A Robust and Rapid Candidate Gene Mapping Pipeline Based on M2 Populations

Zhou, Huangkai; Tang, Kun; Li, Guang; Liu, Wenqiang; Yu, Hui; Yuan, Xiaohui; Yang, Suxin; Bhattacharyya, Madan K.

doi:10.3389/fpls.2021.681816

Cited by 7 publications

(1 citation statement)

References 28 publications

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“…For example, in soybean, these kinds of studies have been widely used to identify genetic loci and candidate genes for seed weight [ 78 ], seed protein and oil contents [ 79 ], pod dehiscence [ 80 ], nitrogen fixation [ 81 ], soybean plant height and primary branches [ 81 ], agronomic traits [ 82 ], disease resistance [ 83 ], and tocopherol concentration [ 84 ]. 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 ].…”

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

Self Cite

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Next Generation Mutation Detection Techniques in Crops

Choudhary,

Padiya,

Ubale

et al. 2023

TILLING and Eco-Tilling for Crop Improvement

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Multi-omics assisted breeding for biotic stress resistance in soybean

et al. 2023

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Biotic stress is a critical factor limiting soybean growth and development. Soybean responses to biotic stresses such as insects, nematodes, and fungal, bacterial, and viral pathogens are governed by complex regulatory and defense mechanisms. Next-generation sequencing has availed research techniques and strategies in genomics and postgenomics. This review summarizes the available information on marker resources, quantitative trait loci, and marker trait associations involved in regulating biotic stress responses in soybean. We discuss the differential expression of related genes and proteins reported in different transcriptomics and proteomics studies and the role of signaling pathways and metabolites reported in metabolomic studies. Recent advances in omics technologies offer opportunities to reshape and improve biotic stress resistance in soybean by altering gene regulation and/or other regulatory networks. We recommend using 'integrated omics' to understand how soybean responds to different biotic stresses. We discuss the potential challenges of integrating multiomics for functional analysis of genes and their regulatory networks and the development of biotic stress-resistant cultivars. This review will help direct soybean breeding programs to develop resistance against different biotic stresses.the rapid identi cation of putative genes/quantitative trait loci (QTLs) associated with biotic stress resistance, the pathways involved, and functional analysis of intermediate or nal proteins and metabolites produced. However, there is a lack of comprehensive information on using omics approaches for biotic stress resistance in soybean. Therefore, we review various omics approaches used in soybean to improve crop performance under different biotic stresses to assist future soybean breeding in developing novel resistant cultivars. Soybean GenomicsIn plant breeding, markers are used to construct genetic linkage maps, perform phylogenetic and evolutionary analyses, select desired alleles, and map genes/QTLs. These activities started with the identi cation of several low-throughput markers, such as RFLP, RAPD, and AFLP. Later, comparatively more abundant, highly polymorphic, and co-dominant markers, known as SSRs, complemented the markers mentioned above in soybean genetics, molecular biology, and breeding research [13]. Large numbers of QTLs associated with different biotic stresses have been identi ed in soybean using these markers (Table 1). In addition, marker-assisted selection (MAS) has sped up the breeding process, particularly for producing disease-and insect pest-resistant cultivars [14].Earlier, a high density genetic map 'Soybean Consensus Map (version 4.0)' was constructed by combining available genetic and physical maps involving SSRs and SNPs [15]. Genomic applications in soybean became more frequent with the availability of the whole genome sequence, one of the rst legumes to be sequenced and published [16]. In addition, sequencing-based genotyping approaches such as genotyping by sequencing (GBS) have be...

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A Robust and Rapid Candidate Gene Mapping Pipeline Based on M2 Populations

Cited by 7 publications

References 28 publications

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

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

Next Generation Mutation Detection Techniques in Crops

Multi-omics assisted breeding for biotic stress resistance in soybean

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