Bayesian LASSO for quantitative trait loci mapping

The mapping of quantitative trait loci (QTL) is to identify molecular markers or genomic loci that influence the variation of complex traits. The problem is complicated by the facts that QTL data usually contain a large number of markers across the entire genome and most of them have little or no effect on the phenotype. In this article, we propose several Bayesian hierarchical models for mapping multiple QTL that simultaneously fit and estimate all possible genetic effects associated with all markers. The proposed models use prior distributions for the genetic effects that are scale mixtures of normal distributions with mean zero and variances distributed to give each effect a high probability of being near zero. We consider two types of priors for the variances, exponential and scaled inverse-chi(2) distributions, which result in a Bayesian version of the popular least absolute shrinkage and selection operator (LASSO) model and the well-known Student's t model, respectively. Unlike most applications where fixed values are preset for hyperparameters in the priors, we treat all hyperparameters as unknowns and estimate them along with other parameters. Markov chain Monte Carlo (MCMC) algorithms are developed to simulate the parameters from the posteriors. The methods are illustrated using well-known barley data.     

Derivation of the shrinkage estimates of quantitative trait locus effects

The shrinkage estimate of a quantitative trait locus (QTL) effect is the posterior mean of the QTL effect when a normal prior distribution is assigned to the QTL. This note gives the derivation of the shrinkage estimate under the multivariate linear model. An important lemma regarding the posterior mean of a normal likelihood combined with a normal prior is introduced. The lemma is then used to derive the Bayesian shrinkage estimates of the QTL effects.     

Robust Bayesian mapping of quantitative trait loci using Student-t distribution for residual

In most quantitative trait loci (QTL) mapping studies, phenotypes are assumed to follow normal distributions. Deviations from this assumption may affect the accuracy of QTL detection, leading to detection of false positive QTL. To improve the robustness of QTL mapping methods, we replace the normal distribution assumption for residuals in a multiple QTL model with a Student-t distribution that is able to accommodate residual outliers. A Robust Bayesian mapping strategy is proposed on the basis of the Bayesian shrinkage analysis for QTL effects. The simulations show that Robust Bayesian mapping approach can substantially increase the power of QTL detection when the normality assumption does not hold and applying it to data already normally distributed does not influence the result. The proposed QTL mapping method is applied to mapping QTL for the traits associated with physics–chemical characters and quality in rice. Similarly to the simulation study in the real data case the robust approach was able to detect additional QTLs when compared to the traditional approach. The program to implement the method is available on request from the first or the corresponding author. Xin Wang and Zhongze Piao contributed equally to this study.     

Extended Bayesian LASSO for Multiple Quantitative Trait Loci Mapping and Unobserved Phenotype Prediction

The Bayesian LASSO (BL) has been pointed out to be an effective approach to sparse model representation and successfully applied to quantitative trait loci (QTL) mapping and genomic breeding value (GBV) estimation using genome-wide dense sets of markers. However, the BL relies on a single parameter known as the regularization parameter to simultaneously control the overall model sparsity and the shrinkage of individual covariate effects. This may be idealistic when dealing with a large number of predictors whose effect sizes may differ by orders of magnitude. Here we propose the extended Bayesian LASSO (EBL) for QTL mapping and unobserved phenotype prediction, which introduces an additional level to the hierarchical specification of the BL to explicitly separate out these two model features. Compared to the adaptiveness of the BL, the EBL is “doubly adaptive” and thus, more robust to tuning. In simulations, the EBL outperformed the BL in regard to the accuracy of both effect size estimates and phenotypic value predictions, with comparable computational time. Moreover, the EBL proved to be less sensitive to tuning than the related Bayesian adaptive LASSO (BAL), which introduces locus-specific regularization parameters as well, but involves no mechanism for distinguishing between model sparsity and parameter shrinkage. Consequently, the EBL seems to point to a new direction for QTL mapping, phenotype prediction, and GBV estimation.REGULARIZATION or shrinkage methods are gaining increasing recognition as a valuable alternative to variable selection techniques in dealing with oversaturated or otherwise ill-defined regression problems in both the classical and Bayesian frameworks (e.g., O''hara and Sillanpää 2009). Many studies (e.g., Xu 2003; Wang et al. 2005; Zhang and Xu 2005; De los Campos et al. 2009; Usai et al. 2009; Wu et al. 2009; Xu et al. 2009) have documented the potential of shrinkage methods for quantitative trait locus (QTL) mapping and genomic breeding value (GBV) estimation using genome-wide dense sets of markers. Lee et al. (2008) make a clear connection between phenotype prediction and GBV estimation, suggesting that methods developed for one are also applicable to the other. We thus use the two concepts interchangeably throughout this article.Regularized regression methods, such as ridge regression (Hoerl and Kennard 1970) or the least absolute shrinkage and selection operator (LASSO) (Tibshirani 1996), are essentially penalized likelihood procedures, where suitable penalty functions are added to the negative log-likelihood to automatically shrink spurious effects (effects of redundant covariates) toward zero, while allowing relevant effects to take values farther from zero.It has been pointed out that these non-Bayesian shrinkage methods are not suitable for oversaturated models. Zou and Hastie (2005) and Park and Casella (2008) noted that the LASSO cannot select a number of nonzero effects exceeding the sample size. Xu (2003) found that for ridge regression to work, the number of model effects should be in the same order as the number of observations. This is impractical for genomic selection, which capitalizes on the variation due to small-marker effects, the number of which can exceed the sample size, by contrast to QTL mapping where interest lies mostly in a small subset of loci with large effects on the focal phenotype. In connection with the LASSO, the Bayesian LASSO (BL) (Park and Casella 2008; Yi and Xu 2008) has been proposed to overcome this limitation by imposing a selective shrinkage across regression parameters. Xu (2003) also proposed a Bayesian shrinkage method for QTL mapping, which extends ridge regression in a similar fashion.Although the BL has been successfully applied to QTL mapping (e.g., Yi and Xu 2008) and to GBV estimation (e.g., De los Campos et al. 2009), it relies on a single parameter known as the regularization parameter to simultaneously regulate the overall model sparsity and the extent to which individual regression coefficients are shrunken. However, this is unrealistic when dealing with a large number of predictors whose effect sizes may differ by orders of magnitude. It is therefore natural to ask whether this practice can be relaxed and how such an attempt may impinge on the model performance (e.g., Sun et al. 2010).Here we propose an extension to the Bayesian LASSO for QTL mapping and unobserved phenotype prediction. Our method, the extended Bayesian LASSO (EBL), introduces locus-specific regularization parameters and utilizes a parameterization that clearly separates the overall model sparsity from the degree of shrinkage of individual regression parameters. We use simulated data to investigate the performance of the EBL relative to the Bayesian LASSO in mapping QTL and in predicting unobserved phenotypes. We also compare the performance of the EBL to the Bayesian adaptive LASSO (BAL) recently proposed by Sun et al. (2010), which also assumes locus-specific regularization parameters.     

A Decision Rule for Quantitative Trait Locus Detection Under the Extended Bayesian LASSO Model

Bayesian shrinkage analysis is arguably the state-of-the-art technique for large-scale multiple quantitative trait locus (QTL) mapping. However, when the shrinkage model does not involve indicator variables for marker inclusion, QTL detection remains heavily dependent on significance thresholds derived from phenotype permutation under the null hypothesis of no phenotype-to-genotype association. This approach is computationally intensive and more importantly, the hypothetical data generation at the heart of the permutation-based method violates the Bayesian philosophy. Here we propose a fully Bayesian decision rule for QTL detection under the recently introduced extended Bayesian LASSO for QTL mapping. Our new decision rule is free of any hypothetical data generation and relies on the well-established Bayes factors for evaluating the evidence for QTL presence at any locus. Simulation results demonstrate the remarkable performance of our decision rule. An application to real-world data is considered as well.     

Genetic diversity of Eucalyptus hybrids estimated by genomic and EST microsatellite markers

The knowledge of breeding impacts on the genetic diversity of hybrids of Eucalyptus is crucial to the exploration of genetic resources. We estimated genetic polymorphic parameters of 112 hybrids of Eucalyptus spp. using 10 genomic simple sequence repeats (SSR) markers and 10 expressed sequence tags (EST) microsatellite markers. According to Student’s t-test, there were no significant differences between genomic SSR and EST-SSR markers. Our results also revealed high polymorphism in the hybrids analyzed, indicating that both markers are appropriate for use in genetic breeding programs.     

Mapping quantitative trait loci associated with regeneration ability of seed callus in rice, Oryza sativa L.

 Quantitative trait loci (QTL) controlling the regeneration ability of rice seed callus were detected using 245 RFLP markers and 98 BC₁F₅ lines derived from two varieties, ‘Nipponbare’ and ‘Kasalath’. Regeneration ability was evaluated by two indices: average number of regenerated shoots per callus (NRS) and regeneration rate (RR). The BC₁F₅ lines showed continuous segregation for both indices. Five putative QTL for NRS (tentatively named qRg1, qRg2, qRg4a, qRg4b and qRg4c) located on chromosomes 1, 2 and 4 were detected. Digenic interaction among these detected QTL was not significant (P<0.01). Among the five QTL detected, four ‘Kasalath’ alleles and one ‘Nipponbare’ allele increased NRS. According to an estimate based on the nearest marker loci, the five QTL accounted for 38.5% of the total phenotypic variation of the BC₁F₅ lines. For RR, four putative QTL were detected on chromosomes 2 and 4, and all of these were in the same chromosomal regions as the NRS QTL. The four RR QTL accounted for 32.6% of the total phenotypic variation. Received: 7 November 1996 / Accepted: 25 April 1997     

Identification of loci contributing to quantitative field resistance to blackleg disease, causal agent Leptosphaeria maculans (Desm.) Ces. et de Not., in Winter rapeseed (Brassica napus L.)

 Blackleg, caused by Leptosphaeria maculans, is one of the most important diseases of Brassica napus. Genomic regions controlling blackleg resistance at the adult plant stage were detected using 152 doubled-haploid (DH) lines derived from the F₁‘Darmor-bzh’בYudal’. The rapeseed genetic map used includes 288 DNA markers on 19 linkage groups. Blackleg resistance of each DH line was evaluated in field tests in 1995 and 1996 by measuring the mean disease index (I) and the percentage of lost plants (P). From notations recovered in 1995, ten quantitative trait loci (QTL) were detected: seven QTL for I and six QTL for P, explaining 57% and 41% of the genotypic variation, respectively. Three of them were common to I and P. From data recovered in 1996, seven QTL were identified: five QTL for I and two different QTL for P, accounting for 50% and 23% of the genotypic variation, respectively. One I QTL, located close to a dwarf gene (bzh), was detected with a very strong effect, masking more QTL detection. It was not revealed at the same position and with the same effect in 1995. Four major genomic regions were revealed from 1995 and from 1996 with the same parental contribution. One of them, located on the DY2 group, has a resistance allele from the susceptible parent. Five- and two-year-specific QTL were detected in 1995 and 1996, respectively. Received: 25 April 1997 / Accepted: 5 August 1997     

Overview of LASSO-related penalized regression methods for quantitative trait mapping and genomic selection

Quantitative trait loci (QTL)/association mapping aims at finding genomic loci associated with the phenotypes, whereas genomic selection focuses on breeding value prediction based on genomic data. Variable selection is a key to both of these tasks as it allows to (1) detect clear mapping signals of QTL activity, and (2) predict the genome-enhanced breeding values accurately. In this paper, we provide an overview of a statistical method called least absolute shrinkage and selection operator (LASSO) and two of its generalizations named elastic net and adaptive LASSO in the contexts of QTL mapping and genomic breeding value prediction in plants (or animals). We also briefly summarize the Bayesian interpretation of LASSO, and the inspired hierarchical Bayesian models. We illustrate the implementation and examine the performance of methods using three public data sets: (1) North American barley data with 127 individuals and 145 markers, (2) a simulated QTLMAS XII data with 5,865 individuals and 6,000 markers for both QTL mapping and genomic selection, and (3) a wheat data with 599 individuals and 1,279 markers only for genomic selection.     

An empirical Bayes method for estimating epistatic effects of quantitative trait loci

Summary .   The genetic variance of a quantitative trait is often controlled by the segregation of multiple interacting loci. Linear model regression analysis is usually applied to estimating and testing effects of these quantitative trait loci (QTL). Including all the main effects and the effects of interaction (epistatic effects), the dimension of the linear model can be extremely high. Variable selection via stepwise regression or stochastic search variable selection (SSVS) is the common procedure for epistatic effect QTL analysis. These methods are computationally intensive, yet they may not be optimal. The LASSO (least absolute shrinkage and selection operator) method is computationally more efficient than the above methods. As a result, it has been widely used in regression analysis for large models. However, LASSO has never been applied to genetic mapping for epistatic QTL, where the number of model effects is typically many times larger than the sample size. In this study, we developed an empirical Bayes method (E-BAYES) to map epistatic QTL under the mixed model framework. We also tested the feasibility of using LASSO to estimate epistatic effects, examined the fully Bayesian SSVS, and reevaluated the penalized likelihood (PENAL) methods in mapping epistatic QTL. Simulation studies showed that all the above methods performed satisfactorily well. However, E-BAYES appears to outperform all other methods in terms of minimizing the mean-squared error (MSE) with relatively short computing time. Application of the new method to real data was demonstrated using a barley dataset.     

Molecular and phenotypic characterization of near isogenic lines at QTL for quantitative resistance to Leptosphaeria maculans in oilseed rape (Brassica napus L.)

The most common and effective way to control phoma stem canker (blackleg) caused by Leptosphaeria maculans in oilseed rape (Brassica napus) is by breeding resistant cultivars. Specific resistance genes have been identified in B. napus and related species but in some B. napus cultivars resistance is polygenic [mediated by quantitative trait loci (QTL)], postulated to be race non-specific and durable. The genetic basis of quantitative resistance in the French winter oilseed rape ‘Darmor’, which was derived from ‘Jet Neuf’, was previously examined in two genetic backgrounds. Stable QTL involved in blackleg resistance across year and genetic backgrounds were identified. In this study, near isogenic lines (NILs) were produced in the susceptible background ‘Yudal’ for four of these QTL using marker-assisted selection. Various strategies were used to develop new molecular markers, which were mapped in these QTL regions. These were used to characterize the length and homozygosity of the ‘Darmor-bzh’ introgressed segment in the NILs. Individuals from each NIL were evaluated in blackleg disease field trials and assessed for their level of stem canker in comparison to the recurrent line ‘Yudal’. The effect of QTL LmA2 was clearly validated and to a lesser extent, QTL LmA9 also showed an effect on the disease level. This work provides valuable material that can be used to study the mode of action of genetic factors involved in L. maculans quantitative resistance.     

Influence of the Selected Antioxidants on the Stability of the Celsior Solution Used for Perfusion and Organ Preservation Purposes

The purpose of the following research was to improve the original Celsior solution in order to obtain a higher degree of stability and effectiveness. The solution was modified by the addition of selected antioxidants such as vitamin C, cysteine, and fumaric acid in the following concentrations: 0.1, 0.3, and 0.5 mmol/l. The solution’s stability was estimated using an accelerated stability test based on changes in histidine concentrations in the solution using Pauly’s method for determining concentrations. Elevated temperatures, the factor accelerating substances’ decomposition reaction rate, were used in the tests. The research was conducted at four temperatures at intervals of 10°C: 60 ± 0.2°C, 70 ± 0.2°C, 80 ± 0.2°C, and 90 ± 0.2°C. It was stated that the studied substances’ decomposition occurred in accordance with the equation for first-order reactions. The function of the logarithmic concentration (log%C) over time was revealed to be rectilinear. This dependence was used to determine the kinetics of decomposition reaction rate parameters (the rate constant of decomposition k, activation energy E _a, and frequency factor A). On the basis of these parameters, the stability of the modified solution was estimated at +5°C. The results obtained show that the proposed antioxidants have a significant effect on lengthening the Celsior solution’s stability. The best results were reached when combining two antioxidants: vitamin C and cysteine in 0.5 mmol/l concentrations. As a result, the Celsior solution’s stability was lengthened from 22 to 299 days, which is 13.5 times. Vitamin C at a concentration of 0.5 mmol/l increased the solution’s stability by 5.2 times (t ₉₀ = 115 days), cysteine at a concentration of 0.5 mmol/l caused a 4.4 times stability increase (t ₉₀ = 96 days), and fumaric acid at a concentration of 0.5 mmol/l extended the stability by 2.1 times (t ₉₀ = 48 days) in relation to the original solution.     

Bayesian functional mapping of dynamic quantitative traits

Without consideration of other linked QTLs responsible for dynamic trait, original functional mapping based on a single QTL model is not optimal for analyzing multiple dynamic trait loci. Despite that composite functional mapping incorporates the effects of genetic background outside the tested QTL in mapping model, the arbitrary choice of background markers also impact on the power of QTL detection. In this study, we proposed Bayesian functional mapping strategy that can simultaneously identify multiple QTL controlling developmental patterns of dynamic traits over the genome. Our proposed method fits the change of each QTL effect with the time by Legendre polynomial and takes the residual covariance structure into account using the first autoregressive equation. Also, Bayesian shrinkage estimation was employed to estimate the model parameters. Especially, we specify the gamma distribution as the prior for the first-order auto-regressive coefficient, which will guarantee the convergence of Bayesian sampling. Simulations showed that the proposed method could accurately estimate the QTL parameters and had a greater statistical power of QTL detection than the composite functional mapping. A real data analysis of leaf age growth in rice is used for the demonstration of our method. It shows that our Bayesian functional mapping can detect more QTLs as compared to composite functional mapping.     

Bayesian shrinkage estimation of quantitative trait loci parameters

Mapping multiple QTL is a typical problem of variable selection in an oversaturated model because the potential number of QTL can be substantially larger than the sample size. Currently, model selection is still the most effective approach to mapping multiple QTL, although further research is needed. An alternative approach to analyzing an oversaturated model is the shrinkage estimation in which all candidate variables are included in the model but their estimated effects are forced to shrink toward zero. In contrast to the usual shrinkage estimation where all model effects are shrunk by the same factor, we develop a Bayesian method that allows the shrinkage factor to vary across different effects. The new shrinkage method forces marker intervals that contain no QTL to have estimated effects close to zero whereas intervals containing notable QTL have estimated effects subject to virtually no shrinkage. We demonstrate the method using both simulated and real data for QTL mapping. A simulation experiment with 500 backcross (BC) individuals showed that the method can localize closely linked QTL and QTL with effects as small as 1% of the phenotypic variance of the trait. The method was also used to map QTL responsible for wound healing in a family of a (MRL/MPJ x SJL/J) cross with 633 F(2) mice derived from two inbred lines.     

Improving rice yield and quality by QTL pyramiding

To facilitate marker-assisted transfer of desirable genes for improvement of yield traits, we used a set of backcross recombinant inbred lines (BRIL) derived from two elite parental lines, ‘Zhenshan97’ and ‘93-11’, to resolve a quantitative trait loci (QTL) cluster for heading date and yield-related traits in rice. Four main-effect QTL (qHD6.1, qHD6.2, qHD7, and qHD8) and four epistatic QTL affecting heading date in the BRIL were detected in two experimental trials. The major QTL (qHD8) was confirmed in three heterogeneous inbred families (HIF) that segregated for this target region, and narrowed down to a 20-kb segment in a large HIF-derived population. qHD8 was found to interact with qHD7 and had a pleiotropic effect responsible for heading date and yield components. To test usability of the identified QTL in rice improvement, we further developed near-isogenic lines (NIL) containing one or more target genes by marker-assisted transfer of ‘93-11’ alleles at qHD8, qHD7, and qHD6.1, and the GS3 gene for grain size into ‘Zhenshan97’. The pyramid line NIL(qHD8 + GS3) had higher yield potential, longer grains, and a more suitable heading date than ‘Zhenshan97’. Comparison of the NIL showed existence of epistasis between alleles at different loci and background effect on qHD8, which are very important for pyramiding of desirable alleles at the target QTL. These results will be particularly useful not only to understand the genetic basis of yield-related traits but also to improve the efficiency of marker-assisted selection for favorable loci in rice breeding programs.     

Identification and validation of a major QTL conferring crown rot resistance in hexaploid wheat

Crown rot (CR), caused by various Fusarium species, is a chronic wheat disease in Australia. As part of our objective of improving the efficiency of breeding CR resistant wheat varieties, we have been searching for novel sources of resistance. This paper reports on the genetic control of one of these newly identified resistant genotypes, ‘CSCR6’. A population derived from a cross between CSCR6 and an Australian variety ‘Lang’ was analyzed using two Fusarium isolates belonging to two different species, one Fusarium pseudograminearum and the other Fusarium graminearum. The two isolates detected QTL with the same chromosomal locations and comparable magnitudes, indicating that CR resistance is not species-specific. The resistant allele of one of the QTL was derived from ‘CSCR6’. This QTL, designated as Qcrs.cpi-3B, was located on the long arm of chromosome 3B and explains up to 48.8% of the phenotypic variance based on interval mapping analysis. Another QTL, with resistant allele from the variety ‘Lang’, was located on chromosome 4B. This QTL explained up to 22.8% of the phenotypic variance. A strong interaction between Qcsr.cpi-3B and Qcsr.cpi-4B was detected, reducing the maximum effect of Qcrs.cpi-3B to 43.1%. The effects of Qcrs.cpi-3B were further validated in four additional populations and the presence of this single QTL reduced CR severity by up to 42.1%. The fact that significant effects of Qcrs.cpi-3B were detected across all trials with different genetic backgrounds and with the use of isolates belonging to two different Fusarium species make it an ideal target for breeding programs as well as for further characterization of the gene(s) involved in its resistance.     

Effects of Migration on Genetic Variability and Parent–Offspring Correlation Coefficients in Anthropometric and Physiometric Phenotypes Among Three Groups of Population in Punjab, India

In the present study, we examined the migration effects on genetic variabilities and heritabilities patterns between three groups of population like parental population in Punjab, migrant from Pakistan, and migrant from other states of India in Punjab using anthropometric and physiometric traits. A total of 500 adult individuals from 300 families were studied. Statistical comparisons were carried out through mean coefficients, Student’s t test, heritability, and regression analysis. The results suggest a significant migration effect on almost all traits. Correlation coefficient for first-degree relatives, the slope factors, and heritabilities for almost all variables have been found significant among the three groups of populations. However, the discrimination is more prominent among migrant from other states of India because of more genetic heterogeneity.     

Fine mapping of <Emphasis Type="Italic">Sta1</Emphasis>, a quantitative trait locus determining stele transversal area,on rice chromosome 9

The stele (root vascular cylinder) in plants plays an important role in the transport of water and nutrients from the root to the shoot. A quantitative trait locus (QTL) on rice chromosome 9 that controls stele transversal area (STA) was previously detected in an F₃ mapping population derived from a cross between the lowland cultivar ‘IR64’, with a small STA, and the upland cultivar ‘Kinandang Patong’, with a large STA. To identify the gene(s) underlying this QTL, we undertook fine mapping of the locus. We screened eight plants from BC₂F₃ lines in which recombination occurred near the QTL. Progeny testing of BC₂F₄ plants was used to determine the genotype classes for the QTL in each BC₂F₃ line. Accordingly, the STA QTL Sta1 (Stele Transversal Area 1) was mapped between the InDel markers ID07_12 and ID07_14. A candidate genomic region for Sta1 was defined more precisely between markers RM566 and RM24334, which delimit a 359-kb interval in the reference cultivar ‘Nipponbare’. A line homozygous for the ‘Kinandang Patong’ allele of Sta1 had an STA approximately 28.4% larger than that of ‘IR64’. However, Sta1 did not influence maximum or total root length, suggesting that this QTL specifically controls STA.     

Jasmonic acid affects mycorrhization of spruce seedlings with Laccaria laccata

 Jasmonic acid (JA) was shown to be an important regulating molecule after wounding and pathogen attack, but it also conveys mycorrhizal signalling. It was therefore applied to the ectomycorrhizal system. Root growth patterns of 2-month-old spruce seedlings mycorrhized with Laccaria laccata after treatment with 0.5 μM JA, 5.0 μM JA, or none for the control, were studied using a paper-sandwich technique. Changes in plant weight, root length, the degree of branching, presence of root hairs, and infection parameters were monitored for 26 days. Data were analysed by means of Student’s t-test and ANOVA factorial. Shoot and root dry weights were significantly increased by mycorrhizal infection, especially when JA was applied. Lateral root formation and the presence of roots with hairs were dramatically decreased in the presence of the fungus. A synergistic effect of JA and mycorrhization on decrease of lateral root length was shown. The first mycorrhizal contact of fungal hyphae with roots was significantly accelerated after treatment with JA. Received: 18 December 1996 / Accepted: 26 May 1997     

Identification and molecular mapping of PdR1, a primary resistance gene to Pierce’s disease in Vitis

A major quantitative trait locus (QTL) controlling resistance to Pierce’s disease (PD) of grape, caused by the bacterium Xylella fastidiosa (Xf), was identified on a Vitis linkage map and denoted as ‘Pierce’s disease resistance 1’ (PdR1). Placement of the locus was accomplished by evaluating a family of full-sib progeny from a cross of two PD-resistant interspecific hybrids with resistance inherited from Vitis arizonica. Resistance was measured under greenhouse conditions by direct quantification of Xf numbers in stem tissues as well as by evaluation of disease symptoms based on leaf scorch and a cane maturation index (CMI). A large QTL (LOD 17.2) accounting for 72% of the phenotypic variance in bacterial numbers was localized to linkage group 14 of the male parent F8909-17. The approximate 95% confidence interval around the QTL peak extended 5.7 cM when using composite interval mapping. The other disease evaluation methods (leaf scorch and CMI, respectively) placed the resistance QTL to the same region on linkage group 14, although at wider 95% confidence intervals (6.0 and 7.5 cM), lower peak LOD scores (11.9 and 7.7) and accounting for less phenotypic variance (59 and 42%). This is the first report of an Xf resistance QTL mapped in any crop species. The relevance of the markers located in the region spanning the QTL will be discussed, addressing their usefulness for the development of PD-resistant grape cultivars.