Genome‐wide association study identifies quantitative trait loci associated with resistance to Verticillium dahliae race 3 in tomato

BackgroundCotton is an important cash crop in China and a key component of the global textile market. Verticillium wilt is a major factor affecting cotton yield. Single nucleotide polymorphism (SNP) markers and phenotypic data can be used to identify genetic markers and loci associated with cotton resistance to Verticillium wilt. We used eight upland cotton parent materials in this study to construct a multiparent advanced generation inter-cross (MAGIC) population comprising 320 lines. The Verticillium wilt resistance of the MAGIC population was identified in the greenhouse in 2019, and the average relative disease index (ARDI) was calculated. A genome-wide association study (GWAS) was performed to discover SNP markers/genes associated with Verticillium wilt resistance.ResultsARDI of the MAGIC population showed wide variation, ranging from 16.7 to 79.4 across three replicates. This variation reflected a diverse range of resistance to Verticillium wilt within the population. Analysis of distribution patterns across the environments revealed consistent trends, with coefficients of variation between 12.25% and 21.96%. Families with higher ARDI values, indicating stronger resistance, were more common, likely due to genetic diversity and environmental factors. Population structure analysis divided the MAGIC population into three subgroups, with Group I showing higher genetic variation and Groups II and III displaying more uniform resistance performance. Principal component analysis (PCA) confirmed these divisions, highlighting the genetic diversity underlying Verticillium wilt resistance. Through GWAS, we identified 19 SNPs significantly associated with Verticillium wilt resistance, distributed across three chromosomes. The screening of candidate genes was performed on the transcriptome derived from resistant and susceptible cultivars, combined with gene annotation and tissue expression patterns, and two key candidate genes, Ghir_A01G006660 and Ghir_A02G008980, were found to be potentially associated with Verticillium wilt resistance. This suggests that these two candidate genes may play an important role in responding to Verticillium wilt.ConclusionThis study aims to dissect the genetic basis of Verticillium wilt resistance in cotton by using a MAGIC population and GWAS. The study seeks to provide valuable genetic resources for marker-assisted breeding and enhance the understanding of resistance mechanisms to improve cotton resilience against Verticillium wilt.

Investigating the genetic regulatory mechanisms underlying complex traits forms the foundation for crop improvement. Verticillium wilt (VW), caused by Verticillium dahliae (V. dahliae), is one of the most devastating diseases affecting crop production worldwide. However, the genetic basis underlying crop resistance to V. dahliae remains largely obscure, hindering progress in the genomic selection for VW resistance breeding. Here, we unraveled the genetic architectures and regulatory landscape of VW resistance in cotton by combining genome‐wide association studies (GWAS) and transcriptome‐wide association studies (TWAS) using 1152 transcriptomes derived from 290 cotton accessions. We identified 10 reliable quantitative trait loci (QTLs) associated with VW resistance across multiple environments. These QTLs showed a pyramiding resistance effect and exhibited promising efficacy in the genomic prediction of cotton's VW resistance supported by an F2:3 population. Moreover, trace analysis of these elite alleles revealed a notably increased utilization of Lsnp1, Lsnp4, Lsnp5, Lsnp8, and Lsnp9, which potentially contribute to the improvement of VW resistance in Chinese cotton breeding since the 1990s. We also identified remarkable gene modules and expression QTL (eQTL) hotspots related to the regulation of reactive oxygen species (ROS) homeostasis and immune response. Furthermore, 15 candidate causal genes were prioritized by TWAS. Knocking down eight genes with a negative effect significantly enhanced cotton resistance to V. dahliae. Among them, GhARM, encoding an armadillo (ARM)‐repeat protein, was verified to modulate cotton resistance to V. dahliae by regulating ROS homeostasis. Overall, this study updates the understanding of the genetic basis and regulatory mechanisms of cotton's VW resistance, providing valuable strategies for VW management through genomic selection in cotton breeding.

Stripe rust is one of the most destructive diseases of wheat (Triticum aestivum L.), caused by Puccinia striiformis f. sp. tritici (Pst), and responsible for significant yield losses worldwide. Single-nucleotide polymorphism (SNP) diagnostic markers were used to identify new sources of resistance at adult plant stage to wheat stripe rust (YR) in 141 CIMMYT advanced bread wheat lines over 3 years in replicated trials at Borlaug Institute for South Asia (BISA), Ludhiana. We performed a genome-wide association study and genomic prediction to aid the genetic gain by accumulating disease resistance alleles. The responses to YR in 141 advanced wheat breeding lines at adult plant stage were used to generate G × E (genotype × environment)-dependent rust scores for prediction and genome-wide association study (GWAS), eliminating variation due to climate and disease pressure changes. The lowest mean prediction accuracies were 0.59 for genomic best linear unbiased prediction (GBLUP) and ridge-regression BLUP (RRBLUP), while the highest mean was 0.63 for extended GBLUP (EGBLUP) and random forest (RF), using 14,563 SNPs and the G × E rust score results. RF and EGBLUP predicted higher accuracies (∼3%) than did GBLUP and RRBLUP. Promising genomic prediction demonstrates the viability and efficacy of improving quantitative rust tolerance. The resistance to YR in these lines was attributed to eight quantitative trait loci (QTLs) using the FarmCPU algorithm. Four (Q.Yr.bisa-2A.1, Q.Yr.bisa-2D, Q.Yr.bisa-5B.2, and Q.Yr.bisa-7A) of eight QTLs linked to the diagnostic markers were mapped at unique loci (previously unidentified for Pst resistance) and possibly new loci. The statistical evidence of effectiveness and distribution of the new diagnostic markers for the resistance loci would help to develop new stripe rust resistance sources. These diagnostic markers along with previously established markers would be used to create novel DNA biosensor-based microarrays for rapid detection of the resistance loci on large panels upon functional validation of the candidate genes identified in the present study to aid in rapid genetic gain in the future breeding programs.

The present study deploys a comparison of Gradient Boosting Machine (GBM), Extreme Gradient Boosting (XGBoost), and Genome Wide Association Studies (GWAS) in selecting optimum subsets of single nucleotide polymorphisms (SNPs) to be used in genomic prediction in cattle. The data simulation was carried out for 6,000 animals and 47,841 SNPs which include 43,633 polygenic markers and 4208 quantitative trait loci (QTL) using QMSim software. The genomic prediction was conducted with the best linear unbiased prediction (BLUP) method using the BLUPF90 program. The accuracy of prediction was computed in three different types, namely, Empirical all SNPs, Empirical QTL, and theoretical accuracy, Accuracy PEV . Among the three models, the highest Empirical all SNPs accuracy 0.79 was derived for GBM followed by 0.77 for XGBoost and 0.76 for GWAS. The Empirical QTL accuracy was almost equal for all three models. The maximum theoretical accuracy was obtained for GWAS which was 0.93, whereas GBM and XGBoost obtained 0.86 and 0.85 accuracy levels respectively. Our results indicate that all three models comparably performed in genomic predictions; however, subsets selected by both GBM and GWAS reported higher prediction accuracies compared to the whole SNP set. The number of QTL selected as a proportion of the total number of SNPs was superior in GWAS. These observations can be validated using real data which could enable further optimization of the analysis process.

BackgroundAlthough the accumulation of whole-genome sequencing (WGS) data has accelerated the identification of mutations underlying complex traits, its impact on the accuracy of genomic predictions is limited. Reliable genotyping data and pre-selected beneficial loci can be used to improve prediction accuracy. Previously, we reported a low-coverage sequencing genotyping method that yielded 11.3 million highly accurate single-nucleotide polymorphisms (SNPs) in pigs. Here, we introduce a method termed selective linkage disequilibrium pruning (SLDP), which refines the set of SNPs that show a large gain during prediction of complex traits using whole-genome SNP data.ResultsWe used the SLDP method to identify and select markers among millions of SNPs based on genome-wide association study (GWAS) prior information. We evaluated the performance of SLDP with respect to three real traits and six simulated traits with varying genetic architectures using two representative models (genomic best linear unbiased prediction and BayesR) on samples from 3579 Duroc boars. SLDP was determined by testing 180 combinations of two core parameters (GWAS P-value thresholds and linkage disequilibrium r2). The parameters for each trait were optimized in the training population by five fold cross-validation and then tested in the validation population. Similar to previous GWAS prior-based methods, the performance of SLDP was mainly affected by the genetic architecture of the traits analyzed. Specifically, SLDP performed better for traits controlled by major quantitative trait loci (QTL) or a small number of quantitative trait nucleotides (QTN). Compared with two commercial SNP chips, genotyping-by-sequencing data, and an unselected whole-genome SNP panel, the SLDP strategy led to significant improvements in prediction accuracy, which ranged from 0.84 to 3.22% for real traits controlled by major or moderate QTL and from 1.23 to 11.47% for simulated traits controlled by a small number of QTN.ConclusionsThe SLDP marker selection method can be incorporated into mainstream prediction models to yield accuracy improvements for traits with a relatively simple genetic architecture, however, it has no significant advantage for traits not controlled by major QTL. The main factors that affect its performance are the genetic architecture of traits and the reliability of GWAS prior information. Our findings can facilitate the application of WGS-based genomic selection.

Simple SummaryBy integrating prior biological information into genomic selection methods using appropriate models, it is possible to improve prediction accuracy for complex traits. In this context, we conducted a comparative assessment of two genomic prediction models, namely, genomic best linear unbiased prediction and genomic feature best linear unbiased prediction. The accuracy of these models in predicting the growth traits of backfat thickness and loin muscle area was evaluated. Our results revealed that the genomic feature best linear unbiased prediction model can effectively integrate prior information into the model, which is superior to the genomic best linear unbiased prediction model in some cases. These findings provide valuable ideas for enhancing the genomic prediction accuracy of growth traits in pigs.Enhancing the accuracy of genomic prediction is a key goal in genomic selection (GS) research. Integrating prior biological information into GS methods using appropriate models can improve prediction accuracy for complex traits. Genome-wide association study (GWAS) is widely utilized to identify potential candidate loci associated with complex traits in livestock and poultry, offering essential genomic insights. In this study, a GWAS was conducted on 685 Duroc × Landrace × Yorkshire (DLY) pigs to extract significant single-nucleotide polymorphisms (SNPs) as genomic features. We compared two GS models, genomic best linear unbiased prediction (GBLUP) and genomic feature BLUP (GFBLUP), by using imputed whole-genome sequencing (WGS) data on 651 Yorkshire pigs. The results revealed that the GBLUP model achieved prediction accuracies of 0.499 for backfat thickness (BFT) and 0.423 for loin muscle area (LMA). By applying the GFBLUP model with GWAS-based SNP preselection, the average prediction accuracies for BFT and LMA traits reached 0.491 and 0.440, respectively. Specifically, the GFBLUP model displayed a 4.8% enhancement in predicting LMA compared to the GBLUP model. These findings suggest that, in certain scenarios, the GFBLUP model may offer superior genomic prediction accuracy when compared to the GBLUP model, underscoring the potential value of incorporating genomic features to refine GS models.

Verticillium wilt (VW), caused by Verticillium dahliae Kleb., is one of the most important diseases in cotton. The objective of this study was to map quantitative trait loci (QTLs) conferring VW resistance using resistance gene analog (RGA)-targeted amplified fragment length polymorphism (RGA-AFLP) markers in an interspecific backcross inbred line mapping population, consisting of 146 lines from a susceptible Sure-Grow 747 (Gossypium hirsutum L.) × resistant Pima S-7 (G. barbadense L.) cross. VW resistance was evaluated in replicated tests based on disease incidence in the field, and disease incidence and severity in the greenhouse. Of 160 polymorphic RGA-AFLP markers, 42 were significantly correlated with one or more VW traits and 41 were placed on a linkage map which covered 1,226 cM of the cotton genome and contained 251 other molecular markers. Three QTLs for VW resistance were detected, each of which explained 12.0–18.6 % of the phenotypic variation. Two of these QTLs for disease incidence and severity detected in the greenhouse inoculation tests using root wounding are located on chromosome c4. Both are closely linked to four RGA-AFLP markers and therefore considered as the same QTL for VW resistance. The other QTL detected in the field test was located on c19 and flanked by several RGA-AFLP markers. The desirable QTL allele on c4 for VW resistance detected in the greenhouse was from the VW susceptible Upland parent and absent from the resistant Pima parent which was more VW susceptible due to the disarmament of the first line of defense mechanism due to root wounding during inoculation. The other desirable VW resistance QTL allele, on c19, was from the resistant parent Pima S-7, consistent with the fact that Pima cotton was more resistant to VW when naturally infected in the field. The results should facilitate the development of more sequence specific markers and the transfer of VW resistance from Pima to Upland cotton through marker-assisted selection.

Genomic selection (GS) plays a crucial role in livestock breeding. However, its implementation in Chinese beef cattle breeding is constrained by a limited reference population and incomplete data records. To address these challenges, this study aimed to identify more effective models for multi-population genomic selection. We simulated five different beef cattle populations and selected three populations with varying levels of kinship to investigate the impact of population relationships on genomic prediction. Utilizing results from a genome-wide association study (GWAS), we preselected different proportions of single nucleotide polymorphism (SNP). Subsequently, we employed three models—genomic best linear unbiased prediction (GBLUP), multi-genomic best linear unbiased prediction (MGBLUP), and weighted multi-genomic best linear unbiased prediction (WMGBLUP)—for within-population and multi-population genomic prediction. Our results showed that increasing the size of the training set improved within-population prediction accuracy. Furthermore, both MGBLUP and WMGBLUP outperformed GBLUP in terms of prediction accuracy for both within-population and multi-population analyses. Among the models evaluated, the WMGBLUP model, which utilized the top 5% of preselected SNPs based on GWAS findings, demonstrated superior performance, yielding an improvement of up to 11.1% in within-population prediction and 16.5% in multi-population prediction. In summary, both WMGBLUP and MGBLUP models exhibit enhanced efficacy in improving genomic prediction accuracy, and the incorporation of GWAS results can further optimize their performance.

Context In beef cattle populations, there is little evidence regarding the minimum number of genetic markers needed to obtain reliable genomic prediction and imputed genotypes. Aims This study aimed to evaluate the impact of single nucleotide polymorphism (SNP) marker density and minor allele frequency (MAF), on genomic predictions and imputation performance for high and low heritability traits using the single-step genomic Best Linear Unbiased Prediction methodology (ssGBLUP) in a simulated beef cattle population. Methods The simulated genomic and phenotypic data were obtained through QMsim software. 735 293 SNPs markers and 7000 quantitative trait loci (QTL) were randomly simulated. The mutation rate (10-5), QTL effects distribution (gamma distribution with shape parameter = 0.4) and minor allele frequency (MAF = 0.02) of markers were used for quality control. A total of 335k SNPs (high density, HD) and 1000 QTLs were finally considered. Densities of 33 500 (35k), 16 750 (16k), 4186 (4k) and 2093 (2k) SNPs were customised through windows of 10, 20, 80 and 160 SNPs by chromosome, respectively. Three marker selection criteria were used within windows: (1) informative markers with MAF values close to 0.5 (HI); (2) less informative markers with the lowest MAF values (LI); (3) markers evenly distributed (ED). We evaluated the prediction of the high-density array and of 12 scenarios of customised SNP arrays, further the imputation performance of them. The genomic predictions and imputed genotypes were obtained with Blupf90 and FImpute software, respectively, and statistics parameters were applied to evaluate the accuracy of genotypes imputed. The Pearson’s correlation, the coefficient of regression, and the difference between genomic predictions and true breeding values were used to evaluate the prediction ability (PA), inflation (b), and bias (d), respectively. Key results Densities above 16k SNPs using HI and ED criteria displayed lower b, higher PA and higher imputation accuracy. Consequently, similar values of PA, b and d were observed with the use of imputed genotypes. The LI criterion with densities higher than 35k SNPs, showed higher PA and similar predictions using imputed genotypes, however lower b and quality of imputed genotypes were observed. Conclusion The results obtained showed that at least 5% of HI or ED SNPs available in the HD array are necessary to obtain reliable genomic predictions and imputed genotypes. Implications The development of low-density customised arrays based on criteria of MAF and even distribution of SNPs, might be a cost-effective and feasible approach to implement genomic selection in beef cattle.

BackgroundA quantitative trait is controlled both by major variants with large genetic effects and by minor variants with small effects. Genome-wide association studies (GWAS) are an efficient approach to identify quantitative trait loci (QTL), and genomic selection (GS) with high-density single nucleotide polymorphisms (SNPs) can achieve higher accuracy of estimated breeding values than conventional best linear unbiased prediction (BLUP). GWAS and GS address different aspects of quantitative traits, but, as statistical models, they are quite similar in their description of the genetic mechanisms that underlie quantitative traits.MethodsHere, we propose a stepwise linear regression mixed model (StepLMM) to unify GWAS and GS in a single statistical model. First, the variance components of the genomic-BLUP (GBLUP) model are estimated. Then, in the SNP selection step, the linear mixed model (LMM) for GWAS is equivalently transformed into a simple linear regression to improve computation speed, and the most significant SNP is selected and included into the evaluation model. In the SNP dropping step, the SNPs in the evaluation model are tested according to the standard errors of their estimated effects. If non-significant SNPs are present, the least significant one is dropped from the model and variance components are re-estimated. We used extended Bayesian information criteria (eBIC) to evaluate the model optimization, i.e. the model with the smallest eBIC is the final one and includes only significant SNPs.ResultsWe simulated scenarios with different heritabilities with 100 QTL. StepLMM estimated heritability accurately and mapped QTL precisely. Genomic prediction accuracy was much higher with StepLMM than with GBLUP. The comparison of StepLMM with other GWAS and GS methods based on a dataset from the 16th QTLMAS Workshop showed that StepLMM had medium mapping power, the lowest rate of false positives for QTL mapping, and the highest accuracy for genomic prediction.ConclusionsStepLMM is a combination of GWAS and GBLUP. GWAS and GBLUP are beneficial to each other in a single statistical model, GWAS improves genomic prediction accuracy, while GBLUP increases mapping precision and decreases the rate of false positives of GWAS. StepLMM has a high performance in both GWAS and GS and is feasible for agricultural breeding programs and human genetic studies.

Cultivated potato, Solanum tuberosum L., is considered an autotetraploid with 12 chromosomes with four homologous phases. However, recent evidence found that, due to frequent large phase deletions in the genome, gene ploidy is not constant across the genome. The elite cultivar "Otava" was found to have an average gene copy number of 3.2 across all loci. Breeding programs for elite potato cultivars rely increasingly on genomic prediction tools for selection breeding and elucidation of quantitative trait loci underpinning trait genetic variance. These are typically based on anonymous single nucleotide polymorphism (SNP) markers, which are usually called from, for example, SNP array or sequencing data using a tetraploid model. In this study, we analyzed the impact of using whole genome markers genotyped as either tetraploid or observed allele frequencies from genotype-by-sequencing data on single-trait additive genomic best linear unbiased prediction (GBLUP) genomic prediction (GP) models and single-marker regression genome-wide association studies of potato to evaluate the implications of capturing varying ploidy on the statistical models employed in genomic breeding. A panel of 762 offspring of a diallel cross of 18 parents of elite breeding material was used for modeling. These were genotyped by sequencing and phenotyped for five key performance traits: chipping quality, length/width ratio, senescence, dry matter content, and yield. We also estimated the read coverage required to confidently discriminate between a heterozygous triploid and tetraploid state from simulated data. It was found that using a tetraploid model neither impaired nor improved genomic predictions compared to using the observed allele frequencies that account for true marker ploidy. In genome-wide associations studies (GWAS), very minor variations of both signal amplitude and number of SNPs supporting both minor and major quantitative trait loci (QTLs) were observed between the two data sets. However, all major QTLs were reproducible using both data sets.

Verticillium dahliae Kleb, the cause of Verticillium wilt, is a particularly destructive soil-borne vascular disease that affects cotton, resulting in serious decline in fiber quality and causing significant losses in cotton production worldwide. However, the progress in identification of wilt-resistance loci or genes in cotton has been limited, most probably due to the highly complex genetic nature of the trait. Nevertheless, the molecular mechanism behind the Verticillium wilt resistance remains poorly understood. In the present study, we investigated the phenotypic variations in Verticillium tolerance and conducted a genome wide association study (GWAS) among a natural population containing 383 accessions of upland cotton germplasm and performed transcriptomic analysis of cotton genotypes with differential responses to Verticillium wilt. GWAS detected 70 significant SNPs and 116 genes associated with resistance loci in two peak signals on D02 and D11 in E1. The transcriptome analysis identified a total of 2689 and 13289 differentially expressed genes (DEGs) among the Verticillium wilt-tolerant (J46) and wilt-susceptible (J11) genotypes, respectively. The DEGs were predominantly enriched in metabolism, plant hormone signal transduction, phenylpropanoid pathway, MAPK cascade pathway and plant-pathogen interaction pathway in GO and KEGG analyses. The identified DEGs were found to comprise several transcription factor (TF) gene families, primarily including AP2/ERF, ZF, WRKY, NAC and MYB, in addition to pentatricopeptide repeat (PPR) proteins and Resistance (R) genes. Finally, by integrating the two results, 34 candidate genes were found to overlap between GWAS and RNA-seq analyses, associated with Verticillium-wilt resistance, including WRKY, MYB, CYP and RGA. This work contributes to our knowledge of the molecular processes underlying cotton responses to Verticillium wilt, offering crucial insights for additional research into the genes and pathways implicated in these responses and paving the way for developing Verticillium wilt-resistant cotton varieties through accelerated breeding by providing a plethora of candidate genes.

Simple SummaryDominance effects play important roles in determining genetic changes with regard to complex traits. We conducted genomic predictions and genome-wide association studies in order to investigate the effects of dominance on carcass weight, dressing percentage, meat percentage, average daily gain, and chuck roll in 1233 Simmental beef cattle. Using dominance models, we improved the predictive abilities and found several candidate single-nucleotide polymorphisms (SNPs) and genes associated with these traits. Our studies helped us to understand causal mutation mapping and genomic selection models with dominance effects in Chinese Simmental beef cattle.Non-additive effects play important roles in determining genetic changes with regard to complex traits; however, such effects are usually ignored in genetic evaluation and quantitative trait locus (QTL) mapping analysis. In this study, a two-component genome-based restricted maximum likelihood (GREML) was applied to obtain the additive genetic variance and dominance variance for carcass weight (CW), dressing percentage (DP), meat percentage (MP), average daily gain (ADG), and chuck roll (CR) in 1233 Simmental beef cattle. We estimated predictive abilities using additive models (genomic best linear unbiased prediction (GBLUP) and BayesA) and dominance models (GBLUP-D and BayesAD). Moreover, genome-wide association studies (GWAS) considering both additive and dominance effects were performed using a multi-locus mixed-model (MLMM) approach. We found that the estimated dominance variances accounted for 15.8%, 16.1%, 5.1%, 4.2%, and 9.7% of the total phenotypic variance for CW, DP, MP, ADG, and CR, respectively. Compared with BayesA and GBLUP, we observed 0.5–1.1% increases in predictive abilities of BayesAD and 0.5–0.9% increases in predictive abilities of GBLUP-D, respectively. Notably, we identified a dominance association signal for carcass weight within RIMS2, a candidate gene that has been associated with carcass weight in beef cattle. Our results suggest that dominance effects yield variable degrees of contribution to the total genetic variance of the studied traits in Simmental beef cattle. BayesAD and GBLUP-D are convenient models for the improvement of genomic prediction, and the detection of QTLs using a dominance model shows promise for use in GWAS in cattle.

BackgroundSingle-step genomic best linear unbiased prediction (SSGBLUP) is a comprehensive method for genomic prediction. Point estimates of marker effects from SSGBLUP are often used for genome-wide association studies (GWAS) without a formal framework of hypothesis testing. Our objective was to implement p-values for single-marker GWAS studies within the single-step GWAS (SSGWAS) framework by deriving computational algorithms and procedures, and by applying these to a large beef cattle population.MethodsP-values were obtained based on the prediction error (co)variances for single nucleotide polymorphisms (SNPs), which were obtained from the prediction error (co)variances of genomic predictions based on the inverse of the coefficient matrix and formulas to estimate SNP effects.ResultsComputation of p-values took a negligible time for a dataset with almost 2 million animals in the pedigree and 1424 genotyped sires, and no inflation of statistics was observed. The SNPs that passed the Bonferroni threshold of 10−5.9 were the same as those that explained the highest proportion of additive genetic variance, but even at the same significance levels and effects, some of them explained less genetic variance due to lower allele frequency.ConclusionsThe use of a p-value for SSGWAS is a very general and efficient strategy to identify quantitative trait loci (QTL). It can be used for complex datasets such as those used in animal breeding, where only a proportion of the pedigreed animals are genotyped.

Verticillium wilt (VW) of alfalfa is a devastating disease that causes forage yield reductions of up to 50% in the northern United States and Canada. The most effective method for controlling the disease is through the development and use of resistant varieties. To identify quantitative trait loci (QTL) for VW resistance in alfalfa, we used a full-sib population segregating for VW resistance. High-density linkage maps for both resistant and susceptible parents were constructed using single-dose alleles of single-nucleotide polymorphism markers generated by genotyping-by-sequencing. Five QTL associated with VW resistance were identified and they were in four linkage groups (4D, 6B, 6D, and 8C). Of those, three QTL (qVW-6D-1, qVW-6D-2, and qVW-8C) had higher logarithm of odds. Two putative candidates of nucleotide-binding site leucine-rich repeat disease resistance genes were identified in the QTL intervals of qVW-6D-2 and qVW-8C, respectively. The result agreed with our previous studies, in which similar resistance loci were identified in an association panel using genome-wide association. The results provide insight into the quantitative resistance to VW in alfalfa. The resistance loci and closely linked markers identified in the present study can be used in developing new alfalfa varieties with enhanced resistance to VW.