Mapping Major Disease Resistance Genes in Soybean by Genome-Wide Association Studies

Ferreira, Everton Geraldo Capote; Marcelino-Guimarães, Francismar Corrêa

doi:10.1007/978-1-0716-2237-7_18

Cited by 5 publications

(4 citation statements)

References 112 publications

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“…These high‐throughput genome‐wide variants greatly facilitate haplotype map construction, and they can be utilized for genomics studies to dissect the genetic architecture of quantitative traits using quantitative trait locus (QTL)‐sequencing and genome‐wide association study (GWAS) (Kumar et al., 2021; Vischi Winck, 2021; Xiao et al., 2017). Utilizing GWAS approaches, several key genes for yield‐related traits (Khan et al., 2021; Liang et al., 2022; Luo et al., 2022; Wang et al., 2019; Zhang, Li Wang, et al., 2021), agronomic traits (Bonnafous et al., 2018; Chen, Yang, et al., 2019; Hu et al., 2022; Yang et al., 2022; Zhang, Zhang, et al., 2017), resistant traits (Ferreira & Marcelino‐Guimarães, 2022; Su et al., 2023; Wang, Wang, et al., 2016; 2022; Wu, Shi, et al., 2022; Zhang et al., 2020; Zhang, Peng, et al., 2022), and nutrition traits (Gonzalez‐Jorge et al., 2016; Körber et al., 2016; Otyama et al., 2022; Yuan et al., 2018; Zhang, Du, et al., 2021) have been identified in crops, providing important gene information for crop improvement in the future.…”

Section: Progress and Application Of Omics In Major Oilseedsmentioning

confidence: 99%

Multi‐omics‐driven advances in the understanding of triacylglycerol biosynthesis in oil seeds

Li,

Che,

Zhu

et al. 2023

The Plant Journal

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SUMMARYVegetable oils are rich sources of polyunsaturated fatty acids and energy as well as valuable sources of human food, animal feed, and bioenergy. Triacylglycerols, which are comprised of three fatty acids attached to a glycerol backbone, are the main component of vegetable oils. Here, we review the development and application of multiple‐level omics in major oilseeds and emphasize the progress in the analysis of the biological roles of key genes underlying seed oil content and quality in major oilseeds. Finally, we discuss future research directions in functional genomics research based on current omics and oil metabolic engineering strategies that aim to enhance seed oil content and quality, and specific fatty acids components according to either human health needs or industrial requirements.

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Section: Progress and Application Of Omics In Major Oilseedsmentioning

confidence: 99%

Multi‐omics‐driven advances in the understanding of triacylglycerol biosynthesis in oil seeds

Li,

Che,

Zhu

et al. 2023

The Plant Journal

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“…Yield in soybean ( Glycine max ) is adversely affected by a wide range of pathogens like fungi, bacteria, viruses, and nematodes. MTAs identified for various diseases are comprehensively reviewed recently by Ferreira and Marcelino-Guimarães (2022) . GWAS identified a single locus on chromosome 2 strongly associated with tobacco ringspot virus sensitivity ( Chang et al., 2016 ).…”

Section: Soybeanmentioning

confidence: 99%

Two decades of association mapping: Insights on disease resistance in major crops

et al. 2022

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Climate change across the globe has an impact on the occurrence, prevalence, and severity of plant diseases. About 30% of yield losses in major crops are due to plant diseases; emerging diseases are likely to worsen the sustainable production in the coming years. Plant diseases have led to increased hunger and mass migration of human populations in the past, thus a serious threat to global food security. Equipping the modern varieties/hybrids with enhanced genetic resistance is the most economic, sustainable and environmentally friendly solution. Plant geneticists have done tremendous work in identifying stable resistance in primary genepools and many times other than primary genepools to breed resistant varieties in different major crops. Over the last two decades, the availability of crop and pathogen genomes due to advances in next generation sequencing technologies improved our understanding of trait genetics using different approaches. Genome-wide association studies have been effectively used to identify candidate genes and map loci associated with different diseases in crop plants. In this review, we highlight successful examples for the discovery of resistance genes to many important diseases. In addition, major developments in association studies, statistical models and bioinformatic tools that improve the power, resolution and the efficiency of identifying marker-trait associations. Overall this review provides comprehensive insights into the two decades of advances in GWAS studies and discusses the challenges and opportunities this research area provides for breeding resistant varieties.

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“…Several studies using the univariate genome-wide association study (GWAS) approach have been used to identify genomic regions associated with important soybean traits, such as disease resistance [5][6][7][8] , seed protein and oil content [9][10][11][12][13] , salt tolerance 14,15 , physiological-related traits [16][17][18] , agronomic traits 10,[19][20][21][22][23][24][25] . Although the univariate GWAS methodology is the most commonly used in a breeding program, multiple correlated traits are often studied simultaneously.…”

Section: Introductionmentioning

confidence: 99%

Structural Equation Models to Interpret Multi-Trait Genome-Wide Association Studies for Morphological and Productive Traits in Soybean [Glycine max (L.) Merr.]

Suela,

Azevedo,

Nascimento

et al. 2023

Preprint

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Understanding the relationship between multiple traits is fundamental in soybean breeding programs because their primary goal is to maximize multiple traits simultaneously, either directly or indirectly. Typically, multi-trait studies are conducted using a multi-trait version of a genome-wide association study (GWAS). However, this approach does not account for phenotypic interrelationships between traits. Therefore, we applied structural equation modeling (SEM) to explore the interrelationship between traits related to morphology (pod thickness - PT) and yield traits (number of pods - NP, number of grains - NG, and hundred grains weight - HGW). We used a dataset containing 96 soybean individuals genotyped with 4,070 single nucleotide polymorphism (SNP) markers. The phenotypic network was modeled using the hill-climbing algorithm, and the structural coefficients were estimated using the SEM approach. According to the sign of the structural coefficient, we identified positive or negative phenotypic interrelationships. We found negative interrelationships between NG and HGW, positive interrelationships between NP and NG, and between HGW and PT. Among these traits, NG, HGW and PT showed indirect SNP effects. In the SEM-GWAS study, we found quantitative trait loci that jointly controlled some and all of the traits. We identified nine candidate genes (i. serine-threonine kinase; ii. protein DA1-related 2; iii. β-1,3-glucanase-like; iv. MYB-like DNA-binding; v. amino acid transporter; vi. Leucine-rich repeat; vii. squamosa promoter-binding-like; viii. monothiol glutaredoxin-s14, and ix. dynamin) that simultaneously acted in the traits. In summary, the SEM-GWAS approach revealed novel relationships among soybean traits, such as PT, thus contributing to soybean breeding programs.

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Mapping Major Disease Resistance Genes in Soybean by Genome-Wide Association Studies

Cited by 5 publications

References 112 publications

Multi‐omics‐driven advances in the understanding of triacylglycerol biosynthesis in oil seeds

Multi‐omics‐driven advances in the understanding of triacylglycerol biosynthesis in oil seeds

Two decades of association mapping: Insights on disease resistance in major crops

Structural Equation Models to Interpret Multi-Trait Genome-Wide Association Studies for Morphological and Productive Traits in Soybean [Glycine max (L.) Merr.]

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