Theoretical and Applied Genetics

, Volume 131, Issue 12, pp 2581–2599 | Cite as

Efficient QTL detection of flowering date in a soybean RIL population using the novel restricted two-stage multi-locus GWAS procedure

  • Liyuan Pan
  • Jianbo He
  • Tuanjie Zhao
  • Guangnan Xing
  • Yufeng Wang
  • Deyue Yu
  • Shouyi Chen
  • Junyi GaiEmail author
Original Article


Key message

Eighty-six R1 QTLs accounting for 89.92% phenotypic variance in a soybean RIL population were identified using RTM-GWAS with SNPLDB marker which performed superior over CIM and MLM-GWAS with BIN/SNPLDB marker.


A population (NJRIKY) composed of 427 recombinant inbred lines (RILs) derived from Kefeng-1 × NN1138-2 (MGII × MGV, MG maturity group) was applied for detecting flowering date (R1) quantitative trait locus (QTL) system in soybean. From a low-depth re-sequencing (~ 0.75 ×), 576,874 SNPs were detected and organized into 4737 BINs (recombination breakpoint determinations) and 3683 SNP linkage disequilibrium blocks (SNPLDBs), respectively. Using the association mapping procedures “Restricted Two-stage Multi-locus Genome-wide Association Study” (RTM-GWAS), “Mixed Linear Model Genome-wide Association Study” (MLM-GWAS) and the linkage mapping procedure “Composite Interval Mapping” (CIM), 67, 36 and 10 BIN-QTLs and 86, 14 and 23 SNPLDB-QTLs were detected with their phenotypic variance explained (PVE) 88.70–89.92% (within heritability 98.2%), 146.41–353.62% (overflowing) and 88.29–172.34% (overflowing), respectively. The RTM-GWAS with SNPLDBs which showed to be more efficient and reasonable than the others was used to identify the R1 QTL system in NJRIKY. The detected 86 SNPLDB-QTLs with their PVE from 0.02 to 30.66% in a total of 89.92% covered 51 out of 104 R1 QTLs in 18 crosses in SoyBase and 26 out of 139 QTLs in a nested association mapping population, while the rest 29 QTLs were novel ones. From the QTL system, 52 candidate genes were annotated, including the verified gene E1, E2, E9 and J, and grouped into 3 categories of biological processes, among which 24 genes were enriched into three protein–protein interaction networks, suggesting gene networks working together. Since NJRIKY involves only MGII and MGV, the QTL/gene system among MG000–MGX should be explored further.



This work was financially supported through the grants from the National Key R&D Program for Crop Breeding in China (2017YFD0101500), the Natural Science Foundation of China (31701447), the MOE 111 Project (B08025), 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, the Fundamental Research Funds for the Central Universities and the Jiangsu JCIC-MCP. The funders had no role in work design, data collection and analysis, and decision and preparation of the manuscript. We would thank the State Key Laboratory of Agricultural Genomics (BGI-Shenzhen, Shenzhen 518083, China) for their service in sequencing the materials. Specific thanks go to Dr. Hans-Peter Piepho for providing consulting in calculation the adjusted heritabilities for unbalanced data.

Compliance with ethical standards

Conflict of interest

The authors declare no conflict of interest.

Supplementary material

122_2018_3174_MOESM1_ESM.docx (396 kb)
Supplementary material 1 (DOCX 395 kb)


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Copyright information

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

Authors and Affiliations

  • Liyuan Pan
    • 1
    • 2
    • 3
    • 4
  • Jianbo He
    • 1
    • 2
    • 3
    • 4
  • Tuanjie Zhao
    • 1
    • 2
    • 3
    • 4
  • Guangnan Xing
    • 1
    • 2
    • 3
    • 4
  • Yufeng Wang
    • 1
    • 2
  • Deyue Yu
    • 1
    • 2
    • 3
    • 4
  • Shouyi Chen
    • 5
  • Junyi Gai
    • 1
    • 2
    • 3
    • 4
    Email author
  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 Enhancement, Jiangsu Collaborative Innovation Center for Modern Crop ProductionNanjing Agricultural UniversityNanjingChina
  5. 5.State Key Lab of Plant Genomics, Institute of Genetics and Developmental BiologyChinese Academy of SciencesBeijingChina

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