Unphased Genotype Data Research Articles

Genotype error biases trio-based estimates of haplotype phase accuracy

Haplotypes can be estimated from unphased genotype data via statistical methods. When parent-offspring trios are available for inferring the true phase from Mendelian inheritance rules, the accuracy of statistical phasing is usually measured by the switch error rate, which is the proportion of pairs of consecutive heterozygotes that are incorrectly phased. We present a method for estimating the genotype error rate from parent-offspring trios and a method for estimating the bias that occurs in the observed switch error rate as a result of genotype error. We apply these methods to 485,301 genotyped UK Biobank samples that include 898 White British trios and to 38,387 sequenced TOPMed samples that include 217 African Caribbean trios and 669 European American trios. We show that genotype error inflates the observed switch error rate and that the relative bias increases with sample size. For the UK Biobank WhiteBritish trios, the observed switch error rate in the trio offspring is 2.4 times larger than the estimated true switch error rate (1.4×10-3 vs 5.8×10-4. We propose an alternate definition of phase error that counts two consecutive switch errors as a single error because back-to-back switch errors arise when a single heterozygote is incorrectly phased with respect to the surrounding heterozygotes. With this definition, we estimate that the average distance between phase errors is 64 megabases in the UK Biobank White British individuals.

Comparison of linkage disequilibrium estimated from genotypes versus haplotypes for crossbred populations

BackgroundLinkage disequilibrium (LD) is commonly measured based on the squared coefficient of correlation left({r}^{2}right) between the alleles at two loci that are carried by haplotypes. LD can also be estimated as the {r}^{2} between unphased genotype dosage at two loci when the allele frequencies and inbreeding coefficients at both loci are identical for the parental lines. Here, we investigated whether {r}^{2} for a crossbred population (F1) can be estimated using genotype data. The parental lines of the crossbred (F1) can be purebred or crossbred.MethodsWe approached this by first showing that inbreeding coefficients for an F1 crossbred population are negative, and typically differ in size between loci. Then, we proved that the expected {r}^{2} computed from unphased genotype data is expected to be identical to the {r}^{2} computed from haplotype data for an F1 crossbred population, regardless of the inbreeding coefficients at the two loci. Finally, we investigated the bias and precision of the {r}^{2} estimated using unphased genotype versus haplotype data in stochastic simulation.ResultsOur findings show that estimates of {r}^{2} based on haplotype and unphased genotype data are both unbiased for different combinations of allele frequencies, sample sizes (900, 1800, and 2700), and levels of LD. In general, for any allele frequency combination and {r}^{2} value scenarios considered, and for both methods to estimate {r}^{2}, the precision of the estimates increased, and the bias of the estimates decreased as sample size increased, indicating that both estimators are consistent. For a given scenario, the {r}^{2} estimates using haplotype data were more precise and less biased using haplotype data than using unphased genotype data. As sample size increased, the difference in precision and biasedness between the {r}^{2} estimates using haplotype data and unphased genotype data decreased.ConclusionsOur theoretical derivations showed that estimates of LD between loci based on unphased genotypes and haplotypes in F1 crossbreds have identical expectations. Based on our simulation results, we conclude that the LD for an F1 crossbred population can be accurately estimated from unphased genotype data. The results also apply for other crosses (F2, F3, Fn, BC1, BC2, and BCn), as long as (selected) individuals from the two parental lines mate randomly.

A fast data-driven method for genotype imputation, phasing and local ancestry inference: MendelImpute.jl.

Current methods for genotype imputation and phasing exploit the volume of data in haplotype reference panels and rely on hidden Markov models (HMMs). Existing programs all have essentially the same imputation accuracy, are computationally intensive and generally require prephasing the typed markers. We introduce a novel data-mining method for genotype imputation and phasing that substitutes highly efficient linear algebra routines for HMM calculations. This strategy, embodied in our Julia program MendelImpute.jl, avoids explicit assumptions about recombination and population structure while delivering similar prediction accuracy, better memory usage and an order of magnitude or better run-times compared to the fastest competing method. MendelImpute operates on both dosage data and unphased genotype data and simultaneously imputes missing genotypes and phase at both the typed and untyped SNPs (single nucleotide polymorphisms). Finally, MendelImpute naturally extends to global and local ancestry estimation and lends itself to new strategies for data compression and hence faster data transport and sharing. Software, documentation and scripts to reproduce our results are available from https://github.com/OpenMendel/MendelImpute.jl. Supplementary data are available at Bioinformatics online.

Sampletrees and Rsampletrees: sampling gene genealogies conditional on SNP genotype data.

: The program sampletrees is a Markov chain Monte Carlo sampler of gene genealogies conditional on either phased or unphased SNP genotype data. The companion program Rsampletrees is for pre- and post-processing of sampletrees files, including setting up the files for sampletrees and storing and plotting the output of a sampletrees run. sampletrees is implemented in C ++. The source code, documentation and test files are available at http://stat.sfu.ca/statgen/research/sampletrees.html The R package Rsampletrees is available on CRAN http://cran.r-project.org/web/packages/Rsampletrees/index.html : kburkett@uottawa.ca Supplementary data are available at Bioinformatics online.

LinkImpute: Fast and Accurate Genotype Imputation for Nonmodel Organisms.

Obtaining genome-wide genotype data from a set of individuals is the first step in many genomic studies, including genome-wide association and genomic selection. All genotyping methods suffer from some level of missing data, and genotype imputation can be used to fill in the missing data and improve the power of downstream analyses. Model organisms like human and cattle benefit from high-quality reference genomes and panels of reference genotypes that aid in imputation accuracy. In nonmodel organisms, however, genetic and physical maps often are either of poor quality or are completely absent, and there are no panels of reference genotypes available. There is therefore a need for imputation methods designed specifically for nonmodel organisms in which genomic resources are poorly developed and marker order is unreliable or unknown. Here we introduce LinkImpute, a software package based on a k-nearest neighbor genotype imputation method, LD-kNNi, which is designed for unordered markers. No physical or genetic maps are required, and it is designed to work on unphased genotype data from heterozygous species. It exploits the fact that markers useful for imputation often are not physically close to the missing genotype but rather distributed throughout the genome. Using genotyping-by-sequencing data from diverse and heterozygous accessions of apples, grapes, and maize, we compare LD-kNNi with several genotype imputation methods and show that LD-kNNi is fast, comparable in accuracy to the best-existing methods, and exhibits the least bias in allele frequency estimates.

β2-Adrenergic receptor promoter haplotype influences the severity of acute viral respiratory tract infection during infancy: a prospective cohort study.

BackgroundDespite the significant interest in β2-Adrenergic receptor (ADRB2) polymorphisms related to asthma, whether ADRB2 genetic variants are similarly associated with acute respiratory tract infections have not been studied. We hypothesized that genetic variants in ADRB2 associated with a response to asthma therapy during an asthma exacerbation were also associated with severity of acute respiratory tract infections.MethodsTo test this hypothesis, we genotyped 5 common polymorphisms in the promoter region and coding block of the ADRB2 gene (loci -2387, -2274, -1343, +46, and +79) from 374 Caucasian and African American term infants who were enrolled at the time of acute respiratory illness over four respiratory viral seasons. Severity of respiratory tract infections was measured using a bronchiolitis severity score (BSS; range = 0-12, clinically significant difference = 0.5) with a higher score indicating more severe disease. We assigned the promoter, coding and combined promoter and coding haplotypes to the unphased genotype data. The associations between each of these five single-nucleotide polymorphisms (SNPs) as well as the haplotypes and infant BSS were analyzed using nonparametric univariate analysis and multivariable proportional odds model separately in Caucasians and African Americans.ResultsThere was no significant association between infant BSS and each of the SNPs in both Caucasians and African Americans. However, promoter haplotype CCA was associated with a decreased BSS in African Americans in a dose dependent manner. The median (interquartile range) BSS of infants with no copies of the CCA haplotype, one copy, and two copies of the CCA haplotype were 5.5 (2.0, 8.0), 4.0 (1.0, 7.5), and 3.0 (1.0, 4.0), respectively. This dose dependent relationship persisted after adjusting for infant age, gender, daycare exposure, secondhand smoke exposure, prior history of breastfeeding, siblings at home, and enrollment season (adjusted odds ratio: 0.59, 95 % confidence interval: 0.36, 0.98). There was no similar protective relationship of haplotype CCA on severity of respiratory tract infections identified in Caucasians.ConclusionsADRB2 genotype may be predictive of severity of acute respiratory tract infections in African Americans, and potentially identify a subset of infants who may respond to beta-agonist therapy.Electronic supplementary materialThe online version of this article (doi:10.1186/s12881-015-0229-3) contains supplementary material, which is available to authorized users.

High-accuracy haplotype imputation using unphased genotype data as the references

Enormously growing genomic datasets present a new challenge on missing data imputation, a notoriously resource-demanding task. Haplotype imputation requires ethnicity-matched references. However, to date, haplotype references are not available for the majority of populations in the world. We explored to use existing unphased genotype datasets as references; if it succeeds, it will cover almost all of the populations in the world. The results showed that our HiFi software successfully yields 99.43% accuracy with unphased genotype references. Our method provides a cost-effective solution to breakthrough the bottleneck of limited reference availability for haplotype imputation in the big data era.

InPHAP: interactive visualization of genotype and phased haplotype data.

BackgroundTo understand individual genomes it is necessary to look at the variations that lead to changes in phenotype and possibly to disease. However, genotype information alone is often not sufficient and additional knowledge regarding the phase of the variation is needed to make correct interpretations. Interactive visualizations, that allow the user to explore the data in various ways, can be of great assistance in the process of making well informed decisions. But, currently there is a lack for visualizations that are able to deal with phased haplotype data.ResultsWe present inPHAP, an interactive visualization tool for genotype and phased haplotype data. inPHAP features a variety of interaction possibilities such as zooming, sorting, filtering and aggregation of rows in order to explore patterns hidden in large genetic data sets. As a proof of concept, we apply inPHAP to the phased haplotype data set of Phase 1 of the 1000 Genomes Project. Thereby, inPHAP’s ability to show genetic variations on the population as well as on the individuals level is demonstrated for several disease related loci.ConclusionsAs of today, inPHAP is the only visual analytical tool that allows the user to explore unphased and phased haplotype data interactively. Due to its highly scalable design, inPHAP can be applied to large datasets with up to 100 GB of data, enabling users to visualize even large scale input data. inPHAP closes the gap between common visualization tools for unphased genotype data and introduces several new features, such as the visualization of phased data. inPHAP is available for download at http://bit.ly/1iJgKmX.

An Effective Filter for IBD Detection in Large Data Sets

Identity by descent (IBD) inference is the task of computationally detecting genomic segments that are shared between individuals by means of common familial descent. Accurate IBD detection plays an important role in various genomic studies, ranging from mapping disease genes to exploring ancient population histories. The majority of recent work in the field has focused on improving the accuracy of inference, targeting shorter genomic segments that originate from a more ancient common ancestor. The accuracy of these methods, however, is achieved at the expense of high computational cost, resulting in a prohibitively long running time when applied to large cohorts. To enable the study of large cohorts, we introduce SpeeDB, a method that facilitates fast IBD detection in large unphased genotype data sets. Given a target individual and a database of individuals that potentially share IBD segments with the target, SpeeDB applies an efficient opposite-homozygous filter, which excludes chromosomal segments from the database that are highly unlikely to be IBD with the corresponding segments from the target individual. The remaining segments can then be evaluated by any IBD detection method of choice. When examining simulated individuals sharing 4 cM IBD regions, SpeeDB filtered out 99.5% of genomic regions from consideration while retaining 99% of the true IBD segments. Applying the SpeeDB filter prior to detecting IBD in simulated fourth cousins resulted in an overall running time that was 10,000x faster than inferring IBD without the filter and retained 99% of the true IBD segments in the output.

Interpreting Whole-Genome Marker Data

The challenges of whole-genome data, when genotypes are available from hundreds of thousands of genetic markers, are explored for four topics in statistical genetics: Hardy-Weinberg testing, estimating linkage disequilibrium from unphased genotypic data, association mapping and characterizing population structure.

WHOLE GENOME IDENTITY-BY-DESCENT DETERMINATION

High-throughput single nucleotide polymorphism genotyping assays conveniently produce genotype data for genome-wide genetic linkage and association studies. For pedigree datasets, the unphased genotype data is used to infer the haplotypes for individuals, according to Mendelian inheritance rules. Linkage studies can then locate putative chromosomal regions based on the haplotype allele sharing among the pedigree members and their disease status. Most existing haplotyping programs require rather strict pedigree structures and return a single inferred solution for downstream analysis. In this research, we relax the pedigree structure to contain ungenotyped founders and present a cubic time whole genome haplotyping algorithm to minimize the number of zero-recombination haplotype blocks. With or without explicitly enumerating all the haplotyping solutions, the algorithm determines all distinct haplotype allele identity-by-descent (IBD) sharings among the pedigree members, in linear time in the total number of haplotyping solutions. Our algorithm is implemented as a computer program iBDD. Extensive simulation experiments using 2 sets of 16 pedigree structures from previous studies showed that, in general, there are trillions of haplotyping solutions, but only up to a few thousand distinct haplotype allele IBD sharings. iBDD is able to return all these sharings for downstream genome-wide linkage and association studies.

Markov chain Monte Carlo sampling of gene genealogies conditional on unphased SNP genotype data

The gene genealogy is a tree describing the ancestral relationships among genes sampled from unrelated individuals. Knowledge of the tree is useful for inference of population-genetic parameters and has potential application in gene-mapping. Markov chain Monte Carlo approaches that sample genealogies conditional on observed genetic data typically assume that haplotype data are observed even though commonly-used genotyping technologies provide only unphased genotype data. We have extended our haplotype-based genealogy sampler, sampletrees, to handle unphased genotype data. We use the sampled haplotype configurations as a diagnostic for adequate sampling of the tree space based on the reasoning that if haplotype sampling is restricted, sampling from the tree space will also be restricted. We compare the distributions of sampled haplotypes across multiple runs of sampletrees, and to those estimated by the phase inference program, PHASE. Performance was excellent for the majority of individuals as shown by the consistency of results across multiple runs. However, for some individuals in some datasets, sampletrees had problems sampling haplotype configurations; longer run lengths would be required for these datasets. For many datasets though, we expect that sampletrees will be useful for sampling from the posterior distribution of gene genealogies given unphased genotype data.

Investigation of Inversion Polymorphisms in the Human Genome Using Principal Components Analysis

Despite the significant advances made over the last few years in mapping inversions with the advent of paired-end sequencing approaches, our understanding of the prevalence and spectrum of inversions in the human genome has lagged behind other types of structural variants, mainly due to the lack of a cost-efficient method applicable to large-scale samples. We propose a novel method based on principal components analysis (PCA) to characterize inversion polymorphisms using high-density SNP genotype data. Our method applies to non-recurrent inversions for which recombination between the inverted and non-inverted segments in inversion heterozygotes is suppressed due to the loss of unbalanced gametes. Inside such an inversion region, an effect similar to population substructure is thus created: two distinct “populations” of inversion homozygotes of different orientations and their 1∶1 admixture, namely the inversion heterozygotes. This kind of substructure can be readily detected by performing PCA locally in the inversion regions. Using simulations, we demonstrated that the proposed method can be used to detect and genotype inversion polymorphisms using unphased genotype data. We applied our method to the phase III HapMap data and inferred the inversion genotypes of known inversion polymorphisms at 8p23.1 and 17q21.31. These inversion genotypes were validated by comparing with literature results and by checking Mendelian consistency using the family data whenever available. Based on the PCA-approach, we also performed a preliminary genome-wide scan for inversions using the HapMap data, which resulted in 2040 candidate inversions, 169 of which overlapped with previously reported inversions. Our method can be readily applied to the abundant SNP data, and is expected to play an important role in developing human genome maps of inversions and exploring associations between inversions and susceptibility of diseases.

Inferring coancestry in population samples in the presence of linkage disequilibrium.

In both pedigree linkage studies and in population-based association studies there has been much interest in the use of modern dense genetic marker data to infer segments of gene identity by descent (ibd) among individuals not known to be related, to increase power and resolution in localizing genes affecting complex traits. In this article, we present a hidden Markov model (HMM) for ibd among a set of chromosomes and describe methods and software for inference of ibd among the four chromosomes of pairs of individuals, using either phased (haplotypic) or unphased (genotypic) data. The model allows for missing data and typing error, but does not model linkage disequilibrium (LD), because fitting an accurate LD model requires large samples from well-studied populations. However, LD remains a major confounding factor, since LD is itself a reflection of coancestry at the population level. To study the impact of LD, we have developed a novel simulation approach to generate realistic dense marker data for the same set of markers but at varying levels of LD. Using this approach, we present results of a study of the impact of LD on the sensitivity and specificity of our HMM model in estimating segments of ibd among sets of four chromosomes and between genotype pairs. We show that, despite not incorporating LD, our model has been quite successful in detecting segments as small as 10(6) bp (1 Mpb); we present also comparisons with fastIBD which uses an LD model in estimating ibd.

Genetic Determinants of Plasma Von Willebrand Factor Antigen Levels: A Target Gene SNP and Haplotype Analysis of the ARIC Cohort

AbstractAbstract 4310 Background and Objectives:Von Willebrand factor (VWF) is an essential component of hemostasis. It is known that multimer size and the amount of circulating VWF impact its hemostatic function. Genetic factors play a major role in regulating VWF synthesis and clearance and are reported to contribute to 66% of the variation in plasma VWF antigen level. We have analyzed 78 SNPs distributed throughout the VWF gene and haplotypes constructed from those SNPs for an association with VWF antigen level in 7,856 subjects of European descent in the Atherosclerosis Risk in Communities cohort. Methods and Results: Blood was drawn after an 8 hour fasting period and VWF antigen level was determined by a commercial ELISA kit. All subjects underwent analysis for 78 SNPs available from Affymetrix 6.0 chip in the region encompassing the VWF gene. We used the fastPHASE 1.2 program to resolve haplotypes from the unphased SNP genotype data. Using Haploview we determined SNPs in strong linkage disequilibrium (LD), their co-segregation rates and underlying haplotypes. Linear models were used to evaluate the association of actual or log VWF antigen levels with each SNP and haplotypes derived from those SNPs. Eighteen (16 are intronic) of the 78 SNPs were found to significantly associate with levels of VWF antigen (Table 1). All are clustered in a 50 kb region of the VWF gene in spite of the fact that the 78 SNPs studied are distributed throughout the VWF gene without apparent clusters. All 78 SNPs are co-segregated in 9 LD haplotype blocks, but a majority of positive SNPs (88.9%) are located in Block 5 and 6, which significantly correlate with VWF antigen level. The O blood type contributes to ∼ 15% of variation in VWF antigen levels. The association for SNPs and Haplotypes grows stronger after ABO effects are removed. Among non-genomic factors, age and BMI are the most significant factors and contribute to 4.8% and 1.4% of variation in VWF antigen level. Conclusions: We have found a strongly positive association between VWF level and SNPs and haplotypes from a strikingly concentrated region of the VWF gene. This region encodes the D2, D' and D3 domains of VWF, including the propeptide and multimerization and Factor VIII binding sites. The physiological significance of these clustered SNPs on VWF synthesis or clearance remains to be further investigated. Our data suggest that this region plays an important role in regulating VWF antigen level and support the idea that combinations of SNPs in LD can provide additive affects. Disclosures:No relevant conflicts of interest to declare.

Phylogenetic nomenclature and evolution of mannose-binding lectin (MBL2) haplotypes

BackgroundPolymorphisms of the mannose-binding lectin gene (MBL2) affect the concentration and functional efficiency of the protein. We recently used haplotype-specific sequencing to identify 23 MBL2 haplotypes, associated with enhanced susceptibility to several diseases.ResultsIn this work, we applied the same method in 288 and 470 chromosomes from Gabonese and European adults, respectively, and found three new haplotypes in the last group. We propose a phylogenetic nomenclature to standardize MBL2 studies and found two major phylogenetic branches due to six strongly linked polymorphisms associated with high MBL production. They presented high Fst values and were imbedded in regions with high nucleotide diversity and significant Tajima's D values. Compared to others using small sample sizes and unphased genotypic data, we found differences in haplotyping, frequency estimation, Fu and Li's D* and Fst results.ConclusionUsing extensive testing for selective neutrality, we confirmed that stochastic evolutionary factors have had a major role in shaping this polymorphic gene worldwide.

Efficient Calculation of P‐value and Power for Quadratic Form Statistics in Multilocus Association Testing

We address the asymptotic and approximate distributions of a large class of test statistics with quadratic forms used in association studies. The statistics of interest take the general form D=X(T)A X, where A is a general similarity matrix which may or may not be positive semi-definite, and X follows the multivariate normal distribution with mean mu and variance matrix Sigma, where Sigma may or may not be singular. We show that D can be written as a linear combination of independent chi(2) random variables with a shift. Furthermore, its distribution can be approximated by a chi(2) or the difference of two chi(2) distributions. In the setting of association testing, our methods are especially useful in two situations. First, when the required significance level is much smaller than 0.05 such as in a genome scan, the estimation of p-values using permutation procedures can be challenging. Second, when an EM algorithm is required to infer haplotype frequencies from un-phased genotype data, the computation can be intensive for a permutation procedure. In either situation, an efficient and accurate estimation procedure would be useful. Our method can be applied to any quadratic form statistic and therefore should be of general interest.

Evaluating NAT2PRED for inferring the individual acetylation status from unphased genotype data

BackgroundGenetically determined differences in N-acetylation capacity have proved to be important determinants of both the effectiveness of therapeutic response and the development of adverse drug reactions and toxicity during drug treatment. NAT2PRED is a web-server that allows a fast determination of NAT2 acetylation phenotype from genotype data without taking the extra step of reconstructing haplotypes for each individual (publicly available at http://nat2pred.rit.albany.edu). However, the classification accuracy of NAT2PRED needs to be assessed before its application can be advocated at a large scale.MethodsThe ability of NAT2PRED to classify individuals according to their acetylation status (slow, intermediate and rapid acetylators) was evaluated in a worldwide dataset composed of 56 population samples (8,489 individuals) from four continental regions.ResultsNAT2PRED correctly identified slow acetylators with a sensitivity above 99% for all populations outside sub-Saharan Africa. Nevertheless, NAT2PRED showed a poor ability to distinguish between intermediate and rapid acetylators, with a classification error rate reaching up to 10% in the non-African samples.ConclusionNAT2PRED is an excellent tool to infer the individual acetylation status from NAT2 genotype data when the main interest is to distinguish slow acetylators from the others. This should facilitate the determination of the individual acetylation status in routine clinical practice and lead to better monitoring of risks associated with cancer and adverse drug reactions.

ATRIUM: Testing Untyped SNPs in Case-Control Association Studies with Related Individuals

In genome-wide association studies, only a subset of all genomic variants are typed by current, high-throughput, SNP-genotyping platforms. However, many of the untyped variants can be well predicted from typed variants, with linkage disequilibrium (LD) information among typed and untyped variants available from an external reference panel such as HapMap. Incorporation of such external information can allow one to perform tests of association between untyped variants and phenotype, thereby making more efficient use of the available genotype data. When related individuals are included in case-control samples, the dependence among their genotypes must be properly addressed for valid association testing. In the context of testing untyped variants, an additional analytical challenge is that the dependence, across related individuals, of the partial information on untyped-SNP genotypes must also be assessed and incorporated into the analysis for valid inference. We address this challenge with ATRIUM, a method for case-control association testing with untyped SNPs, based on genome screen data in samples in which some individuals are related. ATRIUM uses LD information from an external reference panel to specify a one-degree-of-freedom test of association with an untyped SNP. It properly accounts for dependence in the partial information on untyped-SNP genotypes across related individuals. We demonstrate that ATRIUM is robust in that it maintains the nominal type I error rate even when the external reference panel is not well matched to the case-control sample. We apply the method to detect association between type 2 diabetes and variants on chromosome 10 in the Framingham SHARe data.

Linkage disequilibrium in wild French grapevine, Vitis vinifera L. subsp. silvestris

Association mapping based on linkage disequilibrium (LD) can provide high resolution for whole-genome mapping of genes underlying phenotypic variation. This field has received considerable attention over the last decade. We present here the first characterization of LD in wild French grapevine, Vitis vinifera L. subsp. silvestris. To assess the pattern and extent of LD, we used a sample of 85 plants from southern France and 36 microsatellite markers distributed over 5 linkage groups. LD was evaluated with independence tests and multiallelic r(2), using both unphased genotypic data and reconstructed haplotypic data. LD decayed rapidly, with r(2) values decreasing to 0.1 within 2.7 cM for genotypic data and within 1.4 cM for haplotypic data. Compared to the results of a previous study on cultivated grapevine subsp. sativa, where significant LD was found up to 16.8 cM, LD in subsp. silvestris was no longer significant past 1.4 cM. LD was therefore 12 times further extended in cultivated than wild grapevine, even though LD in wild grapevine seemed to extend slightly further than in wild relatives of other crops. Domestication bottlenecks and vegetative propagation are the primary factors responsible for this difference between cultivated and wild grapevine. The rapid decay of LD observed in this study seems promising for future association mapping studies of functional variation in wild V. vinifera grapevine.