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Planta

, Volume 248, Issue 4, pp 947–962 | Cite as

Analysis of QTL–allele system conferring drought tolerance at seedling stage in a nested association mapping population of soybean [Glycine max (L.) Merr.] using a novel GWAS procedure

  • Mueen Alam Khan
  • Fei Tong
  • Wubin Wang
  • Jianbo He
  • Tuanjie Zhao
  • Junyi Gai
Original Article

Abstract

Main conclusion

RTM-GWAS identified 111 DT QTLs, 262 alleles with high proportion of QEI and genetic variation accounting for 88.55–95.92% PV in NAM, from which QTL–allele matrices were established and candidate genes annotated.

Drought tolerance (DT) is one of the major challenges for world soybean production. A nested association mapping (NAM) population with 403 lines comprising two recombinant inbred line (RIL) populations: M8206 × TongShan and ZhengYang × M8206 was tested for DT using polyethylene-glycol (PEG) treatment under spring and summer environments. The population was sequenced using restriction-site-associated DNA sequencing (RAD-seq) filtered with minor allele frequency (MAF) ≥ 0.01, 55,936 single nucleotide polymorphisms (SNPs) were obtained and organized into 6137 SNP linkage disequilibrium blocks (SNPLDBs). The restricted two-stage multi-locus genome-wide association studies (RTM-GWAS) identified 73 and 38 QTLs with 174 and 88 alleles contributed main effect 40.43 and 26.11% to phenotypic variance (PV) and QTL–environment interaction (QEI) effect 24.64 and 10.35% to PV for relative root length (RRL) and relative shoot length (RSL), respectively. The DT traits were characterized with high proportion of QEI variation (37.52–41.65%), plus genetic variation (46.90–58.40%) in a total of 88.55–95.92% PV. The identified QTLs–alleles were organized into main-effect and QEI-effect QTL–allele matrices, showing the genetic and QEI architecture of the three parents/NAM population. From the matrices, the possible best genotype was predicted to have a weighted average value over two indicators (WAV) of 1.873, while the top ten optimal crosses among RILs with 95th percentile WAV 1.098–1.132, transgressive over the parents (0.651–0.773) but much less than 1.873, implying further pyramiding potential. From the matrices, 134 candidate genes were annotated involved in nine biological processes. The present results provide a novel way for molecular breeding in QTL–allele-based genomic selection for optimal cross selection.

Keywords

Gene annotation Optimal cross design QTL–allele matrix Restricted two-stage multi-locus genome-wide association studies (RTM-GWAS) 

Abbreviations

DT

Drought tolerance

NAM

Nested association mapping

PV

Phenotypic variance

QEI

QTL–environment interaction

RIL

Recombinant inbred line

RRL/RSL

Relative root length/relative shoot length

RTM-GWAS

Restricted two-stage multi-locus genome-wide association studies

SNP

Single nucleotide polymorphism

SNPLDBs

SNP linkage disequilibrium blocks

WAV

Weighted average value

Notes

Acknowledgements

This work was supported by the National Key R&D Program for Crop Breeding in China (2017YFD0101500, 2016YFD0100304), the Natural Science Foundation of China (31701447, 31671718, 31571695), the MOE 111 Project (B08025), the MOE Fundamental Research Funds for the Central Universities (KYT201801), the MOE Program for Changjiang Scholars and Innovative Research Team in University (PCSIRT_17R55), the MOA CARS-04 program, the Jiangsu Higher Education PAPD Program, and the Jiangsu JCIC-MCP. The funders had no role in work design, data collection and analysis, and decision and preparation of the manuscript.

Compliance with ethical standards

Conflict of interest

The authors have declared that no competing or conflicts of interest exist.

Supplementary material

425_2018_2952_MOESM1_ESM.docx (1.6 mb)
Supplementary material 1 (DOCX 1644 kb)

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Mueen Alam Khan
    • 1
    • 2
  • Fei Tong
    • 1
    • 2
  • Wubin Wang
    • 1
    • 2
    • 3
    • 4
    • 5
  • Jianbo He
    • 1
    • 2
    • 3
    • 4
  • Tuanjie Zhao
    • 1
    • 2
    • 3
    • 4
    • 5
  • Junyi Gai
    • 1
    • 2
    • 3
    • 4
    • 5
  1. 1.Soybean Research InstituteNanjing Agricultural UniversityNanjingChina
  2. 2.National Center for Soybean ImprovementMinistry of AgricultureNanjingChina
  3. 3.Key Laboratory of Biology and Genetic Improvement of SoybeanMinistry of AgricultureNanjingChina
  4. 4.National Key Laboratory for Crop Genetics and Germplasm EnhancementNanjingChina
  5. 5.Jiangsu Collaborative Innovation Center for Modern Crop ProductionNanjing Agricultural UniversityNanjingChina

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