Molecular Breeding

, 35:88 | Cite as

Genetic mapping in grapevine using SNP microarray intensity values

  • Sean Myles
  • Siraprapa Mahanil
  • James Harriman
  • Kyle M. Gardner
  • Jeffrey L. Franklin
  • Bruce I. Reisch
  • David W. Ramming
  • Christopher L. Owens
  • Lin Li
  • Edward S. Buckler
  • Lance Cadle-Davidson
Article

Abstract

Genotyping microarrays are widely used for genetic mapping, but in high-diversity organisms, the quality of SNP calls can be diminished by genetic variation near the assayed nucleotide. To address this limitation in grapevine, we developed a simple heuristic that uses hybridization intensity to genetically map phenotypes without the need to distinguish between polymorphic states. We applied this approach to the mapping of three previously mapped traits, each controlled by single major effect loci—color, flower sex, and powdery mildew resistance—and confirmed that intensity values outperform SNP calls in all cases. Further, because per sample cost is a major limitation to the adoption of genotyping microarrays in applied genetic research and plant breeding, we tested how many samples were required to map a Mendelian trait in an F1 grape population and found that we could identify the correct genomic region with as few as 12 samples. For high-diversity species for which genotyping arrays are available or under development, our findings suggest a powerful and cost-effective approach to identify large-effect QTL when faced with poor SNP quality.

Keywords

Vitis Grapevine SNP discovery Genotyping microarray 

Supplementary material

11032_2015_288_MOESM1_ESM.tif (33.1 mb)
Figure S1: An assessment of the power of genetic mapping using five different summaries of fluorescence intensity values from a genotyping microarray. Each panel (A-F) shows the genome-wide association scores (-log10(P)) and quantile–quantile (QQ) plots from an association test for powdery mildew resistance. The known causal locus for powdery mildew resistance (Ren4) is on chromosome 18. Each panel shows the result from applying the association test using a different summary of the normalized intensity values from the Vitis9KSNP array, X and Y (see Methods). The summary statistic employed in each panel is shown boxed in the top left portion of each Manhattan plot. The dashed horizontal line in each Manhattan plot is the Bonferroni-corrected genome-wide significance threshold. Chromosome “R” refers to SNPs that are unanchored to the genome assembly. The solid line in the QQ plots shows the distribution the P values would follow under the null hypothesis of no association. Red dots in the QQ plot represent P values that are significant after Bonferroni correction for multiple comparisons. The result of the association test for this phenotype using genotype calls is shown in Fig. 3c. All of the summary statistics of the intensity values outperform the use of SNP calls in their ability to map the locus for powdery mildew resistance. Statistics that use intensities from only one allele (e.g., X, Y) and statistics that use intensities from both alleles (e.g., ln(X/Y), X + Y) both capture sufficient information to successfully map the causal locus. A summary of the effectiveness of these six different summaries of fluorescence intensity values for all three phenotypes considered in the current study is provided in Table S1 (TIFF 33910 kb)
11032_2015_288_MOESM2_ESM.tif (4.1 mb)
Figure S2: Correlation among summary statistics of intensity values. The correlations among all possible pairs of the six tested summary statistics are represented as a heatmap. For each pair of summary statistics across all three phenotypes, a Pearson correlation was performed between all P values generated from the association tests (see Figure S1). The legend shows the shades of green associated with different values of the squared correlation coefficient, R2, resulting from the correlation test. While all summary statistics of the intensity values capture sufficient information for genetic mapping (see Figure S1), they are not all strongly correlated with each other (TIFF 4177 kb)
11032_2015_288_MOESM3_ESM.tif (14.5 mb)
Figure S3: QQ plots from association tests using SNP calls vs. intensity values. Each panel depicts the results for a phenotype, with the name of the phenotype at the top of each plot. On the left are QQ plots from the association tests using SNP calls. On the right are QQ plots from the association tests using the intensity values summary statistic ln(X/Y). The solid line in the QQ plots shows the distribution the P values would follow under the null hypothesis of no association. Red dots in the QQ plot represent P values that are significant after Bonferroni correction for multiple comparisons. The inflation factor (λ) is boxed in and is found in the top left corner of each plot. The inflation factor for all SNPs (λ1) and the inflation factor excluding SNPs that are significant after Bonferroni correction and that map to the appropriate chromosome (λ2) are shown (TIFF 14887 kb)
11032_2015_288_MOESM4_ESM.tif (123 kb)
Figure S4: Power of genetic mapping using intensity values as a function of sample size. The result shown is from the F1 population segregating for color (see Table 1). The x-axis shows case/control sample sizes where every possible combination of cases (white skin) and controls (blue skin) were sampled. For example, for case/control sample size = 2, association analyses using intensity values were performed for every possible combination of two white-skinned offspring vs. two blue-skinned offspring. The proportion of the association analyses that correctly mapped grape skin color to chromosome 2 is shown for each case/control sample size. The mapping was considered correct if the most significant P value was found on chromosome 2 and if it surpassed the Bonferroni-corrected genome-wide significance threshold (TIFF 123 kb)
11032_2015_288_MOESM5_ESM.doc (36 kb)
Table S1: Results from evaluating different methods of using intensity values from the Vitis9KSNP array to map the REN4 locus in grape (DOC 36 kb)

References

  1. Atwell S, Huang YS, Vilhjalmsson BJ, Willems G, Horton M, Li Y, Meng D, Platt A, Tarone AM, Hu TT, Jiang R, Muliyati NW, Zhang X, Amer MA, Baxter I, Brachi B, Chory J, Dean C, Debieu M, de Meaux J, Ecker JR, Faure N, Kniskern JM, Jones JDG, Michael T, Nemri A, Roux F, Salt DE, Tang C, Todesco M, Traw MB, Weigel D, Marjoram P, Borevitz JO, Bergelson J, Nordborg M (2010) Genome-wide association study of 107 phenotypes in Arabidopsis thaliana inbred lines. Nature 465:627–631CrossRefPubMedCentralPubMedGoogle Scholar
  2. Chagne D, Crowhurst RN, Troggio M, Davey MW, Gilmore B, Lawley C, Vanderzande S, Hellens RP, Kumar S, Cestaro A, Velasco R, Main D, Rees JD, Iezzoni A, Mockler T, Wilhelm L, Van de Weg E, Gardiner SE, Bassil N, Peace C (2012) Genome-wide SNP detection, validation, and development of an 8 K SNP array for apple. PLoS ONE 7:e31745. doi:10.1371/journal.pone.0031745 CrossRefPubMedCentralPubMedGoogle Scholar
  3. D’hoop BB, Paulo MJ, Mank RA, van Eck HJ, van Eeuwijk FA (2008) Association mapping of quality traits in potato (Solanum tuberosum L.). Euphytica 161:47–60CrossRefGoogle Scholar
  4. Dalbo MA, Ye GN, Weeden NF, Steinkellner H, Sefc KM, Reisch BI (2000) A gene controlling sex in grapevines placed on a molecular marker-based genetic map. Genome 43:333–340CrossRefPubMedGoogle Scholar
  5. Elshire RJ, Glaubitz JC, Sun Q, Poland JA, Kawamoto K, Buckler ES, Mitchell SE (2011) A robust, simple genotyping-by-sequencing (GBS) approach for high diversity species. PLoS ONE 6:e19379CrossRefPubMedCentralPubMedGoogle Scholar
  6. Fan JB, Oliphant A, Shen R, Kermani BG, Garcia F, Gunderson KL, Hansen M, Steemers F, Butler SL, Deloukas P, Galver L, Hunt S, McBride C, Bibikova M, Rubano T, Chen J, Wickham E, Doucet D, Chang W, Campbell D, Zhang B, Kruglyak S, Bentley D, Haas J, Rigault P, Zhou L, Stuelpnagel J, Chee MS (2003) Highly parallel SNP genotyping. Cold Spring Harb Symp Quant Biol 68:69–78CrossRefPubMedGoogle Scholar
  7. Fechter I, Hausmann L, Daum M, Rosleff Sörensen T, Viehöver P, Weisshaar B, Töpfer R (2012) Candidate genes within a 143 kb region of the flower sex locus in vitis. Mol Genetics Genomics 287:247–259. doi:10.1007/s00438-012-0674-z CrossRefGoogle Scholar
  8. Fournier-Level A, Le Cunff L, Gomez C, Doligez A, Ageorges A, Roux C, Bertrand Y, Souquet J-M, Cheynier V, This P (2009) Quantitative genetic bases of anthocyanin variation in grape (Vitis vinifera L. ssp. sativa) berry: a quantitative trait locus to quantitative trait nucleotide integrated study. Genetics 183:1127–1139. doi:10.1534/genetics.109.103929 CrossRefPubMedCentralPubMedGoogle Scholar
  9. Fu Y, Springer NM, Ying K, Yeh C-T, Iniguez AL, Richmond T, Wu W, Barbazuk B, Nettleton D, Jeddeloh J, Schnable PS (2010) High-resolution genotyping via whole genome hybridizations to microarrays containing long oligonucleotide probes. PLoS ONE 5:e14178CrossRefPubMedCentralPubMedGoogle Scholar
  10. Ganal MW, Durstewitz G, Polley A, Bérard A, Buckler ES, Charcosset A, Clarke JD, Graner E-M, Hansen M, Joets J, Le Paslier M-C, McMullen MD, Montalent P, Rose M, Schön C-C, Sun Q, Walter H, Martin OC, Falque M (2011) A large maize (Zea mays L.) SNP Genotyping array: development and germplasm genotyping, and genetic mapping to compare with the B73 reference genome. PLoS ONE 6:e28334. doi:10.1371/journal.pone.0028334 CrossRefPubMedCentralPubMedGoogle Scholar
  11. Gunderson KL, Steemers FJ, Lee G, Mendoza LG, Chee MS (2005) A genome-wide scalable SNP genotyping assay using microarray technology. Nat Genet 37:549CrossRefPubMedGoogle Scholar
  12. Gunderson KL, Steemers FJ, Ren H, Ng P, Zhou L, Tsan C, Chang W, Bullis D, Musmacker J, King C, Lebruska LL, Barker D, Oliphant A, Kuhn KM, Shen R (2006) Whole-genome genotyping. Methods Enzymol 410:359–376CrossRefPubMedGoogle Scholar
  13. Hamblin MT, Buckler ES, Jannink J-L (2011) Population genetics of genomics-based crop improvement methods. Trend Genetics 27:98CrossRefGoogle Scholar
  14. Heffner EL, Sorrells ME, Jannink J-L (2009) Genomic selection for crop improvement. Crop Sci 49:1–12. doi:10.2135/cropsci2008.08.0512 CrossRefGoogle Scholar
  15. Huang X, Wei X, Sang T, Zhao Q, Feng Q, Zhao Y, Li C, Zhu C, Lu T, Zhang Z, Li M, Fan D, Guo Y, Wang A, Wang L, Deng L, Li W, Lu Y, Weng Q, Liu K, Huang T, Zhou T, Jing Y, Li W, Lin Z, Buckler ES, Qian Q, Zhang QF, Li J, Han B (2010) Genome-wide association studies of 14 agronomic traits in rice landraces. Nat Genet 42:961–967CrossRefPubMedGoogle Scholar
  16. Kijas JW, Townley D, Dalrymple BP, Heaton MP, Maddox JF, McGrath A, Wilson P, Ingersoll RG, McCulloch R, McWilliam S, Tang D, McEwan J, Cockett N, Oddy VH, Nicholas FW, Raadsma H, for the International Sheep Genomics C (2009) A genome wide survey of SNP variation reveals the genetic structure of sheep breeds. PLoS ONE 4:e4668Google Scholar
  17. Kim S, Zhao K, Jiang R, Molitor J, Borevitz JO, Nordborg M, Marjoram P (2006) Association mapping with single-feature polymorphisms. Genetics 173:1125–1133. doi:10.1534/genetics.105.052720 CrossRefPubMedCentralPubMedGoogle Scholar
  18. Kim MY, Lee S, Van K, Kim T-H, Jeong S-C, Choi I-Y, Kim D-S, Lee Y-S, Park D, Ma J, Kim W-Y, Kim B-C, Park S, Lee K-A, Kim DH, Kim KH, Shin JH, Jang YE, Kim KD, Liu WX, Chaisan T, Kang YJ, Lee Y-H, Kim K-H, Moon J-K, Schmutz J, Jackson SA, Bhak J, Lee S-H (2010) Whole-genome sequencing and intensive analysis of the undomesticated soybean (Glycine soja Sieb. and Zucc.) genome. Proc Natl Acad Sci. doi:10.1073/pnas.1009526107
  19. Li JZ, Absher DM, Tang H, Southwick AM, Casto AM, Ramachandran S, Cann HM, Barsh GS, Feldman M, Cavalli-Sforza LL, Myers RM (2008) Worldwide human relationships inferred from genome-wide patterns of variation. Science 319:1100–1104. doi:10.1126/science.1153717 CrossRefPubMedGoogle Scholar
  20. Lijavetzky D, Cabezas JA, Ibanez A, Rodriguez V, Martinez-Zapater JM (2007) High throughput SNP discovery and genotyping in grapevine (Vitis vinifera L.) by combining a re-sequencing approach and SNPlex technology. BMC Genom 8:424CrossRefGoogle Scholar
  21. Lowe KM, Walker MA (2006) Genetic linkage map of the interspecific grape rootstock cross Ramsey (Vitis champinii) × Riparia Gloire (Vitis riparia). Theor Appl Genet 112:1582–1592CrossRefPubMedGoogle Scholar
  22. Mahanil S, Ramming DW, Cadle-Davidson MM, Owens C, Garris AJ, Myles S, Cadle-Davidson L (2012) Development of marker sets useful in the early selection of Ren4 powdery mildew penetration resistance and seedlessness for table and raisin grape breeding. Theor Appl Genet 124:23CrossRefPubMedGoogle Scholar
  23. Marguerit E, Boury C, Manicki A, Donnart M, Butterlin G, Nemorin A, Wiedemann-Merdinoglu S, Merdinoglu D, Ollat N, Decroocq S (2009) Genetic dissection of sex determinism, inflorescence morphology and downy mildew resistance in grapevine. Theor Appl Genet 118:1261–1278CrossRefPubMedGoogle Scholar
  24. Matukumalli LK, Lawley CT, Schnabel RD, Taylor JF, Allan MF, Heaton MP, O’Connell J, Moore SS, Smith TPL, Sonstegard TS, Van Tassell CP (2009) Development and characterization of a high density SNP genotyping assay for cattle. PLoS ONE 4:e5350CrossRefPubMedCentralPubMedGoogle Scholar
  25. Morgante M, Brunner S, Pea G, Fengler K, Zuccolo A, Rafalski A (2005) Gene duplication and exon shuffling by helitron-like transposons generate intraspecies diversity in maize. Nat Genet 37:997–1002Google Scholar
  26. Morrell PL, Buckler ES, Ross-Ibarra J (2012) Crop genomics: advances and applications. Nat Rev Genet 13:85–96Google Scholar
  27. Myles S, Chia J-M, Hurwitz B, Simon C, Zhong GY, Buckler E, Ware D (2010) Rapid genomic characterization of the genus vitis. PLoS ONE 5:e8219CrossRefPubMedCentralPubMedGoogle Scholar
  28. Peiffer DA, Le JM, Steemers FJ, Chang W, Jenniges T, Garcia F, Haden K, Li J, Shaw CA, Belmont J, Cheung SW, Shen RM, Barker DL, Gunderson KL (2006) High-resolution genomic profiling of chromosomal aberrations using infinium whole-genome genotyping. Genome Res 16:1136–1148. doi:10.1101/gr.5402306 CrossRefPubMedCentralPubMedGoogle Scholar
  29. Picq S, Santoni S, Lacombe T, Latreille M, Weber A, Ardisson M, Ivorra S, Maghradze D, Arroyo-Garcia R, Chatelet P, This P, Terral J, Bacilieri R (2014) A small XY chromosomal region explains sex determination in wild dioecious V-vinifera and the reversal to hermaphroditism in domesticated grapevines. BMC Plant Biol 14:229CrossRefPubMedCentralPubMedGoogle Scholar
  30. Rabbee N, Speed TP (2006) A genotype calling algorithm for affymetrix SNP arrays. Bioinformatics 22:7–12. doi:10.1093/bioinformatics/bti741 CrossRefPubMedGoogle Scholar
  31. Ramming DW, Gabler F, Smilanick J, Cadle-Davidson MM, Barba P, Mahanil S, Cadle-Davidson L (2011) A single dominant locus Ren4 confers non-race-specific penetration resistance to grapevine powdery mildew. Phytopathology. doi:10.1094/PHYTO-09-10-0237
  32. Ramos AM, Crooijmans RPMA, Affara NA, Amaral AJ, Archibald AL, Beever JE, Bendixen C, Churcher C, Clark R, Dehais P, Hansen MS, Hedegaard J, Hu Z-L, Kerstens HH, Law AS, Megens H-J, Milan D, Nonneman DJ, Rohrer GA, Rothschild MF, Smith TPL, Schnabel RD, Van Tassell CP, Taylor JF, Wiedmann RT, Schook LB, Groenen MAM (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:e6524CrossRefPubMedCentralPubMedGoogle Scholar
  33. Reisch BI, Owens CL, Cousins PS (2012) Grapes. In: Badenes ML, Byrne DH (eds) Handbook of plant breeding: fruit breeding, vol 8. Springer, USA, pp 225–262. doi:10.1007/978-1-4419-0763-9_7
  34. Riaz S, Krivanek AF, Xu K, Walker MA (2006) Refined mapping of the Pierce’s disease resistance locus, PdR1, and sex on an extended genetic map of Vitis rupestris × V. arizonica. Theor Appl Genet 113:1317–1329. doi:10.1007/s00122-006-0385-0 CrossRefPubMedGoogle Scholar
  35. Springer NM, Ying K, Fu Y, Ji T, Yeh C-T, Jia Y, Wu W, Richmond T, Kitzman J, Rosenbaum H, Iniguez AL, Barbazuk WB, Jeddeloh JA, Nettleton D, Schnable PS (2009) Maize inbreds exhibit high levels of copy number variation (CNV) and presence/absence variation (PAV) in genome content. PLoS Genet 5:e1000734CrossRefPubMedCentralPubMedGoogle Scholar
  36. Stranger BE, Stahl EA, Raj T (2011) Progress and promise of genome-wide association studies for human complex trait genetics. Genetics 187:367–383CrossRefPubMedCentralPubMedGoogle Scholar
  37. Teo YY, Inouye M, Small KS, Gwilliam R, Deloukas P, Kwiatkowski DP, Clark TG (2007) A genotype calling algorithm for the Illumina BeadArray platform. Bioinformatics 23:2741–2746CrossRefPubMedCentralPubMedGoogle Scholar
  38. The French-Italian Public Consortium for Grapevine Genome Characterization (2007) The grapevine genome sequence suggests ancestral hexaploidization in major angiosperm phyla. Nature 449:463–467CrossRefGoogle Scholar
  39. Velasco R, Zharkikh A, Troggio M, Cartwright DA, Cestaro A, Pruss D, Pindo M, Fitzgerald LM, Vezzulli S, Reid J, Malacarne G, Iliev D, Coppola G, Wardell B, Micheletti D, Macalma T, Facci M, Mitchell JT, Perazzolli M, Eldredge G, Gatto P, Oyzerski R, Moretto M, Gutin N, Stefanini M, Chen Y, Segala C, Davenport C, Dematte L, Mraz A, Battilana J, Stormo K, Costa F, Tao Q, Si-Ammour A, Harkins T, Lackey A, Perbost C, Taillon B, Stella A, Solovyev V, Fawcett JA, Sterck L, Vandepoele K, Grando SM, Toppo S, Moser C, Lanchbury J, Bogden R, Skolnick M, Sgaramella V, Bhatnagar SK, Fontana P, Gutin A, Van de Peer Y, Salamini F, Viola R (2007) A high quality draft consensus sequence of the genome of a heterozygous grapevine variety. PLoS ONE 2:e1326CrossRefPubMedCentralPubMedGoogle Scholar
  40. Verde I, Bassil N, Scalabrin S, Gilmore B, Lawley CT, Gasic K, Micheletti D, Rosyara UR, Cattonaro F, Vendramin E, Main D, Aramini V, Blas AL, Mockler TC, Bryant DW, Wilhelm L, Troggio M, Sosinski B, Aranzana MJ, Arús P, Iezzoni A, Morgante M, Peace C (2012) Development and evaluation of a 9 K SNP array for peach by internationally coordinated SNP detection and validation in breeding germplasm. PLoS ONE 7:e35668. doi:10.1371/journal.pone.0035668 CrossRefPubMedCentralPubMedGoogle Scholar
  41. Zhao K, Tung C-W, Eizenga GC, Wright MH, Ali ML, Price AH, Norton GJ, Islam MR, Reynolds A, Mezey J, McClung AM, Bustamante CD, McCouch SR (2011) Genome-wide association mapping reveals a rich genetic architecture of complex traits in Oryza sativa. Nat Commun 2:467CrossRefPubMedCentralPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  • Sean Myles
    • 1
  • Siraprapa Mahanil
    • 2
  • James Harriman
    • 2
    • 3
  • Kyle M. Gardner
    • 1
  • Jeffrey L. Franklin
    • 4
  • Bruce I. Reisch
    • 5
  • David W. Ramming
    • 6
  • Christopher L. Owens
    • 2
  • Lin Li
    • 7
  • Edward S. Buckler
    • 3
  • Lance Cadle-Davidson
    • 2
  1. 1.Department of Plant and Animal Sciences, Faculty of AgricultureDalhousie UniversityTruroCanada
  2. 2.USDA-Agricultural Research Service (ARS) Grape Genetics Research UnitGenevaUSA
  3. 3.Institute for Genomic Diversity, USDA-ARSCornell UniversityIthacaUSA
  4. 4.Agriculture and Agri-Food CanadaKentvilleCanada
  5. 5.Department of Horticulture, New York State Agricultural Experiment StationCornell UniversityGenevaUSA
  6. 6.USDA-ARS San Joaquin Valley Agricultural Sciences CenterParlierUSA
  7. 7.Department of BiostatisticsHarvard UniversityBostonUSA

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