Rapid Identification of Soybean Resistance Genes to Soybean Mosaic Virus by SLAF-seq Bulked Segregant Analysis

Yang, Qinghua; Jin, Hangxia; Yu, Xiaomin; Fu, Xujun; Zhi, Hui; Yuan, Fengjie

doi:10.1007/s11105-020-01227-w

Cited by 8 publications

(6 citation statements)

References 43 publications

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“…Bulk segregant analysis (BSA) and its modified methodologies are currently used in many crops [ 85 , 86 , 87 , 88 , 89 , 90 ]. The NGS-based BSA is becoming a popular approach to identifying candidate genes for various traits, such as the soybean mosaic virus [ 91 ], charcoal rot resistance [ 92 ], flowering time [ 93 ], phytophthora resistance [ 94 ], and powdery-mildew resistance [ 95 ]. Recently, the deep-learning algorithm for BSA (DeepBSA) has been developed for QTL mapping and functional gene cloning in maize [ 96 ].…”

Section: Linking Of Crop Genome To Phenome With Aimentioning

confidence: 99%

Applications of Artificial Intelligence in Climate-Resilient Smart-Crop Breeding

Khan

Wang

et al. 2022

IJMS

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Recently, Artificial intelligence (AI) has emerged as a revolutionary field, providing a great opportunity in shaping modern crop breeding, and is extensively used indoors for plant science. Advances in crop phenomics, enviromics, together with the other “omics” approaches are paving ways for elucidating the detailed complex biological mechanisms that motivate crop functions in response to environmental trepidations. These “omics” approaches have provided plant researchers with precise tools to evaluate the important agronomic traits for larger-sized germplasm at a reduced time interval in the early growth stages. However, the big data and the complex relationships within impede the understanding of the complex mechanisms behind genes driving the agronomic-trait formations. AI brings huge computational power and many new tools and strategies for future breeding. The present review will encompass how applications of AI technology, utilized for current breeding practice, assist to solve the problem in high-throughput phenotyping and gene functional analysis, and how advances in AI technologies bring new opportunities for future breeding, to make envirotyping data widely utilized in breeding. Furthermore, in the current breeding methods, linking genotype to phenotype remains a massive challenge and impedes the optimal application of high-throughput field phenotyping, genomics, and enviromics. In this review, we elaborate on how AI will be the preferred tool to increase the accuracy in high-throughput crop phenotyping, genotyping, and envirotyping data; moreover, we explore the developing approaches and challenges for multiomics big computing data integration. Therefore, the integration of AI with “omics” tools can allow rapid gene identification and eventually accelerate crop-improvement programs.

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Section: Linking Of Crop Genome To Phenome With Aimentioning

confidence: 99%

Applications of Artificial Intelligence in Climate-Resilient Smart-Crop Breeding

Khan

Wang

et al. 2022

IJMS

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“…For example, Song et al (2017) used WGRS to resequence contrasting DNA pools in the BSA analysis, and they have simultaneously identified two major genes regulating the cotyledon color in soybean. In addition, many studies have used NGS‐based techniques in the BSA approach to identify candidate genes for various traits in soybean such as soybean mosaic virus (Yang et al, 2020), charcoal rot resistance (da Silva et al, 2020), flowering time (Watanabe et al, 2017a), phytophthora resistance (Cheng et al, 2017), and powdery mildew resistance (Jiang et al, 2019). The modified approach of BSA such as MutMap based on WGRS of pooled DNA samples collected from the phenotypic extremes of a segregating population (derived from mutant × wild‐type cross) was successfully demonstrated in soybean leading to the identification of mutant gene regulating the mutant phenotype (Al Amin et al, 2019).…”

Section: Ngs‐based Genotyping: Gene Discovery and Mas In Soybeanmentioning

confidence: 99%

High‐throughput NGS‐based genotyping and phenotyping: Role in genomics‐assisted breeding for soybean improvement

Bhat

2021

Legume Science

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Soybean is an important food crop that provides edible protein and oil for human and animal nutrition. Conventional phenotypic‐based breeding approaches have made significant contribution in the last century by developing many improved soybean varieties. However, due to the longer time taken to develop a variety, low genetic gain per unit time, and adverse environmental influence of phenotypic‐based selection, conventional approaches are not sufficient to maintain pace with growing population and climate change. In this context, the recent method of genotypic selection, that is, genomics‐assisted breeding (GAB) is considered a promising approach to address the challenges in soybean breeding. However, to harness the true potential of GAB in soybean improvement, the great coverage and precision in the genotyping and phenotyping are required. Previously, a huge gap was observed between the discovery and practical use of quantitative trait loci (QTLs) in soybean improvement. It has been suggested that low marker density and manually collected phenotypes are the major reasons for this failure. Hence, high‐throughput genotyping (HTG) providing higher genome‐wide marker density, as well as accurate and precise phenotyping using high‐throughput digital phenotyping (HTP) platforms, can significantly increase the success of QTL and candidate gene identification in soybean. These approaches can greatly increase the practical utility of GAB in soybean and also offer a faster characterization of germplasm and breeding materials. This review provides the detailed information on how the recent innovations in genomics and phenomics can assists in improving the efficiency and potential of GAB in soybean improvement.

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“…This technique has been widely used in genetic diversity analysis [5], high-density genetic map construction [6], and germplasm resources identification [7]. At present, it has been widely used to develop high-density molecular labels in plants that include tea plant [8,9], rice [10,11], maize [12,13], barley [14,15], bean [16,17], rape [18,19], peanut [20,21], potato [22], soybean [23,24] and other plants.…”

Section: Introductionmentioning

confidence: 99%

“…Genome-wide association studies (GWASs) use a large number of SNPs to conduct control analysis or association analysis at the genome-wide level, screen the genetic variation most likely to affect a trait according to the significance p value, and then mine the genes related to trait variation. At present, many studies have used the molecular markers developed by SLAF-seq for GWAS analysis, and screened many important functional genes in tea plant [8,9], rice [10,11], barley [14,15], rape [18,19] and soybean [23,24].…”

Section: Introductionmentioning

confidence: 99%

Population Structure Analysis and Genome-Wide Association Study of Tea (Camellia sinensis (L.) Kuntze) Germplasm in Qiannan, China, Based on SLAF-Seq Technology

Zhang¹,

Tian²,

Cen³

et al. 2022

Phyton

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Duyun Maojian tea is a famous tea in China. In this study, the specific-locus amplified fragment (SLAF) sequencing method was used to analyze the population structure and conduct a genome-wide association study (GWAS) of 2 leaf traits of 123 tea plants in Qiannan, China. A total of 462,019 SLAF tags and 11,362,041 single-nucleotide polymorphism (SNP) loci were obtained. The results of phylogenetic tree analysis, cluster analysis, and principal component analysis showed that 123 tea germplasms were clustered into three groups, and the heterozygosity rates for Groups I, II, and II were 0.206, 0.224, and 0.34, respectively. Generally, tea germplasms in a production area are clustered in a group, indicating that tea germplasms in different production areas have certain genetic diversity. The traditional Duyun Maojian tea core production areas, TS and DC-SJ, are clustered into Group I and Group II respectively, while the ZY production area is relatively independent in Group III. Furthermore, based on GWAS analysis, 11 candidate genes related to leaf apex and 7 candidate genes related to leaf shape were obtained. This study clarified the genetic relationship among eight Duyun Maojian tea production areas and obtained candidate genes related to leaf apex and leaf shape development. The results showed that population structure and candidate genes are an effective basis for the breeding of Duyun Maojian tea germplasm.

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Rapid Identification of Soybean Resistance Genes to Soybean Mosaic Virus by SLAF-seq Bulked Segregant Analysis

Cited by 8 publications

References 43 publications

Applications of Artificial Intelligence in Climate-Resilient Smart-Crop Breeding

Applications of Artificial Intelligence in Climate-Resilient Smart-Crop Breeding

High‐throughput NGS‐based genotyping and phenotyping: Role in genomics‐assisted breeding for soybean improvement

Population Structure Analysis and Genome-Wide Association Study of Tea (Camellia sinensis (L.) Kuntze) Germplasm in Qiannan, China, Based on SLAF-Seq Technology

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