Discovery of QTLs for water mining and water use efficiency traits in rice under water-limited condition through association mapping

Raju, B. C.; Mohankumar, M. V.; Sumanth, Kambalimath K; Rajanna, Mavinahalli P.; Udayakumar, M.; Prasad, T. G.; Sheshshayee, M. S.

doi:10.1007/s11032-016-0457-z

Cited by 26 publications

(32 citation statements)

References 79 publications

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“…The number of alleles per loci varied from 2 to 13, with an average of 8.36 per loci. The amount of diversity available within this association panel is comparable with or even higher than that of earlier reports (Agrama et al 2007; Yan et al 2009; Borba et al 2010; Muthukumar et al 2015; Raju et al 2016). In general, higher genetic diversity in any germplasm collection indicates lesser gene flow among the accessions (Rahman et al 2007; Dinesh Raj et al 2010).…”

Section: Discussionsupporting

confidence: 85%

“…This kind of clear differentiation among genotypes of any subspecies may be because of their different growth environments, adaptive traits and evolutionary patterns. The allelic fixation (F st ) values were different among the three subgroups and varied from 0.194 to 0.277, suggesting higher genome compatibility among the genotypes, indicating that crosses made between the genotypes from these distinct subgroups would generate superior lines with better adaptability to moderate to severe drought conditions (N’Goran et al 2000; Raju et al 2016). The expected heterozygosity was higher in all the three subgroups and it was more than 0.544 among the three subgroups.…”

Section: Discussionmentioning

confidence: 99%

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Association Mapping of Yield and Yield-related Traits Under Reproductive Stage Drought Stress in Rice (Oryza sativa L.)

Swamy

Shamsudin

Rahman

et al. 2017

Rice

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BackgroundThe identification and introgression of major-effect QTLs for grain yield under drought are some of the best and well-proven approaches for improving the drought tolerance of rice varieties. In the present study, we characterized Malaysian rice germplasm for yield and yield-related traits and identified significant trait marker associations by structured association mapping.ResultsThe drought screening was successful in screening germplasm with a yield reduction of up to 60% and heritability for grain yield under drought was up to 78%. There was a wider phenotypic and molecular diversity within the panel, indicating the suitability of the population for quantitative trait loci (QTL) mapping. Structure analyses clearly grouped the accessions into three subgroups with admixtures. Linkage disequilibrium (LD) analysis revealed that LD decreased with an increase in distance between marker pairs and the LD decay varied from 5–20 cM. The Mixed Linear model-based structured association mapping identified 80 marker trait associations (MTA) for grain yield (GY), plant height (PH) and days to flowering (DTF). Seven MTA were identified for GY under drought stress, four of these MTA were consistently identified in at least two of the three analyses. Most of these MTA identified were on chromosomes 2, 5, 10, 11 and 12, and their phenotypic variance (PV) varied from 5% to 19%. The in silico analysis of drought QTL regions revealed the association of several drought-responsive genes conferring drought tolerance. The major-effect QTLs are useful in marker-assisted QTL pyramiding to improve drought tolerance.ConclusionThe results have clearly shown that structured association mapping is one of the feasible options to identify major-effect QTLs for drought tolerance-related traits in rice.Electronic supplementary materialThe online version of this article (doi:10.1186/s12284-017-0161-6) contains supplementary material, which is available to authorized users.

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Section: Discussionsupporting

confidence: 85%

Section: Discussionmentioning

confidence: 99%

Association Mapping of Yield and Yield-related Traits Under Reproductive Stage Drought Stress in Rice (Oryza sativa L.)

Swamy

Shamsudin

Rahman

et al. 2017

Rice

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“…The decay of linkage disequilibrium or squared allele‐frequency correlations ( r 2 ) values between the pairs of markers along physical distance was computed for both populations by using the default settings in version 5.1 of Tassel (http://www.maizegenetics.net/tassel). Marker pairs were binned at 1‐kbp intervals, and the binned r 2 values were plotted against the physical distance (Raju et al , ).…”

Section: Methodsmentioning

confidence: 99%

“…maizegenetics.net/tassel). Marker pairs were binned at 1-kbp intervals, and the binned r 2 values were plotted against the physical distance (Raju et al, 2016).…”

Section: Genome Wide Association Mappingmentioning

confidence: 99%

Genome‐wide association mapping for phenotypic plasticity in rice

Kikuchi

Bheemanahalli

Jagadish

et al. 2017

Plant Cell & Environment

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Phenotypic plasticity of plants in response to environmental changes is important for adapting to changing climate. Less attention has been paid to exploring the advantages of phenotypic plasticity in resource-rich environments to enhance the productivity of agricultural crops. Here, we examined genetic variation for phenotypic plasticity in indica rice (Oryza sativa L.) across two diverse panels: (1) a Phenomics of Rice Adaptation and Yield (PRAY) population comprising 301 accessions; and (2) a Multi-parent Advanced Generation Inter-Cross (MAGIC) indica population comprising 151 accessions. Altered planting density was used as a proxy for elevated atmospheric CO response. Low planting density significantly increased panicle weight per plant compared with normal density, and the magnitude of the increase ranged from 1.10 to 2.78 times among accessions for the PRAY population and from 1.05 to 2.45 times for the MAGIC population. Genome-wide-association studies validate three Environmental Responsiveness (ER) candidate alleles (qER1-3) that were associated with relative response of panicle weight to low density. Two of these alleles were tested in 13 genotypes to clarify their biomass responses during vegetative growth under elevated CO in Japan. Our study provides evidence for polymorphisms that control rice phenotypic plasticity in environments that are rich in resources such as light and CO .

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“…The genotypic data of 66 polymorphic markers were used for genetic diversity analysis. Jaccard’s similarity coefficient was calculated by using the NTSYS-PC software package [34, 35]. Cluster analysis was performed using UPGMA and sequential agglomerative hierarchal nested (SHAN) module of NTSYS-PC.…”

Section: Methodsmentioning

confidence: 99%

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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Discovery of QTLs for water mining and water use efficiency traits in rice under water-limited condition through association mapping

Cited by 26 publications

References 79 publications

Association Mapping of Yield and Yield-related Traits Under Reproductive Stage Drought Stress in Rice (Oryza sativa L.)

Association Mapping of Yield and Yield-related Traits Under Reproductive Stage Drought Stress in Rice (Oryza sativa L.)

Genome‐wide association mapping for phenotypic plasticity in rice

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

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