Identification of a major QTL related to resistance to soybean mosaic virus in diverse soybean genetic populations

Chu, Jiahao; Li, Wenlong; Piao, Daxun; Lin, Feng; Huo, Xiaobo; Hua, Zhang; Du, Hui; Kong, Youbin; Yuan, Jiangang; Li, Xihuan; Zhang, Caiying

doi:10.1007/s10681-021-02907-8

Cited by 13 publications

(9 citation statements)

References 56 publications

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“…1 Lin et al ( 2016 ), Zhao et al ( 2016 ) ZL-52 12,091,080 qSC3/7-D1b ss715580960–ss715581063 10,935,557–12,334,435 Greenhouse Screening SMV strains SC3 and SC7 54.2% RIL (279) Qihuang30 Chu et al ( 2021a ) qSC7-D1b ss715581063–ss715581097 12,334,435–12,506,411 Greenhouse Screening SMV strain SC7 27.0% RIL (279) Qihuang30 Chu et al ( 2021a ) – ss715583175 45,170,092 Greenhouse Screening SMV strain SC3 10.6% Cultivars (302) and landraces (77) – Chu et al ( 2021b ) MLG B1 (Chr. 11) – ss715608741 10,304,178 Greenhouse Screening SMV strain SC3 7.0% Cultivars (302) and landraces (77) – Chu et al ( 2021b ) MLG F (Chr. 13) Rsv1 – – Greenhouse Screening SMV Strain SVM-1 – F2 (1739) PI 96983 Kiihl and Hartwig ( 1979 ) SoyHSP176 29,041,694 Greenhouse Screening SMV Strain G1 – …”

Section: Section V Soybean Resistance To Virus Diseasesmentioning

confidence: 99%

“…1 Bai et al ( 2009 ), Ma et al ( 2011 ) MY750 29,594,566 Rsc15ZH – 27,801,314 Greenhouse Screening SMV Strain SC15 – F8 (163) Zhonghuang24 Li et al ( 2020 ) – 27,864,011 qSMV13 ss715614844 29,741,893 Greenhouse Screening SMV strain SC3 8.1% Cultivars (302) and landraces (77) – Chu et al ( 2021b ) ss715614844–ss715614864 29,741,893–29,839,120 Greenhouse Screening SMV strains SC3 and SC7 71.2–76.6% F6:8 (193) Kennong7 Chu et al ( 2021b ) MLG B2 (Chr. 14) – ss715617664 13,092,389 Greenhouse Screening SMV strain SC3 19.0% Cultivars (302) and landraces (77) – Chu et al ( 2021b ) Rsv3 – – – – – OX 686 Buzzell and Tu ( 1989 ) Barc-012953–00,413 45,086,977 Greenhouse Screening SMV Strains G1-G7 – …”

Section: Section V Soybean Resistance To Virus Diseasesmentioning

confidence: 99%

“…14) – ss715617664 13,092,389 Greenhouse Screening SMV strain SC3 19.0% Cultivars (302) and landraces (77) – Chu et al ( 2021b ) Rsv3 – – – – – OX 686 Buzzell and Tu ( 1989 ) Barc-012953–00,413 45,086,977 Greenhouse Screening SMV Strains G1-G7 – Diverse Genotypes (47) – Shi et al ( 2011 ) A519-snp2 46,937,343 Greenhouse Screening SMV Strains G1-G7 – Diverse Genotypes (47) – Shi et al ( 2011 ) A519-snp4 46,937,465 Greenhouse Screening SMV Strains G1-G7 – Diverse Genotypes (47) – Shi et al ( 2011 ) Satt063 45,993,857 Greenhouse Screening SMV Strains G5-G7 – F2:3 (195) L29, Tousan 140 Jeong et al ( 2002 ) A519-f/r 46,937,953 Greenhouse Screening SMV Strains G5-G7 – F2:3 (195) L29, Tousan 140 Jeong et al ( 2002 ...…”

Section: Section V Soybean Resistance To Virus Diseasesmentioning

confidence: 99%

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Breeding for disease resistance in soybean: a global perspective

et al. 2022

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Key message This review provides a comprehensive atlas of QTLs, genes, and alleles conferring resistance to 28 important diseases in all major soybean production regions in the world. Abstract Breeding disease-resistant soybean [Glycine max (L.) Merr.] varieties is a common goal for soybean breeding programs to ensure the sustainability and growth of soybean production worldwide. However, due to global climate change, soybean breeders are facing strong challenges to defeat diseases. Marker-assisted selection and genomic selection have been demonstrated to be successful methods in quickly integrating vertical resistance or horizontal resistance into improved soybean varieties, where vertical resistance refers to R genes and major effect QTLs, and horizontal resistance is a combination of major and minor effect genes or QTLs. This review summarized more than 800 resistant loci/alleles and their tightly linked markers for 28 soybean diseases worldwide, caused by nematodes, oomycetes, fungi, bacteria, and viruses. The major breakthroughs in the discovery of disease resistance gene atlas of soybean were also emphasized which include: (1) identification and characterization of vertical resistance genes reside rhg1 and Rhg4 for soybean cyst nematode, and exploration of the underlying regulation mechanisms through copy number variation and (2) map-based cloning and characterization of Rps11 conferring resistance to 80% isolates of Phytophthora sojae across the USA. In this review, we also highlight the validated QTLs in overlapping genomic regions from at least two studies and applied a consistent naming nomenclature for these QTLs. Our review provides a comprehensive summary of important resistant genes/QTLs and can be used as a toolbox for soybean improvement. Finally, the summarized genetic knowledge sheds light on future directions of accelerated soybean breeding and translational genomics studies.

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Section: Section V Soybean Resistance To Virus Diseasesmentioning

confidence: 99%

Section: Section V Soybean Resistance To Virus Diseasesmentioning

confidence: 99%

Section: Section V Soybean Resistance To Virus Diseasesmentioning

confidence: 99%

See 1 more Smart Citation

Breeding for disease resistance in soybean: a global perspective

et al. 2022

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“… Han et al (2019) constructed a high-density genetic map using 260 RILs derived from the cultivars of Heihe43 and Heihe18, and the constructed map contained 4,953 SLAF markers spanning 1478.86 cM with an average distance between adjacent markers of 0.53 cM. Chu et al (2021) reported on a genetic linkage map constructed by polymorphic 2,234 SNP markers from a SoySNP6K array, which covered a total of 4229.01 cM genetic distance with an average distance of 1.89 cM. Tian et al (2022) constructed a high-density genetic map by re-sequencing technology, which contained a total of 4,011 recombination bin markers with an average distance of 0.78 cM in the entire RILs population.…”

Section: Discussionmentioning

confidence: 99%

Identification of genetic loci conferring seed coat color based on a high-density map in soybean

Yuan

Wang

et al. 2022

Front. Plant Sci.

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Seed coat color is a typical evolutionary trait. Identification of the genetic loci that control seed coat color during the domestication of wild soybean could clarify the genetic variations between cultivated and wild soybean. We used 276 F10 recombinant inbred lines (RILs) from the cross between a cultivated soybean (JY47) and a wild soybean (ZYD00321) as the materials to identify the quantitative trait loci (QTLs) for seed coat color. We constructed a high-density genetic map using re-sequencing technology. The average distance between adjacent markers was 0.31 cM on this map, comprising 9,083 bin markers. We identified two stable QTLs (qSC08 and qSC11) for seed coat color using this map, which, respectively, explained 21.933 and 26.934% of the phenotypic variation. Two candidate genes (CHS3C and CHS4A) in qSC08 were identified according to the parental re-sequencing data and gene function annotations. Five genes (LOC100786658, LOC100801691, LOC100806824, LOC100795475, and LOC100787559) were predicted in the novel QTL qSC11, which, according to gene function annotations, might control seed coat color. This result could facilitate the identification of beneficial genes from wild soybean and provide useful information to clarify the genetic variations for seed coat color in cultivated and wild soybean.

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“…The genotype of RIL population was analysed by the SoySNP6K with 5403 SNPs (core subset from SoySNP50K Illumina Bead Chip array), and the genetic linkage map was constructed using Joinmap v4.0 software in our previous study (Chu, Li, Piao, Lin, et al, 2021). The linkage mapping of SCC in the present study was conducted via the inclusive composite interval mapping by ICIMapping v4.2 software similar as in our previous description (Chu, Li, Piao, Kong, et al, 2021).…”

Section: Methodsmentioning

confidence: 99%

Mining of seed coat cracking related genetic loci and genes in diverse soybean (Glycine max) populations

et al. 2023

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Seed coat cracking (SCC) is a major factor that affects seed appearance quality, pathogen infection, humility absorption and seed storage in soybean. In view of this, a recombinant inbred line (RIL) population and a natural population were genotyped by SoySNP6K and were evaluated for type-I SCC percentage. Eight QTLs were identified on six chromosomes with phenotypic variation explanations ranging from 3.46% to 34.71% in RIL population. Twenty-three SNPs associated with cracking percentage were identified on eight chromosomes in natural population. More importantly, the association SNP ss715593768 in natural population was the left marker of qSCC-C2-1 in RIL population, which were detected across all of the environments and BLUP values. Two genes, Glyma.06G197300 and Glyma.06G202100, were proposed in this QTL region. These two genes showed differential expressions at seed developmental stages between RIL parents in the seed transcriptome sequencing and quantitative reverse transcription-polymerase chain reaction (qRT-PCR) analysis, and also showed differential expressions between two germplasms with different cracking percentage in natural population. Thus, the results provide novel insight into the genetic basis and molecular improvement of SCC in soybean.

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Identification of a major QTL related to resistance to soybean mosaic virus in diverse soybean genetic populations

Cited by 13 publications

References 56 publications

Breeding for disease resistance in soybean: a global perspective

Breeding for disease resistance in soybean: a global perspective

Identification of genetic loci conferring seed coat color based on a high-density map in soybean

Mining of seed coat cracking related genetic loci and genes in diverse soybean (Glycine max) populations

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