Genetic analysis of shoot fresh weight in a cross of wild (G. soja) and cultivated (G. max) soybean
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Shoot fresh weight (SFW) is one of the parameters, used to estimate the total plant biomass yield in soybean. In the present study, a total of 188 F5:8 recombinant inbred lines (RIL) derived from an interspecific cross of PI 483463 (Glycine soja) and Hutcheson (Glycine max) were investigated for SFW variation in the field for three consecutive years. The parental lines and RILs were phenotyped in the field at the R6 stage by measuring total biomass in kg/plot to identify the QTLs for SFW. Three QTLs qSFW6_1, qSFW15_1, and qSFW19_1 influencing SFW were identified on chromosome 6, 15, and 19, respectively. The QTL qSFW19_1 flanked between the markers BARC-044913-08839 and BARC-029975-06765 was the stable QTL expressed in all the three environments. The phenotypic variation explained by the QTLs across all environments ranged from 6.56 to 21.32 %. The additive effects indicated contribution of alleles from both the parents and additive × environment interaction effects affected the expression of SFW QTL. Screening of the RIL population with additional SSRs from the qSFW19_1 region delimited the QTL between the markers SSR19-1329 and BARC-29975-06765. QTL mapping using bin map detected two QTLs, qSFW19_1A and qSFW19_1B. The QTL qSFW19_1A mapped close to the Dt1 gene locus, which affects stem termination, plant height, and floral initiation in soybean. Potential candidate genes for SFW were pinpointed, and sequence variations within their sequences were detected using high-quality whole-genome resequencing data. The findings in this study could be useful for understanding genetic basis of SFW in soybean.
KeywordsSoybean Wild soybean Forage Shoot fresh weight Candidate gene
This work was carried out with the support of the Next-Generation BioGreen 21 Program for Agriculture and Technology Development (Project No. PJ1109201), Rural Development Administration, Republic of Korea.
S Asekova, M. Kim performed field experiment, S. Asekova, K.P. Kulkarni, and G. Patil performed QTL mapping, genotyping, and genetic analysis, S. Asekova and K.P. Kulkarni contributed to drafting the manuscript, and J.G. Shannon, J.T. Song, H.T. Nguyen, and J.D. Lee editing manuscript. All authors read and approved the final manuscript.
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
The authors declare that the experiments comply with the current laws of counties in which the experiments were performed.
- Abdul-Baki AA, Morse RD, Devine TE, Teasdale JR (1997) Broccoli production in forage soybean and foxtail millet cover crop mulches. Hort Sci 32:836–839Google Scholar
- Blount AR, Wright DL, Sprenkel RK, Hewitt TD, Myer RO (2002) Forage soybeans for grazing, hay, and silage. Agronomy Department, UF/IFAS Extension, SS-AGR-180. http://edis.ifas.ufl.edu. Accessed 26 Dec 2015
- Devine TE, Hatley EO, Stamer DE (1998) Registration of ‘Derry’ forage soybean. Crop Sci 38:1719Google Scholar
- Doyle JJ, Doyle JL (1990) Isolation of plant DNA from fresh tissues. Focus 12:13–15Google Scholar
- Feurtado JA, Huang D, Wicki-Stordeur L, Hemstock LE, Potentier MS, Tsang EWT, Cutler AJ (2011) The Arabidopsis C2H2 zinc finger INDETERMINATE DOMAIN1/ENHYDROUS promotes the transition to germination by regulating light and hormonal signaling during seed maturation. Plant Cell 23:1772–1794CrossRefPubMedPubMedCentralGoogle Scholar
- Ingley E, Hemmings BA (1994) Pleckstrin homology (PH) domains in signal transduction. Cell Biochem Biophys 56:436–443Google Scholar
- Kilian B, Graner A (2012) NGS technologies for analyzing germplasm diversity in genebanks. Oxford University Press. doi: 10.1093/bfgp/elr046
- Lee JD, Shannon JG, Chung G, Hwang YH (2011) Wild soybean (Glycine soja Sieb. & Zucc)—a genetic source for soybean variety improvement. Kor Soybean Digest 28:7–15Google Scholar
- Li ZK, Yu SB, Lafitte HR, Huang N, Courtois B, Hittalmani S, Vijayakumar CH, Liu GF, Wang GC, Shashidhar HE, Zhuang JY, Zheng KL, Singh VP, Sidhu JS, Srivantaneeyakul S, Khush GS (2003) QTL × environment interactions in rice. I. Heading date and plant height. Theor Appl Genet 108:141–153CrossRefPubMedGoogle Scholar
- Manavalan LP, Prince SJ, Musket TA, Chaky J, Deshmukh R, Vuong TD, Song L, Cregan PB, Nelson JC, Shannon JG, Specht JE, Nguyen HT (2015) Identification of novel QTL governing root architectural traits in an interspecific soybean population. PLoS ONE. doi: 10.1371/journal.pone.0120490 PubMedPubMedCentralGoogle Scholar
- Probst AH, Judd RW (1973) Origin, U.S. history and development, and world distribution. In: Caldwell BE (ed) Soybeans: improvement, production, and uses. American Society of Agronomy, Madison, pp 1–15Google Scholar
- SAS Institute (2013) SAS/STAT 9.4 user’s guide. SAS Inst. Inc., Cary, NC27513-2414Google Scholar
- SPSS (2007) for Windows. Release 16.0. SPSS Inc, Chicago, ILGoogle Scholar
- Wang S, Basten CJ, and Zeng ZB (2011) Windows QTL cartographer 2.5. Department of Statistics, North Carolina State University, Raleigh, NCGoogle Scholar
- Xu X, Zeng L, Tao Y, Vuong T, Wan J, Boerma R, Noe J, Li Z, Finnerty S, Pathan SM, Shannon JG, Nguyen HT (2013) Pinpointing genes underlying the quantitative trait loci for root-knot nematode resistance in palaeopolyploid soybean by whole genome resequencing. PNAS 110:13469–13474CrossRefPubMedPubMedCentralGoogle Scholar