Molecular mapping of QTLs for yield related traits in recombinant inbred line (RIL) population derived from the popular rice hybrid KRH-2 and their validation through SNP genotyping

Kulkarni, Swapnil Ravindra; Balachandran, S. M.; Ulaganathan, Kandasamy; Balakrishnan, Divya; Praveen, M; Prasad, A.; Fiyaz, R. Abdul; Senguttuvel, P.; Sinha, Pragya; Kale, Ravindra Ramrao; Rekha, G.; Kousik, M. B. V. N.; Harika, G.; Anila, M.; Punniakoti, E.; Dilip, T.; Hajira, S. K.; Pranathi, K.; Das, Moumita; Shaik, Mastanbee; Chaitra, K.; Rao, P. K.; Gangurde, Sunil S.; Pandey, Manish K.; Sundaram, R. M.

doi:10.1038/s41598-020-70637-3

Cited by 31 publications

(22 citation statements)

References 113 publications

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“…Moreover, after cleaning the data of all the NA/NaN values, we had a total of 56,421 data sets with 43,994 No values and 12,427 Yes values. We used this data which is a good sample to perform multiple cross validation experiments to evaluate and propose the high-performing models representing the population 3 , 26 .…”

Section: Data Sourcementioning

confidence: 99%

Automated predictive analytics tool for rainfall forecasting

Raval

Sivashanmugam

Pham

et al. 2021

Sci Rep

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Australia faces a dryness disaster whose impact may be mitigated by rainfall prediction. Being an incredibly challenging task, yet accurate prediction of rainfall plays an enormous role in policy making, decision making and organizing sustainable water resource systems. The ability to accurately predict rainfall patterns empowers civilizations. Though short-term rainfall predictions are provided by meteorological systems, long-term prediction of rainfall is challenging and has a lot of factors that lead to uncertainty. Historically, various researchers have experimented with several machine learning techniques in rainfall prediction with given weather conditions. However, in places like Australia where the climate is variable, finding the best method to model the complex rainfall process is a major challenge. The aim of this paper is to: (a) predict rainfall using machine learning algorithms and comparing the performance of different models. (b) Develop an optimized neural network and develop a prediction model using the neural network (c) to do a comparative study of new and existing prediction techniques using Australian rainfall data. In this paper, rainfall data collected over a span of ten years from 2007 to 2017, with the input from 26 geographically diverse locations have been used to develop the predictive models. The data was divided into training and testing sets for validation purposes. The results show that both traditional and neural network-based machine learning models can predict rainfall with more precision.

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Section: Data Sourcementioning

confidence: 99%

Automated predictive analytics tool for rainfall forecasting

Raval

Sivashanmugam

Pham

et al. 2021

Sci Rep

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“…The genomic DNA was isolated from fresh and healthy leaves of 125 DHLs (D 3 generation) and parents IR58025A-KMR-3R, following the protocol of CTAB MiniPrep method described by [88]. The protocol for setting up the polymerase chain reaction (PCR) including the PCR pro le was followed as described in [87]. The products ampli ed after the PCR were resolved in 4% agarose gel (Sigma, USA) using Protean II gel casting and electrophoresis apparatus (BioRad, USA).…”

Section: Discussionmentioning

confidence: 99%

“…At appropriate growth stage of experimental material, 12 traits namely, days to fty percent owering (DFF), total number of grains per panicle (GP), test (1,000) grain weight (TGW), number of productive tillers (PT), biomass (BM), fertile grains per panicle (FGP), panicle weight (PW), panicle length (PL), ag leaf length (FLL), ag leaf width (FLW), plant height (PH) and total grain yield per plant (YLD) were recorded from ve healthy plants of middle row of each line (of each replication), as per the standard evaluation system recommended by [86] and the collected data was considered for further analysis. The procedure of recording these traits was followed as described in [87].…”

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confidence: 99%

QTL Mapping of Novel Genomic Regions for Yield Related Traits in Doubled Haploid (DH) Population Derived from the Popular Rice Hybrid KRH-2

Kulkarni

Ulaganathan

et al. 2020

Preprint

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Background: Rice, being the principal food crop and major nutritional source for more than half of the global population, is also an important source of livelihood in many South and South-East Asian countries. Amidst diminishing natural resources and many biotic-abiotic stresses, increasing the yield of rice varieties remains a challenging task. Identification of novel and yield augmenting alleles from stable rice hybrids is crucial to facilitate their marker-assisted transfer into various genetic backgrounds. Results: Quantitative trait loci (QTL) mapping using a population of 125 doubled haploid (DH) lines developed from the cross IR58025A/KMR3R and 126 polymorphic SSR; EST-derived SSR markers led to the identification of 12 each of major-minor effect QTLs for yield related traits. Major effect QTLs were detected for traits namely days to fifty percent flowering, test (1,000) grain weight, plant height, panicle weight, panicle length, flag leaf width, flag leaf length, biomass and total grain yield/plant explaining the phenotypic variability in the range of 29.95%-56.75%. QTL hotspots were detected on chromosome 3 for the traits, panicle length and total grain yield/plant and on chromosome 6 for the traits, panicle length, flag leaf length and total grain yield/plant. Though many of these QTLs were noted to co-localize with the QTL regions reported in earlier studies, five novel and major effect QTLs for panicle length, biomass, flag leaf width, panicle weight, plant height and three novel minor effect QTLs for panicle weight and fertile grains per panicle, were identified in this study. Conclusions: Through this study, both major-minor effect novel QTLs for crucial yield related traits, viz., fertile grains per panicle, panicle length, panicle weight were identified. Further, the QTL hotspots identified on two different chromosomes for flag leaf length, panicle length and total grain yield/plant shall not only help in understanding the underlying genetic mechanisms of yield regulation but also would provide an insight into the genetic synchrony among the various yield related traits in contributing for yield heterosis. The identified QTL hotspots after their validation can be deployed in breeding programs targeted towards improvement of yield heterosis.

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“…This information can be utilized for breeding applications to recuperate rice grain shape and yield (Yan et al, 2014). Recently, another RIL population obtained from KRH-2 (IR58025A/KMR3R) was utilized to identify QTLs governing crop yield (Kulkarni et al, 2020). A genetic map of 294.2 cM with 126 simple sequence repeats (SSR) was made.…”

Section: Genomics and Transcriptomics In Rice Trait Improvementmentioning

confidence: 99%

“…Moreso, considerable epistatic interactions were detected for the length of panicle and grain yield per plant. In silico analysis of the QTLs highlighted the functions of candidate genes linked with preferred traits (Kulkarni et al, 2020). The high-yielding RILs harboring the yield associated QTLs were recognized as restorers.…”

Section: Genomics and Transcriptomics In Rice Trait Improvementmentioning

confidence: 99%

Toward Integrated Multi-Omics Intervention: Rice Trait Improvement and Stress Management

Iqbal

Khan

et al. 2021

Front. Plant Sci.

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Rice (Oryza sativa) is an imperative staple crop for nearly half of the world’s population. Challenging environmental conditions encompassing abiotic and biotic stresses negatively impact the quality and yield of rice. To assure food supply for the unprecedented ever-growing world population, the improvement of rice as a crop is of utmost importance. In this era, “omics” techniques have been comprehensively utilized to decipher the regulatory mechanisms and cellular intricacies in rice. Advancements in omics technologies have provided a strong platform for the reliable exploration of genetic resources involved in rice trait development. Omics disciplines like genomics, transcriptomics, proteomics, and metabolomics have significantly contributed toward the achievement of desired improvements in rice under optimal and stressful environments. The present review recapitulates the basic and applied multi-omics technologies in providing new orchestration toward the improvement of rice desirable traits. The article also provides a catalog of current scenario of omics applications in comprehending this imperative crop in relation to yield enhancement and various environmental stresses. Further, the appropriate databases in the field of data science to analyze big data, and retrieve relevant information vis-à-vis rice trait improvement and stress management are described.

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Molecular mapping of QTLs for yield related traits in recombinant inbred line (RIL) population derived from the popular rice hybrid KRH-2 and their validation through SNP genotyping

Cited by 31 publications

References 113 publications

Automated predictive analytics tool for rainfall forecasting

Automated predictive analytics tool for rainfall forecasting

QTL Mapping of Novel Genomic Regions for Yield Related Traits in Doubled Haploid (DH) Population Derived from the Popular Rice Hybrid KRH-2

Toward Integrated Multi-Omics Intervention: Rice Trait Improvement and Stress Management

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