Genome-wide association analysis of stem water-soluble carbohydrate content in bread wheat

Fu, Luping; Wu, Jingchun; Yang, Shurong; Jin, Yi; Liu, Jindong; Yang, Mengjiao; Rasheed, Awais; Xia, Xianchun; Jing, Ruilian; He, Zhonghu; Xiao, Yonggui

doi:10.1007/s00122-020-03640-x

Cited by 27 publications

(41 citation statements)

References 85 publications

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“…Second, a series of high-density SNP genotyping arrays provide versatile scenarios for wheat genotyping for varying costs and applications [126][127][128][142][143][144]. 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].…”

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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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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“…However, after the quality control performed on genotyping data, no SNPs from these genes were represented in the datasets utilized in our association tests. The use of genotyping 90 K arrays has recently confirmed the presence of significantly associated SNPs, belonging to those genes, validating the importance of genes regulating fructan metabolism in the response of wheat to water stress [37].…”

Section: Discussionmentioning

confidence: 82%

“…In our study, the number of significant associations depended on the trait (Table 2) and the chromosome (Supplementary Materials Table S2). The contribution of particular chromosomes within the wheat genome towards yield (e.g., GY) and WSC traits is variable within the literature describing GWAS results from trials established on sites with water limitations [30,35,37,42,43]. Indeed, most of the wheat chromosomes were represented across these studies, and the effect of individual significant SNPs on phenotypic variation, expressed in terms of R 2 , was spread across a wide range of values (up to 16%).…”

Section: Discussionmentioning

confidence: 99%

“…These types of proteins have been implicated in responses to stress, membrane transport, signal transduction, and cell death. Other GWAS analyses of wheat WSC traits have identified candidate genes from two main categories: carbohydrate metabolism and stress responses [35,37]. Differences among studies are expected due to the diversity of experimental factors and the genotyping approaches implemented.…”

Section: Discussionmentioning

confidence: 99%

“…Another study conducted in a large collection (n = 990) of bread wheat genotypes in Australia revealed significant associations between stem WSCs at post-anthesis and molecular markers (SNPs and diversity arrays technology (DArT)) located on chromosomes 1A, 1B, 1D, 2D, and 4A [36]. Recently, Fu et al [37] conducted a GWAS on 166 wheat accessions, from China and other countries, utilizing SNP arrays. They identified 19-36 significant loci associated with WSC (measured by near-infrared spectroscopy) at 10 to 30 days post-anthesis.…”

Section: Introductionmentioning

confidence: 99%

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Genome-Wide Association of Stem Carbohydrate Accumulation and Remobilization during Grain Growth in Bread Wheat (Triticum aestivum L.) in Mediterranean Environments

Guerra

Yáñez

Matus

et al. 2021

Plants

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Water deficit represents an important challenge for wheat production in many regions of the world. Accumulation and remobilization of water-soluble carbohydrates (WSCs) in stems are part of the physiological responses regulated by plants to cope with water stress and, in turn, determine grain yield (GY). The genetic mechanisms underlying the variation in WSC are only partially understood. In this study, we aimed to identify Single Nucleotide Polymorphism (SNP) markers that account for variation in a suite of WSC and GY, evaluated in 225 cultivars and advanced lines of spring wheat. These genotypes were established in two sites in the Mediterranean region of Central Chile, under water-limited and full irrigation conditions, and assessed in two growing seasons, namely anthesis and maturity growth periods. A genome-wide association study (GWAS) was performed by using 3243 SNP markers. Genetic variance accounted for 5 to 52% of phenotypic variation of the assessed traits. A rapid linkage disequilibrium decay was observed across chromosomes (r2 ≤ 0.2 at 2.52 kbp). Marker-trait association tests identified 96 SNPs related to stem weight (SW), WSCs, and GY, among other traits, at the different sites, growing seasons, and growth periods. The percentage of SNPs that were part of the gene-coding regions was 34%. Most of these genes are involved in the defensive response to drought and biotic stress. A complimentary analysis detected significant effects of different haplotypes on WSC and SW, in anthesis and maturity. Our results evidence both genetic and environmental influence on WSC dynamics in spring wheat. At the same time, they provide a series of markers suitable for supporting assisted selection approaches and functional characterization of genes.

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UAV‐based RGB imagery and ground measurements for high‐throughput phenotyping of senescence and QTL mapping in bread wheat

Li,

Hassan,

Song

et al. 2023

Crop Science

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Sustainable wheat production is challenged by climate change events such as drought, salinity, and heat stress. Senescence is a gradual programmed cell death trait occurring post anthesis in wheat (Triticum aestivum), with significant affecting stability of yield and quality‐related traits under climate severities. Phenotyping of complex traits is increasingly perceived as a bottleneck due to elevated labour costs and time in large field conditions. Unmanned aerial vehicle (UAV) based platforms using RGB imagery and ground phenotyping based novel traits can facilitate the repeated non‐destructive measurements of crop canopy traits cost‐effectively. Here, we described combined application of UAV‐based RGB imaging and ground measurements based novel trait to quantify canopy senescence in wheat. We reported senescence related traits with high heritability in a recombinant inbred line (RIL) population derived from the cross Zhongmai 175/Lunxuan 987. Our results showed that the selection of slow senescence genotypes using UAV‐based VIs was equally effective as ground‐based traits and illustrated significant variations among the genotypes. We also identified five quantitative trait loci (QTL) for canopy senescence in both UAV and ground‐based data sets using a 50K single‐nucleotide polymorphism array. QTL for UAV‐based VIs from RGB imaging and ground measurements based traits were mapped on chromosomes 1B, 2B, 3A, and 4B. The integration of both data sets with genetic analysis identified an important slow senescence/stay‐green related locus on chromosome 4B that explained phenotypic variation up to 23.07% for both UAV and ground traits at late grain filling stage. Three QTL (QTL‐caas.1B, QTL‐caas.3A, QTL‐caas.4B2) on chromosomes 1B, 3A, and 4B were also validated for slow senescence with high chlorophyll level and cool canopy temperature in a natural population of 160 lines by developing kompetitive allele‐specific PCR markers. QTL‐caas.4B1 was validated not only for senescence but also for plant height. Our findings suggest that UAV‐based RGB imagery is advantageous for precise and rapid assessment of senescence related traits and genetic studies of senescence in wheat.This article is protected by copyright. All rights reserved

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Genome-wide association analysis of stem water-soluble carbohydrate content in bread wheat

Cited by 27 publications

References 85 publications

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

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

Genome-Wide Association of Stem Carbohydrate Accumulation and Remobilization during Grain Growth in Bread Wheat (Triticum aestivum L.) in Mediterranean Environments

UAV‐based RGB imagery and ground measurements for high‐throughput phenotyping of senescence and QTL mapping in bread wheat

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