The genetic architecture of zinc and iron content in maize grains as revealed by QTL mapping and meta-analysis

Jin, Tiantian; Jin-feng, Zhou; Chen, Jingtang; Zhu, Linghua; Zhao, Yang; Huang, Yaqun

doi:10.1270/jsbbs.63.317

Cited by 74 publications

(69 citation statements)

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“…There has been conflicting reports on identifying co-localized QTLs for the two traits in accordance to the phenotypic correlation between kernel-Zn and Fe (Qin et al 2012; Ŝimić et al 2012; Jin et al 2013, 2015). In our study, we have observed only limited co-localization of the genomic regions controlling these two traits, like the ones on chromosome 4 (161–167 Mb), 7 (173–174 Mb) and 9 (136 Mb).…”

Section: Discussionmentioning

confidence: 99%

Identification and validation of genomic regions influencing kernel zinc and iron in maize

et al. 2018

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Key messageGenome-wide association study (GWAS) on 923 maize lines and validation in bi-parental populations identified significant genomic regions for kernel-Zinc and-Iron in maize.AbstractBio-fortification of maize with elevated Zinc (Zn) and Iron (Fe) holds considerable promise for alleviating under-nutrition among the world’s poor. Bio-fortification through molecular breeding could be an economical strategy for developing nutritious maize, and hence in this study, we adopted GWAS to identify markers associated with high kernel-Zn and Fe in maize and subsequently validated marker-trait associations in independent bi-parental populations. For GWAS, we evaluated a diverse maize association mapping panel of 923 inbred lines across three environments and detected trait associations using high-density Single nucleotide polymorphism (SNPs) obtained through genotyping-by-sequencing. Phenotyping trials of the GWAS panel showed high heritability and moderate correlation between kernel-Zn and Fe concentrations. GWAS revealed a total of 46 SNPs (Zn-20 and Fe-26) significantly associated (P ≤ 5.03 × 10−05) with kernel-Zn and Fe concentrations with some of these associated SNPs located within previously reported QTL intervals for these traits. Three double-haploid (DH) populations were developed using lines identified from the panel that were contrasting for these micronutrients. The DH populations were phenotyped at two environments and were used for validating significant SNPs (P ≤ 1 × 10−03) based on single marker QTL analysis. Based on this analysis, 11 (Zn) and 11 (Fe) SNPs were found to have significant effect on the trait variance (P ≤ 0.01, R2 ≥ 0.05) in at least one bi-parental population. These findings are being pursued in the kernel-Zn and Fe breeding program, and could hold great value in functional analysis and possible cloning of high-value genes for these traits in maize.Electronic supplementary materialThe online version of this article (10.1007/s00122-018-3089-3) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 99%

Identification and validation of genomic regions influencing kernel zinc and iron in maize

et al. 2018

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“…(Sankaran et al, 2009), common bean (Phaseolus vulgaris L.) (Cichy et al, 2009;Blair et al, 2009Blair et al, , 2010Blair et al, , 2011, wheat (Triticum sp.) (Peleg et al, 2009;Tiwari et al, 2009;Xu et al, 2012;Pu et al, 2014;Srinivasa et al, 2014), maize (Zea mays L.) (Simic et al, 2012;Jin et al, 2013), and soybean (Ramamurthy et al, 2014). These studies identified QTLs linked to the genes controlling Fe accumulation in seed.…”

Section: Introductionmentioning

confidence: 99%

QTLs for iron concentration in seeds of the cultivated lentil (Lens culinaris Medic.) via genotyping by sequencing

Aldemir¹,

Ateş²,

Temel³

et al. 2017

Turk J Agric For

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“…BioMercator, a genetic map compilation and QTL meta-analysis software program, offers wizards to run analyses and display input and output maps through an accessible graphical user interface (Arcade et al 2004;Sosnowski et al 2012). Recently, BioMercator has been used for QTL meta-analysis in several crops, especially maize and soybean (Griffiths et al 2009;Jin et al 2013;Li et al 2013;Qi et al 2011;Truntzler et al 2010;Zhang et al 2014). Though a large number of QTLs have been detected, there are a few reports on QTL meta-analysis of rice yield-related traits (Ballini et al 2008;Courtois et al 2009;Swamy and Sarla 2011).…”

Section: Introductionmentioning

confidence: 99%

Quantitative trait loci identification and meta-analysis for rice panicle-related traits

Huang

Tao

et al. 2016

Mol Genet Genomics

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map with 6970 markers and a total map length of 1823.1 centimorgan (cM), on which 837 QTLs were projected. These QTLs were then integrated into 87 meta-quantitative trait loci (MQTLs) by meta-analysis, and the 95 % confidence intervals (CI) of them were smaller than the mean value of the original QTLs. Also, 30 MQTLs covered 47 of the 54 QTLs detected from the cross between Nipponbare and H71D in this study. Among them, the two major and stable QTLs, spp10.1 and sd10.1, were found to be included in MQTL10.4. The three other major QTLs, pl3.1, sb2.1, and sb10.1, were included in MQTL3.3, MQTL2.2, and MQTL10.3, respectively. A total of 21 of the 87 MQTLs' phenotypic variation were >20 %. In total, 24 candidate genes were found in 15 MQTLs that spanned physical intervals <0.2 Mb, including genes that have been cloned previously, e.g., EP3, LP, MIP1, HTD1, DSH1, and OsPNH1. However, it would be beneficial to identify a greater number of candidate genes from these MQTLs. Mining new genes that modulate yield and its related traits would assist researchers to better understand the relevant molecular mechanisms. The MQTLs found in this study that have small physical and genetic intervals are useful not only for marker-assisted selection and pyramiding, but they also provide important information of rice yield and related gene mining for future research.Keywords QTL analysis · Meta-analysis · Rice panicle traits · Yield · MQTL Communicated by B. Yang.Y. Wu and M. Huang are contributed equally to this work. Electronic supplementary materialThe online version of this article (

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The genetic architecture of zinc and iron content in maize grains as revealed by QTL mapping and meta-analysis

Cited by 74 publications

References 62 publications

Identification and validation of genomic regions influencing kernel zinc and iron in maize

Identification and validation of genomic regions influencing kernel zinc and iron in maize

QTLs for iron concentration in seeds of the cultivated lentil (Lens culinaris Medic.) via genotyping by sequencing

Quantitative trait loci identification and meta-analysis for rice panicle-related traits

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