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Diversity analysis and genomic prediction of Sclerotinia resistance in sunflower using a new 25 K SNP genotyping array

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Abstract

Key message

We have developed a SNP array for sunflower containing more than 25 K markers, representing single loci mostly in or near transcribed regions of the genome. The array was successfully applied to genotype a diversity panel of lines, hybrids, and mapping populations and represented well the genetic diversity of cultivated sunflower. Results of PCoA and population substructure analysis underlined the complexity of the genetic composition of current elite breeding material. The performance of this genotyping platform for genome-based prediction of phenotypes and detection of QTL with improved resolution could be demonstrated based on the re-evaluation of a population segregating for resistance to Sclerotinia midstalk rot. Given our results, the newly developed 25 K SNP array is expected to be of great utility for the most important applications in genome-based sunflower breeding and research.

Abstract

Genotyping with a large number of molecular markers is a prerequisite to conduct genome-based genetic analyses with high precision. Here, we report the design and performance of a 25 K SNP genotyping array for sunflower (Helianthus annuus L.). SNPs were discovered based on variant calling in de novo assembled, UniGene-based contigs of sunflower derived from whole genome sequencing and amplicon sequences originating from four and 48 inbred lines, respectively. After inclusion of publically available transcriptome-derived SNPs, in silico design of the Illumina® Infinium iSelect HD BeadChip yielded successful assays for 22,299 predominantly haplotype-specific SNPs. The array was validated in a sunflower diversity panel including inbred lines, open-pollinated varieties, introgression lines, landraces, recombinant inbred lines, and F2 populations. Validation provided 20,502 high-quality bi-allelic SNPs with stable cluster performance whereby each SNP marker represents a single locus mostly in or near transcribed regions of the sunflower genome. Analyses of population structure and quantitative resistance to Sclerotinia midstalk rot demonstrate that this array represents a significant improvement over currently available genomic tools for genetic diversity analyses, genome-wide marker-trait association studies, and genetic mapping in sunflower.

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References

  • Albrecht T, Wimmer V, Auinger HJ, Erbe M, Knaak C, Ouzunova M, Simianer H, Schön CC (2011) Genome-based prediction of testcross values in maize. Theor Appl Genet 123:339–350. doi:10.1007/s00122-011-1587-7

    Article  PubMed  Google Scholar 

  • Al-Chaarani GR, Gentzbittel L, Huang XQ, Sarrafi A (2004) Genotypic variation and identification of QTLs for agronomic traits, using AFLP and SSR markers in RILs of sunflower (Helianthus annuus L.). Theor Appl Genet 109:1353–1360. doi:10.1007/s00122-004-1770-1

    Article  PubMed  Google Scholar 

  • Alexander DH, Novembre J, Lange K (2009) Fast model-based estimation of ancestry in unrelated individuals. Genome Res 19:1655–1664. doi:10.1101/gr.094052.109

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Bachlava E, Taylor CA, Tang S, Bowers JE, Mandel JR, Burke JM, Knapp SJ (2012) SNP discovery and development of a high-density genotyping array for sunflower. PLoS ONE 7:e29814. doi:10.1371/journal.pone.0029814

    PubMed  CAS  Google Scholar 

  • Barb JG, Bowers JE, Renaut S, Rey JI, Knapp SJ, Rieseberg LH, Burke JM (2014) Chromosomal evolution and patterns of introgression in Helianthus. Genetics 197:969–979. doi:10.1534/genetics.114.165548

    Article  PubMed  PubMed Central  Google Scholar 

  • Berrios EF, Gentzbittel L, Kayyal H, Alibert G, Sarrafi A (2000) AFLP mapping of QTLs for in vitro organogenesis traits using recombinant inbred lines in sunflower (Helianthus annuus L.). Theor Appl Genet 101:1299–1306. doi:10.1007/s001220051610

    Article  CAS  Google Scholar 

  • Berry ST, Allen RJ, Barnes SR, Caligari PDS (1994) Molecular marker analysis of Helianthus annuus L. 1. Restriction fragment length polymorphism between inbred lines of cultivated sunflower. Theor Appl Genet 89:435–441. doi:10.1007/BF00225378

    Article  PubMed  CAS  Google Scholar 

  • Bianco L, Cestaro A, Sargent DJ, Banchi E, Derdak S, Di Guardo M, Salvi S, Jansen J, Viola R, Gut I, Laurens F, Chagne D, Velasco R, van de Weg E, Troggio M (2014) Development and validation of a 20 K single nucleotide polymorphism (SNP) whole genome genotyping array for apple (Malus × domestica Borkh). PLoS ONE 9:e110377. doi:10.1371/journal.pone.0110377

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Bowers JE, Bachlava E, Brunick RL, Rieseberg LH, Knapp SJ, Burke JM (2012a) Development of a 10,000 locus genetic map of the sunflower genome based on multiple crosses. G3 (Bethesda) 2:721–729. doi:10.1534/g3.112.002659

  • Bowers JE, Nambeesan S, Corbi J, Barker MS, Rieseberg LH, Knapp SJ, Burke JM (2012b) Development of an ultra-dense genetic map of the sunflower genome based on single-feature polymorphisms. PLoS ONE 7:e51360. doi:10.1371/journal.pone.0051360

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Browning BL, Browning SR (2009) A unified approach to genotype imputation and haplotype-phase inference for large data sets of trios and unrelated individuals. Am J Human Genetics 84:210–223. doi:10.1016/j.ajhg.2009.01.005

    Article  CAS  Google Scholar 

  • Burke JM, Lai Z, Salmaso M, Nakazato T, Tang S, Heesacker A, Knapp SJ, Rieseberg LH (2004) Comparative mapping and rapid karyotypic evolution in the genus Helianthus. Genetics 167:449–457. doi:10.1534/genetics.167.1.449

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Chang CC, Chow CC, Tellier LC, Vattikuti S, Purcell SM, Lee JJ (2015) Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience 4:7. doi:10.1186/s13742-015-0047-8

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Chen HD, Xie WB, He H, Yu HH, Chen W, Li J, Yu RB, Yao Y, Zhang WH, He YQ, Tang XY, Zhou FS, Deng XW, Zhang QF (2014) A high-density SNP genotyping array for rice biology and molecular breeding. Mol Plant 7:541–553. doi:10.1093/Mp/Sst135

    Article  PubMed  CAS  Google Scholar 

  • Churchill GA, Doerge RW (1994) Empirical threshold values for quantitative trait mapping. Genetics 138:963–971

    PubMed  PubMed Central  CAS  Google Scholar 

  • Daetwyler HD, Bansal UK, Bariana HS, Hayden MJ, Hayes BJ (2014) Genomic prediction for rust resistance in diverse wheat landraces. Theor Appl Genet 127:1795–1803. doi:10.1007/s00122-014-2341-8

    Article  PubMed  CAS  Google Scholar 

  • Dalton-Morgan J, Hayward A, Alamery S, Tollenaere R, Mason AS, Campbell E, Patel D, Lorenc MT, Yi B, Long Y, Meng J, Raman R, Raman H, Lawley C, Edwards D, Batley J (2014) A high-throughput SNP array in the amphidiploid species Brassica napus shows diversity in resistance genes. Funct Integr Genomics 14:643–655. doi:10.1007/s10142-014-0391-2

    Article  PubMed  CAS  Google Scholar 

  • Dekkers JC (2007) Marker-assisted selection for commercial crossbred performance. J Anim Sci 85:2104–2114. doi:10.2527/jas.2006-683

    Article  PubMed  CAS  Google Scholar 

  • Dußle CM, Hahn V, Knapp SJ, Bauer E (2004) Pl ARG from Helianthus argophyllus is unlinked to other known downy mildew resistance genes in sunflower. Theor Appl Genet 109:1083–1086. doi:10.1007/s00122-004-1722-9

    Article  PubMed  CAS  Google Scholar 

  • FAOSTAT (2013) http://faostat.fao.org/site/567/DesktopDefault.aspx?PageID=567#ancor. Accessed 19 Jan 2015

  • Fick GN, Miller JF (1997) Sunflower breeding. In: Schneiter AA (ed) Sunflower technology and production, vol 35. American Society of Agronomy, Madison, pp 395–439. doi:10.2134/agronmonogr35.c8

  • Gentzbittel L, Perrault A, Nicolas P (1992) Molecular phylogeny of the Helianthus genus, based on nuclear restriction fragment length polymorphism (RFLP). Mol Biol Evol 9:872–892

    CAS  Google Scholar 

  • Gentzbittel L, Zhang YX, Vear F, Griveau Y, Nicolas P (1994) RFLP studies of genetic relationships among inbred lines of cultivated sunflower, Helianthus annuus L: evidence for distinct restorer and maintainer germplasm pools. Theor Appl Genet 89:419–425. doi:10.1007/BF00225376

    Article  PubMed  CAS  Google Scholar 

  • Gill N, Buti M, Kane N, Bellec A, Helmstetter N, Berges H, Rieseberg LH (2014) Sequence-based analysis of structural organization and composition of the cultivated sunflower (Helianthus annuus L.). Genome Biol (Basel) 3:295–319. doi:10.3390/biology3020295

    CAS  Google Scholar 

  • Gilmour AB, Gogel B, Cullis B, Thompson R (2009) ASReml User Guide Release 3.0. VSN International. Hemel Hempstead, UK

  • Giraud H, Lehermeier C, Bauer E, Falque M, Segura V, Bauland C, Camisan C, Campo L, Meyer N, Ranc N, Schipprack W, Flament P, Melchinger AE, Menz M, Moreno-Gonzalez J, Ouzunova M, Charcosset A, Schön CC, Moreau L (2014) Linkage disequilibrium with linkage analysis of multiline crosses reveals different multiallelic QTL for hybrid performance in the flint and dent heterotic groups of maize. Genetics 198:1717–1734. doi:10.1534/genetics.114.169367

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Goddard ME, Hayes BJ (2007) Genomic selection. J Anim Breed Genet 124:323–330. doi:10.1111/j.1439-0388.2007.00702.x

    Article  PubMed  CAS  Google Scholar 

  • Gower JC (1966) Some distance properties of latent root and vector methods used in multivariate analysis. Biometrika 53:325–338. doi:10.2307/2333639

    Article  Google Scholar 

  • Grassa CJ, Baute GJ, Moyers B, Owens GL, Kane N, Langlade N, Gouzy J, Gill N, Staton SE, Nguyen TN, Hubner S, Bowers JE, Chaidir NI, Bergès H, King M, Lai Z, Bachlava E, Knapp S, Burke JM, Vincourt P, Rieseberg LH (2015) Reference genome of sunflower, line HA412, Version 1.4: a domesticated Compilospecies. In: Paper presented at the PAG XXIII, San Diego, CA, January 10–14

  • Habier D, Fernando RL, Dekkers JC (2007) The impact of genetic relationship information on genome-assisted breeding values. Genetics 177:2389–2397. doi:10.1534/genetics.107.081190

    PubMed  PubMed Central  CAS  Google Scholar 

  • Heesacker AF, Bachlava E, Brunick RL, Burke JM, Rieseberg LH, Knapp SJ (2009) Comparative mapping identifies multiple chromosomal rearrangements and duplications in the common and silverleaf sunflower genomes. Plant Genome 2:1–14. doi:10.3835/plantgenome2009.05.0015

    Article  CAS  Google Scholar 

  • Heffner EL, Jannink JL, Sorrells ME (2011) Genomic selection accuracy using multifamily prediction models in a wheat breeding program. Plant Genome 4:65–75. doi:10.3835/plantgenome2010.12.0029

    Article  Google Scholar 

  • Hofheinz N, Borchardt D, Weissleder K, Frisch M (2012) Genome-based prediction of test cross performance in two subsequent breeding cycles. Theor Appl Genet 125:1639–1645. doi:10.1007/s00122-012-1940-5

    Article  PubMed  Google Scholar 

  • Hongtrakul V (1997) The development and analysis of sequence-based DNA markers in sunflower for DNA fingerprinting and candidate gene analysis. PhD, Oregon State University

  • Hulke BS, Grassa CJ, Bowers JE, Burke JM, Qi L, Talukder ZI, Rieseberg LH (2015) A unified single nucleotide polymorphism map of sunflower (Helianthus annuus L.) derived from current genomic resources. Crop Sci 55:1696–1702. doi:10.2135/cropsci2014.11.0752

    Article  CAS  Google Scholar 

  • INRA (2014) http://lipm-helianthus.toulouse.inra.fr/Web/core/Core_collections_list.html

  • Jan CC, Chandler JM (1988) Registration of a powdery mildew resistant sunflower germplasm pool, PM1. Crop Sci 28:1040. doi:10.2135/cropsci1988.0011183X002800060072x

    Article  Google Scholar 

  • Jombart T (2008) adegenet: a R package for the multivariate analysis of genetic markers. Bioinformatics 24:1403–1405. doi:10.1093/bioinformatics/btn129

    Article  PubMed  CAS  Google Scholar 

  • Kane NC, Gill N, King MG, Bowers JE, Berges H, Gouzy J, Bachlava E, Langlade NB, Lai Z, Stewart M, Burke JM, Vincourt P, Knapp SJ, Rieseberg LH (2011) Progress towards a reference genome for sunflower. Botany-Botanique 89:429–437. doi:10.1139/B11-032

    Article  Google Scholar 

  • Kosambi DD (1944) The estimation of map distance from recombination values. Ann Eugenics 12:172–175

    Article  Google Scholar 

  • Lai Z, Livingstone K, Zou Y, Church SA, Knapp SJ, Andrews J, Rieseberg LH (2005a) Identification and mapping of SNPs from ESTs in sunflower. Theor Appl Genet 111:1532–1544. doi:10.1007/s00122-005-0082-4

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Lai Z, Nakazato T, Salmaso M, Burke JM, Tang S, Knapp SJ, Rieseberg LH (2005b) Extensive chromosomal repatterning and the evolution of sterility barriers in hybrid sunflower species. Genetics 171:291–303. doi:10.1534/genetics.105.042242

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Lorenz AJ, Smith KP, Jannink JL (2012) Potential and optimization of genomic selection for Fusarium Head Blight resistance in six-row barley. Crop Sci 52:1609–1621. doi:10.2135/cropsci2011.09.0503

    Article  Google Scholar 

  • Mammadov J, Aggarwal R, Buyyarapu R, Kumpatla S (2012) SNP markers and their impact on plant breeding. Int J Plant Genomics 1–11. doi:10.1155/2012/728398

  • Mandel JR, Dechaine JM, Marek LF, Burke JM (2011) Genetic diversity and population structure in cultivated sunflower and a comparison to its wild progenitor, Helianthus annuus L. Theor Appl Genet 123:693–704. doi:10.1007/s00122-011-1619-3

    Article  PubMed  CAS  Google Scholar 

  • Mandel JR, Nambeesan S, Bowers JE, Marek LF, Ebert D, Rieseberg LH, Knapp SJ, Burke JM (2013) Association mapping and the genomic consequences of selection in sunflower. PLoS Genet 9:e1003378. doi:10.1371/journal.pgen.1003378

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Manly KF, Cudmore RH Jr, Meer JM (2001) Map Manager QTX, cross-platform software for genetic mapping. Mamm Genome 12:930–932. doi:10.1007/s00335-001-1016-3

    Article  PubMed  CAS  Google Scholar 

  • Meuwissen TH, Hayes BJ, Goddard ME (2001) Prediction of total genetic value using genome-wide dense marker maps. Genetics 157:1819–1829

    PubMed  PubMed Central  CAS  Google Scholar 

  • Micic Z, Hahn V, Bauer E, Schön CC, Melchinger AE (2005) QTL mapping of resistance to Sclerotinia midstalk rot in RIL of sunflower population NDBLOSsel × CM625. Theor Appl Genet 110:1490–1498. doi:10.1007/s00122-005-1984-x

    Article  PubMed  CAS  Google Scholar 

  • Miller JF (1987) Sunflower. In: Fehr WR (ed) Principles of cultivar development, vol 2., Crop SpeciesWiley, New York, pp 626–668

    Google Scholar 

  • Miller JF (1997) Registration of cmsHA 89 (PEF1) cytoplasmic male-sterile, RPEF1 restorer, and two nuclear male-sterile (NMS 373 and 377) sunflower genetic stocks. Crop Sci 37:1984. doi:10.2135/cropsci1997.0011183X003700060063x

    Article  Google Scholar 

  • Murray MG, Thompson WF (1980) Rapid isolation of high molecular weight plant DNA. Nucleic Acids Res 8:4321–4325

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Natali L, Cossu RM, Barghini E, Giordani T, Buti M, Mascagni F, Morgante M, Gill N, Kane NC, Rieseberg L, Cavallini A (2013) The repetitive component of the sunflower genome as shown by different procedures for assembling next generation sequencing reads. BMC Genom 14:686. doi:10.1186/1471-2164-14-686

    Article  CAS  Google Scholar 

  • Paradis E, Claude J, Strimmer K (2004) APE: analyses of phylogenetics and evolution in R language. Bioinformatics 20:289–290. doi:10.1093/bioinformatics/btg412

    Article  PubMed  CAS  Google Scholar 

  • Pegadaraju V, Nipper R, Hulke B, Qi L, Schultz Q (2013) De novo sequencing of sunflower genome for SNP discovery using RAD (Restriction site Associated DNA) approach. BMC Genom 14:556. doi:10.1186/1471-2164-14-556

    Article  CAS  Google Scholar 

  • Pérez Vich B, Berry ST (2010) Molecular Breeding. In: Hu J, Seiler G, Kole C (eds) Genetics, genomics and breeding of sunflower. CRC Press, Pullman

    Google Scholar 

  • Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MAR, Bender D, Maller J, Sklar P, de Bakker PIW, Daly MJ, Sham PC (2007) PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 81:559–575. doi:10.1086/519795

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Qi LL, Seiler GJ, Vick BA, Gulya TJ (2012) Genetics and mapping of the R 11 gene conferring resistance to recently emerged rust races, tightly linked to male fertility restoration, in sunflower (Helianthus annuus L.). Theor Appl Genet 125:921–932. doi:10.1007/s00122-012-1883-x

    Article  PubMed  CAS  Google Scholar 

  • Ramos AM, Crooijmans RP, Affara NA, Amaral AJ, Archibald AL, Beever JE, Bendixen C, Churcher C, Clark R, Dehais P, Hansen MS, Hedegaard J, Hu ZL, Kerstens HH, Law AS, Megens HJ, Milan D, Nonneman DJ, Rohrer GA, Rothschild MF, Smith TP, Schnabel RD, Van Tassell CP, Taylor JF, Wiedmann RT, Schook LB, Groenen MA (2009) Design of a high density SNP genotyping assay in the pig using SNPs identified and characterized by next generation sequencing technology. PLoS ONE 4:e6524. doi:10.1371/journal.pone.0006524

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Reif JC, Zhao YS, Würschum T, Gowda M, Hahn V (2013) Genomic prediction of sunflower hybrid performance. Plant Breed 132:107–114. doi:10.1111/Pbr.12007

    Article  CAS  Google Scholar 

  • Rieseberg LH, Raymond O, Rosenthal DM, Lai Z, Livingstone K, Nakazato T, Durphy JL, Schwarzbach AE, Donovan LA, Lexer C (2003) Major ecological transitions in wild sunflowers facilitated by hybridization. Science (New York, N Y) 301:1211–1216. doi:10.1126/science.1086949

    Article  CAS  Google Scholar 

  • Rutkoski J, Benson J, Jia Y, Brown-Guedira G, Jannink JL, Sorrells M (2012) Evaluation of genomic prediction methods for Fusarium head blight resistance in wheat. Plant Genome 5:51–61. doi:10.3835/plantgenome2012.02.0001

    Article  CAS  Google Scholar 

  • Seiler GJ (1992) Utilization of wild sunflower species for the improvement of cultivated sunflower. Field Crop Res 30:195–230. doi:10.1016/0378-4290(92)90002-Q

    Article  Google Scholar 

  • Seiler G (2010) Utilization of wild Helianthus species in breeding for disease resistance. In: International symposium “Sunflower breeding on resistance to diseases”, Krasnodar, Russia, 2010. All-Russia Research Institute of Oil Crops by V. S. Pustovoit (VNIIMK) and The International Sunflower Association (ISA), pp 37–51

  • Song Q, Hyten DL, Jia G, Quigley CV, Fickus EW, Nelson RL, Cregan PB (2013) Development and evaluation of SoySNP50 K, a high-density genotyping array for soybean. PLoS ONE 8:e54985. doi:10.1371/journal.pone.0054985

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Tajima F (1983) Evolutionary relationship of DNA sequences in finite populations. Genetics 105:437–460

    PubMed  PubMed Central  CAS  Google Scholar 

  • Talukder ZI, Gong L, Hulke BS, Pegadaraju V, Song Q, Schultz Q, Qi L (2014) A high-density SNP Map of sunflower derived from RAD-sequencing facilitating fine-mapping of the rust resistance gene R12. PLoS ONE 9:e98628. doi:10.1371/journal.pone.0098628

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Tang S, Yu JK, Slabaugh MB, Shintani DK, Knapp SJ (2002) Simple sequence repeat map of the sunflower genome. Theor Appl Genet 105:1124–1136. doi:10.1007/s00122-002-0989-y

    Article  PubMed  CAS  Google Scholar 

  • Technow F, Burger A, Melchinger AE (2013) Genomic prediction of northern corn leaf blight resistance in maize with combined or separated training sets for heterotic groups. G3 (Bethesda) 3:197–203. doi:10.1534/g3.112.004630

    Article  Google Scholar 

  • Tosser-Klopp G, Bardou P, Bouchez O, Cabau C, Crooijmans R, Dong Y, Donnadieu-Tonon C, Eggen A, Heuven HCM, Jamli S, Jiken AJ, Klopp C, Lawley CT, McEwan J, Martin P, Moreno CR, Mulsant P, Nabihoudine I, Pailhoux E, Palhiere I, Rupp R, Sarry J, Sayre BL, Tircazes A, Wang J, Wang W, Zhang WG, Consortium IGG (2014) Design and characterization of a 52 K SNP chip for goats. PloS ONE 9. doi:10.1371/journal.pone.0086227

  • Untergasser A, Cutcutache I, Koressaar T, Ye J, Faircloth BC, Remm M, Rozen SG (2012) Primer3-new capabilities and interfaces. Nucleic Acids Res 40:e115. doi:10.1093/nar/gks596

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Unterseer S, Bauer E, Haberer G, Seidel M, Knaak C, Ouzunova M, Meitinger T, Strom TM, Fries R, Pausch H, Bertani C, Davassi A, Mayer KF, Schön CC (2014) A powerful tool for genome analysis in maize: development and evaluation of the high density 600 k SNP genotyping array. BMC Genom 15:823. doi:10.1186/1471-2164-15-823

    Article  Google Scholar 

  • USDA (2006) Sunflower inbred lines. http://www.ag.ndsu.nodak.edu/aginfo/seedstock/varieties/VH-SUNF.htm

  • USDA-ARS (2014) Germplasm resources information network. http://www.ars-grin.gov/npgs/acc/acc_queries.html

  • Utz HF, Melchinger AE (2006) PLABQTL: a computer program to map QTL, version 1.2 edn. Institute of Plant Breeding, Seed Science and Population Genetics, University of Hohenheim, Stuttgart

  • van Ooijen JW (2006) JoinMap 4, software for the calculation of genetic linkage maps in experimental populations. Kyazma, B.V., Wageningen

    Google Scholar 

  • Weir BS, Cockerham CC (1984) Estimating F-statistics for the analysis of population structure. Evolution 38:1358–1370. doi:10.2307/2408641

    Article  Google Scholar 

  • Wigginton JE, Cutler DJ, Abecasis GR (2005) A note on exact tests of Hardy–Weinberg equilibrium. Am J Hum Genet 76:887–893. doi:10.1086/429864

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Wimmer V, Albrecht T, Auinger HJ, Schön CC (2012) Synbreed: a framework for the analysis of genomic prediction data using R. Bioinformatics 28:2086–2087. doi:10.1093/bioinformatics/bts335

    Article  PubMed  CAS  Google Scholar 

  • Yu J-K, Tang S, Slabaugh MB, Heesacker A, Cole G, Herring M, Soper J, Han F, Chu W-C, Webb DM, Thompson L, Edwards KJ, Berry S, Leon AJ, Grondona M, Olungu C, Maes N, Knapp SJ (2003) Towards a saturated molecular genetic linkage map for cultivated sunflower. Crop Sci 43:367–387. doi:10.2135/cropsci2003.3670

    Article  CAS  Google Scholar 

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Acknowledgments

This work has been supported by a grant (FKZ: 0315952A-D) from the German Federal Ministry for Education and Research (BMBF). We are grateful to USDA and INRA (Patrick Vincourt) for providing seeds of sunflower accessions used as core collection during this project. We thank Uwe Scholz and the Research group Bioinformatics and Information Technology at IPK Gatersleben for providing computational resources.

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Authors and Affiliations

  1. Department of Plant Sciences, Plant Breeding, Technische Universität München, Liesel-Beckmann-Strasse 2, 85354, Freising, Germany

    Maren Livaja, Sandra Unterseer, Wiltrud Erath, Christina Lehermeier & Chris-Carolin Schön

  2. TraitGenetics GmbH, Am Schwabeplan 1b, 06466, Gatersleben, Germany

    Ralf Wieseke, Jörg Plieske, Andreas Polley, Hartmut Luerßen & Martin W. Ganal

  3. KWS SAAT SE, Grimsehlstrasse 31, 37555, Einbeck, Germany

    Silke Wieckhorst & Milena Ouzunova

  4. State Plant Breeding Institute, Universität Hohenheim, Fruwirthstrasse 21, 70599, Stuttgart, Germany

    Volker Hahn

  5. Research Group Domestication Genomics, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Corrensstrasse 3, 06466, Gatersleben, Stadt Seeland, Germany

    Martin Mascher

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  1. Maren Livaja
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  2. Sandra Unterseer
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  13. Chris-Carolin Schön
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Corresponding author

Correspondence to Chris-Carolin Schön.

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Conflict of interest

The authors MWG, JP, AP and HL have competing commercial interests as members of TraitGenetics GmbH which is a company that offers marker development and analysis (including this array) for commercial purposes. The authors MO and SW have competing commercial interests as members of KWS SAAT SE which is a sunflower breeding company. This does not alter the authors’ adherence to sharing all data and materials. There are no further products in development or marketed products or patents to declare.

Additional information

Communicated by B. Hulke.

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Figure S1: Representative cluster plots obtained for the sunflower SNP array based on the GenomeStudio software. Genotypes are called for each sample (dot) by their signal intensity (y axis, Norm R) and allele frequency (x axis, Norm Theta) relative to canonical cluster positions for a given SNP (dark shading). Cluster ovals indicate the position of the allele calling areas. Data points are color coded according to their genotype call (homozygous allele A = red, homozygous allele B = blue, heterozygous AB = purple). Data points colored in black are classified as “no calls”. A) Calling of all three genotypes with clearly defined and separated clusters (polymorphic SNP); B) Calling of only one genotype in essentially all 1090 sunflower lines. Marker is considered as successful but monomorphic; C) Pattern showing ambiguous clustering with low signal intensity, considered as a failed SNP marker; D) Multiple clusters indicative of two simultaneously scored polymorphic loci. Such SNP markers cannot be scored accurately and were removed from further analysis (TIFF 2870 kb)

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Figure S2: Venn diagram of polymorphic SNPs among 184 inbred lines with known category assignment. Venn diagram showing the number of polymorphic variants represented on the 25 K array in 184 inbred lines with known category assignment. Numbers indicate the absolute number of SNP markers. Percentages refer to the proportion of class-specific polymorphic SNPs to the total number of 18,825 markers polymorphic for this set of genotypes (TIFF 3443 kb)

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Figure S3: Cross-validation errors from ADMIXTURE. Cross-validation errors as a function of the number of assumed groups K. Shown are standard error estimates from ADMIXTURE for K = 1 to 20 for 243 sunflower inbred lines based on 20,502 SNPs (TIFF 675 kb)

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Figure S4: Distribution and correlation of phenotypic traits. A) Histograms and B) correlation plots of adjusted means averaged over two locations for the 113 RILs. Shown are the data for stem lesion length, speed of fungal growth, leaf lesion length, and leaf length with petiole (TIFF 158 kb)

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Livaja, M., Unterseer, S., Erath, W. et al. Diversity analysis and genomic prediction of Sclerotinia resistance in sunflower using a new 25 K SNP genotyping array. Theor Appl Genet 129, 317–329 (2016). https://doi.org/10.1007/s00122-015-2629-3

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  • DOI: https://doi.org/10.1007/s00122-015-2629-3

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