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


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.


Vitis Grapevine SNP discovery Genotyping microarray 



We thank Jeremiah Degenhardt and Carlos D. Bustamante for useful discussion. This research was undertaken thanks to funding from the USDA-Agricultural Research Service, the National Science and Engineering Research Council (NSERC) of Canada and the Canada Research Chairs program. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Ethical standards

The research presented here did not involve the use of human participants or animal.

Conflict of interest

The authors declare no conflict of interests.

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)


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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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