QTL mapping for tolerance to anaerobic germination in rice from IR64 and the &lt;i&gt;aus&lt;/i&gt; landrace Kharsu 80A

Baltazar, Miriam D.; Ignacio, John Carlos I.; Thomson, Michael J.; Ismail, Abdelbagi M.; Mendioro, Merlyn S.; Septiningsih, Endang M.

doi:10.1270/jsbbs.18159

Cited by 39 publications

(34 citation statements)

References 42 publications

(38 reference statements)

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“…Coleoptile elongation and seedling survival rate under submerged conditions have been widely used as major indicator traits for tolerance phenotyping in QTL mapping and genome-wide association study (GWAS) approach ( Table 2 ). Among these, five QTLs associated with tolerance of submergence during germination were mapped on chromosomes 1 ( qAG-1-2 ), 3 ( qAG-3-1 ), 7( qAG-7-2 ), and 9 ( qAG-9-1 and qAG-9-2 ), respectively [ 87 ]; four QTLs ( qAG7.1 , qAG7.2 , qAG7.3 , and qAG3 ), which derived from aus landrace Kharsu 80A for AG tolerance were identified in a recent study [ 88 ]. Of which, only one QTL ( qAG-9-2 ) has been cloned.…”

Section: Identification Of Qtls/genes For Ag Tolerancementioning

confidence: 99%

The Molecular Regulatory Pathways and Metabolic Adaptation in the Seed Germination and Early Seedling Growth of Rice in Response to Low O2 Stress

Cen

et al. 2020

Plants

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As sessile organisms, flooding/submergence is one of the major abiotic stresses for higher plants, with deleterious effects on their growth and survival. Therefore, flooding/submergence is a large challenge for agriculture in lowland areas worldwide. Long-term flooding/submergence can cause severe hypoxia stress to crop plants and can result in substantial yield loss. Rice has evolved distinct adaptive strategies in response to low oxygen (O2) stress caused by flooding/submergence circumstances. Recently, direct seeding practice has been increasing in popularity due to its advantages of reducing cultivation cost and labor. However, establishment and growth of the seedlings from seed germination under the submergence condition are large obstacles for rice in direct seeding practice. The physiological and molecular regulatory mechanisms underlying tolerant and sensitive phenotypes in rice have been extensively investigated. Here, this review focuses on the progress of recent advances in the studies of the molecular mechanisms and metabolic adaptions underlying anaerobic germination (AG) and coleoptile elongation. Further, we highlight the prospect of introducing quantitative trait loci (QTL) for AG into rice mega varieties to ensure the compatibility of flooding/submergence tolerance traits and yield stability, thereby advancing the direct seeding practice and facilitating future breeding improvement.

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Section: Identification Of Qtls/genes For Ag Tolerancementioning

confidence: 99%

The Molecular Regulatory Pathways and Metabolic Adaptation in the Seed Germination and Early Seedling Growth of Rice in Response to Low O2 Stress

Cen

et al. 2020

Plants

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“…qACE3.1 was successfully detected on the short arm of chromosome 3 in the population of CSSLs which have part of their chromosome segment substituted with Koshihikari in the IR 64 genetic background. In previous studies, many QTLs for seedling establishment have been detected in the Indica-type genetic background to improve adaptability to the wet direct seeding method (Angaji et al, 2010;Baltazar et al, 2014Baltazar et al, , 2019Kim & Reinke, 2018;Septiningsih et al, 2013). In addition, two genetic loci for coleoptile elongation under anaerobic conditions have been detected by GWAS (Hsu & Tung, 2015;Nghi et al, 2019) and QTL analyses (Jiang et al, 2006;Manangkil et al, 2013).…”

Section: Discussionmentioning

confidence: 99%

“…In addition, two genetic loci for coleoptile elongation under anaerobic conditions have been detected by GWAS (Hsu & Tung, 2015;Nghi et al, 2019) and QTL analyses (Jiang et al, 2006;Manangkil et al, 2013). Among these QTLs for seedling establishment, three have been detected on chromosome 3 in populations derived from cross combinations between IR 64 and Khao Hlan On (Angaji et al, 2010), IR 64 and Kharsu 80A (Baltazar et al, 2019), and IR 64 and Nanhi (Baltazar et al, 2014), which were mapped to regions at approximately 30.9, 5.5, and 21.1 Mb, respectively, on chromosome 3. Moreover, using GWAS, a further QTL has been detected on chromosome 3 in a Japonica-type population, which is located at approximately 27.4 Mb (Nghi et al, 2019).…”

Section: Discussionmentioning

confidence: 99%

“…To develop improved breeding materials for seedling establishment under flooded conditions in DSR, quantitative trait loci (QTLs) for the rate of seedling emergence under anaerobic conditions (i.e., anaerobic germination) have been determined (Angaji, Septiningsih, Mackill & Ismail, 2010;Baltazar et al, 2014Baltazar et al, , 2019Kim & Reinke, 2018;Septiningsih et al, 2013). Among these QTLs, a trehalose-6-phosphate phosphatase gene (OsTPP7) was identified as the gene responsible for qAG-9-2 a major quantitative trait locus (QTL) for anaerobic germination (Kretzschmar et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

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Detection and characterization of quantitative trait loci for coleoptile elongation under anaerobic conditions in rice

Nishimura

Sasaki

Yamaguchi

et al. 2020

Plant Production Science

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Low oxygen concentrations in flooded paddy fields cause poor seedling establishment in wet direct seeded rice. We aimed to identify novel genomic regions associated with coleoptile elongation under anaerobic conditions in order to improve seedling establishment. Chromosome segment substitution lines (CSSLs), substituted with Koshihikari (Japonica-type) in the IR 64 genetic background (Indica-type) were evaluated. These lines were imbibed with a hydroponic solution containing Oxyrase to create a stable anaerobic condition and coleoptile lengths were measured six days after incubation at 30°C. Among the CSSLs, only SL2111 had a significantly longer coleoptile (23.9 mm) than that of IR 64 (14.3 mm). From genotype and phenotype analyses, a novel QTL, referred to as qACE3.1, for coleoptile elongation under anaerobic conditions was detected on chromosome 3. To explore the molecular mechanism of qACE3.1, the expression levels of genes encoding enzymes involved in starch degradation and fermentation were assessed. In SL2111 coleoptiles, the expression levels of pyruvate decarboxylase were significantly higher than in IR 64 coleoptiles whereas those of alcohol dehydrogenase were lower. In contrast, no differences were observed in the expression levels of genes associated with starch degradation. These results imply that qACE3.1 may specifically affect fermentative metabolism. In addition, we evaluated the impact of qACE3.1 on seedling establishment under flooded conditions in a paddy field.

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“…Because intensive simulations using deep neural networks are time-consuming, F2 populations of selfing crops (e.g., rice and soybean) for QTL mapping where relatively small numbers of DNA markers (two to three hundred) are often genotyped [22,23] were assumed in this study. The number of segregated markers was assumed to be 200.…”

Section: Simulation Scenariosmentioning

confidence: 99%

Predicting genotypic values associated with gene interactions using neural networks: A simulation study for investigating factors affecting prediction accuracy

Onogi

2019

Preprint

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Genomic prediction has been applied to various species of plants and livestock to enhance breeding efficacy. Neural networks including deep neural networks are attractive candidates to predict phenotypic values. However, the properties of neural networks in predicting non-additive effects have not been clarified. In this simulation study, factors affecting the prediction of genetic values associated with gene interactions (i.e., epistasis) were investigated using multilayer perceptron. The results suggested that (1) redundant markers should be pruned, although markers in LD with QTLs are less harmful, (2) predicting epistatic genetic values with neural networks in real populations would be infeasible using training populations of 1000 samples, (3) neural networks with two or fewer hidden layers and a sufficient number of units per hidden layer would be useful, particularly when a certain number of interactions is involved, and (4) neural networks have greater capability to predict epistatic genetic values than random forests, although neural networks are more sensitive to training population size and the level of epistatic genetic variance. These lessons also would be applicable to other regression problems in which interactions between explanatory variables are expected, e.g., gene-by-environment interactions.

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QTL mapping for tolerance to anaerobic germination in rice from IR64 and the <i>aus</i> landrace Kharsu 80A

Cited by 39 publications

References 42 publications

The Molecular Regulatory Pathways and Metabolic Adaptation in the Seed Germination and Early Seedling Growth of Rice in Response to Low O2 Stress

The Molecular Regulatory Pathways and Metabolic Adaptation in the Seed Germination and Early Seedling Growth of Rice in Response to Low O2 Stress

Detection and characterization of quantitative trait loci for coleoptile elongation under anaerobic conditions in rice

Predicting genotypic values associated with gene interactions using neural networks: A simulation study for investigating factors affecting prediction accuracy

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