Multi-Locus Genome-Wide Association Studies to Characterize Fusarium Head Blight (FHB) Resistance in Hard Winter Wheat

Zhang, Jinfeng; Gill, Harsimardeep S.; Halder, Jyotirmoy; Brar, Navreet K.; Ali, Shaukat; Bernardo, Amy; Amand, Paul St.; Bai, Guihua; Turnipseed, Brent; Sehgal, Sunish K.

doi:10.3389/fpls.2022.946700

Cited by 7 publications

(6 citation statements)

References 90 publications

(135 reference statements)

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“…Linkage mapping and genome‐wide association studies (GWAS) have been intensively used to dissect the genetic basis of complex traits (Liu et al., 2018a). Further, the effectiveness of GWAS has already been established to capture genetic factors for various traits in wheat including agronomic (Sukumaran et al., 2015; Sun et al., 2017), disease resistance (Arruda et al., 2016; Halder et al., 2019; Juliana et al., 2018; Zhang et al., 2022), and end‐use quality (Chen et al., 2019). Likewise, these approaches have been used to identify significant QTL for several yield‐related traits in wheat (Singh et al., 2022).…”

Section: Introductionmentioning

confidence: 99%

Genome‐wide association analysis of spike and kernel traits in the U.S. hard winter wheat

et al. 2023

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A better understanding of the genetic control of spike and kernel traits that have higher heritability can help in the development of high‐yielding wheat varieties. Here, we identified the marker‐trait associations (MTAs) for various spike‐ and kernel‐related traits in winter wheat (Triticum aestivum L.) through genome‐wide association studies (GWAS). An association mapping panel comprising 297 hard winter wheat accessions from the U.S. Great Plains was evaluated for eight spike‐ and kernel‐related traits in three different environments. A GWAS using 15,590 single‐nucleotide polymorphisms (SNPs) identified a total of 53 MTAs for seven spike‐ and kernel‐related traits, where the highest number of MTAs were identified for spike length (16) followed by the number of spikelets per spike (15) and spikelet density (11). Out of 53 MTAs, 14 were considered to represent stable quantitative trait loci (QTL) as they were identified in multiple environments. Five multi‐trait MTAs were identified for various traits including the number of spikelets per spike (NSPS), spikelet density (SD), kernel width (KW), and kernel area (KA) that could facilitate the pyramiding of yield‐contributing traits. Further, a significant additive effect of accumulated favorable alleles on the phenotype of four spike‐related traits suggested that breeding lines and cultivars with a higher number of favorable alleles could be a valuable resource for breeders to improve yield‐related traits. This study improves the understanding of the genetic basis of yield‐related traits in hard winter wheat and provides reliable molecular markers that will facilitate marker‐assisted selection (MAS) in wheat breeding programs.

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Section: Introductionmentioning

confidence: 99%

Genome‐wide association analysis of spike and kernel traits in the U.S. hard winter wheat

et al. 2023

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“…The uncovering of QTLs in such populations leads to high mapping resolution since they harness all historical recombination events [ 12 ]. Various characteristics in wheat, including agro-morphological traits [ 14 ], resistance to stripe rust [ 15 ], stem rust [ 16 ], powdery mildew [ 17 ], and fusarium head blight [ 18 ] have been dissected successfully by association mapping. There are some reports on mapping QTL/genes involved in bread wheat resistance to leaf rust [ 9 , 19 – 23 ], with a potential to marker-assisted selection (MAS).…”

Section: Introductionmentioning

confidence: 99%

Exploring genomic regions involved in bread wheat resistance to leaf rust at seedling/adult stages by using GWAS analysis

Delfan

Bihamta²,

Dadrezaei³

et al. 2023

BMC Genomics

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Background Global wheat productivity is seriously challenged by a range of rust pathogens, especially leaf rust derived from Puccinia triticina. Since the most efficient approach to control leaf rust is genetic resistance, many efforts have been made to uncover resistance genes; however, it demands an ongoing exploration for effective resistance sources because of the advent of novel virulent races. Thus, the current study was focused on detecting leaf rust resistance-related genomic loci against the P. triticina prevalent races by GWAS in a set of Iranian cultivars and landraces. Results Evaluation of 320 Iranian bread wheat cultivars and landraces against four prevalent rust pathotypes of P. triticina (LR-99–2, LR-98–12, LR-98–22, and LR-97–12) indicated the diversity in wheat accessions responses to P. triticina. From GWAS results, 80 leaf rust resistance QTLs were located in the surrounding known QTLs/genes on almost chromosomes, except for 1D, 3D, 4D, and 7D. Of these, six MTAs (rs20781/rs20782 associated with resistance to LR-97–12; rs49543/rs52026 for LR-98–22; rs44885/rs44886 for LR-98–22/LR-98–1/LR-99–2) were found on genomic regions where no resistance genes previously reported, suggesting new loci conferring resistance to leaf rust. The GBLUP genomic prediction model appeared better than RR-BLUP and BRR, reflecting that GBLUP is a potent model for genomic selection in wheat accessions. Conclusions Overall, the newly identified MTAs as well as the highly resistant accessions in the recent work provide an opportunity towards improving leaf rust resistance.

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“…In recent years, various studies have been conducted on modern wheats, particularly bread wheat, to identify new genetic locations associated with different quality parameters. These parameters include yield and related traits ( Maccaferri et al., 2019 ; Arriagada et al., 2020 ; Gupta et al., 2020 ), protein content ( Saini et al., 2022c ), mineral content ( Fatiukha et al., 2020 ), abiotic-related traits ( Bhusal et al., 2018 ), and biotic-related traits such as FHB ( Zhang et al., 2022 ). Nevertheless, research indicates that intensive breeding practices in modern wheats have led to increased productivity at the expense of diminished nutritional value, protein content, and resistance or tolerance to abiotic and biotic stressors ( Cakmak et al., 2010 ; Peng et al., 2011 ; Cabas-Lühmann et al., 2023 ).…”

Section: Discussionmentioning

confidence: 99%

Meta-QTL analysis and candidate genes for quality traits, mineral content, and abiotic-related traits in wild emmer

Cabas-Lühmann,

Schwember,

Arriagada

et al. 2024

Front. Plant Sci.

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Wild emmer (Triticum turgidum ssp. dicoccoides) genotypes were studied for their high-nutritional value and good tolerance to various types of stress; for this reason, several QTL (quantitative trait loci) studies have been conducted to find favorable alleles to be introgressed into modern wheat cultivars. Given the complexity of the QTL nature, their interaction with the environment, and other QTLs, a small number of genotypes have been used in wheat breeding programs. Meta-QTL (MQTL) analysis helps to simplify the existing QTL information, identifying stable genomic regions and possible candidate genes for further allele introgression. The study aimed to identify stable QTL regions across different environmental conditions and genetic backgrounds using the QTL information of the past 14 years for different traits in wild emmer based upon 17 independent studies. A total of 41 traits were classified as quality traits (16), mineral composition traits (11), abiotic-related traits (13), and disease-related traits (1). The analysis revealed 852 QTLs distributed across all 14 chromosomes of wild emmer, with an average of 61 QTLs per chromosome. Quality traits had the highest number of QTLs (35%), followed by mineral content (33%), abiotic-related traits (28%), and disease-related traits (4%). Grain protein content (GPC) and thousand kernel weight (TKW) were associated with most of the QTLs detected. A total of 43 MQTLs were identified, simplifying the information, and reducing the average confidence interval (CI) from 22.6 to 4.78 cM. These MQTLs were associated with multiple traits across different categories. Nine candidate genes were identified for several stable MQTLs, potentially contributing to traits such as quality, mineral content, and abiotic stress resistance. These genes play essential roles in various plant processes, such as carbohydrate metabolism, nitrogen assimilation, cell wall biogenesis, and cell wall extensibility. Overall, this study underscores the importance of considering MQTL analysis in wheat breeding programs, as it identifies stable genomic regions associated with multiple traits, offering potential solutions for improving wheat varieties under diverse environmental conditions.

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Multi-Locus Genome-Wide Association Studies to Characterize Fusarium Head Blight (FHB) Resistance in Hard Winter Wheat

Cited by 7 publications

References 90 publications

Genome‐wide association analysis of spike and kernel traits in the U.S. hard winter wheat

Genome‐wide association analysis of spike and kernel traits in the U.S. hard winter wheat

Exploring genomic regions involved in bread wheat resistance to leaf rust at seedling/adult stages by using GWAS analysis

Meta-QTL analysis and candidate genes for quality traits, mineral content, and abiotic-related traits in wild emmer

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