Fine-mapping and candidate gene analysis for the foxglove aphid resistance gene Raso2 from wild soybean PI 366121

Kim, Ki-Seung; Kim, Jimin; Jung, Jiyeong; Shin, Ilseob; Park, Sumin; Lee, Ju Seok; Jeong, Soon‐Chun; Lee, Jeong‐Dong; Jung, Jin Kyo; Ha, Bo‐Keun; Kang, Sung-Taeg

doi:10.1007/s00122-021-03853-8

Cited by 10 publications

(7 citation statements)

References 35 publications

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“…& Zucc.). Wild soybean is a valuable genetic resource for soybean improvement because of its many agronomically desired traits, including high protein and biotic and abiotic stress tolerances [ 1 , 2 , 3 , 4 , 5 , 6 ]. However, due the presence of undesired traits, such as the indeterminate vine-like habit, lodging and small seed size, a simple cross of G. max and G. soja is unlikely to result in progeny with only the desirable agronomic traits.…”

Section: Introductionmentioning

confidence: 99%

Identification of Genomic Regions Associated with Vine Growth and Plant Height of Soybean

Zhang

Guo

et al. 2022

IJMS

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Vining growth (VG) and high plant height (PH) are the physiological traits of wild soybean that preclude their utilization for domesticated soybean breeding and improvement. To identify VG- and PH-related quantitative trait loci (QTLs) in different genetic resources, two populations of recombinant inbred lines (RILs) were developed by crossing a cultivated soybean, Zhonghuang39 (ZH39), with two wild soybean accessions, NY27-38 and NY36-87. Each line from the two crosses was evaluated for VG and PH. Three QTLs for VG and three for PH, detected in the ZH39 × NY27-38 population of the RILs, co-located on chromosomes 2, 17 and 19. The VG- and PH-related QTL in the ZH39 × NY36-87 population co-located on chromosome 19. A common QTL shared by the two populations was located on chromosome 19, suggesting that this major QTL was consistently selected for in different genetic backgrounds. The results suggest that different loci are involved in the domestication or adaptations of soybean of various genetic backgrounds. The molecular markers presented here would benefit the fine mapping and cloning of candidate genes underlying the VG and PH co-localized regions and thus facilitate the utilization of wild resources in breeding by avoiding undesirable traits.

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Section: Introductionmentioning

confidence: 99%

Identification of Genomic Regions Associated with Vine Growth and Plant Height of Soybean

Zhang

Guo

et al. 2022

IJMS

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“…Genomic DNA from the lines and parents was hybridized into the Affymetrix GeneTitan array system and then scanned using GeneTitan Scanner (Affymetrix, CA, USA) following the manufacturer’s protocol. SNP genotype analysis was conducted based on Axiom Genotyping Solution Data Analysis User Guide ( http://www.affymetrix.com ) 43 .…”

Section: Methodsmentioning

confidence: 99%

Identification of noble candidate gene associated with sensitivity to phytotoxicity of etofenprox in soybean

Kim

Shin

et al. 2022

Sci Rep

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Phytotoxicity is caused by the interaction between plants and a chemical substance, which can cause critical damage to plants. Understanding the molecular mechanism underlying plant-chemical interactions is important for managing pests in crop fields and avoiding plant phytotoxicity by insecticides. The genomic region responsible for sensitivity to phytotoxicity of etofenprox (PE), controlled by a single dominant gene, was detected by constructing high density genetic map using recombination inbred lines (RILs) in soybean. The genomic region of ~ 80 kbp containing nine genes was identified on chromosome 16 using a high-throughput single nucleotide polymorphism (SNP) genotyping system using two different RIL populations. Through resequencing data of 31 genotypes, nonsynonymous SNPs were identified in Glyma.16g181900, Glyma.16g182200, and Glyma.16g182300. The genetic variation in Glyma.16g182200, encoding glycosylphosphatidylinositol-anchored protein (GPI-AP), caused a critical structure disruption on the active site of the protein. This structural variation of GPI-AP may change various properties of the ion channels which are the targets of pyrethroid insecticide including etofenprox. This is the first study that identifies the candidate gene and develops SNP markers associated with PE. This study would provide genomic information to understand the mechanism of phytotoxicity in soybean and functionally characterize the responsive gene.

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“…Further, two important candidate genes Glyma.13G194700 and Glyma.13G195100, encoding Toll Interleukin Receptor (TIR) nucleotide-binding-leucine-rich repeat (NBS-LRR) resistance proteins, were reported in this region (Karthikeyan et al 2018). Most recently in 2021, a foxglove aphid resistance gene Raso2 was ne mapped from 13cM interval to 77Kb region in the soybean genome and one promising candidate gene, Glyma.07g077700 encoding NBS-LRR domain containing protein was identi ed (Kim et al 2021). A QTL, qSCN18 providing resistance to SCN was ne mapped to a 166 kb genomic region containing 23 genes (Usovsky et al 2021).…”

Section: Soybean Genomicsmentioning

confidence: 99%

Multi-omics assisted breeding for biotic stress resistance in soybean: challenges and opportunities

Bisht

Saini

Kaur

et al. 2022

Preprint

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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 post-genomics. This review summarizes the available information on marker resources, quantitative trait loci, and marker trait associations involved in regulating biotic stress responses of 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 multi-omics 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.

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Fine-mapping and candidate gene analysis for the foxglove aphid resistance gene Raso2 from wild soybean PI 366121

Cited by 10 publications

References 35 publications

Identification of Genomic Regions Associated with Vine Growth and Plant Height of Soybean

Identification of Genomic Regions Associated with Vine Growth and Plant Height of Soybean

Identification of noble candidate gene associated with sensitivity to phytotoxicity of etofenprox in soybean

Multi-omics assisted breeding for biotic stress resistance in soybean: challenges and opportunities

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