Meta-GWAS for quantitative trait loci identification in soybean

Shook, Johnathon M.; Zhang, Jiaoping; Jones, Sarah; Singh, Arti; Diers, Brian W.

doi:10.1093/g3journal/jkab117

Cited by 37 publications

(18 citation statements)

References 57 publications

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“…Genome-wide association analysis, based on millions of molecular markers, is currently widely used in the analysis of complex quality traits for crops such as wheat (Yang et al, 2021 ), maize (Zheng et al, 2021 ), pea (Gali et al, 2019 ), soybean (Shook et al, 2021 ), flaxseed (Soto-Cerda et al, 2018 ), and Brassica (Xiao et al, 2019 ). Many reports addressed use of GWAS for the identification of candidate genes associated oil content (Pal et al, 2021 ), fatty acids (Tang et al, 2019 ), glucosinolate (Qu et al, 2015 ), and fiber fractions (Gajardo et al, 2015 ; Körber et al, 2016 ) in B. napus .…”

Section: Discussionmentioning

confidence: 99%

Genomic Regions Associated With Seed Meal Quality Traits in Brassica napus Germplasm

et al. 2022

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The defatted Brassica napus (rapeseed) meal can be high-protein feed for livestock as the protein value of rapeseed meal is higher than that of the majority of other vegetable proteins. Extensive work has already been carried out on developing canola rapeseed where the focus was on reducing erucic acid and glucosinolate content, with less consideration to other antinutritional factors such as tannin, phytate, sinapine, crude fiber, etc. The presence of these antinutrients limits the use and marketing of rapeseed meals and a significant amount of it goes unused and ends up as waste. We investigated the genetic architecture of crude protein, methionine, tryptophan, total phenols, β-carotene, glucosinolates (GLSs), phytate, tannins, sinapine, and crude fiber content of defatted seed meal samples by conducting a genome-wide association study (GWAS), using a diversity panel comprising 96 B. napus genotypes. Genotyping by sequencing was used to identify 77,889 SNPs, spread over 19 chromosomes. Genetic diversity and phenotypic variations were generally high for the studied traits. A total of eleven genotypes were identified which showed high-quality protein, high antioxidants, and lower amount of antinutrients. A significant negative correlation between protein and limiting amino acids and a significant positive correlation between GLS and phytic acid were observed. General and mixed linear models were used to estimate the association between the SNP markers and the seed quality traits and quantile-quantile (QQ) plots were generated to allow the best-fit algorithm. Annotation of genomic regions around associated SNPs helped to predict various trait-related candidates such as ASP2 and EMB1027 (amino acid biosynthesis); HEMA2, GLU1, and PGM (tryptophan biosynthesis); MS3, CYSD1, and MTO1 (methionine biosynthesis); LYC (β-carotene biosynthesis); HDR and ISPF (MEP pathway); COS1 (riboflavin synthesis); UGT (phenolics biosynthesis); NAC073 (cellulose and hemicellulose biosynthesis); CYT1 (cellulose biosynthesis); BGLU45 and BGLU46 (lignin biosynthesis); SOT12 and UGT88A1 (flavonoid pathway); and CYP79A2, DIN2, and GSTT2 (GLS metabolism), etc. The functional validation of these candidate genes could confirm key seed meal quality genes for germplasm enhancement programs directed at improving protein quality and reducing the antinutritional components in B. napus.

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Section: Discussionmentioning

confidence: 99%

Genomic Regions Associated With Seed Meal Quality Traits in Brassica napus Germplasm

et al. 2022

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“…Recently, whole-genome assemblies released from 26 different soybean varieties and lead to the structuring of the soybean pangenome and the sequences of previously cultivated lines in the United States, China, and Japan (Liu et al, 2020). QTLs have been mapped for many quality, biotic and abiotic stress, and agronomic traits in soybean using QTL mapping and GWAS (Merry et al, 2019;Qin et al, 2019;Ravelombola et al, 2020;Shook et al, 2021). The complete details about the adoption of various genomic tools is presented as a timeline in Figure 3C.…”

Section: Overview Of Six Crops Used In This Studymentioning

confidence: 99%

Prospectus of Genomic Selection and Phenomics in Cereal, Legume and Oilseed Breeding Programs

et al. 2022

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The last decade witnessed an unprecedented increase in the adoption of genomic selection (GS) and phenomics tools in plant breeding programs, especially in major cereal crops. GS has demonstrated the potential for selecting superior genotypes with high precision and accelerating the breeding cycle. Phenomics is a rapidly advancing domain to alleviate phenotyping bottlenecks and explores new large-scale phenotyping and data acquisition methods. In this review, we discuss the lesson learned from GS and phenomics in six self-pollinated crops, primarily focusing on rice, wheat, soybean, common bean, chickpea, and groundnut, and their implementation schemes are discussed after assessing their impact in the breeding programs. Here, the status of the adoption of genomics and phenomics is provided for those crops, with a complete GS overview. GS’s progress until 2020 is discussed in detail, and relevant information and links to the source codes are provided for implementing this technology into plant breeding programs, with most of the examples from wheat breeding programs. Detailed information about various phenotyping tools is provided to strengthen the field of phenomics for a plant breeder in the coming years. Finally, we highlight the benefits of merging genomic selection, phenomics, and machine and deep learning that have resulted in extraordinary results during recent years in wheat, rice, and soybean. Hence, there is a potential for adopting these technologies into crops like the common bean, chickpea, and groundnut. The adoption of phenomics and GS into different breeding programs will accelerate genetic gain that would create an impact on food security, realizing the need to feed an ever-growing population.

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“…Genes identified include the previously uncharacterised gene GhIDD7 that was subsequently shown to control fibre length by using gene knockout with CRISPR-Cas9 [ 38 ]. Meta-GWAS was also employed in a soybean study using 17,556 accessions and associated phenotypic data to identify candidate genes related to agronomic traits, reporting several new loci, some of which were associated with multiple traits suggesting pleiotropic effects [ 119 ]. Leveraging previously published studies with biochemical analysis may help bridge the understanding of the effect of SVs on plant morphology.…”

Section: The Breeding Potential Of Under-utilised Crop Speciesmentioning

confidence: 99%

Pangenomes as a Resource to Accelerate Breeding of Under-Utilised Crop Species

Fernandez

Nestor

Danilevicz

et al. 2022

IJMS

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Pangenomes are a rich resource to examine the genomic variation observed within a species or genera, supporting population genetics studies, with applications for the improvement of crop traits. Major crop species such as maize (Zea mays), rice (Oryza sativa), Brassica (Brassica spp.), and soybean (Glycine max) have had pangenomes constructed and released, and this has led to the discovery of valuable genes associated with disease resistance and yield components. However, pangenome data are not available for many less prominent crop species that are currently under-utilised. Despite many under-utilised species being important food sources in regional populations, the scarcity of genomic data for these species hinders their improvement. Here, we assess several under-utilised crops and review the pangenome approaches that could be used to build resources for their improvement. Many of these under-utilised crops are cultivated in arid or semi-arid environments, suggesting that novel genes related to drought tolerance may be identified and used for introgression into related major crop species. In addition, we discuss how previously collected data could be used to enrich pangenome functional analysis in genome-wide association studies (GWAS) based on studies in major crops. Considering the technological advances in genome sequencing, pangenome references for under-utilised species are becoming more obtainable, offering the opportunity to identify novel genes related to agro-morphological traits in these species.

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Meta-GWAS for quantitative trait loci identification in soybean

Cited by 37 publications

References 57 publications

Genomic Regions Associated With Seed Meal Quality Traits in Brassica napus Germplasm

Genomic Regions Associated With Seed Meal Quality Traits in Brassica napus Germplasm

Prospectus of Genomic Selection and Phenomics in Cereal, Legume and Oilseed Breeding Programs

Pangenomes as a Resource to Accelerate Breeding of Under-Utilised Crop Species

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