Identification and Validation of New Stable QTLs for Grain Weight and Size by Multiple Mapping Models in Common Wheat

Cao, Junhui; Shang, Yong; Xu, Dongmei; Xu, Kangle; Cheng, Xinran; Xu, Peng; Liu, Xue; Liu, Mingli; Gao, Chang; Yan, Shengnan; Yao, Hui; Gao, Wei; Lu, Jie; Zhang, Haiping; Chang, Cheng; Xia, Xianchun; Xiao, Shuai; Ma, Chunyan

doi:10.3389/fgene.2020.584859

Cited by 9 publications

(4 citation statements)

References 109 publications

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“…Sixteen QTLs were identified in two or more individual environments, and eight genomic regions affected two or more traits (Table 1 and Table S5). Several QTLs detected for PH in this study were physically close to previously reported QTL [30,[56][57][58]. Two PH QTLs, one was 3.8 Mbp away from a PH QTL located on 4B at 611.1 Mbp, and another one was 10 Mbp away from QTL on 4B at 474.9 Mbp identified by Semagn et al [57].…”

Section: Discussionsupporting

confidence: 87%

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Mapping QTL for Yield and Its Component Traits Using Wheat (Triticum aestivum L.) RIL Mapping Population from TAM 113 × Gallagher

Cerit,

Wang,

Dogan

et al. 2023

Agronomy

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Understanding genetic architectures of yield and yield-related traits of wheat (Triticum aestivum L.) grown under dryland or irrigated conditions is pivotal for developing modern high-yielding germplasm and cultivars. The objectives of this study were to detect quantitative trait loci (QTL) linked to yield and yield components using a mapping population derived from ‘TAM 113’/‘Gallagher’, including 191 recombinant inbred lines (RILs). The population was grown in McGregor, College Station, and Bushland, Texas, for three consecutive years from 2019 to 2021. A high-density genetic map covering all 21 chromosomes was constructed using a set of 8,075 single nucleotide polymorphisms (SNPs) obtained using genotyping-by-sequencing (GBS). A total of 147 QTLs for 16 yield-related traits were identified, which included 16 QTLs consistently detected in multiple experiments and 8 QTLs that showed pleiotropic effects. Of them, five pleiotropic QTLs overlapped with the consistent QTL. They increased grain yield (YLD) up to 37.64 g m−2, thousand kernel weight (TKW) up to 1.33 g, harvest (HI) up to 0.97%, kernel length up to 0.08 mm, and kernel width up to 0.04 mm with Gallagher alleles and increased YLD up to 22.21 g m−2, kernels spike−1 up to 1.77, TKW up to 1.14 g, and HI up to 3.72% with TAM 113 alleles. One major and consistent QTL on chromosome 2D at 34.4 Mbp overlapped with the major photoperiod gene Ppd-D1 and was affected by multiple traits, including kernel diameter (DIAM), TKW, kernel hardness index (KHI), heading date (HD), and plant height (PH). Another QTL cluster region on 7D between 52 and 66 Mbp, encompassing one consistent and three pleiotropic QTLs. One of the pleiotropic QTLs at 52 Mbp increased YLD up to 24.16 g m−2, HI up to 1%, and DIAM up to 0.03 mm. This study dissected genetic loci associated with yield and yield-related traits, providing valuable information on wheat improvement using marker-assisted selection (MAS).

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Section: Discussionsupporting

confidence: 87%

“…The similarity of findings between Dhakal et al [29] and our study can be explained by closely related parents shared by both studies with similar genetic backgrounds. Moreover, two YLD QTLs with relatively small effects on chromosome 6D and 7B at 10.33 Mbp and 4.2 Mbp were reported by previous studies [30,[56][57][58][59].…”

Section: Discussionmentioning

confidence: 61%

Mapping QTL for Yield and Its Component Traits Using Wheat (Triticum aestivum L.) RIL Mapping Population from TAM 113 × Gallagher

Cerit,

Wang,

Dogan

et al. 2023

Agronomy

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“…The marker AX-95103934 linked to the QTL QTGW.iari-6B.2 exhibited a significant marker trait linkage with TGW, and the mean difference between the two allele groups was 2.35%. Similar validation for TGW on chromosomes 1B, 4B, and 7A was done by Cao et al (2020) . The marker AX-94897804 linked to QTL QDH.iari-1B can be used in breeding programs aimed at developing varieties with optimized flowering time under stress conditions, which is a key factor in determining wheat adaptation and performance under changing environmental conditions.…”

Section: Discussionmentioning

confidence: 99%

QTL mapping: insights into genomic regions governing component traits of yield under combined heat and drought stress in wheat

Manjunath,

Krishna,

Devate

et al. 2024

Front. Genet.

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Drought and heat frequently co-occur during crop growth leading to devastating yield loss. The knowledge of the genetic loci governing component traits of yield under combined drought and heat stress is essential for enhancing the climate resilience. The present study employed a mapping population of 180 recombinant inbred lines (RILs) derived from a cross between GW322 and KAUZ to identify quantitative trait loci (QTLs) governing the component traits of yield under heat and combined stress conditions. Phenotypic evaluation was conducted across two consecutive crop seasons (2021–2022 and 2022–2023) under late sown irrigation (LSIR) and late sown restricted irrigation (LSRI) conditions at the Indian Council of Agricultural Research Institute–Indian Agricultural Research Institute (ICAR-IARI), New Delhi. Various physiological and agronomic traits of importance were measured. Genotyping was carried out with 35K SNP Axiom breeder’s genotyping array. The linkage map spanned a length of 6769.45 cM, ranging from 2.28 cM/marker in 1A to 14.21 cM/marker in 5D. A total of 35 QTLs were identified across 14 chromosomes with 6B containing the highest (seven) number of QTLs. Out of 35 QTLs, 16 were major QTLs explaining the phenotypic variance greater than 10%. The study identified eight stable QTLs along with two hotspots on chromosomes 6B and 5B. Five QTLs associated with traits thousand-grain weight (TGW), normalized difference vegetation index (NDVI), and plant height (PH) were successfully validated. Candidate genes encoding antioxidant enzymes, transcription factors, and growth-related proteins were identified in the QTL regions. In silico expression analysis highlighted higher expression of transcripts TraesCS2D02G021000.1, TraesCS2D02G031000, TraesCS6A02G247900, and TraesCS6B02G421700 under stress conditions. These findings contribute to a deeper understanding of the genetic architecture underlying combined heat and drought tolerance in wheat, providing valuable insights for wheat improvement strategies under changing climatic conditions.

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“…Wheat QTL mapping and GWAS using Infinium 90 K SNP assay Drought tolerance [87] GWAS using GBS Spot blotch resistance [88] GWAS using Illumina Infinium 15 K BeadChip Head blast resistance [89] GWAS using 35 K axiom ® arrays Stripe rust resistance [90] GWAS using DArTseq technology Tan spot resistance [91] GWAS using wheat 660 K SNP array Identification of chromosomal regions of root traits [92] Haplotype-based GWAS and genotyping by sequencing Grain yield [93] QTL mapping (RIL population derived from specific locus amplified fragment sequencing (SLAF-seq)) Grain weight and size [94] GWAS using 20 K Infinitum iSelect SNP array and genomic prediction of anther extrusion in hybrid breeding…”

Section: Crop Molecular Breeding Approaches Traits Improved Referencesmentioning

confidence: 99%

Genetic Enhancement of Cereals Using Genomic Resources for Nutritional Food Security

Chaudhary,

Salgotra,

Chauhan

2023

Genes

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Advances in genomics resources have facilitated the evolution of cereal crops with enhanced yield, improved nutritional values, and heightened resistance to various biotic and abiotic stresses. Genomic approaches present a promising avenue for the development of high-yielding varieties, thereby ensuring food and nutritional security. Significant improvements have been made within the omics domain, specifically in genomics, transcriptomics, and proteomics. The advent of Next-Generation Sequencing (NGS) techniques has yielded an immense volume of data, accompanied by substantial progress in bioinformatic tools for proficient analysis. The synergy between genomics and computational tools has been acknowledged as pivotal for unravelling the intricate mechanisms governing genome-wide gene regulation. Within this review, the essential genomic resources are delineated, and their harmonization in the enhancement of cereal crop varieties is expounded upon, with a paramount focus on fulfilling the nutritional requisites of humankind. Furthermore, an encompassing compendium of the available genomic resources for cereal crops is presented, accompanied by an elucidation of their judicious utilization in the advancement of crop attributes.

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Identification and Validation of New Stable QTLs for Grain Weight and Size by Multiple Mapping Models in Common Wheat

Cited by 9 publications

References 109 publications

Mapping QTL for Yield and Its Component Traits Using Wheat (Triticum aestivum L.) RIL Mapping Population from TAM 113 × Gallagher

Mapping QTL for Yield and Its Component Traits Using Wheat (Triticum aestivum L.) RIL Mapping Population from TAM 113 × Gallagher

QTL mapping: insights into genomic regions governing component traits of yield under combined heat and drought stress in wheat

Genetic Enhancement of Cereals Using Genomic Resources for Nutritional Food Security

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