Recent Advances and Applicability of GBS, GWAS, and GS in Maize

Tiwari, Anshuman; Choudhary, Shalu; Padiya, Jayendra; Ubale, Abhijit; Mikkilineni, Venugopal; Char, Bharat

doi:10.1002/9781119745686.ch9

Cited by 7 publications

(4 citation statements)

References 137 publications

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“…For the identification of genes/QTLs based on the linkage disequilibrium (LD), GWAS is one of the effective methods. For the prediction of candidate genes, GWAS has been widely used in several crops using genome-wide dense markers (Liu et al, 2018 ; Srivastava et al, 2020 ; Alseekh et al, 2021 ; Tiwari et al, 2022 ), including wheat (Negisho et al, 2022 ; Zhang et al, 2022 ). Advantages of GWAS is, QTLs for several traits can be found with high resolution in one go.…”

Section: Introductionmentioning

confidence: 99%

Genome-wide association mapping for component traits of drought and heat tolerance in wheat

et al. 2022

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Identification of marker trait association is a prerequisite for marker-assisted breeding. To find markers linked with traits under heat and drought stress in bread wheat (Triticum aestivum L.), we performed a genome-wide association study (GWAS). GWAS mapping panel used in this study consists of advanced breeding lines from the IARI stress breeding programme produced by pairwise and complex crosses. Phenotyping was done at multi locations namely New Delhi, Karnal, Indore, Jharkhand and Pune with augmented-RCBD design under different moisture and heat stress regimes, namely timely sown irrigated (IR), timely sown restricted irrigated (RI) and late sown (LS) conditions. Yield and its component traits, viz., Days to Heading (DH), Days to Maturity (DM), Normalized Difference Vegetation Index (NDVI), Chlorophyll Content (SPAD), Canopy temperature (CT), Plant Height (PH), Thousand grain weight (TGW), Grain weight per spike (GWPS), Plot Yield (PLTY) and Biomass (BMS) were phenotyped. Analysis of variance and descriptive statistics revealed significant differences among the studied traits. Genotyping was done using the 35k SNP Wheat Breeder's Genotyping Array. Population structure and diversity analysis using filtered 10,546 markers revealed two subpopulations with sufficient diversity. A large whole genome LD block size of 7.15 MB was obtained at half LD decay value. Genome-wide association search identified 57 unique markers associated with various traits across the locations. Twenty-three markers were identified to be stable, among them nine pleiotropic markers were also identified. In silico search of the identified markers against the IWGSC ref genome revealed the presence of a majority of the SNPs at or near the gene coding region. These SNPs can be used for marker-assisted transfer of genes/QTLs after validation to develop climate-resilient cultivars.

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Section: Introductionmentioning

confidence: 99%

Genome-wide association mapping for component traits of drought and heat tolerance in wheat

et al. 2022

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“…On the other hand, GBS has been improved for SNP discovery and mapping thanks to the two-enzyme approach, and sequence data software and pipelines have recently been developed [23]. GBS has been extensively used for genetic diversity studies, GWAS, and genomic selection approaches in many crops, including maize [24]. It offers the advantage of not having ascertainment bias, although it can be characterized by a high rate of missing data when the sequencing depth is not optimal, thus strongly reducing the number of usable markers when a strict filtering process is applied to retain high-quality markers [23].…”

Section: Discussionmentioning

confidence: 99%

Genetic Diversity within a Collection of Italian Maize Inbred Lines: A Resource for Maize Genomics and Breeding

Mastrangelo,

Hartings,

Lanzanova

et al. 2024

Plants

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Genetic diversity is fundamental for studying the complex architecture of the traits of agronomic importance, controlled by major and minor loci. Moreover, well-characterized germplasm collections are essential tools for dissecting and analyzing genetic and phenotypic diversity in crops. A panel of 360 entries, a subset of a larger collection maintained within the GenBank at CREA Bergamo, which includes the inbreds derived from traditional Italian maize open-pollinated (OP) varieties and advanced breeding ones (Elite Inbreds), was analyzed to identify SNP markers using the tGBS® genotyping-by-sequencing technology. A total of 797,368 SNPs were found during the initial analysis. Imputation and filtering processes were carried out based on the percentage of missing data, redundant markers, and rarest allele frequencies, resulting in a final dataset of 15,872 SNP markers for which a physical map position was identified. Using this dataset, the inbred panel was characterized for linkage disequilibrium (LD), genetic diversity, population structure, and genetic relationships. LD decay at a genome-wide level indicates that the collection is a suitable resource for association mapping. Population structure analyses, which were carried out with different clustering methods, showed stable grouping statistics for four groups, broadly corresponding to ‘Insubria’, ‘Microsperma’, and ‘Scagliolino’ genotypes, with a fourth group composed prevalently of elite accessions derived from Italian and US breeding programs. Based on these results, the CREA Italian maize collection, genetically characterized in this study, can be considered an important tool for the mapping and characterization of useful traits and associated loci/alleles, to be used in maize breeding programs.

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“…It is on the verge of overtaking wheat as the most heavily traded cereal. Leading net-exporting nations such as the USA, Brazil, Argentina, Ukraine, and Romania are consistently shipping substantial quantities, ranging from 5 to 54 million metric tons annually [7]. Over the last century, maize yield has surged by a factor of eight, thanks to innovations in yield per plant and plant density optimization achieved through harnessing heterosis.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Maize (Zea maysL.) Crop through Advanced Techniques: A Comprehensive Approach

Kumar Singh,

Pundir,

Chaturvedi

et al. 2024

Agricultural Sciences

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Maize (Zea mays L.) is one of the most widely cultivated crops globally, making significant contributions to food, animal feed, and biofuel production. However, maize yield is greatly affected by various climate and soil factors, and it faces hindrances due to abiotic stresses, such as drought, salinity, extreme temperatures, and cold conditions. In confronting these hurdles, the field of crop breeding has transformed thanks to high-throughput sequencing technologies (HSTs). These advancements have streamlined the identification of beneficial quantitative trait loci (QTL), associations between markers and traits (MTAs), as well as genes and alleles that contribute to crop improvement. Presently, well-established omics techniques like genomics, transcriptomics, proteomics, and metabolomics are being integrated into maize breeding studies. These approaches have unveiled new biological markers can enhance maize’s ability to withstand a range of challenges. In this chapter, we explore the current understanding of the morpho-physiological and molecular mechanisms underlying maize resistance and tolerance to biotic and abiotic stresses. We focus on the use of omics techniques to enhance maize’s ability to withstand these challenges. Moreover, it emphasizes the significant potential of integrating multiple omics techniques to tackle the challenges presented by biotic and abiotic stress in maize productivity, contrasting with singular approaches.

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Recent Advances and Applicability of GBS, GWAS, and GS in Maize

Cited by 7 publications

References 137 publications

Genome-wide association mapping for component traits of drought and heat tolerance in wheat

Genome-wide association mapping for component traits of drought and heat tolerance in wheat

Genetic Diversity within a Collection of Italian Maize Inbred Lines: A Resource for Maize Genomics and Breeding

Enhancing Maize (Zea maysL.) Crop through Advanced Techniques: A Comprehensive Approach

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