Comparative mapping combined with homology-based cloning of the rice genome reveals candidate genes for grain zinc and iron concentration in maize

Jin, Tiantian; Chen, Jingtang; Zhu, Linghua; Zhao, Yang; Guo, Jian-Wei; Huang, Yaqun

doi:10.1186/s12863-015-0176-1

Cited by 38 publications

(31 citation statements)

References 101 publications

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“…Their higher phenotypic variance (R 2 ) values indicate that they control a considerable amount of genetic variation in grain mineral contents and could be reliable genetic markers for further improvement of Fe and Zn contents in rice grains. All these markers have been variably reported to be linked with grain micronutrients contents [10,29,[52][53][54][55].…”

Section: Discussionmentioning

confidence: 99%

Grain Fe and Zn Contents Linked SSR Markers Based Genetic Diversity Reveal Perspective for Marker Assisted Biofortification Breeding in Rice

Raza¹,

Riaz²,

Saher³

et al. 2020

Preprint

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Rice is critical for sustainable food and nutritional security; however, nominal micronutrient quantities in grains aggravate malnutrition in rice-eating poor populations. Here, we assessed genetic diversity in grain iron (Fe) and zinc (Zn) contents using trait-linked simple sequence repeat (SSR) markers in 56 fine and coarse grain rice accessions of different geographical origin. Aromatic fine gain accessions contained relatively higher Fe and Zn contents in brown rice (BR) than coarse grain accessions. Genotyping with 24 SSR markers identified 21 polymorphic markers, among which seventeen demonstrated higher gene diversity and polymorphism information content (PIC) values, strongly indicating that markers used in current research were moderate to highly informative for evaluating genetic diversity. Population structure, principal coordinate and phylogenetic analyses classified studied rice accessions into two fine grain specific and one fine and coarse grain admixture subpopulations. Single marker analysis recognized four ZnBR and single FeBR significant marker-trait associations (MTAs), contributing 15.41-39.72% in total observed phenotypic variance. Furthermore, high grain Fe and Zn contents linked marker alleles from significant MTAs were also identified. Collectively, these results indicate availability of wide genetic diversity in rice germplasm and perspective for marker-assisted biofortification breeding.

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Section: Discussionmentioning

confidence: 99%

Grain Fe and Zn Contents Linked SSR Markers Based Genetic Diversity Reveal Perspective for Marker Assisted Biofortification Breeding in Rice

Raza¹,

Riaz²,

Saher³

et al. 2020

Preprint

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“…The qK6.1/qCa6/qZn6/qMn6/qCu6 region for major QTLs was a 29.9-kb region flanked by RM19410 and Si2944, with the enhancing alleles derived from Milyang 46 (Yu et al, 2015). In maize, the NRAMP genes are likely responsible for the observed natural variation in maize grain Zn and Fe concentrations (Jin et al, 2015).…”

Section: Qtl Detection and Pleiotropy For Eight Element Concentrationmentioning

confidence: 99%

Identification of quantitative trait loci for mineral elements in grains and grass powder of barley

Zeng¹,

Du²,

Yang³

et al. 2016

Genet. Mol. Res.

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ABSTRACT. Mineral elements in barley (Hordeum vulgare) play an important physiological role in global human health. In this study, quantitative trait loci (QTLs) for concentration of nine mineral elements in barley grain and grass powder were detected in a population of 193 recombinant inbred lines of the barley cross Ziguangmangluoerling x Schooner and the parents. We observed large genetic variation contributing to element concentrations in both grains and grass powder. The mean K, Ca, and Fe concentrations in grass powder were 6.67, 12.00, and 4.58 times that of regenerating barley grains. In grains, 17 2 Y.W. Zeng et al. Genetics and Molecular Research 15 (4): gmr15049103QTLs that accounted for 6.36-64.08% of the phenotypic variation in Zn, Mg, Ca, K, Na, Mn, Fe, and P concentrations were identified. In grass powder, seven QTLs were identified; these accounted for 6.03-21.86% of the variation in Ca, Zn, Mg, K, Fe, and Cu concentrations. These QTLs affecting elements in grain and grass powder are so far unreported in barley. To our knowledge, QTLs with pleiotropic effects for three elements were also identified for the first time in barley. The qK1/qMg1/qCa1 region between markers Bmag0211 and GBMS0014 on chromosome 1H was shown to have large additive effects for Mg, Ca, and K concentrations in grains. These additive effects indicated that the high element (Mg, Ca, Zn, Mn, and K) alleles were contributed by Ziguangmangluoerling. These results will further our understanding of the genetic basis of mineral elements and help us develop markers linked with mineral elements for marker-assisted selection breeding in barley.

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“…Previous results pertaining to the genomic locations, confidence intervals or phenotypic variance explained by QTLs were inconsistent due to the differences in genetic backgrounds, environments, and/or mapping populations (Jin et al 2015). Quantifying target traits in plants is time consuming, laborious, and expensive.…”

Section: Comparison Of Loci Among Different Populationsmentioning

confidence: 99%

“…Consequently, comparing QTL for different traits detected by independent experiments is important. Evidences for comparative QTLs detected by different populations leading to identify candidate genes which are probably responsible for target traits have been recorded by multiple studies in maize (Prioul et al 1999;Pelleschi et al 1999;Duble et al 2000;Liu et al 2012;Osman et al 2013;Jin et al 2015).…”

Section: Comparison Of Loci Among Different Populationsmentioning

confidence: 99%

“…Based on meta-QTL analysis in rice and maize reported by Jin et al (2015), three MQTL-containing candidate genes in maize were detected and two maize orthologs of rice, GRMZM2G366919 and GRMZM2G178190, were characterized as NRAMP genes likely responsible for the natural variation in maize grain zinc and iron concentration. Combing single environment analysis with multiple environment trial (MET) QTL analysis in six environments, Zhang et al (2017) found five candidate genes for the target traits were identified in the intervals detected by meta-QTLs in the previous study.…”

Section: Introductionmentioning

confidence: 99%

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Identification of quantitative trait loci and the exploration of candidate genes for the tolerance to Zn deficiency in maize

Jian-qin¹,

Zhu²,

Fu³

et al. 2019

Preprint

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Background Zn is essential for plants and Zn deficiency leads to great reduction in quality and quantity of crops. Maize, as one of the most important main staple crops worldwide, is more susceptible to Zn deficiency than any other cereal crops. Therefore, understanding the functional mechanisms in tolerance to Zn deficiency in maize is urgent but is still lacking. In this study, quantitative trait loci (QTL) analysis in K22 and By815 RIL population with high-density bin map was conducted to investigate genetic basis of the mechanisms in maize to tolerate Zn deficiency, subsequently some candidate genes were identified and considered as being associated with Zn metabolisms in plants. Results 21 QTLs were detected and accounted for 5.9% - 16.6% of phenotypic variations. Based on the co-localization in this study and the comparisons with previous studies in different RIL and GWAS populations, 223 candidate genes were identified inside the reduced QTL peak intervals on chromosome 1, 2, 6, 7 and 9. Furthermore, 9 genes detected within the peak bins of valuable genomic regions are suggested to be associated with ions transportation and some redox processes affected by Zn deficiency. Additionally, 5 genes, including ZmIRT1, ZmNRAMP6, ZmEIN2 and ZmHMAs, whose homologous gene have been studied and considered to be responsible for metal cations transportation and ethylene-signaling pathway requiring a transition metal were discovered in 5 loci we mapped. Conclusions 14 target genes identified in 9 loci we mapped in this work were explored to elucidate the potential functions in Zn homeostasis and the direct or indirect effects on mechanisms in Zn deficiency tolerance in maize. It is the first time that ZmIRT1, ZmNRAMP6, ZmHMAs were identified using linkage analysis under Zn deficiency in maize, providing genetic evidence and foundation for further gene functional characterization. Our findings have assisted us untangling the genetic basis of possible mechanisms in response to Zn deficiency in maize.

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Comparative mapping combined with homology-based cloning of the rice genome reveals candidate genes for grain zinc and iron concentration in maize

Cited by 38 publications

References 101 publications

Grain Fe and Zn Contents Linked SSR Markers Based Genetic Diversity Reveal Perspective for Marker Assisted Biofortification Breeding in Rice

Grain Fe and Zn Contents Linked SSR Markers Based Genetic Diversity Reveal Perspective for Marker Assisted Biofortification Breeding in Rice

Identification of quantitative trait loci for mineral elements in grains and grass powder of barley

Identification of quantitative trait loci and the exploration of candidate genes for the tolerance to Zn deficiency in maize

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