Quantitative Trait Loci Mapping of Mineral Element Contents in Brown Rice Using Backcross Inbred Lines Derived From Oryza longistaminata

Liu, Xingdan; Fan, Fengfeng; Liu, Manman; Long, Weixiong; Yu, Yaoxiang; Yuan, Huanran; Pan, Guojing; Li, Nengwu; Li, Shaoqing; Liu, Jianfeng

doi:10.3389/fpls.2020.01229

Cited by 14 publications

(6 citation statements)

References 44 publications

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“…A set of 140 BC 2 F 20 BIL lines derived from O. longistaminata and acceptor parent 9311 were used in the experiment [16][17][18]. The seeds of the 9311 and BILs were placed in an oven at 50 • C for 5 days to break the possible dormancy, then germinated at room temperature for 48 h after surface sterilization with 2% sodium hypochlorite solution for 10 min and rinsing well with distilled water.…”

Section: Plant Materials and Salt Treatmentsmentioning

confidence: 99%

“…A total of 2432 bin markers were used to construct the genetic linkage map covering the whole-genome sequencing (unpublished data). The DNA extraction, single nucleotide polymorphism (SNP) calling, genotyping, bin map, and genetic linkage map construction were all performed following previous reports [16][17][18]. The QTL IciMapping v4.1 software (Chinese Academy of Agricultural Sciences, Beijing, China) was used to construct the linkage map and QTL analysis [36], and the limit of detection (LOD) threshold was set at 2.5.…”

Section: Qtl Mappingmentioning

confidence: 99%

“…Wild rice is an important gene pool for rice yield, quality, and resistance improvement [13,14]. O. longistaminata, an African wild rice species variety of AA genome, has a good performance in abiotic tolerance, but only a few genes have been discovered and there is little research on rice breeding [15][16][17][18]. In order to further explore the alien genes related to salt tolerance and broaden the genetic diversity of rice, 140 backcross inbred lines (BILs) derived from the cross of 9311 and wild rice O. longistaminata were investigated, and the salt injury indexes were scored based on evaluation of the characteristics, including seedling length, root length, seedling weight, and root weight.…”

Section: Introductionmentioning

confidence: 99%

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Quantitative Trait Locus Mapping of Salt Tolerance in Wild Rice Oryza longistaminata

Yuan

Zhang

Xiao

et al. 2022

IJMS

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Salt stress is one of the most severe adverse environments in rice production; increasing salinization is seriously endangering rice production around the world. In this study, a rice backcross inbred line (BIL) population derived from the cross of 9311 and wild rice Oryza longistaminata was employed to identify the favorable genetic loci of O. longistaminata for salt tolerance. A total of 27 quantitative trait loci (QTLs) related to salt tolerance were identified in 140 rice BILs, and 17 QTLs formed seven QTL clusters on different chromosomes, of which 18 QTLs were derived from O. longistaminata, and a QTL for salt injury score (SIS), water content of seedlings (WCS) under salt treatment, and relative water content of seedlings (RWCS) was repeatedly detected and colocalized at the same site on chromosome 2, and a cytochrome P450 86B1 (MH02t0466900) was suggested as the potential candidate gene responsible for the salt tolerance based on sequence and expression analysis. These findings laid the foundation for further improving rice salt tolerance through molecular breeding in the future.

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Section: Plant Materials and Salt Treatmentsmentioning

confidence: 99%

Section: Qtl Mappingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Quantitative Trait Locus Mapping of Salt Tolerance in Wild Rice Oryza longistaminata

Yuan

Zhang

Xiao

et al. 2022

IJMS

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“…Improvement of the character controlled by a polygene is constantly crucial in plant breeding programs. A molecular approach for improving the quantitative character of rice through QTL analysis has been investigated by several researchers (Bakti and Tanaka 2019;Baltazar et al 2019;Jewel et al 2019;Liu et al 2020;Sandhu et al 2021;Zhao et al 2022). However, molecular techniques are expensive, labor-intensive, and difficult to apply in countries with limited resources.…”

Section: Introductionmentioning

confidence: 99%

The shifting genetic diversity pattern of Indonesian rice improved varieties from 1943-2019 based on historical pedigree data

et al. 2022

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Abstract. Anas, Damayanti F, Kadapi M, Carsono N, Sari S. 2022. The shifting genetic diversity pattern of Indonesian rice improved varieties from 1943-2019 based on historical pedigree data. Biodiversitas 23: 4649-4656. For rice plants in Indonesia, stagnation in increasing crop yields due to a reduction in genetic diversity is a significant issue. The issue of using the same parents in breeding programs and consumer preferences for a single main variety are among the causes of the narrowing of rice plants' genetic diversity. The purpose of this study is to figure out which ancestors are significant and how the genetic diversity of improved Indonesian rice cultivars has changed over time. Changes in the genetic background of the Indonesian rice gene pool were decided using pedigree analysis by calculating the coefficient of parentage (COP) among varieties. There are 280 ancestors in the rice gene pool. The pedigree map exemplifies the complexities of rice breeding in Indonesia. The four classical ancestors of DGWG, Taichung Native1, China, and Latisail had a noteworthy influence on all irrigated rice plant types (11.22%) and upland rice plant types (8.30%) in the gene pool. The dominance of the phenomenal variety IR64 has been continued by Inpari 32, which is a direct derivative of Ciherang. In the meantime, Inpago9, Luhur 2, and UPLRI ancestors set the foundation for the upland rice plant type. Inpara7 and Inpara9, along with their IRRI-introduced parents (IRRIpara4 and IRRIpara5), had a significant impact on Indonesian tidal rice plants. The A1, Hipa7, and Hipa3 varieties are heavily influenced by the hybrid rice plants of Indonesia.

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“…Identification of QTLs or genes that control the concentrations of essential mineral nutrients and toxic trace metals (also known as the ionome) in rice grains can increase the speed and efficiency of the variety improvement process. In the past few decades, many efforts have been made in mapping QTLs regulating the rice grain ionome, and hundreds of genetic loci have been identified for individual elements through bi-parental ( Norton et al, 2010 , 2019 ; Zhang et al, 2014 ; Yu et al, 2015 ; Chen et al, 2019 ; Raza et al, 2019 ; Liu et al, 2020 ; Wang et al, 2020 ) or genome wide association (GWA) mapping approaches ( Norton et al, 2014 ; Huang et al, 2015 ; Yang et al, 2018 ; Tan et al, 2020 ). Among these QTLs, only a small number of causal genes have been identified and functionally characterized, including OsHMA3 , OsCAL1 and OsCd1 for Cd ( Ueno et al, 2010 ; Miyadate et al, 2011 ; Luo et al, 2018 ; Sui et al, 2019 ; Yan et al, 2019 ), OsHMA4 for Cu ( Huang et al, 2016 ), and OsMOT1;1 for molybdenum (Mo) ( Yang et al, 2018 ; Huang et al, 2019 ; Wang et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Univariate and Multivariate QTL Analyses Reveal Covariance Among Mineral Elements in the Rice Ionome

et al. 2021

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Rice provides more than one fifth of daily calories for half of the world’s human population, and is a major dietary source of both essential mineral nutrients and toxic elements. Rice grains are generally poor in some essential nutrients but may contain unsafe levels of some toxic elements under certain conditions. Identification of quantitative trait loci (QTLs) controlling the concentrations of mineral nutrients and toxic trace metals (the ionome) in rice will facilitate development of nutritionally improved rice varieties. However, QTL analyses have traditionally considered each element separately without considering their interrelatedness. In this study, we performed principal component analysis (PCA) and multivariate QTL analyses to identify the genetic loci controlling the covariance among mineral elements in the rice ionome. We resequenced the whole genomes of a rice recombinant inbred line (RIL) population, and performed univariate and multivariate QTL analyses for the concentrations of 16 elements in grains, shoots and roots of the RIL population grown in different conditions. We identified a total of 167 unique elemental QTLs based on analyses of individual elemental concentrations as separate traits, 53 QTLs controlling covariance among elemental concentrations within a single environment/tissue (PC-QTLs), and 152 QTLs which determined covariation among elements across environments/tissues (aPC-QTLs). The candidate genes underlying the QTL clusters with elemental QTLs, PC-QTLs and aPC-QTLs co-localized were identified, including OsHMA4 and OsNRAMP5. The identification of both elemental QTLs and PC QTLs will facilitate the cloning of underlying causal genes and the dissection of the complex regulation of the ionome in rice.

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Quantitative Trait Loci Mapping of Mineral Element Contents in Brown Rice Using Backcross Inbred Lines Derived From Oryza longistaminata

Cited by 14 publications

References 44 publications

Quantitative Trait Locus Mapping of Salt Tolerance in Wild Rice Oryza longistaminata

Quantitative Trait Locus Mapping of Salt Tolerance in Wild Rice Oryza longistaminata

The shifting genetic diversity pattern of Indonesian rice improved varieties from 1943-2019 based on historical pedigree data

Univariate and Multivariate QTL Analyses Reveal Covariance Among Mineral Elements in the Rice Ionome

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