Genome-Wide Association Mapping Reveals Multiple QTLs Governing Tolerance Response for Seedling Stage Chilling Stress in Indica Rice

Pandit, Elssa; Tasleem, S; Barik, Saumya Ranjan; Mohanty, Durga P.; Nayak, D. K.; Mohanty, S.; Das, Subrata Kumar; Pradhan, Sharat Kumar

doi:10.3389/fpls.2017.00552

Cited by 51 publications

(58 citation statements)

References 65 publications

(112 reference statements)

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“…Presence of these different groups in the population increased the continuation of linkage disequilibrium and favoured for getting the marker-trait association for Fe-Zn content in rice grain. Similar results of trait mapping through marker-phenotype association were reported earlier for various rice phenotypic traits [17,39,40,43,45,47]. A moderate level of genetic diversity was observed in the studied population for grain Fe and Zn content.…”

Section: Discussionsupporting

confidence: 88%

“…Mapping reports of Fe-Zn content in rice indicated the use of various bi-parental mapping populations for detecting the genes controlling these two micronutrients. Gene mapping using bi-parental mapping populations is more time consuming, costlier and lesser resolution than association mapping [17,[37][38][39][40]. These limitations can be overcome by linkage disequilibrium (LD) mapping or association mapping.…”

Section: Introductionmentioning

confidence: 99%

“…These limitations can be overcome by linkage disequilibrium (LD) mapping or association mapping. Association mapping is based on linkage disequilibrium or variations existing in natural or developed populations [17,37,39,40]. The main purpose of association mapping here is to estimate the correlations between molecular markers with the grain Fe and Zn content in a panel containing representative rice population that possess considerable amount of variability for these two traits.…”

Section: Introductionmentioning

confidence: 99%

“…Only few research reports on association mapping for grain Fe-Zn content are available [41,42]. But, report on QTL mapping through association mapping in rice are available for grain yield and agronomic traits [29,41,43,44]; seedling stage cold tolerance [40,45]; cold tolerance at germination and booting stages [46]; high temperature stress tolerance [39]; grain quality traits [47]; salinity tolerance [48]; drought tolerance traits [49,50]; early seedling vigour [51] and grain protein content [17]. However, the information on association mapping for grain Fe and Zn content in rice is scarce.…”

Section: Introductionmentioning

confidence: 99%

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Linkage disequilibrium mapping for grain Fe and Zn enhancing QTLs useful for nutrient dense rice breeding

et al. 2020

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Background: High yielding rice varieties are usually low in grain iron (Fe) and zinc (Zn) content. These two micronutrients are involved in many enzymatic activities, lack of which cause many disorders in human body. Biofortification is a cheaper and easier way to improve the content of these nutrients in rice grain. Results: A population panel was prepared representing all the phenotypic classes for grain Fe-Zn content from 485 germplasm lines. The panel was studied for genetic diversity, population structure and association mapping of grain Fe-Zn content in the milled rice. The population showed linkage disequilibrium showing deviation of Hardy-Weinberg's expectation for Fe-Zn content in rice. Population structure at K = 3 categorized the panel population into distinct sub-populations corroborating with their grain Fe-Zn content. STRUCTURE analysis revealed a common primary ancestor for each sub-population. Novel quantitative trait loci (QTLs) namely qFe3.3 and qFe7.3 for grain Fe and qZn2.2, qZn8.3 and qZn12.3 for Zn content were detected using association mapping. Four QTLs, namely qFe3.3, qFe7.3, qFe8.1 and qFe12.2 for grain Fe content were detected to be co-localized with qZn3.1, qZn7, qZn8.3 and qZn12.3 QTLs controlling grain Zn content, respectively. Additionally, some Fe-Zn controlling QTLs were co-localized with the yield component QTLs, qTBGW, OsSPL14 and qPN. The QTLs qFe1.1, qFe3.1, qFe5.1, qFe7.1, qFe8.1, qZn6, qZn7 and gRMm9-1 for grain Fe-Zn content reported in earlier studies were validated in this study. Conclusion: Novel QTLs, qFe3.3 and qFe7.3 for grain Fe and qZn2.2, qZn8.3 and qZn12.3 for Zn content were detected for these two traits. Four Fe-Zn controlling QTLs and few yield component QTLs were detected to be colocalized. The QTLs, qFe1.1, qFe3.1, qFe5.1, qFe7.1, qFe8.1, qFe3.3, qFe7.3, qZn6, qZn7, qZn2.2, qZn8.3 and qZn12.3 will be useful for biofortification of the micronutrients. Simultaneous enhancement of Fe-Zn content may be possible with yield component traits in rice.

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Section: Discussionsupporting

confidence: 88%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Linkage disequilibrium mapping for grain Fe and Zn enhancing QTLs useful for nutrient dense rice breeding

et al. 2020

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“…The present association study was conducted on 60 diverse genotypes panel and 85 SSR markers The present association study focused on identification QTLs associated with yield and related traits in relatively small population and with limited markers [75]. Therefore, our study analogous with previous reports with a small, focused group of genotypes and limited marker pairs combination [12,13,60,67,69,75–77].…”

Section: Discussionmentioning

confidence: 78%

Identification of QTLs for high grain yield and component traits in New Plant Types of rice

Donde

Mohapatra

Baksh

et al. 2020

Preprint

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16A panel of 60 genotypes consisting of New Plant Types (NPTs) along with indica, tropical and 17 temperate japonica genotypes were phenotypically evaluated for four seasons in irrigated situation 18 for grain yield per se and component traits. Twenty NPT genotypes were found to be promising with 19 an average grain yield of 5.45 to 8.8 t/ha. A total of 85 SSR markers were used in the study to 20 identify QTLs associated with grain yield per se and related traits. Sixty-six (77.65%) markers were 21 found to be polymorphic. The PIC values varied from 0.516 to 0.92 with an average of 0.704. A 22 moderate level of genetic diversity (0.39) was detected among genotypes. Variation to the tune of 23 8% within genotypes, 68% among the genotypes within the population and 24% among the 24 populations were observed (AMOVA). The association analysis using GLM and MLM models led to 25 the identification of 30 and 10 SSR markers were associated with 70 and 16 QTLs, respectively. 26 Thirty novel QTLs linked with 16 SSRs were identified to be associated with eleven traits, namely, 27 tiller number (qTL-6. 29 total no. of grains (qTG-2.2, qTG-a7.1), thousand-grain weight 30 qTGW-8.1 ), fertile grains (qFG-7.1), seed length-breadth ratio (qSlb-3.1), plant height (qPHT-6.1, 31 qPHT-9.1), days to 50% flowering (qFD-1.1) and grain yield per se 11.1). This information could be useful for identification of highly potential parents for development 33 of transgressive segregants. Moreover, super rice genotypes could be developed through pyramiding 34 of these QTLS for important yield traits for prospective increment in yield potentiality and breaking 35 yield ceiling. 36 Introduction 39Rice (Oryza sativa L.) is a staple crop for more than 3.5 billion people in the globe. In current 40 scenario, rice productivity is increasing rate at 1% per year which is less than the 2.4% per year rate 41 required to double the global production by 2050 [1]. Considering a glimpse of the history, a 42 quantum jump in productivity was achieved due to the green revolution in mid-sixties, which 43 drastically enhanced the rice production of the world. However, a ceiling of productivity potentiality 44 is reported by and large in semi-dwarf inbred indica genotypes since release of , in spite of 45 substantial improvement in yield stability, per day productivity and grain quality [3]. A 46 breakthrough in productivity barrier is necessitated because of increasing competition for natural 47 resources viz., land, water and others given population explosion coupled with expanding 48 industrialization, urbanization and diversion of agricultural land [1,4,5]. This is still aggravated with 49 the abnormal change in weather and climate with significant influence on crop productivity and 50 quality [6,7]. 51Rice scientists are facing many challenges for doubling rice production by 2050. Irrigated 52 rice has a share of 75% of total rice production in the world, although it has a share of about 55% of 53 the total rice area [2]. Therefore, improveme...

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Molecular Breeding for Iron Bio‐Fortification in Rice Grain: Recent Progress and Future Perspectives

Pandit

Pawar

Sanghamitra

et al. 2021

Molecular Breeding for Rice Abiotic Stress Tolerance and Nutritional Quality

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Genome-Wide Association Mapping Reveals Multiple QTLs Governing Tolerance Response for Seedling Stage Chilling Stress in Indica Rice

Cited by 51 publications

References 65 publications

Linkage disequilibrium mapping for grain Fe and Zn enhancing QTLs useful for nutrient dense rice breeding

Linkage disequilibrium mapping for grain Fe and Zn enhancing QTLs useful for nutrient dense rice breeding

Identification of QTLs for high grain yield and component traits in New Plant Types of rice

Molecular Breeding for Iron Bio‐Fortification in Rice Grain: Recent Progress and Future Perspectives

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