Mapping QTL hotspots associated with weed competitive traits in backcross population derived from Oryza sativa L. and O. glaberrima Steud.

Bharamappanavara, Muralidhara; Siddaiah, Anantha M.; Ponnuvel, Senguttuvel; Lokesha, R.; Basavaraj, P. S.; Appaiah, Manoj Chikkahosahalli; Maganti, Sheshu Madhav; Sundaram, R. M.; Shankarappa, Shashidhar Kadadanamari; Tuti, Mangal Deep; Sreedevi, Banugu; Parmar, Brajendra; Rathod, Santosha; Barbadikar, Kalyani M.; Kota, Suneetha; Subbarao, L. V.; Mondal, Tapan Kumar; Gireesh, C.

doi:10.1038/s41598-020-78675-7

Cited by 10 publications

(12 citation statements)

References 73 publications

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“…The quantile-quantile (Q-Q) plot also confirmed the detected associated markers with the growth parameters in rice ( Figure 9 ). Marker–trait associations have been reported previously by many workers in rice [ 24 , 30 , 64 ].…”

Section: Discussionmentioning

confidence: 73%

“…The associations were at 300 cM position on chromosome 4; 159 and 221 cM on chromosome 6; and 27 cM position on chromosome 8. Bharamappanavara et al [ 64 ] reported a QTL on chromosome 2 at 61.59–115.44 cM position and on chromosome 3 within 16.95 cM. However, no reports are available for the locations detected by us for QTLs controlling AGR in rice.…”

Section: Discussionmentioning

confidence: 90%

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Mapping the Genomic Regions Controlling Germination Rate and Early Seedling Growth Parameters in Rice

Mohanty

Nayak

Sanghamitra

et al. 2023

Genes

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Seed vigor is the key performance parameter of good quality seed. A panel was prepared by shortlisting genotypes from all the phenotypic groups representing seedling growth parameters from a total of 278 germplasm lines. A wide variation was observed for the traits in the population. The panel was classified into four genetic structure groups. Fixation indices indicated the existence of linkage disequilibrium in the population. A moderate to high level of diversity parameters was assessed using 143 SSR markers. Principal component, coordinate, neighbor-joining tree and cluster analyses showed subpopulations with a fair degree of correspondence with the growth parameters. Marker–trait association analysis detected eight novel QTLs, namely qAGR4.1, qAGR6.1, qAGR6.2 and qAGR8.1 for absolute growth rate (AGR); qRSG6.1, qRSG7.1 and qRSG8.1 for relative shoot growth (RSG); and qRGR11.1 for relative growth rate (RGR), as analyzed by GLM and MLM. The reported QTL for germination rate (GR), qGR4-1, was validated in this population. Additionally, QTLs present on chromosome 6 controlling RSG and AGR at 221 cM and RSG and AGR on chromosome 8 at 27 cM were detected as genetic hotspots for the parameters. The QTLs identified in the study will be useful for improvement of the seed vigor trait in rice.

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Section: Discussionmentioning

confidence: 73%

Section: Discussionmentioning

confidence: 90%

Mapping the Genomic Regions Controlling Germination Rate and Early Seedling Growth Parameters in Rice

Mohanty

Nayak

Sanghamitra

et al. 2023

Genes

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“…Therefore, weed-suppressive strategies and control methods employed in IWM are preferred. Additionally, there have been a few studies searching for QTLs for weed competitive traits in crops ( Coleman et al, 2001 ; Bharamappanavara et al, 2020 ; Dimaano et al, 2020 ). Although many of these studies have not mentioned specific genes from fine mapping, it is likely that genes involved in growth and development like maturity genes, tillering genes, and leaf development genes will be strong candidates for improvement.…”

Section: Potential Gene Targets For Reducing Weed Fitnessmentioning

confidence: 99%

Biotechnological Road Map for Innovative Weed Management

et al. 2022

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In most agriculture farmlands, weed management is predominantly reliant on integrated weed management (IWM) strategies, such as herbicide application. However, the overuse and misuse of herbicides, coupled with the lack of novel active ingredients, has resulted in the uptrend of herbicide-resistant weeds globally. Moreover, weedy traits that contribute to weed seed bank persistence further exacerbate the challenges in weed management. Despite ongoing efforts in identifying and improving current weed management processes, the pressing need for novel control techniques in agricultural weed management should not be overlooked. The advent of CRISPR/Cas9 gene-editing systems, coupled with the recent advances in “omics” and cheaper sequencing technologies, has brought into focus the potential of managing weeds in farmlands through direct genetic control approaches, but could be achieved stably or transiently. These approaches encompass a range of technologies that could potentially manipulate expression of key genes in weeds to reduce its fitness and competitiveness, or, by altering the crop to improve its competitiveness or herbicide tolerance. The push for reducing or circumventing the use of chemicals in farmlands has provided an added incentive to develop practical and feasible molecular approaches for weed management, although there are significant technical, practical, and regulatory challenges for utilizing these prospective molecular technologies in weed management.

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“…Studies have also reported several sterility loci in interspecific crosses between O. sativa and O. glaberrima (Lorieux et al 2000;Ren et al 2005;Li et al 2008Li et al , 2018Xu et al 2014;Shen et al 2015;Yang et al 2016). Further, there are reports of quantitative trait loci (QTLs) derived from O. glaberrima associated with grain quality (Li et al 2004;Wambugu et al 2018), rice stripe necrosis (Gutiérrez et al 2010), iron toxicity (Dufey et al 2015) and competitiveness against weeds (Bharamappanavara et al 2020). Molecular profiling of interspecific progeny of O. glaberrima has been performed (Semagn et al 2007;Ndjiondjop et al 2008;Bimpong et al 2011;Agnoun et al 2012;Moukoumbi et al 2015) to provide insight into the extent of the O. glaberrima genome inherited in the progenies.…”

Section: Introductionmentioning

confidence: 99%

“…2010), iron toxicity (Dufey et al . 2015) and competitiveness against weeds (Bharamappanavara et al . 2020).…”

Section: Introductionmentioning

confidence: 99%

Genetic analysis and identification of QTLs associated with yield-enhancing traits in Oryza sativa IR64 × Oryza glaberrima interspecific backcross populations

Bharamappanavara

Chikkahosahalli

Basavaraj

et al. 2023

Crop & Pasture Science

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Context Broadening the gene pool of staple food crops by incorporating desirable genes from wild/relative species is way to achieve higher genetic gains. The African rice species Oryza glaberrima Steud. is a source of genes for genetic improvement of Oryza sativa L. to various biotic and abiotic stresses. Aim The study was undertaken to identify genomic regions associated with yield and yield-related traits in interspecific backcross mapping populations derived from variety IR64 (O. sativa indica) × O. glaberrima. Methods The BC1F2 and BC1F2:3 populations were phenotyped for days to 50% flowering, plant height, number of tillers, number of productive tillers, panicle length, spikelet fertility and grain yield under irrigated conditions. The BC1F2 population was genotyped with 99 genome-wide polymorphic SSR markers. Quantitative trait loci (QTLs) associated with yield-enhancing traits were identified through single marker analysis. Key results Significant genetic variability was observed with moderate to high heritability in mapping populations. Genotyping revealed the presence of segregation distortion and skewed genome composition. Single marker analysis identified 20 QTLs linked to the studied traits. A QTL linked to grain yield, qGY-4.1, derived from O. glaberrima was found in both populations with high phenotypic variance. The chromosome region around marker RM510 was identified as a QTL hotspot and putative candidate genes were identified. Conclusions Our study demonstrates that O. glaberrima can serve as a valuable genetic resource for genetic improvement of yield-enhancing traits in O. sativa cultivars. Implications The identified QTLs regions can be employed in molecular breeding. The promising introgression lines identified can be used as pre-breeding lines in future breeding programs.

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Mapping QTL hotspots associated with weed competitive traits in backcross population derived from Oryza sativa L. and O. glaberrima Steud.

Cited by 10 publications

References 73 publications

Mapping the Genomic Regions Controlling Germination Rate and Early Seedling Growth Parameters in Rice

Mapping the Genomic Regions Controlling Germination Rate and Early Seedling Growth Parameters in Rice

Biotechnological Road Map for Innovative Weed Management

Genetic analysis and identification of QTLs associated with yield-enhancing traits in Oryza sativa IR64 × Oryza glaberrima interspecific backcross populations

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