The genus Vitis (the grapevine) is a group of highly diverse, diploid woody perennial vines consisting of approximately 60 species from across the northern hemisphere. It is the world’s most valuable horticultural crop with ~8 million hectares planted, most of which is processed into wine. To gain insights into the use of wild Vitis species during the past century of interspecific grape breeding and to provide a foundation for marker-assisted breeding programmes, we present a principal components analysis (PCA) based ancestry estimation method to calculate admixture proportions of hybrid grapes in the United States Department of Agriculture grape germplasm collection using genome-wide polymorphism data. We find that grape breeders have backcrossed to both the domesticated V. vinifera and wild Vitis species and that reasonably accurate genome-wide ancestry estimation can be performed on interspecific Vitis hybrids using a panel of fewer than 50 ancestry informative markers (AIMs). We compare measures of ancestry informativeness used in selecting SNP panels for two-way admixture estimation, and verify the accuracy of our method on simulated populations of admixed offspring. Our method of ancestry deconvolution provides a first step towards selection at the seed or seedling stage for desirable admixture profiles, which will facilitate marker-assisted breeding that aims to introgress traits from wild Vitis species while retaining the desirable characteristics of elite V. vinifera cultivars.
Citation: Sawler J, Reisch B, Aradhya MK, Prins B, Zhong G-Y, et al. (2013) Genomics Assisted Ancestry Deconvolution in Grape. PLoS ONE 8(11): e80791. doi:10.1371/journal.pone.0080791
Editor: Nicholas A. Tinker, Agriculture and Agri-Food Canada, Canada
Received: July 18, 2013; Accepted: October 7, 2013; Published: November 11, 2013
This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.
Funding: This article was written, in part, thanks to funding from the Canada Research Chairs program and the National Sciences and Engineering Research Council of Canada. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
The genus Vitis (the grapevine) is a group of highly diverse, diploid woody perennial vines consisting of approximately 60 species from across the northern hemisphere . According to the archaeological record, cultivation of the domesticated grapevine, Vitis vinifera, began 6000-8000 years ago in the Near East . Today, the grape is the world’s most valuable horticultural crop with ~8 million hectares planted, most of which is processed into wine (http://faostat.fao.org; Accessed 2013 October 21). Grapes from the domesticated species, V. vinifera, account for more than 95% of the grapes grown worldwide  and the world’s vineyards are dominated by a small number of closely related V. vinifera cultivars that have often been vegetatively propagated for centuries . Because they are perpetually propagated, elite grape cultivars require increasingly intense chemical applications to combat evolving pathogen pressures. It is widely recognized that the exploitation of wild Vitis species’ resistance to disease is crucial to the continued success and expansion of the grape and wine industries, and that the grape is well-poised to benefit from the use of marker-assisted breeding for this purpose [1,4,5].
In plant breeding, marker-assisted backcrossing can be used to incorporate traits into elite cultivars while minimizing the transfer of undesirable alleles from the donor genome . This process involves both foreground and background selection. Foreground selection refers to the screening and selection of offspring based on the presence or absence of a specific allele that is associated with a trait of interest. In contrast, background selection is the selection of offspring on the basis of genomic ancestry estimates. A breeder may wish to introgress a specific trait from a wild species into an elite cultivar, while minimizing the genomic contribution from the wild species unrelated to that trait [4,5]. Recombinant selection (through backcrossing) aims to reduce the size of the chromosomal segment carrying the desired locus. Wild species often possess genes that negatively affect crop performance, making it advantageous to remove any additional background contribution from these wild species to the genomes of the resulting progeny . While backcrossing in many crops is performed by crossing offspring back to one of the parents, “pseudo-backcrossing” is the method used to perform backcrosses in grapes. Pseudo-backcrossing involves crossing hybrid offspring back to a cultivated V. vinifera cultivar that is not one of the parents from the original cross. This form of backcrossing is performed because grapes suffer from severe inbreeding depression and thus crosses between closely related cultivars must be avoided . Background selection relies on accurate estimation of the percentages of the donor and recurrent parental genomes present in the resulting progeny.
To gain insights into the use of wild Vitis species during the past century of interspecific grape breeding and to provide a foundation for background selection in marker-assisted breeding programmes, we present a principal components analysis (PCA) based ancestry estimation method to calculate admixture proportions of hybrid grapes in the US Department of Agriculture (USDA) grape germplasm collection using genome-wide polymorphism data from the Vitis9kSNP microarray . We find that grape breeders have backcrossed to both V. vinifera and wild Vitis species and that reasonably accurate genome-wide ancestry estimation can be achieved in interspecific Vitis hybrids using a panel of fewer than 50 ancestry informative markers (AIMs). Our method of ancestry deconvolution provides a first step towards selection at the seed or seedling stage for desirable admixture profiles, which will facilitate marker-assisted breeding that aims to introgress traits from wild Vitis species while retaining the desirable characteristics of elite V. vinifera cultivars.
Materials and Methods
Sample Collection and Genotype Calling
Leaf tissue was collected from the USDA grape germplasm collections in Davis, California and Geneva, New York. Permission for tissue collection was obtained from the local USDA authority. DNA was extracted using commercial extraction kits. Genotype data were generated from the custom Illumina Vitis9KSNP array, which assays 8,898 single nucleotide polymorphisms (SNPs). After quality filters (GenTrain Score ≥ 0.3 and GenCall ≥ 0.2) 6114 SNPs in 1817 Vitis samples remained for analysis .
Samples with >10% missing data were removed, and SNPs with >10% missing data and minor allele frequency (MAF) <0.10 were removed using PLINK . After these filters, 1599 samples and 2959 SNPs remained. PCA was performed on this data set using SMARTPCA  and 60 samples were removed due to mislabeling. For example, some samples labeled as V. vinifera clustered with wild species and some samples labeled as hybrids clustered with wild or V. vinifera (Figure S1). DNA sample mix-up is an unlikely explanation for these errors because sample processing was done primarily with robotics and no genotype discordance for 145 pairwise comparisons between replicate samples placed randomly across sample plates was observed . Thus, the cases of mislabeling are likely due to curation error. Our ancestry estimates of putatively mislabeled individuals are currently being verified by direct observation in the vineyard and the USDA Germplasm Resources Information Network (GRIN) online database  will be updated accordingly. The data are available from the Dryad Digital Repository: http://dx.doi.org/10.5061/dryad.45hh0.
Our PCA plot of the full data set revealed a clear separation of North American wild species from V. vinifera along the first principal component (PC1; Figure S1). Eurasian wild species fell between these two groups. Although they are occasionally used in grape breeding, we excluded Eurasian wild species and hybrids with known Eurasian wild ancestry from the remaining analysis because the number of samples was low and their position in PC space complicates ancestry estimation. The present study thus focuses on hybrids with ancestry from North American wild Vitis species (hereafter referred to simply as wild Vitis) and V. vinifera.
Principal components were computed using 333 wild Vitis samples and a random sample of 333 V. vinifera samples. Equal sample sizes (N = 333) for ancestral populations were selected as this has been shown to be a crucial factor in accurately inferring genetic relatedness based on PCA . After establishing the PC axes based on these ancestral populations, all 1599 samples were subsequently projected on to these axes, and individuals between -0.02 and 0.02 on PC1 (with the exception of Eurasian wild samples and hybrids with known Eurasian wild ancestry) were considered hybrids for the remainder of the analysis (N = 127 hybrids; Figure 1A). These conservative thresholds were chosen because the projection space between them included all samples labeled as “hybrid” in the USDA Germplasm Resources Information Network database .
(A) PC axis 1 (PC1) and PC2 were calculated using 2959 SNPs from 333 V. vinifera and 333 wild Vitis samples. The proportion of the variance explained by each PC is shown in parentheses along each axis. Subsequently, 1599 samples, including various Vitis species and hybrids, were projected onto these axes (green dots). Samples lying between the dotted vertical lines were considered hybrids for the remaining analyses. (B) Boxplots show the range of PC1 values for the two ancestral populations (V. vinifera and wild Vitis) and the hybrids identified in (A). Boxes denote upper and lower quartiles and whiskers extend to 2.7 SD. Below the boxplots, an illustration of how ancestry proportions are calculated is provided (see Methods for details).
Our method of calculating ancestry coefficients employs the approach described in [11,12], where admixture proportions are equal to the coordinate distance in PC space between the admixed individual (hybrid) and the two ancestral populations (V. vinifera and wild Vitis). For each purported hybrid grape, the genome-wide proportion of V. vinifera is estimated as P = b/(a+b), where b and a are the chord distances from the wild Vitis and V. vinifera centroids, respectively, for the given hybrid along PC1 (Figure 1B). We also estimated ancestry proportions using the model-based software STRUCTURE . STRUCTURE was run with a burn-in period of 20,000 iterations followed by 100,000 iterations using the admixture model where each sample draws some fraction of its genome from each of the K populations where K = 2. As with previous work , our PCA-based ancestry estimates are highly similar to those generated from STRUCTURE (R2 = 0.998; Figure S2).
Measures of Ancestry Informativeness
We ranked 2959 SNPs according to four measures of ancestry informativeness: PC1 Weight, PC1 Positive Weight, FST and a linear model described below. We evaluated the ability of reduced marker sets (1-100 SNPs) based on these measures to predict ancestry relative to the full set of 2959 SNPs. The “classification accuracy” of a set of AIMs is the R2 value generated from a Pearson correlation between the hybrid ancestry estimates based on the reduced marker set and the estimates based on the full set of 2959 SNPs. PC1 Weight is the absolute value of the PC1 loading given to each SNP by SMARTPCA, whereas PC1 Positive Weight excludes any values given a negative loading by the software. FST, a measure of allele frequency difference between the ancestral populations, was calculated according to  for each SNP using allele frequencies output by PLINK. For the Linear Model (LM) measure, linear regression was performed in R  using genome-wide V. vinifera content (estimated from all 2959 markers) as a response variable and genotypes for a given SNP across wild Vitis, V. vinifera and hybrid samples as an explanatory variable (0 = homozygous reference allele; 1 = heterozygous; 2 = homozygous non-reference allele). SNPs were ranked according to their R2 from the linear model as a measure of ancestry informativeness.
Simulations of Admixture
To evaluate the accuracy of our PCA-based ancestry estimation method, in silico crosses between V. vinifera and wild species were simulated in R. Simulated F1 offspring were generated by randomly sampling one of the 333 V. vinifera and one of the 333 wild Vitis as parents. Parental genotypes were combined to produce offspring genotypes by sampling one allele at random from each parent at each SNP. Linkage disequilibrium between SNPs was ignored. This procedure was repeated 10,000 times to generate 10,000 F1 offspring. To generate simulated F2 populations this process was repeated, using the F1 individuals as one ancestral population and either wild or V.vinifera accessions as the other to simulate backcrossing (n = 10000 for F1 backcrossed to wild, and n = 10000 for F1 backcrossed to V. vinifera).
Results and Discussion
PCA-based ancestry estimation
PCA is a useful tool for revealing patterns of population structure and relatedness among samples for which genome-wide SNP data are available [17-19]. A genotyping microarray for the grape, the Vitis9KSNP array, was recently developed with probes designed for SNPs segregating within the domesticated species, V. vinifera, and a small number of probes designed to assay variation among Vitis species . When PCA is applied to Vitis9KSNP array data from a diverse collection of V. vinifera cultivars, wild Vitis species and hybrid cultivars, PC1 clearly separates wild Vitis species from V. vinifera while hybrid cultivars lie between these two groups (Figure 1A, S1). This observation motivated us to apply methods developed previously [11,12] to use a hybrid cultivar’s projected position along PC1 to estimate the proportion of its ancestry derived from V. vinifera and wild Vitis species (Figure 1B). Our PCA-based method provides highly similar ancestry estimates to those generated from the model-based approach in STRUCTURE (Figure S2).
For the present study, we considered a sample a “hybrid” if its projected position along PC1 was between -0.02 and 0.02. After removing obvious errors (see Methods), all samples labeled as “hybrid” in the USDA Germplasm Resources Information Network (GRIN) online database  fall within this range, but many samples labeled as either wild Vitis species or V. vinifera fall within this range as well (Figure S1). Our ancestry estimates are therefore being used to improve the accuracy of the ancestry assignments associated with each accession in the USDA grape germplasm collection. We acknowledge, however, that samples outside of our defined “hybrid” range may in fact represent hybrid samples that resulted from extensive backcrossing to either V. vinifera or wild Vitis species. Further studies will be required to verify the ancestry of such hybrid samples and distinguish them unequivocally from the ancestral groups.
Verification of ancestry estimation method
To verify the accuracy of our PCA-based ancestry estimation method, we simulated F1 (V. vinifera x wild Vitis) and F2 hybrids (F1 simulated hybrids backcrossed to V. vinifera or wild Vitis) using real genotype calls from the ancestral populations. The PCA plot of the simulated progeny and ancestral populations is shown in Figure 2A. The mean estimated genome-wide proportion of V. vinifera in the simulated F1 hybrids was 0.499, 95% CI [0.460, 0.537]. We expect the proportion of V. vinifera in these individuals to be 0.5, with the remainder of the genome (0.5) being contributed from the wild Vitis population. For offspring of the simulated F1 x V. vinifera cross we estimate the mean genome-wide proportion of V. vinifera at 0.743, 95% CI [0.699, 0.782], with an expected value of 0.75. For offspring of the simulated F1 x wild Vitis cross, we estimate the mean genome-wide proportion of V. vinifera at 0.257, 95% CI [0.222, 0.294], with an expected value of 0.25. Distributions of the estimated V. vinifera genomic content of the three simulated crosses using PCA-based ancestry estimation are shown in Figure 2B. These results demonstrate that our PCA-based method provides reasonably accurate ancestry estimates for F1 and F2 backcrossed hybrid grape cultivars generated from a highly diverse collection of grape germplasm.
(A) 10,000 F1 hybrids (green) were generated by simulating V. vinifera x wild Vitis crosses. Using the simulated genotype data, these hybrid samples were then projected onto the PC axes defined by the 333 V. vinifera (red) and 333 wild Vitis samples (blue) and the proportion of each F1 hybrid’s ancestry derived from each ancestral population was estimated using our PCA-based approach. The same method was applied to F2 populations derived from backcrossing F1 hybrids to V. vinifera (pink) and backcrossing F1 hybrids to wild Vitis (light blue). (B) The distribution of V. vinifera ancestry proportions for the F1 and F2 populations.
Grape ancestry estimation
Genome-wide V. vinifera content was estimated for the 127 samples identified as being hybrids with V. vinifera and wild Vitis ancestry (Figure 3; Table S1). The mean proportion of V. vinifera content for these hybrids is 0.474 (Min: 0.2506, Max: 0.7508). The range of observed admixture estimates in hybrid grapes suggests that backcrossing to both wild Vitis and V. vinifera has occurred in the past century of interspecific grape breeding. The relatively large number of samples with approximately 50% V. vinifera contribution to their genome suggests the existence of many first-generation interspecific hybrids in the USDA grape collection. An F1 hybrid included in this analysis, Baco Noir, is a cross between Folle Blanche (V. vinifera) and an accession of Vitis riparia (North American wild Vitis species). Based on its pedigree, we expect ancestry proportions of 50% wild and 50% V. vinifera, and our method using the full 2959 SNPs provides estimates of 49% and 51%, respectively. The cultivar Alicante Ganzin is the result of a cross between Alicante Bouschet (V. vinifera) and Ganzin No. 4. Ganzin No. 4 is an F1 hybrid between V. rupestris (North American wild Vitis) and Aramon Noir (V. vinifera). This pseudo-backcross pedigree suggests the genome-wide V. vinifera content for Alicante Ganzin should be 75%. Our method provides an estimate of 75.1% V. vinifera for this sample (Table S1).
The distribution of V. vinifera ancestry proportions in 127 hybrids from the USDA germplasm repository. Estimates are based on the full set of 2959 SNPs. A table of cultivar names, information and proportion V. vinifera ancestry is provided in Table S1.
According to the distribution in Figure 3, it appears that, unlike breeders of many other crops, grape breeders have not explicitly aimed to introgress specific genetic loci from wild Vitis species by repeatedly backcrossing to the domesticated species, V. vinifera. In fact, the large number of cultivars with low % V. vinifera ancestry suggests that backcrossing to wild Vitis may have been more frequent than backcrossing to V. vinifera. However, hybrids with V. vinifera content outside a particular range are not included in this analysis due to thresholds established in PC space for hybrid classification (see Methods). If breeders have historically aimed to minimize wild Vitis content in hybrid grapes by backcrossing extensively to V. vinifera, it is possible that commercially successful hybrid cultivars fall outside our established thresholds and thus may be underrepresented here. In addition, the sample of hybrids from the USDA collection genotyped in the present study may not be representative of interspecific grape breeding in general. Thus, ancestry estimation of a large sample of hybrid grape cultivars from breeding programmes worldwide is currently underway to verify this claim.
When PCA is applied to the genotypes generated from the Vitis9KSNP array, there is a clear distinction between V. vinifera and wild North American Vitis species along the first principal component (Figure 1A, Figure S1). The genotype data are also sufficient to enable the various wild species to be distinguished from each other. For example, wild Vitis samples clearly cluster by species along PC2 (Figure S1). This suggests that our present method could be extended to identify the precise wild Vitis species that has contributed to a hybrid’s ancestry: a hybrid’s position on PC2 is likely an indicator of the wild Vitis species that has contributed to its ancestry. However, many hybrid grape cultivars have complex pedigrees with genetic contributions from multiple wild Vitis species. For example, the hybrid Brianna derives its ancestry from seven different wild Vitis species and V. vinifera . Extensions of the present method beyond a simple 2-way admixture model and higher density genotype data will be required to generate accurate estimates of the genetic contributions of each individual wild Vitis species in complex hybrids. A high-density set of SNPs for this purpose could be generated using a genotyping by sequencing (GBS) approach .
Selection of Ancestry Informative Markers (AIMs)
To enable ancestry estimation in grapes not included in this study, we investigated several methods for identifying a small number of SNPs, or ancestry informative markers (AIMs), that most effectively capture the ancestry information contained within the full set of 2959 SNPs. In admixed populations, an ideal AIM should have alleles that are fixed between the two ancestral populations and thus have an FST = 1.0 . In addition, PCA generates weights for each SNP indicating the degree to which a SNP contributes to each PC. SNPs with extreme PC1 weights differentiate V. vinifera from wild Vitis along PC1 and are thus also good candidate AIMs . We find that Fst values and PC1 weights are highly correlated (R2 = 0.979; Figure S3) and that both metrics are useful for the selection of AIMs (Figure 4A). This relationship between Fst and the first principal component has been previously described in .
(A) Each measure used to rank AIMs is shown in the legend. For each measure, the proportion of V. vinifera ancestry across the 127 hybrids was estimated using 1-100 SNPs ranked according to that measure and the result was compared to the proportion of V. vinifera ancestry estimated from the full set of 2959 SNPs. The classification accuracy (Y axis) is the squared Pearson correlation coefficient (R2) between the estimate derived from the reduced set of AIMs and the estimate from the full set of SNPs. (B) A Venn diagram showing the overlap of the top 100 AIMs identified using each of the measures.
We reasoned that the effectiveness of an AIM should not only depend on its frequency difference between the ancestral populations, but also on the extent to which the segregation pattern of its alleles in the hybrid population correlates with the ancestry of the hybrids. Thus, we developed a linear model of informativeness (LM; see Methods) and found that it outperformed both Fst and PC1 weights when fewer than 20 SNPs are used, but failed to improve when additional SNPs were added (Figure 4A).
The PC1 weight of a SNP can be interpreted as a scaled regression coefficient that takes on negative values when PC1 values are negatively correlated with genotypes and positive values when this correlation is positive. The sign of the weight depends on how the genotypes are encoded in the input file. For the present study, genotypes were encoded as follows: 0 = homozygous reference allele; 1 = heterozygous; 2 = homozygous alternative allele. Because the grape reference genome is V. vinifera , reference alleles are more frequent in the V. vinifera samples which occupy the lower values along PC1 (Figure 1). This results in a PC1 weight distribution that is highly skewed towards positive values (Figure S4). Thus, SNPs with extreme negative PC1 weights, where V. vinifera are homozygous for the alternative allele and wild Vitis species are homozygous for the reference allele, are rare. We reasoned that most of the useful ancestry information would therefore be contained within the positive PC1 weights. We therefore not only tested the ancestry informativeness of markers based on the absolute value of the PC1 weights as is normally done [8,11,25], but also ranked SNPs by their positive PC1 weights only. We found that ignoring the negative weights and only considering the positive weights significantly reduced the number of AIMs required to accurately infer ancestry (Figure 4A). This observation should serve as a cautionary note to future uses of PC1 weights for the purposes of AIM identification.
The physical coordinates of the AIMs identified in the present study can be found in Table S2. Each of the four measures we used to rank SNPs by ancestry informativeness resulted in a different set of AIMs. The overlap in the top 100 AIMs identified by each measure is shown as a Venn diagram in Figure 4B. Within the four sets of 100 markers, 55 and 41 SNPs were unique to LM and PC1 Positive Weight, respectively. The PC1 Weight and FST panels had 93 SNPs in common. Thus, the selection of AIMs on the basis of PC1 weight and FST produce highly similar marker panels, however additional informative markers are overlooked if other measures are not taken into consideration. Although each measure is useful in identifying a set of AIMs, there is clearly a need for a method that can conclusively identify the optimal set of AIMs that maximizes ancestry informativeness.
Over the past century, grape breeders have generated interspecific hybrid grapes by crossing cultivars of the cultivated V. vinifera species with numerous wild Vitis species. Our PCA-based ancestry estimates of 127 hybrid cultivars indicate that F1 hybrids (V. vinifera x wild Vitis) are common and that backcrossing to wild Vitis was equally or even more frequent than backcrossing to V. vinifera. However, estimates from a representative sample of commercial hybrids with known pedigrees are required to verify this claim. Our method provides a framework for enabling marker-assisted breeding of seedling populations based on ancestry estimates, but the application of such background selection in bi-parental populations will require higher marker densities than those provided by the Vitis9KSNP array.
We identify sets of AIMs and demonstrate that genotypes from only ~50 SNPs are sufficient to accurately estimate the proportion of ancestry a hybrid grape derives from V. vinifera and wild Vitis species. Not only can the AIMs identified here be employed to curate germplasm collections, but they can also be used for forensic purposes. Regulatory and appellation systems around the world like the AOC (France), DOC (Italy), QmP (Germany) and VQA (Canada) exist to verify and guarantee the authenticity of the origin of their wines. Often, these systems only approve the use of cultivars with 100% V. vinifera ancestry, yet the ancestry inferences they employ are often based on questionable morphological analyses, error-prone breeding records or pure conjecture. Although it is widely recognized by the scientific community that the restriction of cultivar use by these organizations poses a serious threat to the future of the wine and grape industry , the set of AIMs and the method presented here provide a robust forensic tool that can be used to definitively verify the ancestry criteria these regulatory agencies attempt to apply.
PCA of 1599 samples from USDA grape germplasm collection. (A) PC axis 1 (PC1) and PC2 were calculated using 2959 SNPs from 333 V. vinifera and 333 wild Vitis samples. The proportion of the variance explained by each PC is shown in parentheses along each axis. Subsequently, 1599 samples, including various Vitis species and hybrids, were projected onto these axes. This is the same plot as Figure 1 in the main manuscript, but each sample is labeled with the species identifier associated with that sample. Species identifiers were obtained from the Germplasm Resources Information Network (GRIN) database managed by the USDA. It is evident that many samples are mislabeled. For example, some samples labeled as V. vinifera clearly cluster far to the right of PC1 with the wild species. In cases where there was an obvious error and it interfered with downstream analyses, the samples were removed from analysis (N = 60). Eurasian wild Vitis samples and hybrids with known ancestry from Eurasian wild species were removed from the analysis. See Materials and Methods on how we defined “hybrid” for the present study.
A comparison of ancestry estimates derived from our PCA-based method and the model-based method STRUCTURE. The proportion V. vinifera ancestry estimated using the PCA-based method and the programme STRUCTURE are shown on the X and Y axes, respectively, for the 127 hybrid samples analysed in the present study.
(A) FST and the absolute value of PC1 weights are highly correlated. (B) The classification accuracy of AIMs ranked by Fst and PC1 absolute weight are highly similar.
The distribution of PC1 weights from running SMARTPCA on 333 V. vinifera and 333 wild Vitis samples. The distribution is skewed towards positive values.
A list of the 127 accessions from the USDA grape germplasm collection considered “hybrid” in the current study based on their positions along PC1 and associated cultivar name for these accessions from the USDA Germplasm Resources Information Network (GRIN) database.
A list of the AIMs identified in the present study. The top 100 AIMs are listed for each of the four measures used in the present study. The AIMs are ranked according to the measure listed at the top of each column. The name of each SNP contains the physical coordinates of the SNP according to the 8x Pinot Noir reference genome, where the chromosome name is separated by the physical position by a colon. Chromosome numbers outside of the range of 1-19 refer to the unanchored contigs found in the 8x Pinot Noir reference genome.
We thank the USDA staff that maintains the USDA grape germplasm collection. We thank Kyle Gardner, Andrew Jamieson, Kasia Bryc, Adam Boyko and Carlos D. Bustamante for useful discussions.
Conceived and designed the experiments: JS SM. Performed the experiments: JS SM. Analyzed the data: JS SM. Contributed reagents/materials/analysis tools: BR MA BP GYZ HS CS EB. Wrote the manuscript: JS SM.
- 1. Reisch B, Owens C, Cousins P (2012) Grape. In: ML BadenesDH Byrne. Fruit Breeding. US: Springer Verlag. pp. 225-262.
- 2. McGovern PE (2003) Ancient Wine: The Search for the Origins of Viniculture. Princeton, NJ: Princeton University Press.
- 3. Myles S, Boyko AR, Owens CL, Brown PJ, Grassi F et al. (2011) Genetic structure and domestication history of the grape. Proc Natl Acad Sci USA 108: 3530-3535. doi:10.1073/pnas.1009363108. PubMed: 21245334.
- 4. Myles S (2013) Improving fruit and wine: what does genomics have to offer? Trends Genet 29: 190-196. doi:10.1016/j.tig.2013.01.006. PubMed: 23428114.
- 5. Di Gaspero G, Cattonaro F (2010) Application of genomics to grapevine improvement. Aust J Grape Wine Res 16: 122-130. doi:10.1111/j.1755-0238.2009.00072.x.
- 6. Collard BCY, Mackill DJ (2008) Marker-assisted selection: an approach for precision plant breeding in the twenty-first century. Philos Trans R Soc Lond B: Biol Sci 363: 557-572. doi:10.1098/rstb.2007.2170. PubMed: 17715053.
- 7. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MAR et al. (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. PubMed: 17701901.
- 8. Patterson N, Price AL, Reich D (2006) Population Structure and Eigenanalysis. PLOS Genet 2: e190. doi:10.1371/journal.pgen.0020190. PubMed: 17194218.
- 9. USDA (2013)ARS. National Genetic Resources Program, Germplasm Resources Information Network; —— (GRIN). Retrieved onpublished at whilst December year 1111 from . http://www.ars-grin.gov/cgi-bin/npgs/html/index. pl. Accessed 2013 October 21.
- 10. McVean G (2009) A Genealogical Interpretation of Principal Components Analysis. PLOS Genet 5: e1000686. doi: 10.1371/journal.pgen.1000686
- 11. Paschou P, Ziv E, Burchard EG, Choudhry S, Rodriguez-Cintron W et al. (2007) PCA-Correlated SNPs for Structure Identification in Worldwide Human Populations. PLOS Genet 3: e160. doi:10.1371/journal.pgen.0030160. PubMed: 17892327.
- 12. Bryc K, Auton A, Nelson MR, Oksenberg JR, Hauser SL et al. (2009) Genome-wide patterns of population structure and admixture in West Africans and African Americans. Proc Natl Acad Sci USA 107: 786-791. PubMed: 20080753.
- 13. Pritchard JK, Stephens M, Donnelly P (2000) Inference of Population Structure Using Multilocus Genotype Data. Genetics 155: 945-959. PubMed: 10835412.
- 14. Ma J, Amos CI (2012) Principal Components Analysis of Population Admixture. PLOS ONE 7: e40115. doi:10.1371/journal.pone.0040115. PubMed: 22808102.
- 15. Weir BS, Cockerham CC (1984) Estimating F-statistics for the analysis of population structure. Evolution 38: 1358-1370. doi:10.2307/2408641.
- 16. R Development Core Team (2008) R: A language and environment for statistical computing. Retrieved onpublished at whilst December year 1111 from http://www.R-project.org. Vienna, Austria: Accessed. p. 2013. Accessed October 21.
- 17. Bovine HapMap Consortium, Gibbs RA, Taylor JF, Van Tassell CP, Barendse W, Eversole KA, et al. (2009) Genome-Wide Survey of SNP Variation Uncovers the Genetic Structure of Cattle Breeds. Science 324: 528-532. doi:10.1126/science.1167936. PubMed: 19390050.
- 18. Boyko AR, Boyko RH, Boyko CM, Parker HG, Castelhano M et al. (2009) Complex population structure in African village dogs and its implications for inferring dog domestication history. Proc Natl Acad Sci USA 106: 13903-13908. doi:10.1073/pnas.0902129106. PubMed: 19666600.
- 19. Novembre J, Johnson T, Bryc K, Kutalik Z, Boyko AR et al. (2008) Genes mirror geography within Europe. Nature 456: 98-101. doi:10.1038/nature07331. PubMed: 18758442.
- 20. Myles S, Chia J-M, Hurwitz B, Simon C, Zhong GY et al. (2010) Rapid Genomic Characterization of the Genus Vitis. PLOS ONE 5: e8219. doi:10.1371/journal.pone.0008219. PubMed: 20084295.
- 21. Robinson J, Harding J, Vouillamoz JF (2012) Wine Grapes: A complete guide to 1368 vine varieties, including their origins and flavours. New York: HarperCollins.
- 22. Elshire RJ, Glaubitz JC, Sun Q, Poland JA, Kawamoto K et al. (2011) A Robust, Simple Genotyping-by-Sequencing (GBS) Approach for High Diversity Species. PLOS ONE 6: e19379. doi:10.1371/journal.pone.0019379. PubMed: 21573248.
- 23. Ding L, Wiener H, Abebe T, Altaye M, Go RC et al. (2011) Comparison of measures of marker informativeness for ancestry and admixture mapping. BMC Genomics 12: 622. doi:10.1186/1471-2164-12-622. PubMed: 22185208.
- 24. the French-Italian Public Consortium for Grapevine Genome Characterization (2007) The grapevine genome sequence suggests ancestral hexaploidization in major angiosperm phyla. Nature 449: 463-467. doi:10.1038/nature06148. PubMed: 17721507.
- 25. Paschou P, Drineas P, Lewis J, Nievergelt CM, Nickerson DA et al. (2008) Tracing Sub-Structure in the European American Population with PCA-Informative Markers. PLOS Genet 4: e1000114. PubMed: 18797516.