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Genome analysis to identify SNPs associated with oil content and fatty acid components in soybean

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Abstract

The nutritional value, flavor and stability of soybean oil are determined by its five dominant fatty acids: saturated palmitic and stearic, monounsaturated oleic, and polyunsaturated linoleic and linolenic acids. Identifying molecular markers or quantitative trait loci associated with these components has the potential to facilitate the development of improved varieties and thus improve soybean oil content and quality. In this study, we used the BARCSoySNP6K BeadChip array to conduct a genome analysis of diverse soybean accessions evaluated for 2 years under Brazilian field conditions. The results demonstrated high broad-sense heritability, suggesting that the soybean genotype panel could be useful for oil trait breeding programs. Moreover, the range of oil trait variation among the plant introductions (PIs) was superior to that among the Brazilian cultivars in this study, indicating that a PI population could be used to find genes controlling these traits. The genome analysis showed that the genetic structure of the soybean germplasm comprised two main genetic groups, and it revealed linkage disequilibrium decay of approximately 300 kb. A total of 19 single-nucleotide polymorphism (SNP) loci on ten different chromosomes significantly associated with palmitic acid, oleic acid and total oil contents were discovered. Analysis of the SNP annotations revealed enzymes associated with several oil-related physiological metabolisms. Loci and specific alleles in our soybean panel that contributed to lower palmitic acid contents and higher oleic acid and total oil contents were identified. Overall, this genome analysis confirmed previous findings and identified SNP markers that may be useful to rapidly improve oil traits in soybean.

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References

  • Aransiola EF, Ojumu TV, Oyekola OO, Madzimbamuto TF, Ikhu-Omoregbe DIO (2014) A review of current technology for biodiesel production: state of the art. Biomass Bioenergy 61:276–297

    Article  CAS  Google Scholar 

  • Bradbury PJ, Zhang Z, Kroon DE, Casstevens TM, Ramdoss Y, Buckler ES (2007) TASSEL: software for association mapping of complex traits in diverse samples. Bioinformatics 23:2633–2635

    Article  CAS  Google Scholar 

  • Cao YC, Li SG, Wang ZL, Chang FG, Kong JJ, Gai JY, Zhao TJ (2017) Identification of major quantitative trait loci for seed oil content in soybeans by combining linkage and genome-wide association mapping. Front Plant Sci 8:1222

    Article  Google Scholar 

  • Clemente TE, Cahoon EB (2009) Soybean oil: genetic approaches for modification of functionality and total content. Plant Physiol 151:1030–1040

    Article  CAS  Google Scholar 

  • Contreras-Soto RI, de Oliveira MB, Costenaro-da-Silva D, Scapim CA, Schuster I (2017) Population structure, genetic relatedness and linkage disequilibrium blocks in cultivars of tropical soybean (Glycine max). Euphytica 213:173

    Article  Google Scholar 

  • Deshmukh R, Sonah H, Patil G, Chen W, Prince S, Mutava R, Vuong T, Valliyodan B, Nguyen HT (2014) Integrating omic approaches for abiotic stress tolerance in soybean. Front Plant Sci 5:244

    Article  Google Scholar 

  • Diers BW, Shoemaker RC (1992) Restriction-fragment-length-polymorphism analysis of soybean fatty-acid content. J Am Oil Chem Soc 69:1242–1244

    Article  CAS  Google Scholar 

  • Earl DA, vonHoldt BM (2012) STRUCTURE HARVESTER: a website and program for visualizing STRUCTURE output and implementing the Evanno method. Conserv Genet Resour 4:359–361

    Article  Google Scholar 

  • Evanno G, Regnaut S, Goudet J (2005) Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol Ecol 14:2611–2620

    Article  CAS  Google Scholar 

  • Fehr WR, Welke GA, Hammond EG, Duvick DN, Cianzio SR (1991) Inheritance of elevated palmitic acid content in soybean seed oil. Crop Sci 31:1522–1524

    Article  CAS  Google Scholar 

  • Feng S, Wang SC, Chen CC, Lan L (2011) GWAPOWER: a statistical power calculation software for genome-wide association studies with quantitative traits. BMC Genet 12:12

    Article  Google Scholar 

  • Ginestet C (2011) GGPLOT2: elegant graphics for data analysis. J R Stat Soc Ser A Stat Soc 174:245–246

    Article  Google Scholar 

  • Gizlice Z, Carter TE, Burton JW (1994) Genetic base for North-American public soybean cultivars released between 1947 and 1988. Crop Sci 34:1143–1151

    Article  Google Scholar 

  • Graef G, LaVallee BJ, Tenopir P, Tat M, Schweiger B, Kinney AJ, Van Gerpen JH, Clemente TE (2009) A high-oleic-acid and low-palmitic-acid soybean: agronomic performance and evaluation as a feedstock for biodiesel. Plant Biotechnol J 7:411–421

    Article  CAS  Google Scholar 

  • Hiromoto DM, Vello NA (1986) The genetic base of Brazilian soybean (Glycine-max (L) Merrill) cultivars. Braz J Genet 9:295–306

    Google Scholar 

  • Hubisz MJ, Falush D, Stephens M, Pritchard JK (2009) Inferring weak population structure with the assistance of sample group information. Mol Ecol Resour 9:1322–1332

    Article  Google Scholar 

  • Hwang EY, Song QJ, Jia GF, Specht JE, Hyten DL, Costa J, Cregan PB (2014) A genome-wide association study of seed protein and oil content in soybean. BMC Genom 15:1

    Article  Google Scholar 

  • Hyten DL, Pantalone VR, Saxton AM, Schmidt ME, Sams CE (2004) Molecular mapping and identification of soybean fatty acid modifier quantitative trait loci. J Am Oil Chem Soc 81:1115–1118

    Article  CAS  Google 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 50:960–968

    Article  CAS  Google Scholar 

  • Jakobsson M, Rosenberg NA (2007) CLUMPP: a cluster matching and permutation program for dealing with label switching and multimodality in analysis of population structure. Bioinformatics 23:1801–1806

    Article  CAS  Google Scholar 

  • Keenan K, McGinnity P, Cross TF, Crozier WW, Prodöh PA (2013) diveRsity: an R package for the estimation and exploration of population genetics parameters and their associated errors. Methods Ecol Evol 4:782–788

    Article  Google Scholar 

  • Koboldt DC, Steinberg KM, Larson DE, Wilson RK, Mardis ER (2013) The next-generation sequencing revolution and its impact on genomics. Cell 155:27–38

    Article  CAS  Google Scholar 

  • Leamy LJ, Zhang HY, Li CB, Chen CY, Song BH (2017) A genome-wide association study of seed composition traits in wild soybean (Glycine soja). BMC Genom 18:18

    Article  Google Scholar 

  • Lee JD, Bilycu KD, Shannon JG (2015a) Genetics and breeding for modified fatty acid profile in soybean seed oil. J Crop Sci Biotech 10:201–210

    Google Scholar 

  • Lee YG, Jeong N, Kim JH, Lee K, Kim KH, Pirani A, Ha BK, Kang ST, Park BS, Moon JK, Kim N, Jeong SC (2015b) Development, validation and genetic analysis of a large soybean SNP genotyping array. Plant J 81:625–636

    Article  CAS  Google Scholar 

  • Li YH, Guan RX, Liu ZX, Ma YS, Wang LX, Li LH, Lin FY, Luan WJ, Chen PY, Yan Z, Guan Y, Zhu L, Ning XC, Smulders MJM, Li W, Piao RH, Cui YH, Yu ZM, Guan M, Chang RZ, Hou AF, Shi AN, Zhang B, Zhu SL, Qiu LJ (2008) Genetic structure and diversity of cultivated soybean (Glycine max (L.) Merr.) landraces in China. Theor Appl Genet 117:857–871

    Article  CAS  Google Scholar 

  • Li HW, Zhao TJ, Wang YF, Yu DY, Chen SY, Zhou RB, Gai JY (2011) Genetic structure composed of additive QTL, epistatic QTL pairs and collective unmapped minor QTL conferring oil content and fatty acid components of soybeans. Euphytica 182:117–132

    Article  Google Scholar 

  • Li YH, Reif JC, Ma YS, Hong HL, Liu ZX, Chang RZ, Qiu LJ (2015) Targeted association mapping demonstrating the complex molecular genetics of fatty acid formation in soybean. BMC Genom 16:841

    Article  Google Scholar 

  • Mackay TFC, Stone EA, Ayroles JF (2009) The genetics of quantitative traits: challenges and prospects. Nat Rev Genet 10:565–577

    Article  CAS  Google Scholar 

  • Mensink R, Katan M (1990) Effect of dietary trans fatty acids on high-density and low-density lipoprotein cholesterol levels in healthy subjects. N Engl J Med 323:439–445

    Article  CAS  Google Scholar 

  • Monteros MJ, Burton JW, Boerma HR (2008) Molecular mapping and confirmation of QTLs associated with oleic acid content in N00-3350 soybean. Crop Sci 48:2223–2234

    Article  Google Scholar 

  • Nyquist WE (1991) Estimation of heritability and prediction of selection response in plant-populations. CRC Crit Rev Plant Sci 10:235–322

    Article  Google Scholar 

  • Panthee DR, Pantalone VR, Saxton AM (2006) Modifier QTL for fatty acid composition in soybean oil. Euphytica 152:67–73

    Article  CAS  Google Scholar 

  • Paradis E, Claude J, Strimmer K (2004) APE: analyses of phylogenetics and evolution in R language. Bioinformatics 20:289–290

    Article  CAS  Google Scholar 

  • Priolli RHG, Campos JB, Stabellini NS, Pinheiro JB, Vello NA (2015) Association mapping of oil content and fatty acid components in soybean. Euphytica 203:83–96

    Article  CAS  Google Scholar 

  • Pritchard JK, Stephens M, Donnelly P (2000) Inference of population structure using multilocus genotype data. Genetics 155:945–959

    CAS  PubMed  PubMed Central  Google Scholar 

  • R Development Core Team (2015) R: a language and environment for statistical computing [Internet]. R Foundation for Statistical Computing, Vienna

    Google Scholar 

  • Smallwood CJ, Gillman JD, Saxton AM, Bhandari HS, Wadl PA, Fallen BD, Hyten DL, Song Q, Pantalone VR (2017) Identifying and exploring significant genomic regions associated with soybean yield, seed fatty acids, protein and oil. J Crop Sci Biotech 20(4):243–253

    Article  Google Scholar 

  • Snedecor GW, Cochran WG (1989) Statistical methods, 8th edn. Iowa State University Press, Ames

    Google Scholar 

  • Sneller CH (1994) Pedigree analysis of elite soybean lines. Crop Sci 34:1515–1522

    Article  Google Scholar 

  • Sneller CH (2003) Impact of transgenic genotypes and subdivision on diversity within elite North American soybean germplasm. Crop Sci 43:409–414

    Article  Google Scholar 

  • Song QJ, Hyten DL, Jia GF, Quigley CV, Fickus EW, Nelson RL, Cregan PB (2013) Development and evaluation of SoySNP50K, a high-density genotyping array for soybean. PLoS ONE 8:e54985

    Article  CAS  Google Scholar 

  • Song Q, Jia G, Quigley C, Fickus E, Hyten D, Nelson R, Cregan P (2014) Soybean BARCSoySNP6K Beadchip—a tool for soybean genetics research. In: Plant animal genome XXII, Jan 10–15, 2014, San Diego. Abstract No. P306. https://pag.confex.com/pag/xxii/webprogram/Paper10932.html. Accessed 28 June 2018

  • Spencer MM, Pantalone VR, Meyer EJ, Landau-Ellis D, Hyten DL (2003) Mapping the Fas locus controlling stearic acid content in soybean. Theor Appl Genet 106:615–619

    Article  CAS  Google Scholar 

  • Ude GN, Kenworthy WJ, Costa JM, Cregan PB, Alvernaz J (2003) Genetic diversity of soybean cultivars from China, Japan, North America, and North American ancestral lines determined by amplified fragment length polymorphism. Crop Sci 43:1858–1867

    Article  CAS  Google Scholar 

  • Vaughn JN, Nelson RL, Song QJ, Cregan PB, Li ZL (2014) The genetic architecture of seed composition in soybean is refined by genome-wide association scans across multiple populations. G3 (Bethesda) 4:2283–2294

    Article  Google Scholar 

  • Vello NA, Fehr WR, Bahrenfus JB (1984) Genetic-variability and agronomic performance of soybean populations developed from plant introductions. Crop Sci 24:511–514

    Article  Google Scholar 

  • Vuong TD, Sonah H, Meinhardt CG, Deshmukh R, Kadam S, Nelson RL, Shannon JG, Nguyen HT (2015) Genetic architecture of cyst nematode resistance revealed by genome-wide association study in soybean. BMC Genom 16:593

    Article  CAS  Google Scholar 

  • Wang J, Chu SS, Zhang HR, Zhu Y, Cheng H, Yu DY (2016) Development and application of a novel genome-wide SNP array reveals domestication history in soybean. Sci Rep 6:20728

    Article  CAS  Google Scholar 

  • Wilson R, Burton JW, Pantalone VR, Dewey RE (2002) New gene combinations governing saturated and unsaturated FA composition in soybean. In: Kuo TM, Gardner HW (eds) Lipid biotechnology. Marcel Dekker Inc, New York, pp 95–113

    Google Scholar 

  • Wysmierski PT, Vello NA (2013) The genetic base of Brazilian soybean cultivars: evolution over time and breeding implications. Genet Mol Biol 36:547–555

    Article  Google Scholar 

  • Zhang ZW, Ersoz E, Lai CQ, Todhunter RJ, Tiwari HK, Gore MA, Bradbury PJ, Yu JM, Arnett DK, Ordovas JM, Buckler ES (2010) Mixed linear model approach adapted for genome-wide association studies. Nat Genet 42:355-U118

    Google Scholar 

  • Zhang HY, Song QJ, Griffin JD, Song BH (2017) Genetic architecture of wild soybean (Glycine soja) response to soybean cyst nematode (Heterodera glycines). Mol Genet Genom 292:1257–1265

    Article  CAS  Google Scholar 

  • Zimmermann FJP (2014) Estatística aplicada à pesquisa agrícola, 2nd edn. Embrapa, Brasília

    Google Scholar 

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Acknowledgements

This work was supported by the Sao Paulo Research Foundation (FAPESP, project FAPESP/BIOEN 2016/01823-9). M.M. Bajay thanks Coordination for the Improvement of Higher Education Personnel (CAPES); J.B. P and N.A. Vello thank the National Council for Scientific and Technological Development (CNPq) for scholarships.

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Authors and Affiliations

  1. FIFO, UNISANTA, R. Oswaldo Cruz 266, Santos, SP, 11045-970, Brazil

    R. H. G. Priolli

  2. NEPA, UNICAMP, Av. Albert Einstein 291, Campinas, SP, 13083-852, Brazil

    R. H. G. Priolli

  3. IAC, CPDRGV, Av. Barao de Itapura 1481, Campinas, SP, 13001-970, Brazil

    C. R. L. Carvalho

  4. CERES, UDESC, R. Cel. Fernandes Martins 270, Laguna, SC, 88790-000, Brazil

    M. M. Bajay

  5. Department of Genetics, ESALQ, USP, Av Padua Dias 11, Piracicaba, SP, 13400-970, Brazil

    J. B. Pinheiro & N. A. Vello

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  1. R. H. G. Priolli
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Correspondence to R. H. G. Priolli.

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10681_2019_2378_MOESM6_ESM.jpg

Supplemental Figure 1 Pairwise LD values (r2) plotted against the genetic distance of each chromosome estimated among 5,220 SNP loci and 96 soybean accessions (JPEG 1446 kb)

10681_2019_2378_MOESM7_ESM.jpg

Supplemental Figure 2 Quantile-quantile plot using the MLM model for the 96 soybean accessions. Observed and expected -log P values (a) oil content; (b) palmitic acid (c) oleic acid (JPEG 86 kb)

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Priolli, R.H.G., Carvalho, C.R.L., Bajay, M.M. et al. Genome analysis to identify SNPs associated with oil content and fatty acid components in soybean. Euphytica 215, 54 (2019). https://doi.org/10.1007/s10681-019-2378-5

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