Identification of Novel Genomic Regions and Superior Alleles Associated with Zn Accumulation in Wheat Using a Genome-Wide Association Analysis Method

Zhou, Zhengfu; Shi, Xia; Zhao, Gan-Qing; Qin, Maomao; Ibba, María Itria; Wang, Yahuan; Li, Wenxu; Pan, Yang; Wu, Zhengqing; Lei, Zhensheng; Wang, Jiansheng

doi:10.3390/ijms21061928

Cited by 31 publications

(32 citation statements)

References 56 publications

(67 reference statements)

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“…For grain zinc, a total of six QTL (QGZn.iari-2D.1, QGZn.iari-3B, QGZn.iari-7D.1, QGZn.iari-7D.2, QGZn.iari-1D, and QGZn.iari-2D.2) were identified. The localization of QTL for GZnC reported in earlier studies on 3B (30,39), 1D (39), 2D (39), and 7D (30) in different mapping populations corroborate the involvement of these chromosomes. For GPC, 10 QTL were identified and designated as QGPC.iari-5B.1, QGPC.iari-7A, QGPC.iari-1A, QGPC.iari-4D, QGPC.iari-5D, QGPC.iari-5B.2, QGPC.iari-7D.1, QGPC.iari-6B, QGPC.iari-3D, and QGPC.iari-7D.2.…”

Section: Discussionsupporting

confidence: 78%

“…In this study, four stable QTL (QTkw.iari-7D, QGFe.iari-7D.2, QGFe.iari-7D.1, and QGPC.iari-7D.2) were identified in two or more environments. QTkw.iari-7D was identified in all the six tested environments and pooled mean, followed by QGFe.iari-7D.2 and QGFe.iari-7D.1, which were identified in three environments (39,47,68), GFeC and GZnC (30), and GFeC (33,37). Identification of the best combination of QTL effects by estimation of additive effects of the stable QTL will provide an opportunity to utilize RILs with the best combination as donors.…”

Section: Discussionmentioning

confidence: 98%

“…Another region flanked between Xwmc550-Xgwm350 was also associated with three co-localized QTL (QGFe.iari-7D.1, QGZn.iari-7D.1, and QGPC.iari-7D.1). Some of the other studies (13,15,16,(37)(38)(39)(40) have also identified such pleiotropic region(s) associated with two or more traits, namely, GFeC, GZnC, GPC, and TKW. High positive correlations observed in this study also strongly support the co-localization of genomic regions governing GFeC, GZnC, GPC, and TKW.…”

Section: Discussionmentioning

confidence: 98%

“…QTL have been identified for grain iron (19,(26)(27)(28)(29)(30)(31)(32)(33)(34)(35)(36)(37)(38), grain zinc (13,19,(27)(28)(29)(30)(31)(32)(33)(35)(36)(37)(38)(39)(40)(41), grain protein content (19,27,31,35,36,(42)(43)(44)(45)(46)(47)(48)(49)(50), and thousand kernel weight (19,27,29,45,(51)(52)(53)(54). However, most investiga...…”

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confidence: 99%

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Identification of Novel Genomic Regions for Biofortification Traits Using an SNP Marker-Enriched Linkage Map in Wheat (Triticum aestivum L.)

et al. 2021

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Micronutrient and protein malnutrition is recognized among the major global health issues. Genetic biofortification is a cost-effective and sustainable strategy to tackle malnutrition. Genomic regions governing grain iron concentration (GFeC), grain zinc concentration (GZnC), grain protein content (GPC), and thousand kernel weight (TKW) were investigated in a set of 163 recombinant inbred lines (RILs) derived from a cross between cultivated wheat variety WH542 and a synthetic derivative (Triticum dicoccon PI94624/Aegilops tauschii [409]//BCN). The RIL population was genotyped using 100 simple-sequence repeat (SSR) and 736 single nucleotide polymorphism (SNP) markers and phenotyped in six environments. The constructed genetic map had a total genetic length of 7,057 cM. A total of 21 novel quantitative trait loci (QTL) were identified in 13 chromosomes representing all three genomes of wheat. The trait-wise highest number of QTL was identified for GPC (10 QTL), followed by GZnC (six QTL), GFeC (three QTL), and TKW (two QTL). Four novel stable QTL (QGFe.iari-7D.1, QGFe.iari-7D.2, QGPC.iari-7D.2, and QTkw.iari-7D) were identified in two or more environments. Two novel pleiotropic genomic regions falling between Xgwm350–AX-94958668 and Xwmc550–Xgwm350 in chromosome 7D harboring co-localized QTL governing two or more traits were also identified. The identified novel QTL, particularly stable and co-localized QTL, will be validated to estimate their effects on different genetic backgrounds for subsequent use in marker-assisted selection (MAS). Best QTL combinations were identified by the estimation of additive effects of the stable QTL for GFeC, GZnC, and GPC. A total of 11 RILs (eight for GZnC and three for GPC) having favorable QTL combinations identified in this study can be used as potential donors to develop bread wheat varieties with enhanced micronutrients and protein.

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Section: Discussionsupporting

confidence: 78%

Section: Discussionmentioning

confidence: 98%

Section: Discussionmentioning

confidence: 98%

mentioning

confidence: 99%

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Identification of Novel Genomic Regions for Biofortification Traits Using an SNP Marker-Enriched Linkage Map in Wheat (Triticum aestivum L.)

et al. 2021

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“…These wheat SNP-genotyping arrays have been comprehensively compared and reviewed [145]. More importantly, these SNP arrays increase the number of SNPs available for QTL mapping from several thousand to tens or hundreds of thousand at affordable costs, allowing the accurate mapping of minor QTLs [146][147][148]. Third, as the complements to array-based genotyping, datasets of wheat whole-genome resequencing [149][150][151], representative sequencing (such as GBS) [131,132] and exome sequencing [152,153] have been recently published, enriching the repertoire of wheat genomic resources.…”

Section: Conclusion and Future Perspectivementioning

confidence: 99%

Toward the Genetic Basis and Multiple QTLs of Kernel Hardness in Wheat

Tu¹,

Li²

2020

Plants

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Kernel hardness is one of the most important single traits of wheat seed. It classifies wheat cultivars, determines milling quality and affects many end-use qualities. Starch granule surfaces, polar lipids, storage protein matrices and Puroindolines potentially form a four-way interaction that controls wheat kernel hardness. As a genetic factor, Puroindoline polymorphism explains over 60% of the variation in kernel hardness. However, genetic factors other than Puroindolines remain to be exploited. Over the past two decades, efforts using population genetics have been increasing, and numerous kernel hardness-associated quantitative trait loci (QTLs) have been identified on almost every chromosome in wheat. Here, we summarize the state of the art for mapping kernel hardness. We emphasize that these steps in progress have benefitted from (1) the standardized methods for measuring kernel hardness, (2) the use of the appropriate germplasm and mapping population, and (3) the improvements in genotyping methods. Recently, abundant genomic resources have become available in wheat and related Triticeae species, including the high-quality reference genomes and advanced genotyping technologies. Finally, we provide perspectives on future research directions that will enhance our understanding of kernel hardness through the identification of multiple QTLs and will address challenges involved in fine-tuning kernel hardness and, consequently, food properties.

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Micronutrient Toxicity and Deficiency

Langridge

2022

Wheat Improvement

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Micronutrients are essential for plant growth although required in only very small amounts. There are eight micronutrients needed for healthy growth of wheat: chlorine, iron, boron, manganese, zinc, copper, nickel and molybdenum. Several factors will influence the availability of micronutrients, including levels in the soil, and mobility or availability. Zinc deficiency is the most significant problem globally followed by boron, molybdenum, copper, manganese and iron. Deficiency is usually addressed through application of nutrients to seeds, or through foliar spays when symptoms develop. There is considerable genetic variation in the efficiency of micronutrient uptake in wheat, but this is not a major selection target for breeding programs given the agronomic solutions. However, for some micronutrients, the concentrations in the soil can be very high and result in toxicity. Of the micronutrients, the narrowest range between deficiency and toxicity is for boron and toxicity is a significant problem in some regions. Although not a micronutrient, aluminium toxicity is also a major factor limiting yield in many areas, usually associated with a low soil pH. Agronomic solutions for boron and aluminium toxicity are difficult and expensive. Consequently, genetic approaches have dominated the strategies for addressing toxicity and good sources of tolerance are available.

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Identification of Novel Genomic Regions and Superior Alleles Associated with Zn Accumulation in Wheat Using a Genome-Wide Association Analysis Method

Cited by 31 publications

References 56 publications

Identification of Novel Genomic Regions for Biofortification Traits Using an SNP Marker-Enriched Linkage Map in Wheat (Triticum aestivum L.)

Identification of Novel Genomic Regions for Biofortification Traits Using an SNP Marker-Enriched Linkage Map in Wheat (Triticum aestivum L.)

Toward the Genetic Basis and Multiple QTLs of Kernel Hardness in Wheat

Micronutrient Toxicity and Deficiency

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