Genome-wide association mapping of root system architecture traits in common wheat (Triticum aestivum L.)

Liu, Peng; Jin, Yi; Liu, Jindong; Liu, Caiyun; HongPing, Yao; Luo, Fuyi; Guo, Zhihui; Xia, Xianchun; He, Zhonghu

doi:10.1007/s10681-019-2452-z

Cited by 23 publications

(24 citation statements)

References 56 publications

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“…Some of these SNP loci are closely located to the known loci associated with root traits based on the reference genome of IWGSC V1.1. For example, under drought stress condition, three SNP loci including Affx-88733278, Affx-109672297, and Affx-110800753 for root length on chromosome 2B are 2.29, 1.79, and 3.39 Mb away from AX_111251784, AX_94405934, and AX_108756976, which were associated with total root length in a previous study (Liu et al, 2019). Three SNPs (Affx-110656000, Affx-111718859, and Affx-88565514) on chromosome 3A for root number was about 0.7 Mb away from an SNP (S7_12487861) for branched root length (Beyer et al, 2019).…”

Section: Discussionmentioning

confidence: 81%

“…The stable SNPs in the three experiments were selected for the favorable allele analysis. In the analysis of allele effects on each trait, alleles with positive effects leading to higher values of root traits were described as "favorable alleles, " whereas those with lower values were "unfavorable alleles" (Liu et al, 2019). For the investigation of potential candidate genes, the EnsemPlants database (http://plants.ensembl.org) was used to download the genes within 4 Mb LD block on both sides of the significant SNPs in both controlled and drought environments (Jin et al, 2020).…”

Section: Genome-wide Association Analysis and Prediction Of Candidatementioning

confidence: 99%

“…To date, a large number of quantitative trait loci (QTL) related to various root traits including root dry weight, seminal root number, total root length, root diameter, number of root tips, root number, etc., have been reported in wheat (Bai et al, 2013;Liu et al, 2013Liu et al, , 2019Ayalew et al, 2017;Xie et al, 2017;Alahmad et al, 2019;Beyer et al, 2019). Of these QTL, some QTL were specific to water stress conditions.…”

Section: Introductionmentioning

confidence: 99%

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Genome-Wide Association Mapping of Seedling Biomass and Root Traits Under Different Water Conditions in Wheat

Fatima

Gao

et al. 2021

Front. Genet.

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Drought is a major threat to global wheat production. In this study, an association panel containing 200 Chinese wheat germplasms was used for genome-wide association studies (GWASs) of genetic loci associated with eight root and seedling biomass traits under normal water and osmotic stress conditions. The following traits were investigated in wheat seedlings at the four-leaf stage: root length (RL), root number (RN), root fresh weight (RFW), root dry weight (RDW), shoot fresh weight (SFW), shoot dry weight (SDW), total fresh weight (TFW), and total dry weight (TDW). A total of 323 and 286 SNPs were detected under two water environments, respectively. Some of these SNPs were near known loci for root traits. Eleven SNPs on chromosomes 1B, 2B, 4B, and 2D had pleiotropic effects on multiple traits under different water conditions. Further analysis indicated that several genes located inside the 4 Mb LD block on each side of these 11 SNPs were known to be associated with plant growth and development and thus may be candidate genes for these loci. Results from this study increased our understanding of the genetic architecture of root and seedling biomass traits under different water conditions and will facilitate the development of varieties with better drought tolerance.

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Section: Discussionmentioning

confidence: 81%

Section: Genome-wide Association Analysis and Prediction Of Candidatementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Genome-Wide Association Mapping of Seedling Biomass and Root Traits Under Different Water Conditions in Wheat

Fatima

Gao

et al. 2021

Front. Genet.

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“…They identified 31 candidate genes for root system architecture in landraces and modern wheat varieties using a GWAS study [ 9 ]. Two root trait architecture candidate genes, AX - 110092021 (encoding protein kinase) and AX - 95681859 (encoding E3 ubiquitin-protein ligase), were identified on chromosomes 5BL and 1BL in Chinese wheat cultivars [ 10 ]. Protein kinase genes were expressed stably on wheat roots at different stages, and played important roles in root anisotropic growth [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

Identification of Candidate Genes for Root Traits Using Genotype–Phenotype Association Analysis of Near-Isogenic Lines in Hexaploid Wheat (Triticum aestivum L.)

Halder

Liu

Chen

et al. 2021

IJMS

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Global wheat (Triticum aestivum L.) production is constrained by different biotic and abiotic stresses, which are increasing with climate change. An improved root system is essential for adaptability and sustainable wheat production. In this study, 10 pairs of near-isogenic lines (NILs)—targeting four genomic regions (GRs) on chromosome arms 4BS, 4BL, 4AS, and 7AL of hexaploid wheat—were used to phenotype root traits in a semi-hydroponic system. Seven of the 10 NIL pairs significantly differed between their isolines for 11 root traits. The NIL pairs targeting qDSI.4B.1 GR varied the most, followed by the NIL pair targeting qDT.4A.1 and QHtscc.ksu-7A GRs. For pairs 5–7 targeting qDT.4A.1 GR, pair 6 significantly differed in the most root traits. Of the 4 NIL pairs targeting qDSI.4B.1 GR, pairs 2 and 4 significantly differed in 3 and 4 root traits, respectively. Pairs 9 and 10 targeting QHtscc.ksu-7A GR significantly differed in 1 and 4 root traits, respectively. Using the wheat 90K Illumina iSelect array, we identified 15 putative candidate genes associated with different root traits in the contrasting isolines, in which two UDP-glycosyltransferase (UGT)-encoding genes, TraesCS4A02G185300 and TraesCS4A02G442700, and a leucine-rich repeat receptor-like protein kinase (LRR-RLK)-encoding gene, TraesCS4A02G330900, also showed important functions for root trait control in other crops. This study characterized, for the first time, that these GRs control root traits in wheat, and identified candidate genes, although the candidate genes will need further confirmation and validation for marker-assisted wheat breeding.

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“…Five significant root length QTLs were identified across four chromosomes in wheat by using genome-wide association mapping (GWAS) with a total of 25,125 marker pairs [ 75 ]. In contrast, the large effect markers related to root architecture were also selected in wheat by using a mixed linear model of GWAS with single nucleotide polymorphism (SNP) arrays [ 76 ].…”

Section: Introductionmentioning

confidence: 99%

Intricate genetic variation networks control the adventitious root growth angle in apple

et al. 2020

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Background The root growth angle (RGA) typically determines plant rooting depth, which is significant for plant anchorage and abiotic stress tolerance. Several quantitative trait loci (QTLs) for RGA have been identified in crops. However, the underlying mechanisms of the RGA remain poorly understood, especially in apple rootstocks. The objective of this study was to identify QTLs, validate genetic variation networks, and develop molecular markers for the RGA in apple rootstock. Results Bulked segregant analysis by sequencing (BSA-seq) identified 25 QTLs for RGA using 1955 hybrids of the apple rootstock cultivars ‘Baleng Crab’ (Malus robusta Rehd., large RGA) and ‘M9’ (M. pumila Mill., small RGA). With RNA sequencing (RNA-seq) and parental resequencing, six major functional genes were identified and constituted two genetic variation networks for the RGA. Two single nucleotide polymorphisms (SNPs) of the MdLAZY1 promoter damaged the binding sites of MdDREB2A and MdHSFB3, while one SNP of MdDREB2A and MdIAA1 affected the interactions of MdDREB2A/MdHSFB3 and MdIAA1/MdLAZY1, respectively. A SNP within the MdNPR5 promoter damaged the interaction between MdNPR5 and MdLBD41, while one SNP of MdLBD41 interrupted the MdLBD41/MdbHLH48 interaction that affected the binding ability of MdLBD41 on the MdNPR5 promoter. Twenty six SNP markers were designed on candidate genes in each QTL interval, and the marker effects varied from 0.22°-26.11°. Conclusions Six diagnostic markers, SNP592, G122, b13, Z312, S1272, and S1288, were used to identify two intricate genetic variation networks that control the RGA and may provide new insights into the accuracy of the molecular markers. The QTLs and SNP markers can potentially be used to select deep-rooted apple rootstocks.

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Genome-wide association mapping of root system architecture traits in common wheat (Triticum aestivum L.)

Cited by 23 publications

References 56 publications

Genome-Wide Association Mapping of Seedling Biomass and Root Traits Under Different Water Conditions in Wheat

Genome-Wide Association Mapping of Seedling Biomass and Root Traits Under Different Water Conditions in Wheat

Identification of Candidate Genes for Root Traits Using Genotype–Phenotype Association Analysis of Near-Isogenic Lines in Hexaploid Wheat (Triticum aestivum L.)

Intricate genetic variation networks control the adventitious root growth angle in apple

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