have been fine-mapped and cloned. Here, four loci controlling tomato fruit weight were identified in an F2 population through QTL seq.; fruit weight 6.3 (fw6.3) was a major-effect QTL and its percentage of variation explanation (R2) was 0.118. This QTL was fine-mapped to a 62.6 kb interval on chromosome 6. According to the annotated tomato genome (version SL4.0, annotation ITAG4.0), this interval contained seven genes, including Solyc06g074350 (the SELF-PRUNING gene), which was likely the candidate gene underlying variation in fruit weight. The SELF-PRUNING gene contained a single-nucleotide polymorphism that resulted in an amino acid substitution in the protein sequence. The large-fruit allele of fw6.3 (fw6.3HG) was overdominant to the small-fruit allele fw6.3RG. The soluble solids content was also increased by fw6.3HG. These findings provide valuable information that will aid the cloning of the FW6.3 gene and ongoing efforts to breed tomato plants with higher yield and quality via molecular marker-assisted selection. Full article
(This article belongs to the Special Issue Genetic Analysis of Quantitative Traits in Plants)
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