Linkage mapping for grain iron and zinc content in F<sub>2</sub>population derived from the cross between PAU201 and Palman 579 in rice (<i>Oryza sativa</i>L.)

Kumar, Jitendra; Jain, Sunita; Jain, R. K.

doi:10.1556/crc.42.2014.3.3

Cited by 35 publications

(25 citation statements)

References 16 publications

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“…Three QTLs were detected to be significantly associated with K, one of which (only detected by FarmCPU) was also detected in previous studies [56]. For Zn, ten significant QTLs were identified, three of which co-located with previously reported loci [56][57][58][59]. Among them, one significant QTL posited around 1,8001,929 bp of Chromosome 7 was detected by both univariate and multivariate methods, which located near reported QTL qZN-7 [59].…”

Section: Correlation Of Different Traitssupporting

confidence: 63%

Genome-wide association studies of ionomic and agronomic traits in USDA minicore collection of rice and comparative analyses of different mapping methods

Liu

Zhong

Meng

et al. 2020

Preprint

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Rice is an important human staple food vulnerable to heavy metal contamination due to its unique physiology and growth environment. High yield with low heavy metal contamination is a common but highly challenging goal for rice breeders worldwide due to lack of genetic knowledge. In this report, a comprehensive GWAS analyses for ionomic and agronomic traits based on 3,259,478 SNPs were performed using two univariate methods and two multivariate methods. Under the criterium p-value <1.53×10-8, 106, 47, and 97 QTLs were identified for ionomics in flooded environment, unflooded environment, and agronomic traits, respectively. Detailed analysis of the QTLs revealed that many of the identified QTLs are co-localized with the QTLs reported in prior ionomic and agronomic studies or posited near the genes with known functions in the related traits, suggesting that our GWAS analyses are reliable. Our results further showed that each of the four GWAS methods can identify unique as well as common QTLs. When univariate methods failed to identify QTLs for a trait, the multivariate methods frequently detected QTLs. However, when many QTLs were detected by univariate methods, the number of QTLs detected by multivariate methods were reduced in many cases. These analyses suggest that using multiple GWAS methods can complement each other in QTL identification and some methods may be more powerful with less false discovery rate. In addition, several candidate genes involved in ionomic and agronomic traits control were identified by sequence analysis of the QTL regions. This research provides novel insights into the genetic basis of both ionomic and agronomic variations in rice, establishing an important foundation for further studies on reducing heavy-metal contamination and improving crop yields.

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Section: Correlation Of Different Traitssupporting

confidence: 63%

Genome-wide association studies of ionomic and agronomic traits in USDA minicore collection of rice and comparative analyses of different mapping methods

Liu

Zhong

Meng

et al. 2020

Preprint

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“…Zhang et al () identified QTL for iron and zinc concentrations in a RIL from a cross between the japonica variety Lemont and the indica variety TeQing and a BIL obtained by introgressing TeQing into Lemont. Kumar et al () mapped QTL associated with grain mineral content in an F 2 population that originated from an intra‐ indica cross between PAU201 and Palman 579. They identified QTL for iron on chromosomes 2, 4, and 7 and QTL for zinc on chromosomes 2, 7 and 10.…”

Section: Discussionmentioning

confidence: 99%

“…QTL for rice grain iron and zinc contents have been mapped by using different biparental mapping populations originating from crosses between indica and japonica varieties and crosses involving wild rice relatives. Mapping populations include F 2 progeny (Kumar, Jain, & Jain, ), recombinant inbred lines (RILs, Anuradha et al, ; Zhang et al, ), doubled haploid (DH) lines (Stangoulis, Huynh, Welch, Choi, & Graham, ; Swamy, Descalsota, et al, , Descolta‐Empleo et al ), backcross inbred lines (BILs, Garcia‐Oliveira, Tan, Fu, & Sun, ) and introgression lines (Xu, Li, Yang, & Xu, ). DH lines are favoured as QTL mapping populations because their true breeding nature and convenient production enable accurate phenotyping with replications.…”

Section: Introductionmentioning

confidence: 99%

Analysis of QTL responsible for grain iron and zinc content in doubled haploid lines of rice (Oryza sativa) derived from an intra‐japonica cross

et al. 2020

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Quantitative trait loci (QTL) related to the grain iron and zinc contents of brown rice were mapped by using a doubled haploid population derived from an intra-japonica cross between 'Hwaseonchal' and 'Goami 2'. QTL-QTL, background-QTL, and backgroundbackground interactions and candidate genes that affect grain iron and zinc contents were preliminarily identified. Twenty-one iron-and zinc-related QTL were found. The major-effect QTL qFe7 and qZn7 provided the highest contribution to phenotypic variance for grain iron and zinc contents. The colocation of zinc-and iron-related QTL on chromosomes 1, 4, 7 and 11 may account for the strong correlation between iron and zinc contents. A region on chromosome 7 and epistatic interaction between loci on chromosomes 2 and 10 affected iron content. qZn7 and qZn11.3 exerted additive effects on zinc content. Eleven iron-and zinc-related candidate genes colocated with qFe7, qZn7 and the region on chromosome 7 with an additive effect on iron content.The major-effect QTL identified here may be useful for breeding biofortified rice. K E Y W O R D Scandidate gene, doubled haploid, iron, japonica rice, quantitative trait loci, zinc | 345 JEONG Et al.

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“…Besides, Garcia-Oliveira et al 17 detected one major effect QTL explaining most significant proportion of PVE (11%-19%) for zinc, flanking by SSR marker RM152 on chromosome 8. In other various studies, several QTLs have been reported which explained a large amount of PVE either for zinc or for both iron and zinc contents [45][46][47][48][49][50][51] .…”

Section: Zn and Fe Contents In Ricementioning

confidence: 99%

A Review of Important QTLs for Biofortification Traits in Rice

Sharma¹,

Saini²,

Kumar³

et al. 2019

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Rice (Oryza sativa L.) supplies nourishment to about half of the population of the world's inhabitants. Of them, more than 2 billion people suffer from "hidden hunger" in which they are unable to meet the recommended nutrients or micronutrients from their daily dietary intake. Bio-fortification refers to developing micronutrient-rich diet foods using traditional breeding methods and modern biotechnology, a promising approach to nutrition enrichment as part of an integrated strategy for food systems. To improve the profile of rice grain for the biofortification related traits, understanding the genetics of important biofortification traits is required. Moreover, these attributes are quantitative in nature and are influenced by several genes and environmental variables. In the course of past decades, several endeavours such, as finding the important quantitative trait loci (QTLs) for improving the nutrient profile of rice seeds were successfully undertaken. In this review, we have presented the information regarding the QTLs identified for the biofortification traits in the rice.

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Linkage mapping for grain iron and zinc content in F₂population derived from the cross between PAU201 and Palman 579 in rice (Oryza sativaL.)

Cited by 35 publications

References 16 publications

Genome-wide association studies of ionomic and agronomic traits in USDA minicore collection of rice and comparative analyses of different mapping methods

Genome-wide association studies of ionomic and agronomic traits in USDA minicore collection of rice and comparative analyses of different mapping methods

Analysis of QTL responsible for grain iron and zinc content in doubled haploid lines of rice (Oryza sativa) derived from an intra‐japonica cross

A Review of Important QTLs for Biofortification Traits in Rice

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Linkage mapping for grain iron and zinc content in F2population derived from the cross between PAU201 and Palman 579 in rice (Oryza sativaL.)

Cited by 35 publications

References 16 publications

Genome-wide association studies of ionomic and agronomic traits in USDA minicore collection of rice and comparative analyses of different mapping methods

Genome-wide association studies of ionomic and agronomic traits in USDA minicore collection of rice and comparative analyses of different mapping methods

Analysis of QTL responsible for grain iron and zinc content in doubled haploid lines of rice (Oryza sativa) derived from an intra‐japonica cross

A Review of Important QTLs for Biofortification Traits in Rice

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Linkage mapping for grain iron and zinc content in F₂population derived from the cross between PAU201 and Palman 579 in rice (Oryza sativaL.)