Genome-wide prediction and association mapping of potato common scab with historical data

Genome-wide prediction and association mapping of potato common scab with historical data | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Genome-wide prediction and association mapping of potato common scab with historical data Fatima Latif Azam, Matthijs Brouwer, David Douches, Joseph Coombs, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8543262/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Common scab, caused by Streptomyces spp ., is worldwide an important skin disease of potato, capable of significant reductions in marketable value. Resistant varieties developed through phenotypic selection have been the most effective strategy thus far. Previous studies on the genetics of resistance have identified only minor-effect QTLs. In the current study, we explored the value of historical data for genetic analysis, derived from 52 sources. Based on partial replication of the 3500 + varieties, generalized (entry-mean) heritability was estimated at 0.67. For a subset of 292 varieties with genome-wide markers, the genomic (narrow-sense) heritability was only 0.10. The historical data was combined with a contemporary US dataset spanning 6 environments and 416 varieties. Genome-wide association studies identified four QTLs, which together explained 7.5% of the variation. The median reliability (r 2 ) of genomic-estimated breeding values for marker-based selection was higher for the contemporary US chip group (0.5) than for European varieties from the historical dataset (0.3). This difference can be explained by the higher genomic heritability of the contemporary US dataset (0.24), its larger population size and higher degree of relatedness. This study has illustrated the potential for leveraging historical data for genomics-assisted breeding, but genetic gain for potato common scab resistance continues to be limited by low heritability and high polygenicity. Common scab Streptomyces spp. potato Solanum tuberosum Genome wide association study (GWAS) genomic prediction (GP) Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Potato is generally considered the most important non-cereal food crop in the world. Each year there are numerous diseases that affect potato quality and marketable yield. One of the major bacterial diseases is common scab of potato, which manifests as superficial, raised or deep-pitted lesions on the tuber surface (Fig. 1 ). It is caused by soil-borne Streptomyces spp ., which can survive in soil as a saprophyte for long periods in the absence of host plants surviving on decaying organic matter, either in its vegetative mycelial form or as spores (Loria et al., 2006 ). Strategies to control common scab include the application of organic amendments (e.g., soy and bone meal), crop rotation, site selection, soil acidification, maintaining high soil moisture through irrigation (Wolf and Boer, 2007; Dees and Wanner, 2012 ), and the use of resistant varieties. Disease resistance to common scab presents as a quantitative trait. Previous studies have sought to identify causal loci or at least genetic markers, but thus far only minor-effect quantitative trait loci (QTLs) have been identified, explaining less than 20% of the variance (Bradshaw et al., 2008 ; Braun et al., 2017 ; Kaiser et al., 2020 ; Yuan et al., 2019 ; Koizumi et al., 2021 ). For highly polygenic traits, genomic prediction (GP) is more effective than tracking individual markers (Bernardo and Yu, 2007 ; Heffner et al., 2011 ), and the feasibility of GP for common scab resistance was previously demonstrated (Enciso-Rodriguez et al., 2018 ). Varietal assessment for common scab resistance goes back as far as 1929 (van Loon et al., 2023 ). Phenotypic data has been collected over decades by many stakeholders, including breeding companies, research institutes, and through value for cultivation and use (VCU) trials worldwide. The present study sought to leverage this historical, global dataset and a contemporary US dataset for genomics-assisted breeding, with the aim of improving upon the traditional practice of phenotypic selection. Materials and methods Phenotypic data The historical phenotypic data (File S1) was compiled from 52 sources, such as breeding company catalogues, national trials for the value of cultivation and use (VCU), webpages and databases (Table S1 ). Sources needed at least 5 varieties for inclusion. Each variety name was checked for spelling and, if needed, changed to match the Potato Pedigree Database (van Berloo et al., 2007 ). For different varieties with the same name, the year of release and/or country of origin was added to the name. Sources with multiple years of phenotypic data, (e.g., Nederland's Potato Consultative Foundation (NIVAP), Geniteurslijst, Averis Seed and STET Holland) were combined by averaging the scores for overlapping varieties. For several companies (e.g., Agrico Research, Meijer Potato, HZPC Holland), data were collected from the European Cultivated Potato database and the company website as separate sources because there was a little overlap and different values, suggesting different origins. Each data source that did not conform with a scale ranging from 1 (susceptible) to 9 (resistant), was converted on an individual basis (Table S2 and Table S3 ). Text was converted into numbers using a reference midpoint; if no information was available on the full scale for disease scoring in a dataset, the scoring was left unchanged. The US phenotypic data (File S2) was collected in 3 locations (Antigo, Wisconsin; Hancock, Wisconsin; Lakeview, Michigan) across 4 years, for a total of 6 environments: Antigo24, Lakeview23, Lakeview24, Hancock21, Hancock22, Hancock23. The Hancock and Antigo environments were dedicated scab nurseries, using a randomized complete block design with 3 replicates. Partially replicated designs were used for the field trials in Lakeview. Potato clones in the Hancock and Lakeview environments were entries in the US National Chip Processing Trial. Potato clones in Antigo were from the UW-Madison chip breeding program. Scab resistance scores were transformed from the original scale of 0 (resist.) to 5 (susc.) to the 1–9 scale described above using the linear equation y = -1.6x + 9. Marker data Marker data (File S3) for varieties in the historical dataset was compiled from the 20K SNP array data of Vos et al. ( 2015 ), generating 14,377 SNPs. Marker data (File S4) for varieties in the contemporary US dataset was generated using a 22K SNP array that combined SNPs from the 12K SolCAP SNP array (Felcher et al., 2012 ) with the 20K array of Vos et al. ( 2015 ), generating 15,133 SNPs. A third marker dataset (File S5) was created by identifying common SNPs between the historical and US marker data, using the shared variety “Atlantic” as a reference. Markers with matching (3225) or complementary (2908) reference alleles across both datasets were retained, while markers with unknown reference alleles (1079) in either dataset or differing reference alleles (7) between datasets were removed. The final merged dataset contained 6133 SNP markers, with bp position based on the DMv6.1 reference genome (Pham et al., 2020 ). Data analysis Coefficient of variation (CV) analysis used a linear mixed model in ASReml-R v4.2 (Butler et al., 2023 ), specifying variety as a random effect and source as a fixed effect. CV was calculated as the standard deviation of the residuals for each source divided by the mean of the Best Linear Unbiased Estimates (BLUEs) for that source. Principal component analysis of the marker data utilized prcomp in base R (R Core Team 2025) and ggplot2 for visualization (Wickham, 2016 ). Analysis of the multi-environment trial data was based on a two-stage approach (Piepho et al., 2012 ; Damesa et al., 2017 ), using R package StageWise v1.13 (Endelman, 2023 ) and Asreml-R v4.2 (Butler et al., 2023 ). In Stage 1, BLUEs of each variety were computed per environment. The historical dataset was treated as one environment, with “source” (52 levels) modeled as a random effect. For US environments with row-column information, R package SpATS (Rodríguez-Álvarez et al., 2018 ) was used to remove micro-environmental variation. Entry-mean heritability in the broad-sense was computed for each environment, following the definition of generalized heritability from Oakey et al. ( 2006 ). For Stage 2, the response variable \(\:{y}_{ij\left(k\right)}\) was the Stage 1 BLUE for genotype i in environment j of dataset k , where k had two levels: historical and contemporary US. The linear model was: \(\:{y}_{ij\left(k\right)}={E}_{j\left(k\right)}+g{L}_{ik}+{r}_{i}+{s}_{ij\left(k\right)}+{\epsilon\:}_{ij\left(k\right)}\) [1] where \(\:{E}_{j\left(k\right)}\) was the fixed effect of environment, \(\:g{L}_{ik}\) was the random effect of genotype within dataset, \(\:{r}_{i}\) was a residual genetic effect, and \(\:{s}_{ij\left(k\right)}\) was the Stage 1 error. The \(\:{s}_{ij\left(k\right)}\) effect was multivariate normal with variance-covariance (var-cov) equal to the direct sum of the var-cov matrices for each environment (Damesa et al., 2017 ). The \(\:g{L}_{ik}\) effect was multivariate normal with var-cov equal to the direct product \(\:\mathbf{G}⨂\varvec{\Sigma\:}\) , where G is the genomic relationship matrix and \(\:\varvec{\Sigma\:}\:\) is the 2x2 var-cov matrix for the two datasets. Genomic (narrow-sense) heritability was calculated from the diagonal elements of \(\:\varvec{\Sigma\:}\) , which are the genomic additive variances per location, and accounted for G according to Legarra ( 2016 ). The residual genetic effect \(\:{r}_{i}\) and overall model residual \(\:\:{\epsilon\:}_{ij\left(k\right)}\) were both multivariate normal, with var-cov matrices equal to the direct sum of identity matrices multiplied by variance components specific for each dataset. To account for population structure, a structured genomic relationship matrix was used, based on the theory of Wientjes et al. ( 2017 ). A 3x3 partitioned matrix was used based on the three population groups (US chip, European, Other) identified by PCA. The diagonal blocks of the matrix are the standard tetraploid extension of VanRaden Method 1 (VanRaden, 2008 ; Endelman et al., 2018 ), using marker allele frequencies for each population. The off-diagonal blocks are computed analogously (see Wientjes et al., 2017 ) to model relationships between individuals from different groups. The reliability ( r 2 ) of the genomic-estimated breeding values (GEBV) from R/StageWise was based on the expected squared correlation between predicted and true values. GWAS The association analysis was carried out using two different structure correction models. The models K and P + K were studied using the R/GWASpoly package v2.13 (Rosyara et al., 2016 ). The K model uses a marker-derived relationship matrix, and the P + K model also includes principal components as fixed effects. The P + K model was applied only when a principal component explained 10% or more of the variance. We tested for scab associations in the historical, contemporary US, and integrated datasets. Scab associations were evaluated by specifying environment as a fixed effect and using the Stage 1 BLUEs as the response variable. Five genome scans were effectively conducted for each dataset, corresponding to the additive model, two simplex dominant models (1-dom-alt and 1-dom-ref), and two duplex dominant models (2-dom-alt and 2-dom-ref). P-value inflation was evaluated for both the K and P + K structure correction models via QQ-plots (Fig. S2 ). To remove markers with rare alleles and therefore low statistical power, the maximum genotype frequency was set at 1–5/N, where N is the number of genotypes. The effective number of markers (Moskvina and Schmidt, 2008 ) was used to establish a 0.05 significance level adjusted for multiple testing. The leave-one-chromosome-out (LOCO) method (Yang et al., 2014 ) was used to calculate a different covariance matrix for each chromosome based on the markers from all other chromosomes, avoiding the so-called “proximal contamination” (Listgarten et al., 2012 ). Moreover, the “population parameters previously determined” (P3D)/EMMAX approach (Kang et al., 2010 ; Zhang et al., 2010 ) was used to estimate variance components by REML only for the baseline (no QTL) model, making the analysis less computationally intensive. Multi-QTL models were built within GWASpoly for all scenarios with more than 1 significant SNP. Backwards elimination was employed to sequentially remove SNPs based on their p-values, beginning with the highest. Only SNPs with p-value < 0.05 were retained. All the results were visualised with functions within the GWASpoly package. Data Availability and Reproducibility The phenotypic and marker data needed to reproduce our results are provided in Files S1, S2 S3, S4, S5. R code to generate the results is provided in Markdown format in File S6. Results Phenotypic data The historical dataset had 52 sources and 3552 varieties. The five major sources AHDB, SASA, IPK, GRIN Czech and Geniteurslijst 1954-85 contributed more than 300 varieties each; nine sources contributed 100–300 varieties; 32 sources contributed 11–100 varieties; and six sources contributed up to ten varieties (Table S1 ). Twelve varieties (e.g., Désirée, Agria, Spunta) were present in more than 10 sources, 111 varieties in 6–10 sources, 1376 varieties in 2–5 sources, and the remaining 2053 varieties in only 1 source. The most studied varieties, with data from up to 8 different sources, did not seem to have a consensus score (Fig. S1 ). Using the partially replicated nature of the historical dataset, 67% of the observed variation was attributed to genetic differences. Some sources showed greater residual variation than others. NEIKER, Germicopa netted scab, Danespo, Potato Research Institute and Nordic Genetic Resource Center had coefficients of variation (CV) greater than 15%, with the highest value being 22%, while eighteen sources had a CV less than 10% (Table S4 ). The contemporary US dataset consisted of 718 clones evaluated in dedicated scab nurseries at three different locations over four years, resulting in six distinct environments (location-year combination). The number of varieties per environment ranged from 126 to 268. The Hancock, Wisconsin, location showed the lowest entry-mean heritability (H 2 ), with values of 0.53–0.59 over 3 years. The Antigo, Wisconsin, environment had H 2 = 0.66, and the Lakeview location, Michigan, had H 2 = 0.75–0.82. The top five most tested varieties in the contemporary US dataset are Atlantic, Lamoka, Snowden, Dundee, and Bliss, with up to 29 replications across the different environments. Table 1 Key characteristics of the datasets analysed in this study. Population Environments No. genotyped clones No. markers Broad-sense Heritability (H 2 ) a Historical dataset 46 292 14,377 0.67 Contemporary US dataset 6 416 15,133 [0.53–0.82] a Entry-mean heritability was estimated using all clones, both genotyped and ungenotyped. Population structure Genome-wide marker data was available for 416 varieties in the contemporary US dataset (from all 6 environments) and 292 varieties (from 46 sources) in the historical dataset. With one variety (‘Atlantic’) in common between historical and contemporary US datasets, the integrated dataset had 707 varieties and 6133 SNP markers. The 1st two principal components explained 13.1% of the variance (Fig. 2 A). PC1 separated the European varieties (highlighted in red) from the US germplasm developed for the round white potato chip market (highlighted in blue). Varieties intermediate between these two groups, including North American russets and some historical varieties, were assigned to a third group “Other" (highlighted in dark green). For a PCA within the European group, the 1st two PCs explained 6.9% of the variance (Fig. 2 B). PC1 separated varieties bred for the starch processing market, while PC2 separated British from continental European varieties. The population structure of the European group was not confounded with common scab resistance scores, as indicated by an extremely low correlation of 0.06 between PC1 and the resistance score (estimated BLUEs). GEBV reliability Given the polygenic nature of common scab resistance, the potential for genomic prediction was explored. In the integrated dataset, the genomic heritability was only 10% and 24% for the historical and contemporary US datasets, respectively (Table 2 ). Because the historical dataset was modelled as a single environment, the residual genetic (i.e., clone) and genotype x environment (GxE) effects were confounded in the Stage 2 model and explained 61% of the variation in the Stage 1 BLUEs. For the contemporary US dataset, these two effects could be estimated separately, with 16% of the variation explained as residual genetic and 26% as GxE. Stage 1 errors accounted for 29% and 35% of the variation in the historical and contemporary US locations, respectively. Reliability was estimated for genome-wide, marker-assisted prediction from BLUP theory, which is the expected squared correlation between predicted and true values when phenotypes for the selection candidates are included in the training set. Reliability of GEBVs for the US chip group was higher (median 0.50) than for the European group (median 0.31), and the "Other" group was in between (Fig. 3 ). Due to the different environments and populations in the two datasets, the phenotype data were modelled as correlated traits. The estimated additive genomic correlation was very high. With the residual genetic model, it was 0.999, but because of its proximity to the upper boundary (maximum of 1), no SE was returned by REML (File S6). When the residual genetic effect was replaced with a dominance effect, the estimate was 0.95 with a SE of 0.5; the low precision of the estimate was likely due to the low genomic heritability of the historical dataset. Nonetheless, the high correlation provided justification to perform genetic discovery by GWAS on the integrated dataset. Table 2 Variance components and percent variance explained (PVE) for each location in the integrated dataset. Historical Contemporary US Variance PVE Variance PVE env 0.000 NA 0.279 NA additive 0.132 10.4% 0.201 24.0% g.resid 0.776 61.0% 0.132 15.8% g x env NA NA 0.212 25.5% Stage1.error 0.363 28.6% 0.289 34.6% GWAS There were no significant associations in either the contemporary US or historical datasets, and 4 unique SNPs were identified in the integrated dataset (Fig. 4 ), using three different marker-effect models (additive, 1-dom, 2-dom). The integrated dataset retained 4 markers that explained from 1.6% to 2.2% of the variance, accounting for a total of 7.5% collectively (Table 3 ). The effect sizes ranged from − 0.47 units to + 0.37 units and, independent of the marker-effect model, we observed a pair of SNPs in LD. PotVar0041300 and PotVar0042350 are around 1 Mb apart on chromosome 1. The SNP minor allele frequencies (MAF) in Table 3 are based on the integrated population. Considering commonly used MAF thresholds, the SNPs in the multi-QTL model were divided into two categories: two SNPs with MAF between 5 and 20% were classified as intermediate variants; and 2 SNPs with MAF greater than 20% were considered common variants. Based on the analysis of Vos et al. ( 2015 ), all the identified SNPs were first observed in older varieties. PotVar0042350 and PotVar0117603 were first observed in Yam; and PotVar0041300 and PotVar0099669 were first detected in Katahdin and Peerless, respectively. Table 3 SNPs retained in the multi-QTL model for the integrated dataset. PVE = percent variance explained under backward elimination. Marker Chrom Position (bp) Model Score Effect PVE MAF (%) Minor Genotype Freq. (%) a PotVar0041300 chr01 75095240 1-dom-alt 5.13 -0.35 1.6 12.9 36.0 PotVar0099669 chr01 87350385 1-dom-ref 4.54 0.35 1.6 8.8 28.7 PotVar0042350 chr01 76374420 2-dom-alt 6.56 0.37 2 28.8 31.7 PotVar0117603 chr02 24247561 2-dom-alt 5.36 -0.47 2.2 38.7 18.4 a Genotypes with the same effect on the phenotype in a dominance model are treated as equivalent when calculating the minor genotype frequency. Discussion This study investigated the potential use of historical phenotypic data, both alone and combined with contemporary data, for genetic discovery and genomic prediction. Combining and harmonizing scab data from different sources had significant challenges, as many factors influence disease development. The trials contributing to these data were conducted at different locations and under different environmental conditions. Different scab species may have been recorded based on the lesion type, even though Streptomyces scabies is the most common scab-causing species. Different scab-causing species can produce slightly different symptoms, and not all species are equally pathogenic (Hudec et al., 2021 ). Our estimates of entry-mean, broad-sense heritability (0.53 to 0.82) are similar to many previous studies (Haynes et al., 1997 ; Bradshaw et al., 2008 ; Yuan et al., 2019 ; Braun et al., 2017 ; Enciso-Rodriguez et al., 2018 ; Pereira et al., 2021; Sharma et al., 2024), although lower values have also been reported (Haynes et al., 2009 ; Zorrilla et al., 2014). Consistent with previous studies, only minor QTL were discovered, with 1–2% PVE (percent variance explained). The region [email protected] Mb identified in this study coincides with QTL reported by Kaiser et al. ( 2020 ) in a tetraploid cross for scab tuber coverage and by Koizumi et al. ( 2021 ) in a panel of tetraploid varieties and advanced breeding clones. Our QTL [email protected] Mb is also near the position of a QTL reported by Kaiser et al. ( 2020 ). It is noteworthy that all four QTL in the final model were based on dominance effects (Table 3 ). This is consistent with the polygenic model used for genomic variance partitioning (Table 2 ), in which the residual genetic (i.e., non-additive) effect accounted for 40% of the total genetic variance in the contemporary US dataset and 85% in the historical dataset, although the latter is likely inflated due to confounding with the GxE effect. But the best-fit model for a marker may not match the gene action at the unobserved QTL, because the marker PVE is also influenced by marker-QTL LD (Rosyara et al., 2016 ). Unlike with pedigreed mapping populations, in which LD decays smoothly, LD patterns are very complex in variety panels (Vos et al., 2017 ); even adjacent SNPs can have near-zero LD when derived from different haplotypes. Most GWAS studies use additive models, for which genetic variance (and therefore PVE) increases with MAF and the effect magnitude. In Table 2 , however, the chr02 QTL has similar PVE to the others despite having higher MAF and larger effect magnitude. This is because for the type of dominance models used in GWASpoly, PVE is determined by the frequency of the minor genotype "equivalence class", not the minor allele. For the 2-dom model, which is only applicable to polyploids, it is the combined frequency of the zero-and single-dose genotypes, which was lower for the chr02 QTL (18% vs. 29–36%). The average (or median) reliability of breeding values (BVs) provides an estimate of the squared accuracy of selection (Laloë, 1993 ; Clark et al., 2012 ), analogous to narrow-sense heritability as a measure of squared accuracy for phenotypic selection. Under marker-assisted genomic selection, which implies the use of both phenotypes and marker-based predictions for the selection candidates (Lande and Thompson, 1990; Bernardo, 2020 ), reliability is higher than using only phenotype data alone (i.e., narrow-sense heritability) or only marker-based prediction (Riedelsheimer et al., 2013 ; Endelman et al., 2014 ). This difference is evident when comparing Fig. 3 with Table 2 , as median GEBV reliability for the US chip group was 0.50 vs. 0.24 for the genomic heritability. Although some North American lines were present in the historical dataset, it predominantly consists of data for European varieties in European environments. Since genomic heritability for the historical dataset was lower than the contemporary US dataset (0.10 vs. 0.24), it follows that median GEBV reliability for the European group would be lower in the integrated data analysis (0.3 vs. 0.5). The larger size and higher degree of relatedness within the US chip group compared to the European group may also have contributed to the higher GEBV reliability, as these factors have been observed to increase prediction accuracy in many studies (Lorenz et al., 2011 ). Conclusion Although phenotypic selection for resistance has been practiced for decades, potato common scab remains a challenging disease to manage. This study has confirmed earlier research that resistance is a complex trait controlled by many small-effect loci. The dataset from this study can be included in future research to increase statistical power, but uncertainty about trial conditions in the historical dataset will likely remain a limitation. In the future, accurate reporting of environmental factors and pathogen populations would facilitate merging phenotypic data from multiple sources, as would the adoption of a common rating scale. Empirical studies of scab resistance over multiple cycles of genomic selection are needed to validate the practical utility of the methodology. Declarations Conflict of interest H.J. van Eck is the Editor-in-Chief of Euphytica. The authors are not aware of other relevant financial or non-financial interests to disclose. Competing Interests H.J. van Eck is the Editor-in-Chief of Euphytica. Funding F.L.A. was supported by the Teagasc Walsh Scholarship programme. Collection of the US data was supported by the USDA National Institute of Food & Agriculture Award 2023-34141-41020 and by the Wisconsin Potato & Vegetable Growers Association. Author Contribution Conceptualization and funding acquisition: HJvE, DG, JE. Data collection: FLA, MB, DD, JC, AW, JE. Data analysis: FLA, JE. Supervision: MCH, DM, HJvE, JE. Writing original draft: FLA, JE. Manuscript editing: FLA, DM, DG, HJvE, JE. 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Potato Research 63 (2):253-266. doi:10.1007/s11540-019-09437-w Zhang Z, Ersoz E, Lai CQ, Todhunter RJ, Tiwari HK, Gore MA, Bradbury PJ, Yu J, Arnett DK, Ordovas JM, Buckler ES (2010) Mixed linear model approach adapted for genome-wide association studies. Nat Genet 42 (4):355-360. doi:10.1038/ng.546 Zorrilla C, Navarro F, Vega‐Semorile S, Palta J (2021) QTL for pitted scab, hollow heart, and tuber calcium identified in a tetraploid population of potato derived from an Atlantic × Superior cross. Crop Science 61 (3):1630-1651. doi:10.1002/csc2.20388 Additional Declarations Competing interest reported. H.J. van Eck is the Editor-in-Chief of Euphytica. Supplementary Files FileS1.csv File S1: Historical phenotypic data with transformed scores FileS2.csv File S2: Contemporary US phenotypic data FileS3.csv File S3: Marker genotype data for historical dataset FileS4.csv File S4: Marker genotype data for contemporary US dataset FileS5.csv File S5: Combined marker genotype data FileS6.pdf File S6: R Markdown file with complete data analysis FileS7.pdf File S7: Supplemental tables and figures Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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1","display":"","copyAsset":false,"role":"figure","size":816709,"visible":true,"origin":"","legend":"\u003cp\u003eRaised common scab lesions on the surface of potato tubers.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8543262/v1/c7d1edef24e644f6e42124f7.png"},{"id":100361423,"identity":"32b7b227-d784-4122-9b22-12e6cbeec45a","added_by":"auto","created_at":"2026-01-16 07:45:08","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":350427,"visible":true,"origin":"","legend":"\u003cp\u003ePrincipal Component Analysis (PCA) of genetic variation in two populations. (A) 707 potato varieties in the integrated dataset, with different symbols for “US chip” (blue diamonds), “European” (red circles) and “Other” (dark green plus symbol). (B) 275 potato varieties in the historical dataset of European origin, with starch varieties separated along PC1 and varieties with British origin distinguished along PC2.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8543262/v1/9cc2937c3c5739e7f7b5f393.png"},{"id":100362072,"identity":"fbff00c4-a757-46e6-88d9-20e7aba99c50","added_by":"auto","created_at":"2026-01-16 07:46:10","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":162612,"visible":true,"origin":"","legend":"\u003cp\u003eViolin plot showing the reliability of genomic estimated breeding values (GEBVs) according to BLUP theory, grouped by population structure in the integrated dataset. The width of each violin represents the density at different values, with wider areas indicating higher density. The central line within each violin indicates the median.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8543262/v1/66be7999ffa52176257ea0df.png"},{"id":100361601,"identity":"e56fac72-e176-40ff-ae54-cf2b5d535d70","added_by":"auto","created_at":"2026-01-16 07:45:21","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":335259,"visible":true,"origin":"","legend":"\u003cp\u003eManhattan plots of genome-wide association results for common scab resistance in the integrated dataset. The x-axis shows the chromosomal position of each SNP, while the y-axis represents the –log10(p-value) for association. Each dot corresponds to a SNP, coloured by chromosome. The horizontal line indicates the genome-wide significance threshold calculated using the M.eff method.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8543262/v1/3aba13489fc30ae9abf11969.png"},{"id":101513978,"identity":"6d6742eb-df63-4d3b-aa30-fb97d739fd76","added_by":"auto","created_at":"2026-01-30 15:40:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2536541,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8543262/v1/83e168c2-636c-43d9-a00f-9344882bfac3.pdf"},{"id":100361347,"identity":"93e766ea-e048-4052-b13b-9f6622c3dad0","added_by":"auto","created_at":"2026-01-16 07:45:00","extension":"csv","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":232920,"visible":true,"origin":"","legend":"\u003cp\u003eFile S1: Historical phenotypic data with transformed scores\u003c/p\u003e","description":"","filename":"FileS1.csv","url":"https://assets-eu.researchsquare.com/files/rs-8543262/v1/3426f1534464d16a04274c45.csv"},{"id":100012471,"identity":"80a41df8-bb44-4955-ac6f-9dd25a7d54f1","added_by":"auto","created_at":"2026-01-12 06:14:04","extension":"csv","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":100637,"visible":true,"origin":"","legend":"\u003cp\u003eFile S2: Contemporary US phenotypic data\u003c/p\u003e","description":"","filename":"FileS2.csv","url":"https://assets-eu.researchsquare.com/files/rs-8543262/v1/1ce13ac8beb7ffc8ae6431b3.csv"},{"id":100012491,"identity":"cf5b456c-c8c6-4a4f-96b1-822a24072721","added_by":"auto","created_at":"2026-01-12 06:14:04","extension":"csv","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":8962733,"visible":true,"origin":"","legend":"\u003cp\u003eFile S3: Marker genotype data for historical dataset\u003c/p\u003e","description":"","filename":"FileS3.csv","url":"https://assets-eu.researchsquare.com/files/rs-8543262/v1/84ca820cd8624a61c728b3df.csv"},{"id":100012492,"identity":"1f23cf10-883e-492d-b500-707d19423f41","added_by":"auto","created_at":"2026-01-12 06:14:04","extension":"csv","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":13150557,"visible":true,"origin":"","legend":"\u003cp\u003eFile S4: Marker genotype data for contemporary US dataset\u003c/p\u003e","description":"","filename":"FileS4.csv","url":"https://assets-eu.researchsquare.com/files/rs-8543262/v1/a16a43951278cc8504bf2134.csv"},{"id":100362007,"identity":"0adde8d6-0cf3-4206-bb65-cbcb3ddfb44a","added_by":"auto","created_at":"2026-01-16 07:46:03","extension":"csv","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":8891892,"visible":true,"origin":"","legend":"\u003cp\u003eFile S5: Combined marker genotype data\u003c/p\u003e","description":"","filename":"FileS5.csv","url":"https://assets-eu.researchsquare.com/files/rs-8543262/v1/fd92bb95a39119d37fde5fab.csv"},{"id":100012485,"identity":"7707e6b0-7f74-4165-8197-21caf1f494f4","added_by":"auto","created_at":"2026-01-12 06:14:04","extension":"pdf","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":2756007,"visible":true,"origin":"","legend":"\u003cp\u003eFile S6: R Markdown file with complete data analysis\u003c/p\u003e","description":"","filename":"FileS6.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8543262/v1/7c8f5c7c7c0289c6c1271f39.pdf"},{"id":100012479,"identity":"6d6434f2-26d8-46f5-9a91-a859704c2622","added_by":"auto","created_at":"2026-01-12 06:14:04","extension":"pdf","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":1062378,"visible":true,"origin":"","legend":"\u003cp\u003eFile S7: Supplemental tables and figures\u003c/p\u003e","description":"","filename":"FileS7.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8543262/v1/7353ec1c0780322df98aed2b.pdf"}],"financialInterests":"Competing interest reported. H.J. van Eck is the Editor-in-Chief of Euphytica.","formattedTitle":"Genome-wide prediction and association mapping of potato common scab with historical data","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePotato is generally considered the most important non-cereal food crop in the world. Each year there are numerous diseases that affect potato quality and marketable yield. One of the major bacterial diseases is common scab of potato, which manifests as superficial, raised or deep-pitted lesions on the tuber surface (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). It is caused by soil-borne Streptomyces \u003cem\u003espp\u003c/em\u003e., which can survive in soil as a saprophyte for long periods in the absence of host plants surviving on decaying organic matter, either in its vegetative mycelial form or as spores (Loria et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Strategies to control common scab include the application of organic amendments (e.g., soy and bone meal), crop rotation, site selection, soil acidification, maintaining high soil moisture through irrigation (Wolf and Boer, 2007; Dees and Wanner, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), and the use of resistant varieties.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDisease resistance to common scab presents as a quantitative trait. Previous studies have sought to identify causal loci or at least genetic markers, but thus far only minor-effect quantitative trait loci (QTLs) have been identified, explaining less than 20% of the variance (Bradshaw et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Braun et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Kaiser et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Yuan et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Koizumi et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). For highly polygenic traits, genomic prediction (GP) is more effective than tracking individual markers (Bernardo and Yu, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Heffner et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), and the feasibility of GP for common scab resistance was previously demonstrated (Enciso-Rodriguez et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eVarietal assessment for common scab resistance goes back as far as 1929 (van Loon et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Phenotypic data has been collected over decades by many stakeholders, including breeding companies, research institutes, and through value for cultivation and use (VCU) trials worldwide. The present study sought to leverage this historical, global dataset and a contemporary US dataset for genomics-assisted breeding, with the aim of improving upon the traditional practice of phenotypic selection.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePhenotypic data\u003c/h2\u003e \u003cp\u003eThe historical phenotypic data (File S1) was compiled from 52 sources, such as breeding company catalogues, national trials for the value of cultivation and use (VCU), webpages and databases (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Sources needed at least 5 varieties for inclusion. Each variety name was checked for spelling and, if needed, changed to match the Potato Pedigree Database (van Berloo et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). For different varieties with the same name, the year of release and/or country of origin was added to the name. Sources with multiple years of phenotypic data, (e.g., Nederland's Potato Consultative Foundation (NIVAP), Geniteurslijst, Averis Seed and STET Holland) were combined by averaging the scores for overlapping varieties. For several companies (e.g., Agrico Research, Meijer Potato, HZPC Holland), data were collected from the European Cultivated Potato database and the company website as separate sources because there was a little overlap and different values, suggesting different origins. Each data source that did not conform with a scale ranging from 1 (susceptible) to 9 (resistant), was converted on an individual basis (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e and Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e). Text was converted into numbers using a reference midpoint; if no information was available on the full scale for disease scoring in a dataset, the scoring was left unchanged.\u003c/p\u003e \u003cp\u003eThe US phenotypic data (File S2) was collected in 3 locations (Antigo, Wisconsin; Hancock, Wisconsin; Lakeview, Michigan) across 4 years, for a total of 6 environments: Antigo24, Lakeview23, Lakeview24, Hancock21, Hancock22, Hancock23. The Hancock and Antigo environments were dedicated scab nurseries, using a randomized complete block design with 3 replicates. Partially replicated designs were used for the field trials in Lakeview. Potato clones in the Hancock and Lakeview environments were entries in the US National Chip Processing Trial. Potato clones in Antigo were from the UW-Madison chip breeding program. Scab resistance scores were transformed from the original scale of 0 (resist.) to 5 (susc.) to the 1\u0026ndash;9 scale described above using the linear equation y = -1.6x\u0026thinsp;+\u0026thinsp;9.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMarker data\u003c/h3\u003e\n\u003cp\u003eMarker data (File S3) for varieties in the historical dataset was compiled from the 20K SNP array data of Vos et al. (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), generating 14,377 SNPs. Marker data (File S4) for varieties in the contemporary US dataset was generated using a 22K SNP array that combined SNPs from the 12K SolCAP SNP array (Felcher et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) with the 20K array of Vos et al. (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), generating 15,133 SNPs. A third marker dataset (File S5) was created by identifying common SNPs between the historical and US marker data, using the shared variety \u0026ldquo;Atlantic\u0026rdquo; as a reference. Markers with matching (3225) or complementary (2908) reference alleles across both datasets were retained, while markers with unknown reference alleles (1079) in either dataset or differing reference alleles (7) between datasets were removed. The final merged dataset contained 6133 SNP markers, with bp position based on the DMv6.1 reference genome (Pham et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003eCoefficient of variation (CV) analysis used a linear mixed model in ASReml-R v4.2 (Butler et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), specifying variety as a random effect and source as a fixed effect. CV was calculated as the standard deviation of the residuals for each source divided by the mean of the Best Linear Unbiased Estimates (BLUEs) for that source. Principal component analysis of the marker data utilized \u003cem\u003eprcomp\u003c/em\u003e in base R (R Core Team 2025) and \u003cem\u003eggplot2\u003c/em\u003e for visualization (Wickham, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAnalysis of the multi-environment trial data was based on a two-stage approach (Piepho et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Damesa et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), using R package StageWise v1.13 (Endelman, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and Asreml-R v4.2 (Butler et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In Stage 1, BLUEs of each variety were computed per environment. The historical dataset was treated as one environment, with \u0026ldquo;source\u0026rdquo; (52 levels) modeled as a random effect. For US environments with row-column information, R package SpATS (Rodr\u0026iacute;guez-\u0026Aacute;lvarez et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) was used to remove micro-environmental variation. Entry-mean heritability in the broad-sense was computed for each environment, following the definition of generalized heritability from Oakey et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFor Stage 2, the response variable \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{y}_{ij\\left(k\\right)}\\)\u003c/span\u003e\u003c/span\u003e was the Stage 1 BLUE for genotype \u003cem\u003ei\u003c/em\u003e in environment \u003cem\u003ej\u003c/em\u003e of dataset \u003cem\u003ek\u003c/em\u003e, where \u003cem\u003ek\u003c/em\u003e had two levels: historical and contemporary US. The linear model was:\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{y}_{ij\\left(k\\right)}={E}_{j\\left(k\\right)}+g{L}_{ik}+{r}_{i}+{s}_{ij\\left(k\\right)}+{\\epsilon\\:}_{ij\\left(k\\right)}\\)\u003c/span\u003e \u003c/span\u003e [1]\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{E}_{j\\left(k\\right)}\\)\u003c/span\u003e\u003c/span\u003e was the fixed effect of environment, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:g{L}_{ik}\\)\u003c/span\u003e\u003c/span\u003e was the random effect of genotype within dataset, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{r}_{i}\\)\u003c/span\u003e\u003c/span\u003e was a residual genetic effect, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{s}_{ij\\left(k\\right)}\\)\u003c/span\u003e\u003c/span\u003e was the Stage 1 error. The \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{s}_{ij\\left(k\\right)}\\)\u003c/span\u003e\u003c/span\u003e effect was multivariate normal with variance-covariance (var-cov) equal to the direct sum of the var-cov matrices for each environment (Damesa et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:g{L}_{ik}\\)\u003c/span\u003e\u003c/span\u003e effect was multivariate normal with var-cov equal to the direct product \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\mathbf{G}⨂\\varvec{\\Sigma\\:}\\)\u003c/span\u003e\u003c/span\u003e, where \u003cb\u003eG\u003c/b\u003e is the genomic relationship matrix and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varvec{\\Sigma\\:}\\:\\)\u003c/span\u003e\u003c/span\u003eis the 2x2 var-cov matrix for the two datasets. Genomic (narrow-sense) heritability was calculated from the diagonal elements of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varvec{\\Sigma\\:}\\)\u003c/span\u003e\u003c/span\u003e, which are the genomic additive variances per location, and accounted for \u003cb\u003eG\u003c/b\u003e according to Legarra (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The residual genetic effect \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{r}_{i}\\)\u003c/span\u003e\u003c/span\u003e and overall model residual\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:{\\epsilon\\:}_{ij\\left(k\\right)}\\)\u003c/span\u003e\u003c/span\u003e were both multivariate normal, with var-cov matrices equal to the direct sum of identity matrices multiplied by variance components specific for each dataset.\u003c/p\u003e \u003cp\u003eTo account for population structure, a structured genomic relationship matrix was used, based on the theory of Wientjes et al. (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). A 3x3 partitioned matrix was used based on the three population groups (US chip, European, Other) identified by PCA. The diagonal blocks of the matrix are the standard tetraploid extension of VanRaden Method 1 (VanRaden, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Endelman et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), using marker allele frequencies for each population. The off-diagonal blocks are computed analogously (see Wientjes et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) to model relationships between individuals from different groups.\u003c/p\u003e \u003cp\u003eThe reliability (\u003cem\u003er\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e) of the genomic-estimated breeding values (GEBV) from R/StageWise was based on the expected squared correlation between predicted and true values.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eGWAS\u003c/h3\u003e\n\u003cp\u003eThe association analysis was carried out using two different structure correction models. The models K and P\u0026thinsp;+\u0026thinsp;K were studied using the R/GWASpoly package v2.13 (Rosyara et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The K model uses a marker-derived relationship matrix, and the P\u0026thinsp;+\u0026thinsp;K model also includes principal components as fixed effects. The P\u0026thinsp;+\u0026thinsp;K model was applied only when a principal component explained 10% or more of the variance. We tested for scab associations in the historical, contemporary US, and integrated datasets.\u003c/p\u003e \u003cp\u003eScab associations were evaluated by specifying environment as a fixed effect and using the Stage 1 BLUEs as the response variable. Five genome scans were effectively conducted for each dataset, corresponding to the additive model, two simplex dominant models (1-dom-alt and 1-dom-ref), and two duplex dominant models (2-dom-alt and 2-dom-ref). P-value inflation was evaluated for both the K and P\u0026thinsp;+\u0026thinsp;K structure correction models via QQ-plots (Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). To remove markers with rare alleles and therefore low statistical power, the maximum genotype frequency was set at 1\u0026ndash;5/N, where N is the number of genotypes. The effective number of markers (Moskvina and Schmidt, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) was used to establish a 0.05 significance level adjusted for multiple testing.\u003c/p\u003e \u003cp\u003eThe leave-one-chromosome-out (LOCO) method (Yang et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) was used to calculate a different covariance matrix for each chromosome based on the markers from all other chromosomes, avoiding the so-called \u0026ldquo;proximal contamination\u0026rdquo; (Listgarten et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Moreover, the \u0026ldquo;population parameters previously determined\u0026rdquo; (P3D)/EMMAX approach (Kang et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) was used to estimate variance components by REML only for the baseline (no QTL) model, making the analysis less computationally intensive.\u003c/p\u003e \u003cp\u003eMulti-QTL models were built within GWASpoly for all scenarios with more than 1 significant SNP. Backwards elimination was employed to sequentially remove SNPs based on their p-values, beginning with the highest. Only SNPs with p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were retained. All the results were visualised with functions within the GWASpoly package.\u003c/p\u003e\n\u003ch3\u003eData Availability and Reproducibility\u003c/h3\u003e\n\u003cp\u003eThe phenotypic and marker data needed to reproduce our results are provided in Files S1, S2 S3, S4, S5. R code to generate the results is provided in Markdown format in File S6.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003ePhenotypic data\u003c/h2\u003e \u003cp\u003eThe historical dataset had 52 sources and 3552 varieties. The five major sources AHDB, SASA, IPK, GRIN Czech and Geniteurslijst 1954-85 contributed more than 300 varieties each; nine sources contributed 100\u0026ndash;300 varieties; 32 sources contributed 11\u0026ndash;100 varieties; and six sources contributed up to ten varieties (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Twelve varieties (e.g., D\u0026eacute;sir\u0026eacute;e, Agria, Spunta) were present in more than 10 sources, 111 varieties in 6\u0026ndash;10 sources, 1376 varieties in 2\u0026ndash;5 sources, and the remaining 2053 varieties in only 1 source. The most studied varieties, with data from up to 8 different sources, did not seem to have a consensus score (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eUsing the partially replicated nature of the historical dataset, 67% of the observed variation was attributed to genetic differences. Some sources showed greater residual variation than others. NEIKER, Germicopa netted scab, Danespo, Potato Research Institute and Nordic Genetic Resource Center had coefficients of variation (CV) greater than 15%, with the highest value being 22%, while eighteen sources had a CV less than 10% (Table \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe contemporary US dataset consisted of 718 clones evaluated in dedicated scab nurseries at three different locations over four years, resulting in six distinct environments (location-year combination). The number of varieties per environment ranged from 126 to 268. The Hancock, Wisconsin, location showed the lowest entry-mean heritability (H\u003csup\u003e2\u003c/sup\u003e), with values of 0.53\u0026ndash;0.59 over 3 years. The Antigo, Wisconsin, environment had H\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.66, and the Lakeview location, Michigan, had H\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.75\u0026ndash;0.82. The top five most tested varieties in the contemporary US dataset are Atlantic, Lamoka, Snowden, Dundee, and Bliss, with up to 29 replications across the different environments.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eKey characteristics of the datasets analysed in this study.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePopulation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEnvironments\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo. genotyped clones\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo. markers\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBroad-sense Heritability (H\u003csup\u003e2\u003c/sup\u003e) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistorical dataset\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14,377\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContemporary\u003c/p\u003e \u003cp\u003eUS dataset\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e416\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15,133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e[0.53\u0026ndash;0.82]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003csup\u003ea\u003c/sup\u003e Entry-mean heritability was estimated using all clones, both genotyped and ungenotyped.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePopulation structure\u003c/h3\u003e\n\u003cp\u003eGenome-wide marker data was available for 416 varieties in the contemporary US dataset (from all 6 environments) and 292 varieties (from 46 sources) in the historical dataset.\u003c/p\u003e \u003cp\u003eWith one variety (\u0026lsquo;Atlantic\u0026rsquo;) in common between historical and contemporary US datasets, the integrated dataset had 707 varieties and 6133 SNP markers. The 1st two principal components explained 13.1% of the variance (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). PC1 separated the European varieties (highlighted in red) from the US germplasm developed for the round white potato chip market (highlighted in blue). Varieties intermediate between these two groups, including North American russets and some historical varieties, were assigned to a third group \u0026ldquo;Other\" (highlighted in dark green).\u003c/p\u003e \u003cp\u003eFor a PCA within the European group, the 1st two PCs explained 6.9% of the variance (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). PC1 separated varieties bred for the starch processing market, while PC2 separated British from continental European varieties. The population structure of the European group was not confounded with common scab resistance scores, as indicated by an extremely low correlation of 0.06 between PC1 and the resistance score (estimated BLUEs).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eGEBV reliability\u003c/h2\u003e \u003cp\u003eGiven the polygenic nature of common scab resistance, the potential for genomic prediction was explored. In the integrated dataset, the genomic heritability was only 10% and 24% for the historical and contemporary US datasets, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Because the historical dataset was modelled as a single environment, the residual genetic (i.e., clone) and genotype x environment (GxE) effects were confounded in the Stage 2 model and explained 61% of the variation in the Stage 1 BLUEs. For the contemporary US dataset, these two effects could be estimated separately, with 16% of the variation explained as residual genetic and 26% as GxE. Stage 1 errors accounted for 29% and 35% of the variation in the historical and contemporary US locations, respectively.\u003c/p\u003e \u003cp\u003eReliability was estimated for genome-wide, marker-assisted prediction from BLUP theory, which is the expected squared correlation between predicted and true values when phenotypes for the selection candidates are included in the training set. Reliability of GEBVs for the US chip group was higher (median 0.50) than for the European group (median 0.31), and the \"Other\" group was in between (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDue to the different environments and populations in the two datasets, the phenotype data were modelled as correlated traits. The estimated additive genomic correlation was very high. With the residual genetic model, it was 0.999, but because of its proximity to the upper boundary (maximum of 1), no SE was returned by REML (File S6). When the residual genetic effect was replaced with a dominance effect, the estimate was 0.95 with a SE of 0.5; the low precision of the estimate was likely due to the low genomic heritability of the historical dataset. Nonetheless, the high correlation provided justification to perform genetic discovery by GWAS on the integrated dataset.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eVariance components and percent variance explained (PVE) for each location in the integrated dataset.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eHistorical\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eContemporary US\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eVariance\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003ePVE\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eVariance\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003ePVE\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eenv\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eadditive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eg.resid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.776\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eg x env\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage1.error\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eGWAS\u003c/h2\u003e \u003cp\u003eThere were no significant associations in either the contemporary US or historical datasets, and 4 unique SNPs were identified in the integrated dataset (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), using three different marker-effect models (additive, 1-dom, 2-dom). The integrated dataset retained 4 markers that explained from 1.6% to 2.2% of the variance, accounting for a total of 7.5% collectively (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The effect sizes ranged from \u0026minus;\u0026thinsp;0.47 units to +\u0026thinsp;0.37 units and, independent of the marker-effect model, we observed a pair of SNPs in LD. PotVar0041300 and PotVar0042350 are around 1 Mb apart on chromosome 1.\u003c/p\u003e \u003cp\u003eThe SNP minor allele frequencies (MAF) in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e are based on the integrated population. Considering commonly used MAF thresholds, the SNPs in the multi-QTL model were divided into two categories: two SNPs with MAF between 5 and 20% were classified as intermediate variants; and 2 SNPs with MAF greater than 20% were considered common variants. Based on the analysis of Vos et al. (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), all the identified SNPs were first observed in older varieties. PotVar0042350 and PotVar0117603 were first observed in Yam; and PotVar0041300 and PotVar0099669 were first detected in Katahdin and Peerless, respectively.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSNPs retained in the multi-QTL model for the integrated dataset. PVE\u0026thinsp;=\u0026thinsp;percent variance explained under backward elimination.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarker\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChrom\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePosition (bp)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eScore\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEffect\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePVE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMAF (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMinor Genotype Freq.\u0026nbsp;(%)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePotVar0041300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e75095240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1-dom-alt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e12.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e36.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePotVar0099669\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e87350385\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1-dom-ref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e28.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePotVar0042350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e76374420\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2-dom-alt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e28.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e31.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePotVar0117603\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echr02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24247561\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2-dom-alt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e38.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e18.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003csup\u003ea\u003c/sup\u003e Genotypes with the same effect on the phenotype in a dominance model are treated as equivalent when calculating the minor genotype frequency.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study investigated the potential use of historical phenotypic data, both alone and combined with contemporary data, for genetic discovery and genomic prediction. Combining and harmonizing scab data from different sources had significant challenges, as many factors influence disease development. The trials contributing to these data were conducted at different locations and under different environmental conditions. Different scab species may have been recorded based on the lesion type, even though \u003cem\u003eStreptomyces scabies\u003c/em\u003e is the most common scab-causing species. Different scab-causing species can produce slightly different symptoms, and not all species are equally pathogenic (Hudec et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Our estimates of entry-mean, broad-sense heritability (0.53 to 0.82) are similar to many previous studies (Haynes et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Bradshaw et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Yuan et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Braun et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Enciso-Rodriguez et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Pereira et al., 2021; Sharma et al., 2024), although lower values have also been reported (Haynes et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Zorrilla et al., 2014).\u003c/p\u003e \u003cp\u003eConsistent with previous studies, only minor QTL were discovered, with 1\u0026ndash;2% PVE (percent variance explained). The region [email protected] Mb identified in this study coincides with QTL reported by Kaiser et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) in a tetraploid cross for scab tuber coverage and by Koizumi et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) in a panel of tetraploid varieties and advanced breeding clones. Our QTL [email protected] Mb is also near the position of a QTL reported by Kaiser et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). It is noteworthy that all four QTL in the final model were based on dominance effects (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). This is consistent with the polygenic model used for genomic variance partitioning (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), in which the residual genetic (i.e., non-additive) effect accounted for 40% of the total genetic variance in the contemporary US dataset and 85% in the historical dataset, although the latter is likely inflated due to confounding with the GxE effect. But the best-fit model for a marker may not match the gene action at the unobserved QTL, because the marker PVE is also influenced by marker-QTL LD (Rosyara et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Unlike with pedigreed mapping populations, in which LD decays smoothly, LD patterns are very complex in variety panels (Vos et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2017\u003c/span\u003e); even adjacent SNPs can have near-zero LD when derived from different haplotypes.\u003c/p\u003e \u003cp\u003eMost GWAS studies use additive models, for which genetic variance (and therefore PVE) increases with MAF and the effect magnitude. In Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, however, the chr02 QTL has similar PVE to the others despite having higher MAF and larger effect magnitude. This is because for the type of dominance models used in GWASpoly, PVE is determined by the frequency of the minor genotype \"equivalence class\", not the minor allele. For the 2-dom model, which is only applicable to polyploids, it is the combined frequency of the zero-and single-dose genotypes, which was lower for the chr02 QTL (18% vs. 29\u0026ndash;36%).\u003c/p\u003e \u003cp\u003eThe average (or median) reliability of breeding values (BVs) provides an estimate of the squared accuracy of selection (Lalo\u0026euml;, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; Clark et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), analogous to narrow-sense heritability as a measure of squared accuracy for phenotypic selection. Under marker-assisted genomic selection, which implies the use of both phenotypes and marker-based predictions for the selection candidates (Lande and Thompson, 1990; Bernardo, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), reliability is higher than using only phenotype data alone (i.e., narrow-sense heritability) or only marker-based prediction (Riedelsheimer et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Endelman et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). This difference is evident when comparing Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e with Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, as median GEBV reliability for the US chip group was 0.50 vs. 0.24 for the genomic heritability. Although some North American lines were present in the historical dataset, it predominantly consists of data for European varieties in European environments. Since genomic heritability for the historical dataset was lower than the contemporary US dataset (0.10 vs. 0.24), it follows that median GEBV reliability for the European group would be lower in the integrated data analysis (0.3 vs. 0.5). The larger size and higher degree of relatedness within the US chip group compared to the European group may also have contributed to the higher GEBV reliability, as these factors have been observed to increase prediction accuracy in many studies (Lorenz et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eAlthough phenotypic selection for resistance has been practiced for decades, potato common scab remains a challenging disease to manage. This study has confirmed earlier research that resistance is a complex trait controlled by many small-effect loci. The dataset from this study can be included in future research to increase statistical power, but uncertainty about trial conditions in the historical dataset will likely remain a limitation. In the future, accurate reporting of environmental factors and pathogen populations would facilitate merging phenotypic data from multiple sources, as would the adoption of a common rating scale. Empirical studies of scab resistance over multiple cycles of genomic selection are needed to validate the practical utility of the methodology.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflict of interest\u003c/h2\u003e \u003cp\u003eH.J. van Eck is the Editor-in-Chief of Euphytica. The authors are not aware of other relevant financial or non-financial interests to disclose.\u003c/p\u003e \u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003cp\u003eH.J. van Eck is the Editor-in-Chief of Euphytica.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eF.L.A. was supported by the Teagasc Walsh Scholarship programme. Collection of the US data was supported by the USDA National Institute of Food \u0026amp; Agriculture Award 2023-34141-41020 and by the Wisconsin Potato \u0026amp; Vegetable Growers Association.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization and funding acquisition: HJvE, DG, JE. Data collection: FLA, MB, DD, JC, AW, JE. Data analysis: FLA, JE. Supervision: MCH, DM, HJvE, JE. Writing original draft: FLA, JE. Manuscript editing: FLA, DM, DG, HJvE, JE.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe phenotypic and marker data needed to reproduce our results are provided in Files S1, S2, S3, S4, S5. R code to generate the results is provided in Markdown format in File S6.\u003c/p\u003e"},{"header":"References","content":"\u003cp\u003eBernardo R (2020) Upgrading a maize breeding program via two‐cycle genomewide selection: Same cost, same or less time, and larger gains. Crop Science 61 (4):2444-2455. doi:10.1002/csc2.20516\u003c/p\u003e\n\u003cp\u003eBernardo R, Yu J (2007) Prospects for Genomewide Selection for Quantitative Traits in Maize. Crop Science 47 (3):1082-1090. doi:10.2135/cropsci2006.11.0690\u003c/p\u003e\n\u003cp\u003eBradshaw JE, Hackett CA, Pande B, Waugh R, Bryan GJ (2008) QTL mapping of yield, agronomic and quality traits in tetraploid potato (Solanum tuberosum subsp. tuberosum). 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Nat Genet 46 (2):100-106. doi:10.1038/ng.2876\u003c/p\u003e\n\u003cp\u003eYuan J, Bizimungu B, De Koeyer D, Rosyara U, Wen Z, Lagüe M (2019) Genome-Wide Association Study of Resistance to Potato Common Scab. Potato Research 63 (2):253-266. doi:10.1007/s11540-019-09437-w\u003c/p\u003e\n\u003cp\u003eZhang Z, Ersoz E, Lai CQ, Todhunter RJ, Tiwari HK, Gore MA, Bradbury PJ, Yu J, Arnett DK, Ordovas JM, Buckler ES (2010) Mixed linear model approach adapted for genome-wide association studies. Nat Genet 42 (4):355-360. doi:10.1038/ng.546\u003c/p\u003e\n\u003cp\u003eZorrilla C, Navarro F, Vega‐Semorile S, Palta J (2021) QTL for pitted scab, hollow heart, and tuber calcium identified in a tetraploid population of potato derived from an Atlantic × Superior cross. Crop Science 61 (3):1630-1651. doi:10.1002/csc2.20388\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Common scab, Streptomyces spp., potato, Solanum tuberosum, Genome wide association study (GWAS), genomic prediction (GP)","lastPublishedDoi":"10.21203/rs.3.rs-8543262/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8543262/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCommon scab, caused by \u003cem\u003eStreptomyces spp\u003c/em\u003e., is worldwide an important skin disease of potato, capable of significant reductions in marketable value. Resistant varieties developed through phenotypic selection have been the most effective strategy thus far. Previous studies on the genetics of resistance have identified only minor-effect QTLs. In the current study, we explored the value of historical data for genetic analysis, derived from 52 sources. Based on partial replication of the 3500\u0026thinsp;+\u0026thinsp;varieties, generalized (entry-mean) heritability was estimated at 0.67. For a subset of 292 varieties with genome-wide markers, the genomic (narrow-sense) heritability was only 0.10. The historical data was combined with a contemporary US dataset spanning 6 environments and 416 varieties. Genome-wide association studies identified four QTLs, which together explained 7.5% of the variation. The median reliability (r\u003csup\u003e2\u003c/sup\u003e) of genomic-estimated breeding values for marker-based selection was higher for the contemporary US chip group (0.5) than for European varieties from the historical dataset (0.3). This difference can be explained by the higher genomic heritability of the contemporary US dataset (0.24), its larger population size and higher degree of relatedness. This study has illustrated the potential for leveraging historical data for genomics-assisted breeding, but genetic gain for potato common scab resistance continues to be limited by low heritability and high polygenicity.\u003c/p\u003e","manuscriptTitle":"Genome-wide prediction and association mapping of potato common scab with historical data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-12 06:13:56","doi":"10.21203/rs.3.rs-8543262/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1ba9baac-621a-45b4-adc2-35c890ccb1b2","owner":[],"postedDate":"January 12th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-30T15:40:20+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-12 06:13:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8543262","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8543262","identity":"rs-8543262","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}