Identification of QTL, QTL-by-environment interactions, and their candidate genes for resistance HG Type 0 and HG Type 1.2.3.5.7 in soybean using 3VmrMLM

Jiang, Haipeng; Lv, Suchen; Zhou, Changjun; Qu, Shuo; Liu, Fang; Sun, Haowen; Zhao, Xue; Han, Yingpeng

doi:10.3389/fpls.2023.1177345

Cited by 4 publications

(3 citation statements)

References 45 publications

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“…However, there were no overlapping sites between QEI and QTNs detected based on two environmental data, indicating that all QEIs had no significant additive or dominant effect, but only the interaction effect between additive or dominant and environment, while all the QTNs in twoenvironment jointly analyze were opposite. Using the same 3VmrMLM method in previous studies, the overlapping sites between QEI and QTNs were also few, ranging from 1-3 sites (Han et al, 2022;He et al, 2022;Yu et al, 2022;Zhang et al, 2022;Jiang et al, 2023;Zhao et al, 2023), except for the study of Zou et al (2022), which found 13 overlapping sites. From the perspective of the effect of QEI, since most QEIs do not have a significant additive or dominant effect, their reliability needs to be further confirmed.…”

Section: Discussionmentioning

confidence: 87%

Genome-wide association study of cooking-caused grain expansion in rice (Oryza sativa L.)

Zheng,

Thi,

Lin

et al. 2023

Front. Plant Sci.

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Cooking-caused rice grain expansion (CCRGE) is a critical trait for evaluating the cooking quality of rice. Previous quantitative trait locus (QTL) mapping studies on CCRGE have been limited to bi-parental populations, which restrict the exploration of natural variation and mapping resolution. To comprehensively and precisely dissect the genetic basis of CCRGE, we performed a genome-wide association study (GWAS) on three related indices: grain breadth expansion index (GBEI), grain length expansion index (GLEI), and grain length-breadth ratio expansion index (GREI), using 345 rice accessions grown in two years (environments) and 193,582 SNP markers. By analyzing each environment separately using seven different methods (3VmrMLM, mrMLM, FASTmrMLM, FASTmrEMMA, pLARmEB, pKWmEB, ISIS EM-BLASSO), we identified a total of 32, 19 and 27 reliable quantitative trait nucleotides (QTNs) associated with GBEI, GLEI and GREI, respectively. Furthermore, by jointly analyzing the two environments using 3VmrMLM, we discovered 19, 22 and 25 QTNs, as well as 9, 5 and 7 QTN-by-environment interaction (QEIs) associated with GBEI, GLEI and GREI, respectively. Notably, 12, 9 and 15 QTNs for GBEI, GLEI and GREI were found within the intervals of previously reported QTLs. In the vicinity of these QTNs or QEIs, based on analyses of mutation type, gene ontology classification, haplotype, and expression pattern, we identified five candidate genes that are related to starch synthesis and endosperm development. The five candidate genes, namely, LOC_Os04g53310 (OsSSIIIb, near QTN qGREI-4.5s), LOC_Os05g02070 (OsMT2b, near QTN qGLEI-5.1s), LOC_Os06g04200 (wx, near QEI qGBEI-6.1i and QTNs qGREI-6.1s and qGLEI-6.1t), LOC_Os06g12450 (OsSSIIa, near QTN qGLEI-6.2t), and LOC_Os08g09230 (OsSSIIIa, near QTN qGBEI-8.1t), are predicted to be involved in the process of rice grain starch synthesis and to influence grain expansion after cooking. Our findings provide valuable insights and will facilitate genetic research and improvement of CCRGE.

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Section: Discussionmentioning

confidence: 87%

Genome-wide association study of cooking-caused grain expansion in rice (Oryza sativa L.)

Zheng,

Thi,

Lin

et al. 2023

Front. Plant Sci.

View full text Add to dashboard Cite

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“…Some of these genes, for example found at the rhg2 locus, remain to be cloned (Suzuki et al 2020). Other sources of genetic resistance to H. glycines have since been identified as it is an ongoing process (Wen et al 2017; Hua et al 2018; Jiang et al 2023; Almeida-Silva and Venancio, 2023).…”

Section: Introductionmentioning

confidence: 99%

Glycine maxpolygalacturonase inhibiting protein 11 (GmPGIP11) functions in the root to suppressHeterodera glycinesparasitism

Acharya,

Troell,

Billingsley

et al. 2024

Preprint

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Pathogen-secreted polygalacturonases (PGs) alter plant cell wall structure by cleaving the α- (1→4) linkages between D-galacturonic acid residues in homogalacturonan (HG), macerating the cell wall, facilitating infection. Plant PG inhibiting proteins (PGIPs) disengage pathogen PGs, impairing infection. The soybean cyst nematode,Heterodera glycines,obligate root parasite produces secretions, generating a multinucleate nurse cell called a syncytium, a byproduct of the merged cytoplasm of 200-250 root cells, occurring through cell wall maceration. The common cytoplasmic pool, surrounded by an intact plasma membrane, provides a source from whichH. glycinesderives nourishment but without killing the parasitized cell during a susceptible reaction. The syncytium is also the site of a naturally-occurring defense response that happens in specificG. maxgenotypes. Transcriptomic analyses of RNA isolated from the syncytium undergoing the process of defense have identified that one of the 11G. max PGIPs,GmPGIP11, is expressed during defense. Functional transgenic analyses show roots undergoingGmPGIP11overexpression (OE) experience an increase in its relative transcript abundance (RTA) as compared to theribosomal protein 21(GmRPS21) control, leading to a decrease inH. glycinesparasitism as compared to the overexpression control. TheGmPGIP11undergoing RNAi experiences a decrease in its RTA as compared to theGmRPS21control with transgenic roots experiencing an increase inH. glycinesparasitism as compared to the RNAi control. Pathogen associated molecular pattern (PAMP) triggered immunity (PTI) and effector triggered immunity (ETI) components are shown to influenceGmPGIP11expression while numerous agricultural crops are shown to have homologs.

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“…It employs the association analysis to obtain all single-nucleotide polymorphisms (SNP) in the whole genome of a species and perform the correlation analysis with target traits. It estimates the effects of QTN through different models and then statistically tests the estimated effects, so as to discover the genetic variations underlying complex traits (Harold et al, 2009;Jiang et al, 2023). In addition, the rst whole-genome sequence of the variety Williams 82, completed in 2010, provides a powerful resource for soybean functional genomic research (Schmutz et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Genome-wide association analyses for revealing QTN, QTN-by-environment and QTN-by-QTN interactions in soybean phenology

Yan,

Liang

et al. 2024

Preprint

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Context: Phenology plays an important role in determining the yield and environmental adaptation of soybean, but easily affected by quantitative trait nucleotides (QTN)-by-environment interactions (QEI) and QTN-by-QTN interactions (QQI). Detailed understanding of the genetic basis and the interactions between genome and environments is critical for the development of cultivars with geographical-appropriate phenology. Methods: A compressed variance component mixed model (3VmrMLM) was used to detect QTN, QEI and QQI for four key phenological traits of 345 soybean accessions. These traits include days from emergence to first flower (R1), pod beginning (R3), seed formation (R5) and maturity initiation (R7). Meanwhile, QTN, QEI and QQI were identified in at least ten environments and Best Linear Unbiased Prediction (BLUP) value. Results: (i). A total of 110-193 QTN, 10-31 QEI and 4-8 QQI were identified for each trait. (ii). 40 regions were then divided based on the linkage disequilibrium distance as 500 kb around the above site. (iii). Further differential expression analysis and functional enrichment analysis were finished in 2339 genes and identified 40 genes involved in biological pathways such as flowering and seed maturation. (iv). Haplotype difference analysis revealed that Glyma.02g152200, Glyma.02g152800 and Glyma.02g155200are possibly associated with phenology around a QQI, and Glyma.17g212700 may be useful around a QEI for flowering time to maturity. Conclusions: Extensive genetic analysis of the QTN-QTN with QTN-environment interaction was conducted on key phenological stages in soybean. The candidate genes predicted provide valuable information for functional validation to elucidate the molecular mechanism underlying the soybean phenology.

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Identification of QTL, QTL-by-environment interactions, and their candidate genes for resistance HG Type 0 and HG Type 1.2.3.5.7 in soybean using 3VmrMLM

Cited by 4 publications

References 45 publications

Genome-wide association study of cooking-caused grain expansion in rice (Oryza sativa L.)

Genome-wide association study of cooking-caused grain expansion in rice (Oryza sativa L.)

Glycine maxpolygalacturonase inhibiting protein 11 (GmPGIP11) functions in the root to suppressHeterodera glycinesparasitism

Genome-wide association analyses for revealing QTN, QTN-by-environment and QTN-by-QTN interactions in soybean phenology

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