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Search Results (1,730)

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20 pages, 10732 KiB  
Article
Pangenome Data Analysis Reveals Characteristics of Resistance Gene Analogs Associated with Sclerotinia sclerotiorum Resistance in Sunflower
by Yan Lu, Jiaying Huang, Dongqi Liu, Xiangjiu Kong, Yang Song and Lan Jing
Life 2024, 14(10), 1322; https://doi.org/10.3390/life14101322 - 17 Oct 2024
Abstract
The sunflower, an important oilseed crop and food source across the world, is susceptible to several pathogens, which cause severe losses in sunflower production. The utilization of genetic resistance is the most economical, effective measure to prevent infectious diseases. Based on the sunflower [...] Read more.
The sunflower, an important oilseed crop and food source across the world, is susceptible to several pathogens, which cause severe losses in sunflower production. The utilization of genetic resistance is the most economical, effective measure to prevent infectious diseases. Based on the sunflower pangenome, in this study, we explored the variability of resistance gene analogs (RGAs) within the species. According to a comparative analysis of RGA candidates in the sunflower pangenome using the RGAugury pipeline, a total of 1344 RGAs were identified, comprising 1107 conserved, 199 varied, and 38 rare RGAs. We also identified RGAs associated with resistance against Sclerotinia sclerotiorum (S. sclerotiorum) in sunflower at the quantitative trait locus (QTL). A total of 61 RGAs were found to be located at four quantitative trait loci (QTLs). Through a detailed expression analysis of RGAs in one susceptible and two tolerant sunflower inbred lines (ILs) across various time points post inoculation, we discovered that 348 RGAs exhibited differential expression in response to Sclerotinia head rot (SHR), with 17 of these differentially expressed RGAs being situated within the QTL regions. In addition, 15 RGA candidates had gene introgression. Our data provide a better understanding of RGAs, which facilitate genomics-based improvements in disease resistance in sunflower. Full article
(This article belongs to the Section Plant Science)
14 pages, 3179 KiB  
Article
Combined BSA-Seq and RNA-Seq Analysis to Identify Candidate Genes Associated with Aluminum Toxicity in Rapeseed (Brassica napus L.)
by Huiwen Zhou, Paolan Yu, Lanhua Wu, Depeng Han, Yang Wu, Wei Zheng, Qinghong Zhou and Xiaojun Xiao
Int. J. Mol. Sci. 2024, 25(20), 11190; https://doi.org/10.3390/ijms252011190 - 17 Oct 2024
Abstract
Exchangeable aluminum (Al) ions released from acidic soils with pH < 5.5 inhibit root elongation of crops, ultimately leading to yield reduced. It is necessary to identify the quantitative trait locus (QTLs) and candidate genes that confer toxicity resistance to understand the mechanism [...] Read more.
Exchangeable aluminum (Al) ions released from acidic soils with pH < 5.5 inhibit root elongation of crops, ultimately leading to yield reduced. It is necessary to identify the quantitative trait locus (QTLs) and candidate genes that confer toxicity resistance to understand the mechanism and improve tolerance of rapeseed. In this study, an F2 segregating population was derived from a cross between Al-tolerance inbred line FDH188 (R178) and -sensitive inbred line FDH152 (S169), and the F2:3 were used as materials to map QTLs associated with the relative elongation of taproot (RET) under Al toxicity stress. Based on bulked segregant analysis sequencing (BSA-seq), three QTLs (qAT-A07-1, qAT-A07-2, and qAT-A09-1) were detected as significantly associated with RET, and 656 candidate genes were screened. By combined BSA and RNA-seq analysis, 55 candidate genes showed differentially expressed, including genes encoding ABC transporter G (ABCG), zinc finger protein, NAC, ethylene-responsive transcription factor (ERF), etc. These genes were probably positive factors in coping with Al toxicity stress in rapeseed. This study provides new insight into exploring the QTLs and candidate genes’ response to Al toxicity stress by combined BSA-seq and RNA-seq and is helpful to further research on the mechanism of Al resistance in rapeseed. Full article
(This article belongs to the Section Molecular Genetics and Genomics)
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Figure 1
<p>The hydroponic phenotypic of R178 (ATL) and S169 (ASL) under Al toxicity stress. Asterisks indicate significant differences between ATL and ASL (<span class="html-italic">t</span> test, ** <span class="html-italic">p</span> &lt; 0.01).</p>
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<p>The frequency distribution of RET of the F<sub>2:3</sub> population under Al toxicity stress.</p>
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<p>SNP-index distribution map on the whole genome. The black line is for the delta-SNP-index curve and the red line is the threshold at 99% confidence interval.</p>
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<p>The Indel-index distribution map on the whole genome. The black line is for the delta-Indel-index curve and the red line is the threshold at 99% confidence interval.</p>
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16 pages, 3474 KiB  
Article
Quantitative Trait Locus Mapping Combined with RNA Sequencing Identified Candidate Genes for Resistance to Powdery Mildew in Bitter Gourd (Momordica charantia L.)
by Rukui Huang, Jiazuo Liang, Xixi Ju, Yuhui Huang, Xiongjuan Huang, Xiaofeng Chen, Xinglian Liu and Chengcheng Feng
Int. J. Mol. Sci. 2024, 25(20), 11080; https://doi.org/10.3390/ijms252011080 (registering DOI) - 15 Oct 2024
Viewed by 280
Abstract
Improving the powdery mildew resistance of bitter gourd is highly important for achieving high yield and high quality. To better understand the genetic basis of powdery mildew resistance in bitter gourd, this study analyzed 300 lines of recombinant inbred lines (RILs) formed by [...] Read more.
Improving the powdery mildew resistance of bitter gourd is highly important for achieving high yield and high quality. To better understand the genetic basis of powdery mildew resistance in bitter gourd, this study analyzed 300 lines of recombinant inbred lines (RILs) formed by hybridizing the powdery mildew-resistant material MC18 and the powdery mildew-susceptible material MC402. A high-density genetic map of 1222.04 cM was constructed via incorporating 1,996,505 SNPs generated by resequencing data from 180 lines, and quantitative trait locus (QTL) positioning was performed using phenotypic data at different inoculation stages. A total of seven QTLs related to powdery mildew resistance were identified on four chromosomes, among which qPm-3-1 was detected multiple times and at multiple stages after inoculation. By selecting 18 KASP markers that were evenly distributed throughout the region, 250 lines and parents were genotyped, and the interval was narrowed to 207.22 kb, which explained 13.91% of the phenotypic variation. Through RNA-seq analysis of the parents, 11,868 differentially expressed genes (DEGs) were screened. By combining genetic analysis, gene coexpression, and sequence comparison analysis of extreme materials, two candidate genes controlling powdery mildew resistance in bitter gourd were identified (evm.TU.chr3.2934 (C3H) and evm.TU.chr3.2946 (F-box-LRR)). These results represent a step forward in understanding the genetic regulatory network of powdery mildew resistance in bitter gourd and lay a molecular foundation for the genetic improvement in powdery mildew resistance. Full article
(This article belongs to the Section Molecular Plant Sciences)
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Figure 1
<p>Distribution of bin markers on bitter gourd chromosomes.</p>
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<p>Chromosomal distribution of PM-resistant QTLs in a bitter gourd RIL population.</p>
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<p>Fine mapping of <span class="html-italic">qPm-3-1</span>.</p>
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<p>(<b>a</b>) Number of upregulated and downregulated DEGs within the materials. (<b>b</b>) Venn diagram of DEGs within the materials. (<b>c</b>) Number of upregulated and downregulated DEGs at different stages within the materials. (<b>d</b>) Venn diagram of DEGs at different stages within the materials.</p>
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<p>(<b>a</b>) GO enrichment analysis of all DEGs. (<b>b</b>) KEGG enrichment analysis of all DEGs.</p>
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<p>(<b>a</b>) Hierarchical clustering tree of genes identified via coexpression network analysis. (<b>b</b>) Heatmap of significant correlations between modules and different inoculation periods. (<b>c</b>) Gene coexpression network within the red, green, pink, and tan modules.</p>
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<p>(<b>a</b>) Process of identifying candidate genes in the <span class="html-italic">qPm-3-1</span> interval by combining QTL mapping, fine mapping, differential expression analysis, coexpression network, and sequence comparison analysis. (<b>b</b>) qRT–PCR detection of candidate gene expression, n = 3, * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01. (<b>c</b>) SNPs or Indels in <span class="html-italic">evm.TU.chr3.2934</span> between parents and extreme materials and the difference between the disease severity rates (DSRs) of the two genotypes. (<b>d</b>) SNPs or Indels in <span class="html-italic">evm.TU.chr3.2946</span> between parents and extreme materials and the difference between the DSR of the two genotypes.</p>
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25 pages, 1476 KiB  
Review
Unraveling the Secrets of Early-Maturity and Short-Duration Bread Wheat in Unpredictable Environments
by Charan Singh, Sapna Yadav, Vikrant Khare, Vikas Gupta, Umesh R. Kamble, Om P. Gupta, Ravindra Kumar, Pawan Saini, Rakesh K. Bairwa, Rinki Khobra, Sonia Sheoran, Satish Kumar, Ankita K. Kurhade, Chandra N. Mishra, Arun Gupta, Bhudeva S. Tyagi, Om P. Ahlawat, Gyanendra Singh and Ratan Tiwari
Plants 2024, 13(20), 2855; https://doi.org/10.3390/plants13202855 - 12 Oct 2024
Viewed by 695
Abstract
In response to the escalating challenges posed by unpredictable environmental conditions, the pursuit of early maturation in bread wheat has emerged as a paramount research endeavor. This comprehensive review delves into the multifaceted landscape of strategies and implications surrounding the unlocking of early [...] Read more.
In response to the escalating challenges posed by unpredictable environmental conditions, the pursuit of early maturation in bread wheat has emerged as a paramount research endeavor. This comprehensive review delves into the multifaceted landscape of strategies and implications surrounding the unlocking of early maturation in bread wheat varieties. Drawing upon a synthesis of cutting-edge research in genetics, physiology, and environmental science, this review elucidates the intricate mechanisms underlying early maturation and its potential ramifications for wheat cultivation in dynamic environments. By meticulously analyzing the genetic determinants, physiological processes, and environmental interactions shaping early maturation, this review offers valuable insights into the complexities of this trait and its relevance in contemporary wheat breeding programs. Furthermore, this review critically evaluates the trade-offs inherent in pursuing early maturation, navigating the delicate balance between accelerated development and optimal yield potential. Through a meticulous examination of both challenges and opportunities, this review provides a comprehensive framework for researchers, breeders, and agricultural stakeholders to advance our understanding and utilization of early maturation in bread wheat cultivars, ultimately fostering resilience and sustainability in wheat production systems worldwide. Full article
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<p>Characteristics of early-maturing wheat genotypes.</p>
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<p>The procedure of speed breeding in variety release through shortening the life cycle of wheat.</p>
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<p>The proposed mechanisms by [<a href="#B44-plants-13-02855" class="html-bibr">44</a>,<a href="#B126-plants-13-02855" class="html-bibr">126</a>,<a href="#B130-plants-13-02855" class="html-bibr">130</a>,<a href="#B131-plants-13-02855" class="html-bibr">131</a>,<a href="#B141-plants-13-02855" class="html-bibr">141</a>] illustrate the molecular basis of the genetic control of earliness.</p>
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14 pages, 2640 KiB  
Article
SNP-Based and Kmer-Based eQTL Analysis Using Transcriptome Data
by Mei Ge, Chenyu Li and Zhiyan Zhang
Animals 2024, 14(20), 2941; https://doi.org/10.3390/ani14202941 - 11 Oct 2024
Viewed by 359
Abstract
Traditional expression quantitative trait locus (eQTL) mapping associates single nucleotide polymorphisms (SNPs) with gene expression, where the SNPs are derived from large-scale whole-genome sequencing (WGS) data or transcriptome data. While WGS provides a high SNP density, it also incurs substantial sequencing costs. In [...] Read more.
Traditional expression quantitative trait locus (eQTL) mapping associates single nucleotide polymorphisms (SNPs) with gene expression, where the SNPs are derived from large-scale whole-genome sequencing (WGS) data or transcriptome data. While WGS provides a high SNP density, it also incurs substantial sequencing costs. In contrast, RNA-seq data, which are more accessible and less expensive, can simultaneously yield gene expressions and SNPs. Thus, eQTL analysis based on RNA-seq offers significant potential applications. Two primary strategies were employed for eQTL in this study. The first involved analyzing expression levels in relation to variant sites detected between populations from RNA-seq data. The second approach utilized kmers, which are sequences of length k derived from RNA-seq reads, to represent variant sites and associated these kmer genotypes with gene expression. We discovered 87 significant association signals involving eGene on the basis of the SNP-based eQTL analysis. These genes include DYNLT1, NMNAT1, and MRLC2, which are closely related to neurological functions such as motor coordination and homeostasis, play a role in cellular energy metabolism, and function in regulating calcium-dependent signaling in muscle contraction, respectively. This study compared the results obtained from eQTL mapping using RNA-seq identified SNPs and gene expression with those derived from kmers. We found that the vast majority (23/30) of the association signals overlapping the two methods could be verified by haplotype block analysis. This comparison elucidates the strengths and limitations of each method, providing insights into their relative efficacy for eQTL identification. Full article
(This article belongs to the Section Animal Genetics and Genomics)
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<p>Schematic diagram of the study design. (<b>A</b>) SNP consistency checkup using WGS and RNA-seq data (left panel, green); (<b>B</b>) assessment of the two methods of eQTL analysis (right panel, blue).</p>
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<p>Comparison of the consistency of SNPs identified by WGS and RNA-seq. The <span class="html-italic">x</span> axis indicates the name of the sample, and the <span class="html-italic">y</span> axis indicates the percentage of consistency.</p>
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<p>Manhattan plots of the association results of the representative genes. Manhattan plots of (<b>A</b>) <span class="html-italic">DYNLT1</span>, (<b>B</b>) <span class="html-italic">NMNAT1</span>, and (<b>C</b>) <span class="html-italic">MRLC2</span>. The red dashed line in the Manhattan plots represents the level of significance.</p>
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<p>Manhattan plots of the kmer-based association results of the representative genes. Manhattan plots of (<b>A</b>) <span class="html-italic">DAAM2</span>, (<b>B</b>) <span class="html-italic">CEP70</span>, and (<b>C</b>) <span class="html-italic">LRRC8B</span>. The red dashed line in the Manhattan plots represents the level of significance.</p>
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<p>Manhattan plots of both association results of the representative genes. Manhattan plots of (<b>A</b>) <span class="html-italic">ZWILCH</span>, (<b>B</b>) <span class="html-italic">ZWILCH</span>, (<b>C</b>) <span class="html-italic">PCMTD2</span>, and (<b>D</b>) <span class="html-italic">PCMTD2</span>. The top panel shows the association results based on SNPs and the bottom panel shows the association results based on kmers. The red dashed line in the Manhattan plots represents the level of significance.</p>
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<p>Boxplots of the validation results of the representative genes. Boxplots of (<b>A</b>) <span class="html-italic">DYNLT1</span>, (<b>B</b>) <span class="html-italic">NMNAT1</span>, (<b>C</b>) <span class="html-italic">MRLC2</span>, and (<b>D</b>) <span class="html-italic">PCMTD2</span>.</p>
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16 pages, 2672 KiB  
Article
QTL Mapping-Based Identification of Visceral White-Nodules Disease Resistance Genes in Larimichthys polyactis
by Qian Li, Jiajie Zhu, Sifang Liu, Haowen Liu, Tianle Zhang, Ting Ye, Bao Lou and Feng Liu
Int. J. Mol. Sci. 2024, 25(20), 10872; https://doi.org/10.3390/ijms252010872 - 10 Oct 2024
Viewed by 374
Abstract
Disease outbreaks in aquaculture have recently intensified. In particular, visceral white-nodules disease, caused by Pseudomonas plecoglossicida, has severely hindered the small yellow croaker (Larimichthys polyactis) aquaculture industry. However, research on this disease is limited. To address this gap, the present [...] Read more.
Disease outbreaks in aquaculture have recently intensified. In particular, visceral white-nodules disease, caused by Pseudomonas plecoglossicida, has severely hindered the small yellow croaker (Larimichthys polyactis) aquaculture industry. However, research on this disease is limited. To address this gap, the present study employed a 100K SNP chip to genotype individuals from an F1 full-sib family, identify single nucleotide polymorphisms (SNPs), and construct a genetic linkage map for this species. A high-density genetic linkage map spanning a total length of 1395.72 cM with an average interval of 0.08 cM distributed across 24 linkage groups was obtained. Employing post-infection survival time as an indicator of disease resistance, 13 disease resistance-related quantitative trait loci (QTLs) were detected, and these regions included 169 genes. Functional enrichment analyses pinpointed 11 candidate disease resistance-related genes. RT-qPCR analysis revealed that the genes of chmp1a and arg1 are significantly differentially expressed in response to P. plecoglossicida infection in spleen and liver tissues, indicating their pivotal functions in disease resistance. In summary, in addition to successfully constructing a high-density genetic linkage map, this study reports the first QTL mapping for visceral white-nodules disease resistance. These results provide insight into the intricate molecular mechanisms underlying disease resistance in the small yellow croaker. Full article
(This article belongs to the Section Molecular Genetics and Genomics)
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<p>Genetic linkage map. Length and marker distribution of 24 linkage groups (LGs) in the bin map. The ordinate indicates the genetic distance. The abscissa indicates the linkage groups. Green represents the chromosome, and blue represents the bin markers and their genetic distance on the linkage groups.</p>
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<p>Mapping of disease resistance-related QTLs. The horizontal axis at the top indicates linkage group numbers. The horizontal axis at the bottom indicates the genetic distance for each linkage group. The vertical axis represents LOD values. The red line indicates the determined LOD threshold (=3).</p>
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<p>GO functional enrichment analysis for the genes. Top 30 significant enriched GO terms. Most of the genes were significantly assigned to the category of the protein complex, nuclear part, establishment of localization in the cell, cellular localization, intracellular transport, cytokine receptor binding, and so on.</p>
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<p>KEGG functional enrichment analysis for genes. The top 20 significant enriched pathways are shown, in which the most enriched pathways included endocytosis, the MAPK signaling pathway, the Fanconi anemia pathway, the biosynthesis of amino acids, sphingolipid metabolism, inositol phosphate metabolism, arginine and proline metabolism, and so on.</p>
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<p>Expression levels in spleen (<b>A</b>) and liver (<b>B</b>) tissues. In the test group, fish were injected with <span class="html-italic">P. plecoglossicida</span> for 72 h; in the control group, fish were injected with TSB solution for 72 h. In spleen tissue, <span class="html-italic">pten, chmp1a</span>, <span class="html-italic">arg1</span>, <span class="html-italic">chmp2a</span>, <span class="html-italic">chmp6</span>, and <span class="html-italic">map2k6</span> levels differed significantly between groups. In liver tissue, <span class="html-italic">tat</span>, <span class="html-italic">asah2</span>, <span class="html-italic">chmp1a</span>, and <span class="html-italic">arg1</span> levels differed significantly between groups. * <span class="html-italic">p</span> ≤ 0.05; ** <span class="html-italic">p</span> ≤ 0.01; *** <span class="html-italic">p</span> ≤ 0.001.</p>
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21 pages, 3802 KiB  
Article
Mining of Oil Content Genes in Recombinant Maize Inbred Lines with Introgression from Temperate and Tropical Germplasm
by Mengfei Shi, Jiachen Sun, Fuyan Jiang, Ranjan K. Shaw, Babar Ijaz and Xingming Fan
Int. J. Mol. Sci. 2024, 25(19), 10813; https://doi.org/10.3390/ijms251910813 - 8 Oct 2024
Viewed by 383
Abstract
The oil content of maize kernels is essential to determine its nutritional and economic value. A multiparent population (MPP) consisting of five recombinant inbred line (RIL) subpopulations was developed to elucidate the genetic basis of the total oil content (TOC) in maize. The [...] Read more.
The oil content of maize kernels is essential to determine its nutritional and economic value. A multiparent population (MPP) consisting of five recombinant inbred line (RIL) subpopulations was developed to elucidate the genetic basis of the total oil content (TOC) in maize. The MPP used the subtropical maize inbred lines CML312 and CML384, along with the tropical maize inbred lines CML395, YML46, and YML32 as the female parents, and Ye107 as the male parent. A genome-wide association study (GWAS) was performed using 429 RILs of the multiparent population across three environments, employing 584,847 high-quality single nucleotide polymorphisms (SNPs). Furthermore, linkage analysis was performed in the five subpopulations to identify quantitative trait loci (QTL) linked to TOC in maize. Through QTL mapping and GWAS, 18 QTLs and 60 SNPs that were significantly associated with TOC were identified. Two novel candidate genes, Zm00001d029550 and Zm00001d029551, related to TOC in maize and located on chromosome 1 were reported, which have not been previously reported. These genes are involved in biosynthesis, lipid signal transduction, plant development and metabolism, and stress responses, potentially influencing maize TOC. Haplotype analysis of Zm00001d029550 and Zm00001d029551 revealed that Hap3 could be considered a superior haplotype for increasing TOC in maize. A co-located SNP (SNP-75791466) on chromosome 1, located 5648 bp and 11,951 bp downstream of the candidate genes Zm00001d029550 and Zm00001d029551, respectively, was found to be expressed in various maize tissues. The highest expression was observed in embryos after pollination, indicating that embryos are the main tissue for oil accumulation in maize. This study provides a theoretical basis for understanding the genetic mechanisms underlying maize TOC and developing high-quality, high-oil maize varieties. Full article
(This article belongs to the Special Issue Plant Physiology and Molecular Nutrition)
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<p>Correlations between pop1, pop2, pop3, pop4, and pop5 for TOC in three environments. (<b>a</b>) Correlation of pop1 for TOC between the 22YS, 23JH, and 21YS environments; (<b>b</b>) correlation of pop2 for TOC between the 22YS, 23JH, and 21YS environments; (<b>c</b>) correlation of pop3 for TOC between the 22YS, 23JH, and 21YS environments; (<b>d</b>) correlation of pop4 for TOC response between the 22YS, 23JH, and 21YS environments; (<b>e</b>) correlation of pop5 for TOC between the 22YS, 23JH, and 21YS environments. ** indicates <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>Genetic diversity analysis of the 429 RILs of the MPP. (<b>a</b>) Phylogenetic tree. (<b>b</b>) Principal component analysis. (<b>c</b>) Bayesian clustering plots of 429 maize RILs at K = 5.</p>
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<p>The LD decay plot of the MPP.</p>
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<p>Manhattan map (<b>left</b>) and Q–Q plots (<b>right</b>) for (<b>a</b>) 22YS environment, (<b>b</b>) 23JH environ–ment, and (<b>c</b>) 21YS environment; (<b>d</b>) BLUP values indicate the SNPs associated with TOC. In the Manhattan plot, each dot represents an SNP, and the black line denotes the threshold value. Differ–ent colors in the Manhattan plot represent different chromosomes. In the Q–Q plot, the red line denotes the expected significance value, while the blue dots represent the observed significance val–ues.</p>
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<p>QTL mapping for TOC in Pop3. Blue bands represent the bin markers and orange boxes indicate the significant QTLs linked to TOC.</p>
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<p>The identification of candidate genes related to maize TOC (<b>a</b>) QTLs identified on different chromosomes of maize in the 23JH environment in pop3. (<b>b</b>) The location of the most significant SNPs on chromosome 1, as identified through GWAS. (<b>c</b>) Haplotype analysis of candidate gene <span class="html-italic">Zm00001d029550</span> for TOC in the MPP. (<b>d</b>) The haplotype distribution of <span class="html-italic">Zm00001d029550</span> in five subpopulations, ** represents <span class="html-italic">p</span> ≤ 0.01. (<b>e</b>) The expression levels (FPKM) of the candidate gene <span class="html-italic">Zm00001d029550</span> in different tissues, with the highest level observed in embryos after pollination. (<b>f</b>) The position of <span class="html-italic">Zm0001d029550</span> and the associated SNP.</p>
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<p>(<b>a</b>) The location of the significant SNPs on chromosome 1 identified through GWAS. (<b>b</b>) Haplotype analysis of candidate gene <span class="html-italic">Zm00001d029551</span> for TOC in the MPP. (<b>c</b>) The haplotype dis–tribution of <span class="html-italic">Zm00001d029551</span> in five subpopulations. ** represents <span class="html-italic">p</span> ≤ 0.01. (<b>d</b>) The position of <span class="html-italic">Zm0001d029551</span> and the associated SNP.</p>
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15 pages, 2057 KiB  
Article
Screening Germplasms and Detecting Quantitative Trait Loci for High Sucrose Content in Soybean
by Se-Hee Kang, Seo-Young Shin, Byeong Hee Kang, Sreeparna Chowdhury, Won-Ho Lee, Woon Ji Kim, Jeong-Dong Lee, Sungwoo Lee, Yu-Mi Choi and Bo-Keun Ha
Plants 2024, 13(19), 2815; https://doi.org/10.3390/plants13192815 - 8 Oct 2024
Viewed by 450
Abstract
Sucrose is a desirable component of processed soybean foods and animal feed, and thus, its content is used as an important characteristic for assessing the quality of soybean seeds. However, few studies have focused on the quantitative trait loci (QTLs) associated with sucrose [...] Read more.
Sucrose is a desirable component of processed soybean foods and animal feed, and thus, its content is used as an important characteristic for assessing the quality of soybean seeds. However, few studies have focused on the quantitative trait loci (QTLs) associated with sucrose regulation in soybean seeds. This study aims to measure the sucrose content of 1014 soybean accessions and identify genes related to high sucrose levels using QTL analysis. Colorimetric analysis based on the enzymatic reaction of invertase (INV) and glucose oxidase (GOD) was employed to test the germplasms. A total of six high-sucrose genetic resources (IT186230, IT195321, IT263138, IT263276, IT263286, and IT276521) and two low-sucrose genetic resources (IT025668 and IT274054) were identified. Two F2:3 populations, IT186230 × IT025668 and Ilmi × IT186230, were then established from these germplasms. QTL analysis identified four QTLs (qSUC6.1, qSUC11.1, qSUC15.1, and qSUC17.1), explaining 7.3–27.6% of the phenotypic variation in the sugar content. Twenty candidate genes were found at the four QTLs. Notably, Glyma.17G152300, located in the qSUC17.1 QTL region, exhibited a 17-fold higher gene expression in the high-sucrose germplasm IT186230 compared to the control germplasm Ilmi, confirming its role as a major gene regulating the sucrose content in soybean. These results may assist in marker-assisted selection for breeding programs that aim to develop soybean lines with a higher sucrose content. Full article
(This article belongs to the Special Issue Genomic Selection and Marker-Assisted Breeding in Crops)
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<p>Distribution of sucrose content in 1014 soybean accessions analyzed using GOD/INV method.</p>
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<p>Frequency distribution of sucrose content in F<sub>2:3</sub> populations derived from crosses of (<b>A</b>) IT186230 × IT025668 and (<b>B</b>) Ilmi × IT186230.</p>
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<p>Results of QTL mapping analysis. QTL peak map for soybean Chr. 06 from the F<sub>2:3</sub> population derived from a cross between IT186230 and IT0255668.</p>
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<p>Results of QTL mapping analysis. The QTL peak map for soybean (<b>A</b>) Chr. 11, (<b>B</b>) Chr. 15, and (<b>C</b>) Chr. 17 from the F<sub>2:3</sub> population derived from a cross between Ilmi and IT186230.</p>
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<p>Relative expression levels of three candidate genes in seeds of IT186230 and Ilmi during the intermediate stage between R5 and R6 in seed development. (<b>A</b>) <span class="html-italic">Glyma.15G210400</span>. (<b>B</b>) <span class="html-italic">Glyma.17G137500</span>. (<b>C</b>) <span class="html-italic">Glyma.17G152300</span>. <span class="html-italic">GmActin11</span> was used as an internal control. Results are expressed as the mean and standard error (SE). * Indicates a significant difference in relative expression levels between IT186230 and Ilmi at the 0.05 level, determined using Student’s <span class="html-italic">t</span>-tests.</p>
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17 pages, 12032 KiB  
Article
Investigating the Causal Effects of Exercise-Induced Genes on Sarcopenia
by Li Wang and Song Zhang
Int. J. Mol. Sci. 2024, 25(19), 10773; https://doi.org/10.3390/ijms251910773 - 7 Oct 2024
Viewed by 445
Abstract
Exercise is increasingly recognized as an effective strategy to counteract skeletal muscle aging and conditions such as sarcopenia. However, the specific exercise-induced genes responsible for these protective effects remain unclear. To address this, we conducted an eight-week aerobic exercise regimen on late-middle-aged mice [...] Read more.
Exercise is increasingly recognized as an effective strategy to counteract skeletal muscle aging and conditions such as sarcopenia. However, the specific exercise-induced genes responsible for these protective effects remain unclear. To address this, we conducted an eight-week aerobic exercise regimen on late-middle-aged mice and developed an integrated approach that combines mouse exercise-induced genes with human GWAS datasets to identify causal genes for sarcopenia. This approach led to significant improvements in the skeletal muscle phenotype of the mice and the identification of exercise-induced genes and miRNAs. By constructing a miRNA regulatory network enriched with transcription factors and GWAS signals related to muscle function and traits, we focused on 896 exercise-induced genes. Using human skeletal muscle cis-eQTLs as instrumental variables, 250 of these exercise-induced genes underwent two-sample Mendelian randomization analysis, identifying 40, 68, and 62 causal genes associated with sarcopenia and its clinical indicators—appendicular lean mass (ALM) and hand grip strength (HGS), respectively. Sensitivity analyses and cross-phenotype validation confirmed the robustness of our findings. Consistently across the three outcomes, RXRA, MDM1, RBL2, KCNJ2, and ADHFE1 were identified as risk factors, while NMB, TECPR2, MGAT3, ECHDC2, and GINM1 were identified as protective factors, all with potential as biomarkers for sarcopenia progression. Biological activity and disease association analyses suggested that exercise exerts its anti-sarcopenia effects primarily through the regulation of fatty acid oxidation. Based on available drug–gene interaction data, 21 of the causal genes are druggable, offering potential therapeutic targets. Our findings highlight key genes and molecular pathways potentially responsible for the anti-sarcopenia benefits of exercise, offering insights into future therapeutic strategies that could mimic the safe and mild protective effects of exercise on age-related skeletal muscle degeneration. Full article
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<p>Histological assessment and RNA-Seq analysis of aging skeletal muscle under exercise intervention. (<b>a</b>) Representative images of hematoxylin and eosin (H&amp;E)-stained cross-sections of quadriceps muscle from old and oldEx groups. Scale bars, 100 μm. (<b>b</b>) Sample similarity clustering. Pairwise correlation (Pearson) among all samples was calculated from the gene expression matrix. (<b>c</b>) Heatmap displaying expression profiles of the DEGs (oldEx vs. old, adjusted <span class="html-italic">p</span>-value &lt; 0.05) through hierarchical clustering. Each column represents a sample, and each row represents a gene. The color scale indicates the raw Z-score, ranging from red (high expression) to blue (low expression). (<b>d</b>,<b>e</b>) The top 20 GO biological processes (<b>d</b>) and top 20 KEGGs signaling pathways (<b>e</b>) enriched by the DEGs.</p>
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<p>Construction of an exercise-induced miRNA regulatory network and analysis of its upstream TFs. (<b>a</b>) Heatmap displaying expression profiles of the DEmiRs (oldEx vs. old, adjusted <span class="html-italic">p</span>-value &lt; 0.05) through hierarchical clustering. Each column represents a sample, and each row represents a gene. The color scale indicates the raw Z-score, ranging from red (high expression) to blue (low expression). (<b>b</b>) Potential miRNA–target relationships between DEGs and DEmiRs. DEGs and DEmiRs exhibiting inverse expression changes were further filtered by the starBase database based on predictions of targeted degradation. (<b>c</b>) The enrichment analysis of upstream TFs for these miRNAs in the network is conducted through a hypergeometric test. Red dots represent significantly enriched TFs (<span class="html-italic">p</span>-value &lt; 0.05). The upstream TFs of miRNAs are sourced from the TransmiR database. (<b>d</b>) Enrichment analysis of GWAS trait-related SNPs within the TRRs of the network. These SNPs were sourced from the GWAS catalog database and showed significant associations with the target traits, meeting a stringent significance threshold of <span class="html-italic">p</span> &lt; 5 × 10<sup>−8</sup>. Red dots indicate FDR &lt; 0.05.</p>
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<p>Causal estimates of genes associated with sarcopenia-related traits. (<b>a</b>) An overview of the MR framework. The genes under investigation are derived from the miRNA–target network. IV, instrumental variables; <span class="html-italic">cis</span>-eQTLs, cis expression quantitative trait loci; IVW, inverse variance weighted. (<b>b</b>–<b>d</b>) Identification of causal genes for sarcopenia (<b>b</b>), ALM (<b>c</b>), and HGS (<b>d</b>). Genes meeting the significance criteria for MR assessment but exhibiting horizontal pleiotropy or heterogeneity in IVs were excluded. OR (Odds Ratio) is used for binary outcomes, while beta values are used for continuous outcome variables in the MR analysis to quantify the magnitude of causal effects. pve, proportion of variance explained. (<b>e</b>) Intersection of causal genes for sarcopenia, ALM, and HGS.</p>
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<p>Causal effects of 10 shared genes associated with sarcopenia-related traits. (<b>a</b>–<b>c</b>) Forest plots showing the two-sample MR estimation of the association between 10 shared causal genes and three sarcopenia-related traits: sarcopenia (<b>a</b>), ALM (<b>b</b>), and HGS (<b>c</b>). nsnp, number of single nucleotide polymorphisms; CI, confidence interval.</p>
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<p>Functions and druggable potential of causal genes in sarcopenia. (<b>a</b>–<b>d</b>) The Enrichr platform was used to explore the significant biological processes (<b>a</b>), cellular signaling pathways (<b>b</b>), human diseases (<b>c</b>), and drugs (<b>d</b>) associated with these genes using the GO Biological Process 2023, WikiPathway 2023 Human, ClinVar 2019, and DSigDB options, respectively. Red indicates a significance level of padj &lt; 0.05, while blue indicates a significance level of padj ≥ 0.05. (<b>e</b>) Screening of druggable genes among the 113 causal genes for sarcopenia. A total of 21 druggable genes were identified and are displayed within the rectangle.</p>
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31 pages, 4454 KiB  
Article
Exploring Novel Genomic Loci and Candidate Genes Associated with Plant Height in Bulgarian Bread Wheat via Multi-Model GWAS
by Tania Kartseva, Vladimir Aleksandrov, Ahmad M. Alqudah, Matías Schierenbeck, Krasimira Tasheva, Andreas Börner and Svetlana Misheva
Plants 2024, 13(19), 2775; https://doi.org/10.3390/plants13192775 - 3 Oct 2024
Viewed by 480
Abstract
In the context of crop breeding, plant height (PH) plays a pivotal role in determining straw and grain yield. Although extensive research has explored the genetic control of PH in wheat, there remains an opportunity for further advancements by integrating genomics with growth-related [...] Read more.
In the context of crop breeding, plant height (PH) plays a pivotal role in determining straw and grain yield. Although extensive research has explored the genetic control of PH in wheat, there remains an opportunity for further advancements by integrating genomics with growth-related phenomics. Our study utilizes the latest genome-wide association scan (GWAS) techniques to unravel the genetic basis of temporal variation in PH across 179 Bulgarian bread wheat accessions, including landraces, tall historical, and semi-dwarf modern varieties. A GWAS was performed with phenotypic data from three growing seasons, the calculated best linear unbiased estimators, and the leveraging genotypic information from the 25K Infinium iSelect array, using three statistical methods (MLM, FarmCPU, and BLINK). Twenty-five quantitative trait loci (QTL) associated with PH were identified across fourteen chromosomes, encompassing 21 environmentally stable quantitative trait nucleotides (QTNs), and four haplotype blocks. Certain loci (17) on chromosomes 1A, 1B, 1D, 2A, 2D, 3A, 3B, 4A, 5B, 5D, and 6A remain unlinked to any known Rht (Reduced height) genes, QTL, or GWAS loci associated with PH, and represent novel regions of potential breeding significance. Notably, these loci exhibit varying effects on PH, contribute significantly to natural variance, and are expressed during seedling to reproductive stages. The haplotype block on chromosome 6A contains five QTN loci associated with reduced height and two loci promoting height. This configuration suggests a substantial impact on natural variation and holds promise for accurate marker-assisted selection. The potentially novel genomic regions harbor putative candidate gene coding for glutamine synthetase, gibberellin 2-oxidase, auxin response factor, ethylene-responsive transcription factor, and nitric oxide synthase; cell cycle-related genes, encoding cyclin, regulator of chromosome condensation (RCC1) protein, katanin p60 ATPase-containing subunit, and expansins; genes implicated in stem mechanical strength and defense mechanisms, as well as gene regulators such as transcription factors and protein kinases. These findings enrich the pool of semi-dwarfing gene resources, providing the potential to further optimize PH, improve lodging resistance, and achieve higher grain yields in bread wheat. Full article
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<p>Frequency distribution (<b>a</b>) and boxplots (<b>b</b>) for plant height (in cm) in 179 wheat accessions from Bulgaria in three crop seasons and with the best linear unbiased estimator (BLUE) values. The boxplots show the median as a dot and the 1st and 3rd quartile as a box; whiskers depict the non-outlier range.</p>
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<p>Manhattan plots for genome-wide associated scan of plant height (PH) in 179 Bulgarian wheat accessions, detected by (<b>a</b>) a single-locus model, and (<b>b</b>) two multivariate models. The colored horizontal lines indicate the significance threshold at −log<sub>10</sub> (<span class="html-italic">p</span>-value) = FDR (False Discovery Rate), at a significance level of adjusted <span class="html-italic">p</span>-value &lt; 0.001 (in (<b>a</b>)) and <span class="html-italic">p</span>-value &lt; 0.01 (in (<b>b</b>)), computed for each environment. Arrows point to the novel loci associated with PH. In haplotype blocks on chromosomes 1B, 3A, and 6A, only peak SNPs are marked and underlined (see also <a href="#plants-13-02775-t002" class="html-table">Table 2</a>).</p>
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<p>Manhattan plots for genome-wide associated scan of plant height (PH) in 179 Bulgarian wheat accessions, detected by (<b>a</b>) a single-locus model, and (<b>b</b>) two multivariate models. The colored horizontal lines indicate the significance threshold at −log<sub>10</sub> (<span class="html-italic">p</span>-value) = FDR (False Discovery Rate), at a significance level of adjusted <span class="html-italic">p</span>-value &lt; 0.001 (in (<b>a</b>)) and <span class="html-italic">p</span>-value &lt; 0.01 (in (<b>b</b>)), computed for each environment. Arrows point to the novel loci associated with PH. In haplotype blocks on chromosomes 1B, 3A, and 6A, only peak SNPs are marked and underlined (see also <a href="#plants-13-02775-t002" class="html-table">Table 2</a>).</p>
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<p>Scatter plots showing linear regressions (solid red lines) and 95% confidence intervals of the regressions (dashed red lines) of nine selected SNPs with (<b>a</b>) high reducing and (<b>b</b>) high increasing effects on plant height (PH) with PH BLUE values in 179 wheat accessions. The blue dots represent the PH BLUE values of accessions with corresponding number of SNPs.</p>
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<p>Allelic effects on plant height (PH) phenotype showing differences in PH BLUE values between accessions with PH-affecting alleles (left) and accessions with alternative alleles (right) at nine selected stable SNP loci. (<b>a</b>) PH-reducing SNPs; (<b>b</b>) PH-increasing SNPs. In parentheses, the full name of a QTL is noted for the SNPs belonging to a haplotype block; for the remaining SNPs, only the chromosome is annotated. The boxplots show the mean as a dot and the mean ± SE (standard error) as a box; whiskers depict the mean ± 1.96SE range. *, **, *** indicate significant difference at <span class="html-italic">p</span> ≤ 0.05, 0.01, 0.001, respectively, as calculated by Student’s <span class="html-italic">t</span> test.</p>
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<p>Allelic effects on plant height (PH) phenotype showing differences in PH BLUE values between accessions with PH-affecting alleles (left) and accessions with alternative alleles (right) at nine selected stable SNP loci. (<b>a</b>) PH-reducing SNPs; (<b>b</b>) PH-increasing SNPs. In parentheses, the full name of a QTL is noted for the SNPs belonging to a haplotype block; for the remaining SNPs, only the chromosome is annotated. The boxplots show the mean as a dot and the mean ± SE (standard error) as a box; whiskers depict the mean ± 1.96SE range. *, **, *** indicate significant difference at <span class="html-italic">p</span> ≤ 0.05, 0.01, 0.001, respectively, as calculated by Student’s <span class="html-italic">t</span> test.</p>
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<p>Heatmap showing gene expression levels (log<sub>2</sub> tpm, transcripts per million) of selected putative genes controlling plant height in wheat: (<b>a</b>) genes containing significant SNPs or very close to them; (<b>b</b>) genes in the support interval around significant SNPs and haplotype blocks.</p>
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<p>Heatmap showing gene expression levels (log<sub>2</sub> tpm, transcripts per million) of selected putative genes controlling plant height in wheat: (<b>a</b>) genes containing significant SNPs or very close to them; (<b>b</b>) genes in the support interval around significant SNPs and haplotype blocks.</p>
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20 pages, 2121 KiB  
Article
Genome-Wide Association Studies of Agronomic and Quality Traits in Durum Wheat
by Stefan Tsonev, Rangel Dragov, Krasimira Taneva, Nikolai Kirilov Christov, Violeta Bozhanova and Elena Georgieva Todorovska
Agriculture 2024, 14(10), 1743; https://doi.org/10.3390/agriculture14101743 - 3 Oct 2024
Viewed by 473
Abstract
Durum wheat is mainly used for products for human consumption, the quality of which depends on the content of protein and yellow pigments in the semolina. The challenges faced by modern breeding, related to population growth and climate change, imply improvement of both [...] Read more.
Durum wheat is mainly used for products for human consumption, the quality of which depends on the content of protein and yellow pigments in the semolina. The challenges faced by modern breeding, related to population growth and climate change, imply improvement of both grain yields and quality in durum wheat germplasm well adapted to specific agro-climatic conditions. To address those challenges, a better understanding of the genetic architecture of agronomic and quality traits is needed. In the current study we used the Genome-Wide Association Study (GWAS) approach in a panel of Bulgarian and foreign genotypes to define loci controlling agronomic and quality traits in durum wheat. We mapped 26 marker traits associations (MTAs) for four of the six studied traits—grain yield, grain protein content, seed yellow colour (CIELAB b*), and plant height. The greatest number of MTAs was detected for grain yield. Seven MTAs were detected for each grain protein content and seed colour, and one MTA for plant height. Most of the reported associations had confidence intervals overlapping with already reported quantitative trait loci (QTLs). Two loci controlling grain yield were not reported previously. The MTAs reported here may be a valuable tool in future breeding for improvement of both grain yield and quality in durum wheat. Full article
(This article belongs to the Special Issue Breeding and Genetic Research of Cereal Grain Quality)
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<p>Violin plots of the studied traits with pairwise comparison of seasons’ means. GY—grain yield (t/ha); PH—plant height (cm); GPC—grain protein content (%); SL—spike length (cm); TKW—thousand kernel weight (g); SC—seed colour.</p>
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<p>Phenotypic correlations between studied traits in the three seasons: 2019, 2020, and 2021, and the joint analysis across seasons (3Yrs). The correlation indices on a white background are non-significant.</p>
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<p>Genetic structure of the studied association panel. Most probable number of clusters, defined with Evanno method (<b>a</b>). Clusters according to STRUCTURE analysis (<b>b</b>). Most probable number of groups for DAPC was defined using BIC plotted against the number of clusters (<b>c</b>). Grouping of the genotypes in DAPC (<b>d</b>).</p>
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<p>Graphical representation of whole-genome LD decay in the experimental panel. The red line represents the LD decay with physical distance, calculated by the method of Remington et al. [<a href="#B36-agriculture-14-01743" class="html-bibr">36</a>]. The black horizontal line designates a critical value of <span class="html-italic">r</span><sup>2</sup> = 0.3, and the green horizontal line shows <span class="html-italic">r</span><sup>2</sup> = 0.21 derived as 95th percentile of the distribution of LD values between unlinked loci.</p>
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16 pages, 1678 KiB  
Article
Genome-Wide Association Analyses Defined the Interplay between Two Major Loci Controlling the Fruit Texture Performance in a Norwegian Apple Collection (Malus × domestica Borkh.)
by Liv Gilpin, Fabrizio Costa, Dag Røen and Muath Alsheikh
Horticulturae 2024, 10(10), 1049; https://doi.org/10.3390/horticulturae10101049 - 1 Oct 2024
Viewed by 423
Abstract
Increasing consumption of apples (Malus domestica Borkh.) produced in Norway requires the availability of superior cultivars and extended marketability. Favorable texture and slow softening are important traits for consumer appreciation and postharvest performance. Apple texture has been well characterized using both sensory [...] Read more.
Increasing consumption of apples (Malus domestica Borkh.) produced in Norway requires the availability of superior cultivars and extended marketability. Favorable texture and slow softening are important traits for consumer appreciation and postharvest performance. Apple texture has been well characterized using both sensory evaluation and instrumental assessments, and major quantitative trait loci (QTL) have been detected. With texture being targeted as an important trait and markers being publicly available, marker-assisted selection has already been implemented into several breeding programs. When focusing solely on a limited set of markers linked to well-investigated major QTLs, most minor-effect QTLs are normally excluded. To find novel potential SNP markers suitable to assist in selection processes, we selected a subset of accessions from a larger apple collection established in Norway based on the favorable alleles of two markers previously associated with texture, enabling the investigation of a minor part of the variance initially masked by the effect of major loci. The subset was employed to conduct a genome-wide association study aiming to search for associations with texture dynamics and retainability. QTL regions related to texture at harvest, postharvest, and for the storage index were identified on chromosomes 3, 12, and 16. Specifically, the SNPs located on chromosome 12 were shown to be potential novel markers for selection of crispness retention during storage, a valuable storability trait. These newly detected QTLs and underlying SNPs will represent a potential set of markers for the selection of the most favorable accessions characterized by superior fruit texture properties in ongoing breeding programs. Full article
(This article belongs to the Special Issue Advanced Postharvest Technology in Processed Horticultural Products)
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<p>Map of identified texture-related SNP markers across the 60 accessions in NAAC2 (green) and previously reported texture-related genetic markers [<a href="#B25-horticulturae-10-01049" class="html-bibr">25</a>,<a href="#B37-horticulturae-10-01049" class="html-bibr">37</a>,<a href="#B38-horticulturae-10-01049" class="html-bibr">38</a>,<a href="#B39-horticulturae-10-01049" class="html-bibr">39</a>,<a href="#B40-horticulturae-10-01049" class="html-bibr">40</a>,<a href="#B41-horticulturae-10-01049" class="html-bibr">41</a>,<a href="#B42-horticulturae-10-01049" class="html-bibr">42</a>] in apple (black). The two markers (SNP_FB_0003490 and the MdACO1 SNP marker) used for preselecting the subset defined as the NAAC2 in the NAAC1 are marked on chromosome 10, and the identification of the SNP_FB_0003490 marker in the NAAC1 is depicted in the Manhattan plot, using the MLMM GWAS model and FDR correction. Created with <a href="http://BioRender.com" target="_blank">BioRender.com</a>, accessed on 30 September 2024.</p>
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<p>Two-dimensional PCA plots of variables illustrating the fruit texture variability evaluated in the NAAC2, at harvest (<b>A</b>), postharvest (<b>B</b>), and for the storage index (<b>C</b>). The loading variables yield force (P1), maximum force (P2), final force (P3), number of force peaks (P4), force strain area (P5), force linear distance (P6), Young’s Modulus (P7), mean force (P8), number of acoustic peaks (P9), maximum acoustic pressure (P10), mean acoustic pressure (P11), and acoustic linear distance (P12) are colored according to group: mechanical (red) and acoustic (blue). The mechanical parameter “number of force peaks” has been reported [<a href="#B7-horticulturae-10-01049" class="html-bibr">7</a>] to correlate with the acoustic parameters, hence the yellow coloration.</p>
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<p>Manhattan plots using FarmCPU and MLMM with a minor allele frequency ≥5% and FDR corrected significance thresholds of the NAAC2 genotypes, together with phenotypic data collected at harvest, postharvest, and for the storage index in 2022. The mechanical texture signature is depicted at the upper half of the figure (Dim1), and significant associations were detected on chromosome 3 at postharvest and chromosome 16 for the storage index. The acoustic texture signature is depicted at the lower half of the figure (Dim2), and for this texture parameter, chromosome 12 was mapped as a novel region at both harvest and for the storage index.</p>
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<p>Marker associations for Dim1 postharvest/storage index and Dim2 harvest/storage index with a narrowed-in view on chromosomes 3, 12, and 16, with the most significant markers marked in red. The left panels depict the phenotypic distribution of the markers with the most significant signal for the storage index among NAAC2 apple accessions grouped according to their genotype in this SNP. Different lowercase letters are significant differences, defined by Tukey’s multiple comparisons of means at 95% family-wise confidence level between the homozygous and heterozygous alleles.</p>
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26 pages, 5873 KiB  
Article
Genome-Wide Association Study on Body Conformation Traits in Xinjiang Brown Cattle
by Menghua Zhang, Yachun Wang, Qiuming Chen, Dan Wang, Xiaoxue Zhang, Xixia Huang and Lei Xu
Int. J. Mol. Sci. 2024, 25(19), 10557; https://doi.org/10.3390/ijms251910557 - 30 Sep 2024
Viewed by 315
Abstract
Body conformation traits are linked to the health, longevity, reproductivity, and production performance of cattle. These traits are also crucial for herd selection and developing new breeds. This study utilized pedigree information and phenotypic (1185 records) and genomic (The resequencing of 496 Xinjiang [...] Read more.
Body conformation traits are linked to the health, longevity, reproductivity, and production performance of cattle. These traits are also crucial for herd selection and developing new breeds. This study utilized pedigree information and phenotypic (1185 records) and genomic (The resequencing of 496 Xinjiang Brown cattle generated approximately 74.9 billion reads.) data of Xinjiang Brown cattle to estimate the genetic parameters, perform factor analysis, and conduct a genome-wide association study (GWAS) for these traits. Our results indicated that most traits exhibit moderate to high heritability. The principal factors, which explained 59.12% of the total variance, effectively represented body frame, muscularity, rump, feet and legs, and mammary system traits. Their heritability estimates range from 0.17 to 0.73, with genetic correlations ranging from −0.53 to 0.33. The GWAS identified 102 significant SNPs associated with 12 body conformation traits. A few of the SNPs were located near previously reported genes and quantitative trait loci (QTLs), while others were novel. The key candidate genes such as LCORL, NCAPG, and FAM184B were annotated within 500 Kb upstream and downstream of the significant SNPs. Therefore, factor analysis can be used to simplify multidimensional conformation traits into new variables, thus reducing the computational burden. The identified candidate genes from GWAS can be incorporated into the genomic selection of Xinjiang Brown cattle, enhancing the reliability of breeding programs. Full article
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<p>Rank correlation coefficients between the estimated breeding values for each body conformation trait and the estimated breeding values for factor scores for Xinjiang Brown cattle.Note: ST is stature; BD is body depth; CW is chest width; WW is wither width; HLHC is half of leg circumstance; RLH is rear leg height; RAB is rib and bone; RL is rump length; RA is rump angle; HD is heel depth; FA is feet angle; RLSV is rear leg side view; RUH is rear udder height; RUW is rear udder width; MS is medium; UD is udder depth; FUL is fore udder length; FTL is fore teat length; FTD is fore teat diameter; FUA is fore udder attachment; RUL is rear udder length; UB is udder balance; FTP is fore teat placement; RTP is rear teat placement; DC is dairy character; and LS is loin strength. Red means that the correlation coefficient is greater than 0.3, and the darker the color, the greater the correlation coefficient; blue means that the correlation coefficient is less than 0.3, and the darker the color, the smaller the correlation coefficient.</p>
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<p>Factor loading coefficients of body conformation traits for Xinjiang Brown cattle. Note: ST is stature; BD is body depth; CW is chest width; WW is wither width; HLHC is half of leg circumstance; RLH is rear leg height; RAB is rib and bone; RL is rump length; RA is rump angle; HD is heel depth; FA is feet angle; RLSV is rear leg side view; RUH is rear udder height; RUW is rear udder width; MS is medium; UD is udder depth; FUL is fore udder length; FTL is fore teat length; FTD is fore teat diameter; FUA is fore udder attachment; RUL is rear udder length; UB is udder balance; FTP is fore teat placement; RTP is rear teat placement; DC is dairy character; and LS is loin strength. Red means that the correlation coefficient is greater than 0.3, and the darker the color, the greater the correlation coefficient; blue means that the correlation coefficient is less than 0.3, and the darker the color, the smaller the correlation coefficient.</p>
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<p>Population structure map showing the first three principal components of Xinjiang Brown cattle.</p>
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<p>Quantile–quantile (QQ) plot of GWAS analysis for body conformation traits of Xinjiang Brown cattle. Note: ST is stature; CW is chest width; BD is body depth; WW is wither width; HLHC is half of leg circumstance; RLH is rear leg height; RAB is rib and bone; RL is rump length; RW is rump width; RA is rump angle; FA is rump angle; RLSV is rear leg side view; BQ is bone quality; and RLRV is rear leg rear view.</p>
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<p>Quantile–quantile (QQ) plot of GWAS analysis for body conformation traits of Xinjiang Brown cattle. Note: ST is stature; CW is chest width; BD is body depth; WW is wither width; HLHC is half of leg circumstance; RLH is rear leg height; RAB is rib and bone; RL is rump length; RW is rump width; RA is rump angle; FA is rump angle; RLSV is rear leg side view; BQ is bone quality; and RLRV is rear leg rear view.</p>
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<p>Manhattan plots of body frame traits (stature, body depth, and chest width) in Xinjiang Brown cattle. Note: ST is stature of body frame traits, BD is body depth of body frame traits and CW is chest width body frame traits. Different colors represent different chromosomes, and the number on the horizontal coordinate is the chromosome number.</p>
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<p>Manhattan plots of muscularity traits (wither width, half of leg circumstance, rear leg height, and rib and bone) in Xinjiang Brown cattle. Note: WW is wither width; HLHC is half of leg circumstance; RLH is rear leg height; and RAB is rib and bone. Different colors represent different chromosomes, and the number on the horizontal coordinate is the chromosome number.</p>
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<p>Manhattan plots of muscularity traits (wither width, half of leg circumstance, rear leg height, and rib and bone) in Xinjiang Brown cattle. Note: WW is wither width; HLHC is half of leg circumstance; RLH is rear leg height; and RAB is rib and bone. Different colors represent different chromosomes, and the number on the horizontal coordinate is the chromosome number.</p>
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<p>Manhattan plots of rump traits (rump length, rump width, and rump angle) in Xinjiang Brown cattle. Note: RL is rump length; RW is rump width; and RA is rump angle. Different colors represent different chromosomes, and the number on the horizontal coordinate is the chromosome number.</p>
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<p>Manhattan plots of rump traits (rump length, rump width, and rump angle) in Xinjiang Brown cattle. Note: RL is rump length; RW is rump width; and RA is rump angle. Different colors represent different chromosomes, and the number on the horizontal coordinate is the chromosome number.</p>
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<p>Manhattan plots of rump traits (feet angle, rear leg side view, bone quality, and rear leg rear view) in Xinjiang Brown cattle. Note: FA is feet angle; RLSV is rear leg side view; BQ is bone quality; and RLRV is rear leg rear view. Different colors represent different chromosomes, and the number on the horizontal coordinate is the chromosome number.</p>
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<p>Manhattan plots of rump traits (feet angle, rear leg side view, bone quality, and rear leg rear view) in Xinjiang Brown cattle. Note: FA is feet angle; RLSV is rear leg side view; BQ is bone quality; and RLRV is rear leg rear view. Different colors represent different chromosomes, and the number on the horizontal coordinate is the chromosome number.</p>
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<p>Manhattan plots of udder traits (rear udder length, fore teat length, and rear teat placement) in Xinjiang Brown cattle. Note: RUL is rear udder length; FTL is fore teat length; and RTP is rear teat placement. Different colors represent different chromosomes, and the number on the horizontal coordinate is the chromosome number.</p>
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<p>Description and measurement sites of 27 body conformation traits. Note: 1. stature (cm); 2. body depth (cm); 3. chest width (cm); 4. withers width (cm); 5. hind leg half circumference (cm); 6. rear leg height (cm); 7. rib and bone (points); 8. rump length (cm); 9. rump width (cm); 10. rump angle (cm); 11. heel depth (cm); 12. foot angle (points); 13. rear legs side view (points); 14. bone quality (points); 15. rear legs rear view (points); 16. rear udder height (cm); 17. rear udder width (cm); 18. median suspensory (cm); 19. udder depth (cm); 20. fore udder length (cm); 21. front teat length (cm); 22. front teat diameter (cm); 23. fore udder attachment (points); 24. rear udder length (points); 25. udder balance (points); 26. fore teat placement (points); and 27. rear teat placement (points).</p>
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17 pages, 2094 KiB  
Article
Identification of Candidate Genes for Cold Tolerance at Seedling Stage by GWAS in Rice (Oryza sativa L.)
by Huimin Shi, Wenyu Zhang, Huimin Cao, Laiyuan Zhai, Qingxin Song and Jianlong Xu
Biology 2024, 13(10), 784; https://doi.org/10.3390/biology13100784 - 30 Sep 2024
Viewed by 507
Abstract
Due to global climate change, cold temperatures have significantly impacted rice production, resulting in reduced yield and quality. In this study, we investigated two traits related to the cold tolerance (CT) of 1992 diverse rice accessions at the seedling stage. Geng accessions exhibited [...] Read more.
Due to global climate change, cold temperatures have significantly impacted rice production, resulting in reduced yield and quality. In this study, we investigated two traits related to the cold tolerance (CT) of 1992 diverse rice accessions at the seedling stage. Geng accessions exhibited higher levels of CT compared to xian accessions, with the GJ-tmp subgroup displaying the strongest CT. However, extreme CT accessions were also identified within the xian subspecies. Through GWAS analysis based on the survival rate (SR) and leaf score of cold tolerance (SCT), a total of 29 QTLs associated with CT at the seedling stage were identified, among which four QTLs (qSR3.1a, qSR4.1a, qSR11.1x, and qSR12.1a) were found to be important. Furthermore, five candidate genes (LOC_Os03g44760, LOC_Os04g06900, LOC_Os04g07260, LOC_Os11g40610, and LOC_Os12g10710) along with their favorable haplotypes were identified through gene function annotation and haplotype analysis. Pyramiding multiple favorable haplotypes resulted in a significant improvement in CT performance. Subsequently, three selected accessions (CX534, B236, and IRIS_313-8565), carrying different superior alleles for CT, were selected and recommended for molecular breeding for CT using marker-assisted selection (MAS). The findings from this study provide valuable resources for enhancing rice’s ability for CT while laying a foundation for the future cloning of novel genes involved in conferring CT. Full article
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Figure 1
<p>Phenotypic variations in cold tolerance and identification of QTLs affecting cold tolerance through GWAS analysis of rice accessions from the 3K-RGP. (<b>a</b>) Box-plots of survival rate (SR) for the whole population, and <span class="html-italic">xian</span> (XI) and <span class="html-italic">geng</span> (GJ) subpopulations. (<b>b</b>) Box-plots of SR among <span class="html-italic">GJ-adm</span>, GJ-subtropical (<span class="html-italic">GJ-sbtrp</span>), GJ-temperate (<span class="html-italic">GJ-tmp</span>), GJ-tropical (<span class="html-italic">GJ-trp</span>), XI-1A, XI-1B, XI-2, XI-3, and XI-adm accessions. (<b>c</b>) Box-plots of SCT for the whole population, and XI and GJ subpopulations. (<b>d</b>) Box-plots of SCT among GJ-adm, GJ-sbtrp, GJ-tmp, GJ-trp, XI-1A, XI-1B, XI-2, XI-3, and XI-adm accessions. (<b>e</b>) Manhattan and Q-Q plots of GWAS results for SR. (<b>f</b>) Manhattan and Q-Q plots of GWAS results for SCT. Horizontal lines indicate in the Manhattan plots indicate the genomewide suggestive thresholds. In (<b>a</b>–<b>d</b>), different letters indicate significant differences (<span class="html-italic">p</span> &lt; 0.05, Duncan’s multiple range posthoc test).</p>
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<p>Genome-wide association study of cold tolerance-related traits in the GJ and XI subpopulations. (<b>a</b>) SR in the GJ subpopulation. (<b>b</b>) SCT in the GJ subpopulation. (<b>c</b>) SR in the XI subpopulation. (<b>d</b>) SCT in the XI subpopulation. In (<b>a</b>–<b>d</b>), the horizontal red lines represent the suggestive significant threshold. Horizontal lines indicate in the Manhattan plots indicate the genome-wide suggestive thresholds.</p>
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<p>Candidate gene analysis of <span class="html-italic">qSR4.1a</span> for cold tolerance. (<b>a</b>) Local Manhattan plot (top) of 150 kb upstream and downstream around the lead SNP rs4_3,633,378 (<span class="html-italic">LOC_Os04g06900</span>) and rs4_3,855,187 (<span class="html-italic">LOC_Os04g07260</span>). Codon-haplotypes of <span class="html-italic">LOC_Os04g06900</span> (<b>b</b>) and <span class="html-italic">LOC_Os04g07260</span> (<b>f</b>). The distribution of SR in the accessions for haplotypes (n &gt; 40 accessions) of <span class="html-italic">LOC_Os04g06900</span> (<b>c</b>) and <span class="html-italic">LOC_Os04g07260</span> (<b>g</b>). Different letters above each boxplot indicate significant differences among haplotypes (<span class="html-italic">p</span> &lt; 0.05, Duncan’s multiple range post-hoc test). Haplotype frequency distribution of <span class="html-italic">LOC_Os04g06900</span> (<b>d</b>) and <span class="html-italic">LOC_Os04g07260</span> (<b>h</b>) in different subpopulations. The type of each accession was from the metadata of the 3K-RGP [<a href="#B15-biology-13-00784" class="html-bibr">15</a>]. Frequency of haplotypes of <span class="html-italic">LOC_Os04g06900</span> I and <span class="html-italic">LOC_Os04g07260</span> (<b>i</b>) in the landrace and modern variety populations. Letter n indicates the number of rice accessions belonging to the corresponding subpopulations in (<b>c</b>,<b>d</b>,<b>g</b>,<b>h</b>), or the variety type in (<b>e</b>,<b>i</b>), respectively. Horizontal lines indicate in the Manhattan plots indicate the genome-wide suggestive thresholds.</p>
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<p>Candidate gene analysis of <span class="html-italic">qSR12.1a</span> and <span class="html-italic">qSR3.1a</span> for cold tolerance. Local Manhattan plot (top) of 150 kb upstream and downstream around the lead SNP rs12_5,753,724 (<span class="html-italic">LOC_Os12g10710</span>) (<b>a</b>) and rs3_25,249,852 (<span class="html-italic">LOC_Os03g44760</span>) (<b>f</b>). CDS-haplotypes of <span class="html-italic">LOC_Os12g10710</span> (<b>b</b>) and <span class="html-italic">LOC_Os03g44760</span> (<b>g</b>). The distribution of SR in the accessions for haplotypes (n &gt; 40 accessions) of <span class="html-italic">LOC_Os12g10710</span> (<b>c</b>) and <span class="html-italic">LOC_Os03g44760</span> (<b>h</b>). Different letters above each boxplot indicate significant differences among haplotypes (<span class="html-italic">p</span> &lt; 0.05, Duncan’s multiple range post-hoc test). Haplotype frequency distribution of <span class="html-italic">LOC_Os12g10710</span> (<b>d</b>) and <span class="html-italic">LOC_Os03g44760</span> (<b>i</b>) in different subpopulations. The type of each accession was from the metadata of the 3K-RGP [<a href="#B15-biology-13-00784" class="html-bibr">15</a>]. Frequency of haplotypes of <span class="html-italic">LOC_Os12g10710</span> (<b>e</b>) and <span class="html-italic">LOC_Os03g44760</span> (<b>j</b>) in the landrace and modern variety populations. Letter n indicates the number of rice accessions belonging to the corresponding subpopulation in (<b>c</b>,<b>d</b>,<b>h</b>,<b>i</b>), or the variety type in (<b>e</b>,<b>j</b>), respectively. Horizontal lines indicate in the Manhattan plots indicate the genome-wide suggestive thresholds.</p>
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<p>Candidate gene analysis of <span class="html-italic">qSR11.1x</span> for cold tolerance. (<b>a</b>) Local Manhattan plot (top) of 150 kb upstream and downstream. (<b>b</b>) CDS-haplotypes of <span class="html-italic">LOC_Os11g40610</span>. (<b>c</b>) The distribution of SR in the accessions for haplotypes (n &gt; 40 accessions) of <span class="html-italic">LOC_Os11g40610</span>. Different letters above each boxplot indicate significant differences among haplotypes (<span class="html-italic">p</span> &lt; 0.05, Duncan’s multiple range post-hoc test). (<b>d</b>) Haplotype frequency distribution of <span class="html-italic">LOC_Os11g40610</span> in different subpopulations. The type of each accession was from the metadata of the 3K-RGP [<a href="#B15-biology-13-00784" class="html-bibr">15</a>]. (<b>e</b>) Frequency of haplotypes of <span class="html-italic">LOC_Os11g40610</span> in the landrace and modern variety of the 3K-RGP. Letter n indicates the number of rice accessions belonging to the corresponding subpopulation in (<b>c</b>,<b>d</b>), or the variety type in (<b>e</b>), respectively. Horizontal lines indicate in the Manhattan plots indicate the genome-wide suggestive thresholds.</p>
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<p>Optimal combinations of four favorable haplotypes for cold tolerance. (<b>a</b>) Five combinations of four favorable haplotypes at <span class="html-italic">LOC_Os04g07260</span>, <span class="html-italic">LOC_Os12g10710</span>, <span class="html-italic">LOC_Os03g44760</span>, and <span class="html-italic">LOC_Os11g40610</span>, and the distribution patterns of these accessions across different subpopulations. “+” and “−” represent favorable and inferior haplotypes, respectively. <sup>a</sup> means other subpopulations except <span class="html-italic">geng</span> and <span class="html-italic">xian</span> subspectASies. (<b>b</b>) Comparisons of the SR among accessions with different haplotype combinations. Different letters above each histogram indicate significant differences at <span class="html-italic">p</span> &lt; 0.05 (least significant difference test).</p>
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20 pages, 1340 KiB  
Article
Eco-Physiological and Genetic Basis of Drought Response Index in Rice—Integration Using a Temperate Japonica Mapping Population
by Poornima Ramalingam, An Thi Ha Nguyen and Akihiko Kamoshita
Agronomy 2024, 14(10), 2256; https://doi.org/10.3390/agronomy14102256 - 29 Sep 2024
Viewed by 605
Abstract
The drought response index (DRI) is an indicator of drought tolerance after adjustment for variation in flowering date and potential yield under well-watered conditions. Using a temperate japonica mapping population of 97 recombinant inbred lines from a cross between Otomemochi (OTM) and Yumenohatamochi [...] Read more.
The drought response index (DRI) is an indicator of drought tolerance after adjustment for variation in flowering date and potential yield under well-watered conditions. Using a temperate japonica mapping population of 97 recombinant inbred lines from a cross between Otomemochi (OTM) and Yumenohatamochi (YHM), we evaluated DRI during the reproductive stage under very severe drought in one year and under severe drought in the next year. DRI under very severe drought (−6.4 to 15.9) and severe drought (−3.9 to 8.3) positively correlated with grain dry weight under drought. Three QTLs for DRI were identified: RM3703–RM6911–RM6379 and RM6733–RM3850 both on chromosome 2 in both years combined; and RM8120–RM2615–RM7023 on chromosome 6 in the second year. The latter collocated with putative genes for signaling and defense mechanisms (e.g., PIN1B, BZIP46) revealed by database analysis. Top DRI lines retained root dry weight and had bigger steles. QTL-by-environment interaction had a greater relative contribution than the main effects of QTLs. Comparison with three previous studies revealed that the QTLs for DRI were unique to each experiment and/or population; most of them closely colocalized with reported drought-yield QTLs. Full article
(This article belongs to the Topic Crop Ecophysiology: From Lab to Field, 2nd Volume)
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Figure 1
<p>Relationship of grain dry weight under drought with (<b>a</b>,<b>e</b>) time of 50% flowering in the control, (<b>b</b>,<b>f</b>) grain dry weight in the control, and (<b>c</b>,<b>g</b>) drought response index (DRI); (<b>d</b>,<b>h</b>) relationship of grain dry weight in the control with time of 50% flowering in the control in the Otomemochi × Yumenohatamochi (OY) population in Experiment 1 (<b>a</b>–<b>d</b>) and Experiment 2 (<b>e</b>–<b>h</b>).</p>
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<p>Twenty key genomic regions for drought response index (DRI) and production traits for Otomemochi × Yumenohatamochi (OY) population under very severe drought intensity in Experiment 1 and severe drought.</p>
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