Mapping QTLs underpin nutrition components in aromatic rice germplasm

Islam, Mohammad Zahidul; Arifuzzaman, Md.; Banik, Sarbani; Hossain, Mahmud; Ferdous, Jannatul; Khalequzzaman, M; Pittendrigh, Barry R.; Tomita, Motonori; Ali, M. P.

doi:10.1371/journal.pone.0234395

Cited by 18 publications

(6 citation statements)

References 38 publications

(49 reference statements)

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“…Clusters IV and VI are solitary clusters. Similar results were observed by Islam et al (2020), as they used 113 rice genotypes for D 2 -analysis and observed that these genotypes were grouped into 4 clusters. Diversity analysis by Tejawini et al (2018) and Salem and Sallam (2016) have also shown similar findings.…”

Section: Diversity Analysis Using Morphological Datasupporting

confidence: 83%

Morpho-molecular Diversity Analysis in Rice (Oryza sativa L.) Genotypes using Microsatellite Markers

Singh

Korada

et al. 2022

IJARe

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Background: The insight concerning genetic diversity and relationship among the rice genotypes is a basic contribution to the crop improvement programs. The present field experiment was conducted to study the level of diversity present in 47 rice genotypes using both morphological and molecular analysis. Methods: The experiment was carried out at Agricultural Research Farm, Banaras Hindu University, Varanasi (U.P.), during Kharif-2019 with 47 rice genotypes. Mahalanobis’ D2 analysis was carried out to evaluate the morphological diversity present among the genotypes and 24 polymorphic SSR markers were used for molecular analysis using the NTSYSpc software. Result: Mahalanobis’ D2 grouped 47 rice genotypes into 6 clusters based on the inter-se genetic distance. The highest inter-cluster distance (1134.14) was observed between clusters II and IV indicating the genotypes present in these clusters to be highly divergent. Molecular diversity analysis grouped the 47 rice genotypes into 3 main clusters i.e., cluster I, cluster II and cluster III, which were further divided into sub-clusters. Polymorphic Information Content varied from 0.12 to 0.86, with an average of 0.465. The highest PIC value was observed for locus RM 507 (0.86). All the 24 primers showed polymorphism and the number of alleles was common for all i.e., 2. Together, the morphological and molecular diversity analysis revealed that Desi Dhan and IR 91143-AC 293-1, Desi Dhan and BD 105, IR 85850 and Lal Sundiya, Chauli and Swarna were the most diverse genotypes among the 47 rice genotypes included in the study.

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Section: Diversity Analysis Using Morphological Datasupporting

confidence: 83%

Morpho-molecular Diversity Analysis in Rice (Oryza sativa L.) Genotypes using Microsatellite Markers

Singh

Korada

et al. 2022

IJARe

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“…Most of the QTLs for Fe and Zn content were identified through single-nucleotide polymorphism (SNP) and DArT-SSR markers in RIL and BIL populations, but some of them were also from DH lines ( Table 1 ). Recently, many new QTLs for Fe and Zn content have been mapped mainly through SSR markers in DH and BIL mapping populations ( 26 , 27 , 29 , 34 – 36 ) ( Table 1 ). Maize hybrids and varieties having a high yield potential along with 25–30% more Fe and Zn than common cultivars have been developed as part of the HarvestPlus program ( 108 ).…”

Section: Introductionmentioning

confidence: 99%

Enhancing the Nutritional Quality of Major Food Crops Through Conventional and Genomics-Assisted Breeding

et al. 2020

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Nutritional stress is making over two billion world population malnourished. Either our commercially cultivated varieties of cereals, pulses, and oilseed crops are deficient in essential nutrients or the soils in which these crops grow are becoming devoid of minerals. Unfortunately, our major food crops are poor sources of micronutrients required for normal human growth. To overcome the problem of nutritional deficiency, greater emphasis should be laid on the identification of genes/quantitative trait loci (QTLs) pertaining to essential nutrients and their successful deployment in elite breeding lines through marker-assisted breeding. The manuscript deals with information on identified QTLs for protein content, vitamins, macronutrients, micro-nutrients, minerals, oil content, and essential amino acids in major food crops. These QTLs can be utilized in the development of nutrient-rich crop varieties. Genome editing technologies that can rapidly modify genomes in a precise way and will directly enrich the nutritional status of elite varieties could hold a bright future to address the challenge of malnutrition.

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“…This locus has been frequently reported from aus derived populations. Several studies have also identified similar genomic regions ( Lu et al., 2008 ; Garcia-Oliveira et al, 2009 ; Zhang et al., 2014 ; Descalsota-Empleo et al., 2019b ; Islam et al, 2020 ).…”

Section: Discussionmentioning

confidence: 89%

Molecular dissection of connected rice populations revealed important genomic regions for agronomic and biofortification traits

Palanog

Nha²,

Descalsota-Empleo³

et al. 2023

Front. Plant Sci.

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Breeding staple crops with increased micronutrient concentration is a sustainable approach to address micronutrient malnutrition. We carried out Multi-Cross QTL analysis and Inclusive Composite Interval Mapping for 11 agronomic, yield and biofortification traits using four connected RILs populations of rice. Overall, MC-156 QTLs were detected for agronomic (115) and biofortification (41) traits, which were higher in number but smaller in effects compared to single population analysis. The MC-QTL analysis was able to detect important QTLs viz: qZn5.2, qFe7.1, qGY10.1, qDF7.1, qPH1.1, qNT4.1, qPT4.1, qPL1.2, qTGW5.1, qGL3.1, and qGW6.1, which can be used in rice genomics assisted breeding. A major QTL (qZn5.2) for grain Zn concentration has been detected on chromosome 5 that accounted for 13% of R2. In all, 26 QTL clusters were identified on different chromosomes. qPH6.1 epistatically interacted with qZn5.1 and qGY6.2. Most of QTLs were co-located with functionally related candidate genes indicating the accuracy of QTL mapping. The genomic region of qZn5.2 was co-located with putative genes such as OsZIP5, OsZIP9, and LOC_OS05G40490 that are involved in Zn uptake. These genes included polymorphic functional SNPs, and their promoter regions were enriched with cis-regulatory elements involved in plant growth and development, and biotic and abiotic stress tolerance. Major effect QTL identified for biofortification and agronomic traits can be utilized in breeding for Zn biofortified rice varieties.

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Mapping QTLs underpin nutrition components in aromatic rice germplasm

Cited by 18 publications

References 38 publications

Morpho-molecular Diversity Analysis in Rice (Oryza sativa L.) Genotypes using Microsatellite Markers

Morpho-molecular Diversity Analysis in Rice (Oryza sativa L.) Genotypes using Microsatellite Markers

Enhancing the Nutritional Quality of Major Food Crops Through Conventional and Genomics-Assisted Breeding

Molecular dissection of connected rice populations revealed important genomic regions for agronomic and biofortification traits

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