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
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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.
KeywordsGene annotation Optimal cross design QTL–allele matrix Restricted two-stage multi-locus genome-wide association studies (RTM-GWAS)
Nested association mapping
Recombinant inbred line
Relative root length/relative shoot length
Restricted two-stage multi-locus genome-wide association studies
Single nucleotide polymorphism
SNP linkage disequilibrium blocks
Weighted average value
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.
- Blum A (1988) Plant breeding for stress environments. CRC Press Inc, Boca RatonGoogle Scholar
- Fragoso CA, Moreno M, Wang Z, Heffelfinger C, Arbelaez LJ, Aguirre JA, Franco N, Romero LE, Labadie K, Zhao H, Dellaporta SL, Lorieux M (2017) Genetic architecture of a rice nested association mapping population. G3 Genes Genomes Genet 7(6):1913–1926Google Scholar
- Hyten DL, Choi IY, Song QJ, Specht JE, Carter TE, Shoemaker RC, Hwang EY, Matukumalli LK, Cregan PB (2010) A high density integrated genetic linkage map of soybean and the development of a 1536 universal soy linkage panel for quantitative trait locus mapping. Crop Sci 50:960–968CrossRefGoogle Scholar
- McCouch SR, Cho YG, Yano M, Paul E, Blinst RM, Morishima H, Kinoshita T (1997) Report on QTL nomenclature. Rice Genet Newslett 14:11–13Google Scholar
- Ogata T, Nagatoshi Y, Yamagishi N, Yoshikawa N, Fujita Y (2017) Virus-induced down-regulation of GmERA1A and GmERA1B genes enhances the stomatal response to abscisic acid and drought resistance in soybean. PLoS ONE 12(4):e0175650. https://doi.org/10.1371/journal.pone.0175650 CrossRefPubMedPubMedCentralGoogle Scholar
- SAS Institute Inc. (2004) SAS® 9.1.2 qualification tools user’s guide. SAS Institute Inc, CaryGoogle Scholar
- Tran LS, Nguyen HT (2009) Future biotechnology of legumes. In: Emerich WD, Krishnan H (eds) Nitrogen fixation in crop production. ASA-CSA-SSSA, Madison, pp 265–308Google Scholar
- Zhang Y, He J, Wang Y, Xing G, Zhao J, Li Y, Yang S, Palmer RG, Zhao T, Gai J (2015) Establishment of a 100-seed weight quantitative trait locus–allele matrix of the germplasm population for optimal recombination design in soybean breeding programmes. J Exp Bot 66:6311–6325CrossRefPubMedGoogle Scholar