Spinacia oleracea) is a popular leafy vegetable crop and commercial production is centered in California and Arizona in the US. The oomycete Peronospora effusa causes the most important disease in spinach, downy mildew. A total of nineteen races of P. effusa are known, with more than 15 documented in the last three decades, and the regular emergence of new races is continually overcoming the genetic resistance to the pathogen. This study aimed to finely map the downy mildew resistance locus RPF3 in spinach, identify single nucleotide polymorphism (SNP) markers associated with the resistance, refine the candidate genes responsible for the resistance, and evaluate the prediction performance using multiple machine learning genomic prediction (GP) methods. Segregating progeny population developed from a cross of resistant cultivar Whale and susceptible cultivar Viroflay to race 5 of P. effusa was inoculated under greenhouse conditions to determine downy mildew disease response across the panel. The progeny panel and the parents were resequenced at low coverage (1x) to identify genome wide SNP markers. Association analysis was performed using disease response phenotype data and SNP markers in TASSEL, GAPIT, and GENESIS programs and mapped the race 5 resistance loci (RPF3) to 1.25 and 2.73 Mb of Monoe-Viroflay chromosome 3 with the associated SNP in the 1.25 Mb region was 0.9 Kb from the NBS-LRR gene SOV3g001250. The RPF3 locus in the 1.22-1.23 Mb region of Sp75 chromosome 3 is 2.41-3.65 Kb from the gene Spo12821 annotated as NBS-LRR disease resistance protein. This study extended our understanding of the genetic basis of downy mildew resistance in spinach cultivar Whale and mapped the RPF3 resistance loci close to the NBS-LRR gene providing a target to pursue functional validation. Three SNP markers efficiently selected resistance based on multiple genomic selection (GS) models. The results from this study have added new genomic resources, generated an informed basis of the RPF3 locus resistant to spinach downy mildew pathogen, and developed markers and prediction methods to select resistant lines.Spinach ( Spinacia oleracea L.) is an important cool-season leafy vegetable crop. The US annually cultivates spinach on 58,000 acres with a value of 500 million dollars  have been hypothesized to provide resistance to races of P. effusa  based SNP markers in segregating populations inoculated with P. effusa race 13 to 0.84 Mb  are being approached that may open alternative avenues to develop resistant cultivars by loss-of-function of the S-genes  mapping and genome wide association study (GWAS) for small breeding programs to identify, map, and characterizes genomic regions controlling the phenotypic expression. Genetic resistance to downy mildew pathogens has been extensively studied in many crops . BiparenRPF3 allele and provide resistance to P. effusa race 1, 3, 5, 8-9, 11-12,14, 16, and 19  and a cross between resultant F1 males and female plants. Seeds were harvested from each of the female plants representing a progeny population. Initially, 10-20 F2 seeds and parent lines were evaluated for disease response upon inoculation with P. effusa race 5. After an initial screening, two progeny populations (VW #10 and VW #3), comprising 137 and 251 seedlings, were inoculated with P. effusa race 5 at the Rosen Alternative Pest Control Center, University of Arkansas. Parental cultivars and the differentials, including NIL1, NIL3, and Viroflay, were included in the disease screening as controls. Seeds were sown in 25 x 50-cm plastic trays filled with potting soil . Each plant tray contained ten rows, and 10-15 seeds per row were planted. After germination, 6-8 plants were kept per row and were labeled using a plant tag. Plants were grown in the greenhouse (25°C) for two weeks, watered daily, and fertilized weekly using Miracle-Gro® All Purpose Plant Food.The cultivar Whale contains 5 spores per ml was used to inoculate using a Badger basic spray gun (model 250) until the leaves were wet. Inoculated plants in trays were incubated in a dew chamber (18°C) for 24 h in the dark, moved to a growth chamber  for five days, and finally returned to the dew chamber (18°C) for 24 h to induce sporulation. The disease reactions of each plant were rated seven days post inoculation (dpi) based on the presence and absence of sporulation on cotyledons and true leaves on a scale of 0 to 4, with 0 = no sporulation; 1 = up to 25% leaf area with sporulation; 2 = 26 to 50% leaf area with sporulation; 3 = 51 to 75% leaf area with sporulation; and 4 = 76 to 100% leaf area with sporulation  in an automated KingFisher Flex extraction system . Extracted DNA was quantified using a Qubit Fluorometer, sample integrity was tested on 1% agarose gel electrophoresis, and DNA meeting the requirements were submitted for sequencing at the Texas A&M genomics facility.The whole genome resequencing (WGR) was pursued to generate around 1 Gb sequence reads per sample, approximating 1x genome coverage. Variants were called by mapping the sequence reads to the Monoe-Viroflay reference genome  using thNext, SNPs from six chromosomes were extracted and further filtered using BCFtools  to removGenetic diversity and principal component analysis (PCA) were performed using the 8189 SNPs in GAPIT3  programsInitial GWAS analysis was performed using single marker regression (SMR), general linear model (GLM) with two PCA matrices, and mixed linear model (MLM), including in-built kinship and PCA matrices in TASSEL 5.2.82 . A seconSignificantly associated SNPs identified from multiple association models and programs were used to search for candidate genes up to 20 Kb on either side of the Monoe-Virofaly genome assembly. Genes near the peak associated SNPs were examined for annotated functions. Genes predicted to provide disease resistance against plant pathogens were considered potential candidate genes, and their predicted functions were reported. In the same way, candidate genes were searched for GWAS associated SNPs based on the Sp75 assembly .Prediction performance of resistance to downy mildew pathogen in this population was explored using five different machine learning methods: ridge regression best linear unbiased prediction (rrBLUP), Bayesian models Bayes B, Bayesian LASSO, and Bayesian ridge regression (BRR), and support vector machine (SVM). The SVM model was included as it is more suitable for non-linear data fitting, while other models are more suitable for linear functions. The rrBLUP was fitted using the rrBLUP R package , the BayGP was performed following a five-fold cross-validation scheme where individuals are randomly assigned into five groups, retaining four groups as the training set  and the remaining fifth group  serving as the validation set to predict genomic estimated breeding values (GEBV). The cross-validations were replicated 100 times and prediction accuracy (PA) was determined by averaging the Pearson correlation coefficient (r) between predicted GEBV values obtained from five-fold cross-validations and observed phenotype values in the validation set. Four sets of marker datasets were evaluated with each of the five GP models to compare prediction accuracies among full marker data sets and a smaller number of trait-associated marker sets and to determine the optimum number of markers to obtain high PA for resistance to downy mildew pathogen. The first marker set was the full set of 8,189 SNPs based on the Monoe-Viroflay assembly used for the GWAS analysis. The second set contained 215 SNP markers associated with resistance with LOD ≥ 3.0 in the SMR model in TASSEL. The third set contained 20 SNP with LOD > 6 in all three programs , and the fourth set contained three selected significant SNPs.P. effusa in all greenhouse inoculation experiments. The F2 progeny populations following the greenhouse inoculation show segregation for resistance to downy mildew pathogen . However, of the 251 seedlings of the second population, VW #3, 179 were resistant and 72 susceptible and fit the 3:1 segregating ratio . The progeny populations VW #3 segregating for RPF3 locus from Whale fitted to a 3:1 expected ratio for a single dominant gene; thus, 190 seedlings of this progeny plus the two parents  were selected for sequencing and to investigate the genetic resistance.The resistant parent Whale and susceptible parent Viroflay showed expected responses with race 5 of pathogen Figure 2In total, 183.57 Gb data containing 1,223.82 million raw reads were generated from the Illumina sequencing with an average of 6.37 million reads/sample and average genome coverage of 1.07x. High-quality bases (> Q30) and excluding duplicates and clipped bases were aligned to the spinach reference genome  using thSpinach lines in this panel were differentiated into two main subpopulations in the NJ dendrogram and PCA plot generated by the GAPIT3 program  explained by the SNP loci associated with resistance to downy mildew pathogen in the SMR model ranged from a minimum of 0.30 for Chr3_2737221 and was 0.57 for Chr3_1253998 and Chr3_125400820. The GENESIS model showed phenotypic variance explained by these 20 markers in the range of 0.19-0.40 and from 0.23-0.35 in the GAPIT GLM models  explained by these SNPs in the SMR model ranged from 0.36 for SNP marker Chr3_1222338 to 0.63 for SNP marker Chr3_1239348, while the R2 value for the same SNP was 0.40 and 0.57 in GENESIS model  in five GS models. Average PA from 100 runs ranged from 0.42-0.66 among five different models in the full SNP dataset, with Bayesian B providing the highest prediction accuracy of 0.66 and the lowest standard deviation . Similar was the case with a minimum difference in mean PA across models with 20 SNP (PA in the range of 0.72 to 0.76) and 3 SNP sets (PA in the range of 0.73 to 0.75). It is important to note that the PA obtained using only three SNPs was equivalent to that of the 215 SNP set that provided the highest PA in this study. For the 3 SNP set, SVM was highest with PA of 0.75 ± 0.11, rrBLUP ranked second with PA of 0.74 ± 0.07, and the three Bayesian models ranked third with equal PA of 0.73 ± 0.07, making the SVM and rrBLUP the model of choice when in predicting downy mildew with a smaller number of markers. However, looking at the average PA of all four SNP sets, the Bayesian B model best predicted the downy mildew resistance in this population with a PA of 0.71, as other models averaged between 0.66-0.69. The PA obtained from three SNP markers appears to be a cost-effective approach and could facilitate predicting resistance to the RPF alleles effective against multiple races of P. effusa from two parents are available , as pursued in this study to genotype the population panel, leads to high missing data . High miRPF3 locus to a narrow region, in 1.25 Mb and 2.72 Mb of chromosome 3  unique to associate with the RPF3 resistance locus.Resistance to downy mildew pathogen in spinach is hypothesized to be governed mainly by a major gene with a substantial effect on phenotype. The RRPF resistance loci against downy mildew pathogen in spinach in the same region as previous studies  were explored for the presence of disease resistance candidate genes near the associated regions Table 2.e region . This stcessions . Similar defense , and theRPF loci-associated regions but expand to short genomic regions and are in high LD, and this is why the three SNP markers provided equal or higher PA than the 20 markers. A relatively small number of GWAS-associated markers estimated comparable prediction to that of a larger SNP set providing an optimized marker set for no reduction in predictive ability. Equivalent PA obtained from a small SNP panel could attract adoption as it minimizes the cost of genotyping and favors using a small number of GWAS-associated SNP in GS. A commercial cultivar containing multiple resistant genes is an attractive option for the spinach industry as the cultivar will probably be durably resistant. Overall, this study showed the potential of accurately predicting and implementing GS for a major gene using nonparametric and parametric machine learning models, which may benefit from increased accuracy by including additional traits for selection.The objective was to compare multiple machine learning models and the influence of a different set of markers in the prediction performance of downy mildew race-specific resistance in spinach. GS has recently been evaluated in spinach for resistance to white rust , field eRPF3 resistance loci in the cultivar Whale have transmitted in a 3:1 ratio fitting the expected Mendelian segregation for a trait controlled by a major dominant locus. The RPF3 resistance regions were mapped to the 1.25 Mb and 2.73-2.74 Mb of Monoe-Viroflay chromosomes 3 based on the significant and consistent association of 20 SNP markers across three GWAS programs and ten models. The RPF3 locus was mapped to 1.19, 1.22, 1.23, 1.75, and 1.76 Mb of Sp75 chromosome 3. In this study, the 1.25 Mb associated with the RPF3 locus from cultivar Whale is 0.9 Kb from the gene SOV3g001250, an NBS-LRR gene that encodes disease resistance protein. The RPF3 locus in the 1.22-1.23 Mb region of Sp75 chromosome 3 is 2.41-3.65 Kb from the gene Spo12821 annotated as CC-NBS-LRR disease resistance protein. These genes can be targeted for functional tests of the RPF3 locus to regulate resistance to downy mildew pathogens. This report further presented the GS tools with the ability to use three SNP markers to predict the RPF3 resistance locus in spinach. Implementing GS will be a practical and attractive option with low-cost genotyping resources, incorporating other important traits, and simultaneous prediction of multi-trait data may provide increased PA. Continuous studies on genetics and molecular aspects of qualitative and quantitative host resistance, the evolution of pathogen races and the specificity of virulence factors, and the molecular mechanism and dynamics of host-pathogen interactions can provide innovative options to improve the development of downy mildew resistance cultivars.The https://doi.org/10.6084/m9.figshare.20443017.The datasets generated by this study are available in FigShare: AS and GB conceived the study. GB and DO maintain the downy mildew pathogen and performed phenotyping. JC managed the pathogen. GB performed sequencing and genotyping, planned and performed data analysis, and wrote the draft. AS, BM, and JC made edits, and GB made revisions. All authors contributed to the article and approved the submitted version.This research was supported by USDA-SCRI grant 2017-51181-26830, USDA-AMS SCMP grant 16SCCMAR0001, and USDA NIFA Hatch project ARK0VG2018 and ARK02440 to AS.The authors would like to acknowledge the funding support from USDA-AMS and USDA-SCRI, Genomics and Bioinformatics Service, Texas A&M for sequencing and genotyping. This research used the computational resources available through the Arkansas High Performance Computing Center at the University of Arkansas.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. 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Anatomical collections have been used for centuries for research and teaching purposes. By the example of selected preparations of fetal development from the Ruysch collection (17th–18th centuries) and the Meckel collections (18th–19th century), this article aims to trace the changing purposes, specifics and methods of creating specimens as well as the development of anatomy during that period. The selected specimens are compared and analyzed implementing the historical-critical method. Regarding the appearance of the preparations, we see a transition from the visually aesthetic specimens (Ruysch) to exact ones (Meckel collections). If Ruysch’s preparations were compared in his time to works of art, specimens created by three anatomists of the Meckel dynasty were primarily created for teaching and research purposes. However, Ruysch’s preparations tracing fetal circulation were scientific discoveries of the time. As for preparations of fetal development from the Meckel collections, we see both specimens of physiological processes already known at that time and experimental ones. Regarding teratology, Ruysch and Meckel the Younger tried to explain malformations, but their anatomical preparations could hardly give answers to the cause of deviations from the norm. The differences between the collections can be explained by the different stages of development of anatomy of the time and by the research interests of the anatomists themselves. Anatomical collections of medical educational institutions are an indispensable part of research and visual aids for students. The early anatomical collections were also a testament of the skill of the anatomist himself, and even perceived by contemporaries as pieces of art. While some of these collections are stored in museums and belong to the cultural heritage, others are still being used for research and teaching purposes. The first specialized collections of anatomical specimens emerged in the late 16th century and early 17th century, the largest of which were at the Universities in Padua and Leiden but consisted mostly of skeletons [World famous at the time was the anatomical collection of the Dutch physician Frederick Ruysch 1638–1731), who has been called “the apostle of the injection method” . He beca8–1731, wAnatomical collections began to spread widely in the second half of the 18th century and at the beginning of the 19th century in Europe and were created for the purpose of teaching and research. The Meckel collections in Halle  are the most extensive of their kind in Germany and one of the biggest in Europe, with over 8,000 exhibits and are divided into a human-anatomical and a comparative-anatomical collection area ,7. The cDespite the work of educational institutions and museums in describing their anatomical collections and the history of their creation ,9,10,11,Research works of Ruysch and anatomists from the Meckel dynasty and published literature on the history of their anatomical collections were analyzed and anatomical preparations from both of them were evaluated. To examine works and specimens of Ruysch and the Meckels, we implemented the historical-critical method, which involves the stages of acquisition of primary sources and literature, the critical evaluation of information contained in primary sources and the presentation of historical data in historical context in terms of objectivity and significance . As the We have selected those preparations of fetal development of Ruysch and Meckel that allow us to make a comparison according to the following characteristics: (1) the appearance of the preparation and the anatomical techniques used to create it; (2) specimens of fetuses with abnormalities of physiological development , their purpose and explanation of congenital abnormalities; (3) preparations that allow us to trace physiological processes during embryonic or fetal development. For each group, we selected three to five specimens. Thus, 8 specimens of the Ruysch collection and 11 preparations of the Meckel collections were selected according to the following criteria. On the one hand, they show various preparation techniques: wet preparations and dry ones . The number of preparations from the Meckel collection is larger because in two cases several preparations were made from a single individual. On the other hand, we selected both typical preparations for collections  and rare preparations . We also chose the most well-preserved preparations with which the anatomical techniques can be well traced. We also focused on similar preparations in both collections  to see the difference in their creation techniques.Ruysch’s collection of about 800 preparations includes 93 preparations of fetuses from two to nine months, as well as a child of two years. The overwhelming number of preparations are wet and only eight preparations are dry: three skeletons and five mummified specimens. Most of the preparations represent the fetuses of normal development. At least nine specimens can be classified as teratological. As for the Meckel collections, it contains around 207 fetus specimens and about 70 embryo specimens. The exact number is difficult to determine because sometimes there are several fetuses or embryos in one jar, which has only one inventory number. In total, 113 preparations out of this number are teratological and were created by anatomists of the Meckel dynasty.As for the Meckel collections, we should note that we selected the preparations regardless of the anatomist who had created them. This is explained, on the one hand, by the fact that the Meckels did not make an inventory of their private collections. Thus, it is not always possible to trace the exact provenience of the majority of their specimens. On the other hand, the anatomists themselves regarded preparations as an integrated whole. Meckel the Younger, who was especially focused on embryology and teratology and created the vast majority of teratological preparations, often used specimens of his father and grandfather for his research. Considering that not all preparations have survived to today, we also took into account the description of typical preparations, so that our sample would be representative. We chose those preparations that we were able to contextualize within the research of the anatomists. Photographs of all preparations from the Ruysch collection are available for research on the website of Peter the Great Museum of Anthropology and Ethnography (Kunstkamera) and published with permission. Photos of all preparations from the Meckel collections are retrieved from a database available at the Institute of Anatomy and Cell Biology at Martin Luther University Halle-Wittenberg. Table II from Meckel’s “Descriptio monstrorum nonnullorum cum corollariis anatomico-physiologicis”  in FigurSince Ruysch’s and Meckels’ specimens were obtained prior to the era of informed consent, this raises questions about their ethical use. The creation and presentation of these specimens, which were lawful according to the principles of the time, appear to be unlawfully obtained according to our current assessment. For example, Ruysch often bought corpses of fetuses and stillborn babies with abnormalities from their parents or made a contract with them under which parents were allowed to visit his anatomical collection for free . Meckel Researchers who deal with the issue of the use of historical collections of embryonic and fetal specimens based on ethical considerations also suggest that these have to be maintained and appropriately used for research and education if a demand is justified ,21.In view of the above, we consider it possible to use selected preparations from the Ruysch and Meckel collections for historical research purposes, which emphasize the uniqueness of the preparations and their importance for a deeper understanding of the history of medicine. It should be noted here that the creation of anatomical preparations such as those made by Ruysch and the anatomists of the Meckel dynasty is not a current practice anymore. The use of human tissues for anatomical research purposes or medical training is possible when a person donates his or her own body, as well as in the case of perinatal autopsy, where a consent of the bereaved parents is required. Since the time of Ruysch and Meckels, anatomy teaching aids have been modernized so that anatomical preparations are not a necessity nowadays. Digital platforms, including 3D images and displays and 3D printed models are at the disposal of students. However, anatomical preparations are still an important part of research and visual aids used in the teaching process.We can divide Ruysch’s preparations of fetal development into those that show normally developed child fetuses , fetusesThree of the eight selected specimens are teratological preparations, which shows the following malformations: congenital hydrocephalus  of the brain filled with cerebrospinal fluid) A, intracThe fetal development preparations of Meckel the Younger, and also the preparations of his predecessors that he used in his research, can be divided into two categories: teratological preparations  and specThe fact that Meckel the Younger created preparations for scientific and educational purposes can be seen in the example of the specimens 114/2/2, 86/2/5 and 119/2/2 A,B. FromRuysch paid great attention to the aesthetic appearance of his anatomical preparations, as seen in the specimens 4070–784, 4070–799 and 4070–910 A–C. He cDescriptio monstrorum nonnullorum cum corollariis anatomico-physiologicis in 1826 [Often the anatomists of the Meckel dynasty created several preparations from one individual in order to identify associated malformations. Unlike Ruysch, it was not about the aesthetic representation of the specimen, but about the precise examination of the individual in order to identify any additional malformed organs and thus also to collect teaching material for pathological-anatomical lessons. As an example, the fetus shown in  in 1826 . The wetThe reason for the transition in the creation of “elegant” to “exact” specimens were changes in the development of anatomy. The publication and distribution of detailed handbooks on dissecting techniques has contributed to the dissemination of knowledge. Over time, the creation of anatomical preparations became more common. While at the beginning of the 18th century pathology was more of a theoretical discipline, by the end of the century it had become experimental as a part of anatomy teaching . TherefoSince the concept behind the exhibition of Ruysch’s anatomical collection was that it had to be “pleasing to the eye” of visitors, preparations of deformities or “monstrous” babies were hidden in his cabinet and shown to the viewer only on request . AlthougRuysch diagnosed another specimen 4070–906 B with hyThe teratological part of Ruysch’s collection contained not only wet but also dry preparations and skeletons, mostly conjoined twins. Handbuch der pathologischen Anatomie  [Handbuch der pathologischen Anatomie [Teratological preparations also occupied an important place in the Meckel collections. One of the greatest achievements of Meckel the Younger was in the field of pathology. His Anatomy)  became aAnatomy) . The aimAnatomy) ,32. He nAnatomy) . To descAnatomie  served aAnatomie .Abbildungen und Beschreibungen einiger Misgeburten, die sich ehemals auf dem anatomischen Theater zu Cassel befand  [The preparations from the Meckel collections are still used today for teaching and research. For example, specimen 117/4/1 C,D was r Kassel) . Meckel  Kassel) ,36.As an obstetrician, Ruysch created many preparations of embyos and fetuses in all stages of growth in order to show the development of a human being. His collection included a range of preparations, from embryos of the size of a grain of sand to full-term babies . Ruysch His specimens of embryos in the first weeks of pregnancy that impressed his contemporaries so much have not remained to the present day . Ruysch’Ruysch also studied fetal circulatory system. Although blood circulation was discovered in 1628 by the English physician William Harvey (1578–1657), there was no exact knowledge about fetal blood circulation. Ruysch’s alcohol preparation of a 3-month-old fetus with placenta injected with red (arteries) and white (veins) wax A,B is anMeckel’s cartilage after him. With the development of the bone skeleton, Meckel’s cartilage is moved away by the mandibular bones that develop around it, and is reduced, giving origin to some auditory ossicles. Through this and other research, Meckel refuted the common view of the preformationism, according to which an organism develops from a fully formed miniature version of itself that has existed since the beginning of creation [Turning to the preparations of fetal development created by Meckel the Younger, we see that some of them were created for educational purposes, because they did not carry any new knowledge of the time. These include preparations of fetuses in the maternal placenta A. This pcreation .As a result of comparing the Ruysch and the Meckel collections, we can draw several conclusions. Regarding the appearance of the preparations and the anatomical techniques used to create them, we should note a transition from the “elegant” preparations (Ruysch) to exact ones (Meckel). While in his time, Ruysch’s preparations were compared to works of art, Meckels’ specimens were primarily created for teaching and research purposes. However, Ruysch’s collection was not simply a collection of skillfully created preparations that were equated with objects of art or a “cabinet of curiosity”. As we see in the preparations tracing the fetal circulation, they served research purposes and were scientific discoveries of the time. As for Meckels’ preparations of fetal development, we see both preparations of physiological processes already known at that time and experimental ones, which in turn confirms the educational and scientific purposes of their creation. Regarding teratology, both Ruysch and Meckel the Younger tried to explain malformations, but their anatomical preparations could hardly provide answers to the cause of deviations from norm. Nevertheless, they provided descriptions of the malformation and thereby accumulated new knowledge.The differences between the collections can be explained not only by the different stages of development of anatomy and medicine of the time, but also by the research interests of the anatomists themselves. Unlike the Meckels, comparative fetal osteology was not a subject of Ruysch’s research, so we do not find such osteological preparations in his collection. Moreover, fetal blood circulation, sufficiently explored by the 19th century, was not a research focus for anatomists of the Meckel dynasty. The numerous teratological specimens in the Meckel collections can also be explained by the fact that at that time pathology was developing as an independent discipline. Thus, teratological specimens were an essential teaching aid for medical students. The goals of the collection and the scientific interests of the anatomists, in turn, determined the composition and choice of anatomical techniques in the creation of the specimens.


Carcass traits are important in pig breeding programs for improving pork production. Understanding the genetic variants underlies complex phenotypes can help explain trait variation in pigs. In this study, we integrated a weighted single-step genome-wide association study (wssGWAS) and copy number variation (CNV) analyses to map genetic variations and genes associated with loin muscle area (LMA), loin muscle depth (LMD) and lean meat percentage (LMP) in Duroc pigs.DOK7, ARAP1, ELMO2 and SLC13A3) were highlighted as promising candidates according to their function.Firstly, we performed a genome-wide analysis for CNV detection using GeneSeek Porcine SNP50 Bead chip data of 3770 pigs. A total of 11,100 CNVs were detected, which were aggregated by overlapping 695 CNV regions (CNVRs). Next, we investigated CNVs of pigs from the same population by whole-genome resequencing. A genome-wide analysis of 21 pigs revealed 23,856 CNVRs that were further divided into three categories , which covered 190.8 Mb (~ 8.42%) of the pig autosomal genome. Further, the identified CNVRs were used to determine an overall validation rate of 68.5% for the CNV detection accuracy of chip data. CNVR association analyses identified one CNVR associated with LMA, one with LMD and eight with LMP after applying stringent Bonferroni correction. The wssGWAS identified eight, six and five regions explaining more than 1% of the additive genetic variance for LMA, LMD and LMP, respectively. The CNVR analyses and wssGWAS identified five common regions, of which three regions were associated with LMA and two with LMP. Four genes (We determined an overall validation rate for the CNV detection accuracy of low-density chip data and constructed a genomic CNV map for Duroc pigs using resequencing, thereby proving a value genetic variation resource for pig genome research. Furthermore, our study utilized a composite genetic strategy for complex traits in pigs, which will contribute to the study for elucidating the genetic architecture that may be influenced and regulated by multiple forms of variations.The online version contains supplementary material available at 10.1186/s12864-022-08804-1. In recent decades, pork has made up a large share of total worldwide meat production to meet demands for more animal protein products and accommodate growing human consumption . For theThe main objective of this study was to identify genetic variations and genes associated with carcass traits including LMA, LMD and LMP, by integrating a wssGWAS and CNV analyses in approximately 3770 Duroc pigs that were genotyped with the GeneSeek Porcine SNP50 Bead chip. Herein, we also investigated CNVs of 21 Duroc pigs from this population by NGS data and then, we used the resequencing pigs to determine an overall validation rate for the CNV detection accuracy of chip data. These two approaches (CNVR and SNP based GWAS) were complementary to each other and such a utilization of composite genetic strategy for complex traits in pigs provides valuable insights into elucidating the genetic architecture that may be influenced and regulated by multiple forms of variations.n = 851), loss , and mixed (n = 726) CNVRs, covering 190.8 Mb (~ 8.42%) of the pig autosomal genome  were detected, which were aggregated by overlapping 695 CNVRs. The length of these CNVRs ranged from 11.80 kb to 4.25 Mb  were successfully validated . As shown in Table COL4A2, RAB20, NAXD, CARS2, ING1, ANKRD10, ARHGEF7, TUBGCP3, ATP11A, MCF2L, PCID2, CUL4A, GRTP1, DCUN1D2, TMCO3, TFDP1, and ATP4B were detected and highlighted as potential candidates for LMP. Other prominent CNVRs associated with LMP were also analyzed, and genes within these CNVRs were detected according to the porcine reference genome annotation from the Ensembl database , SSC8 (1.87 – 3.85 Mb), and SSC9 (7.16 – 7.64 Mb) that were associated with LMA, and on SSC13 (197.09 – 197.58 Mb) and SSC17 (48.23 – 48.71 Mb) that were associated with LMP  and 75 significant GO terms. Detailed information on the significant pathways and terms is listed in Additional file P value threshold. This led to a limited number CNVRs were used in the association analyses. Although the findings of identified CNVRs may help elucidate the genetic architecture of carcass traits that may be influenced and regulated by multiple forms of variations, the translation of the CNVs in these regions into applications in the pig industry will not be as direct as that of SNPs in the genomic selection for LMA, LMD and LMP [In this study, the combination of a region-based GWAS with CNVR analyses improved the detection efficiency of genomic regions and genes associated with LMA, LMD and LMP and consequently identified additional genetic variants and genes. LMA and LMD, which are regarded as carcass traits, play essential roles in the determination of LMP and other growth traits . Previou and LMP . These v and LMP , and the and LMP . Moreove and LMP . In the Sscrofa 11.1 genome assembly using the liftOver tool. In total, 56.69% (394/695) of the CNVRs in the present study overlapped with the merged CNVRs of previous studies by at least 1 bp  and pigs [The validation results of CNVRs detected in 50 K SNP array implied that the CNV detection accuracy of chip data in the present study were relatively low, since the overall validation rate between resequencing data and microarray data in human was 83.5%, which utilized a population-level of 266 paired-end Illumina data to call CNVs . In thisand pigs . NumerouACOT7, located in CNVR241, encodes a member of the acyl coenzyme family, and the encoded protein hydrolyzes the CoA thioester of palmitoyl-CoA and other long-chain fatty acids [ACOT7 gene is associated with the metabolism and transport of fatty acids or lipids in the longissimus muscle in pigs [longissimus muscle development. The GADD45B and JSRP1 genes are mapped to the CNVR71 region (SSC2: 76.01 – 76.54 Mb) and were found to be associated with LMD and LMP traits in pigs. The GADD45B gene is a member of a group of genes whose transcript levels are increased following stressful growth arrest conditions and treatment with DNA-damaging agents. The functions of the GADD45B gene include the regulation of growth and apoptosis [GADD45B has been suggested to be overexpressed in beef longissimus thoracis muscles [JSRP1 gene encodes a protein that is involved in excitation–contraction coupling at the sarcoplasmic reticulum, and JSRP1 interacts with CACNA1S, CACNB1, and calsequestrin to help regulate calcium influx and efflux in skeletal muscle [SDK1 gene, located on the CNVR101 region, and the PTTG1IP gene, located on the CNVR534 region, were identified to be associated with LMP. The SDK1 gene encodes a protein that is a member of the immunoglobulin superfamily. Previous GWASs have shown that SDK1 is a candidate gene for pork meat quality [SDK1 gene involvement in muscle development has not been clearly defined. The PTTG1IP gene encodes a single-pass type I integral membrane protein, which binds to pituitary tumor-transforming 1 protein (PTTG1). A recent report regarding transcriptome analyses of genes revealed that the PTTG1IP gene is involved in muscle development and was also found to be alternatively spliced among the muscle tissues in chickens [PTTG1IP as a candidate gene for muscle development in pigs, and it might also be a candidate gene for meat content.Copy number variation has been considered a major source of genomic variation, and multiple CNVR-based association analyses have been conducted to provide evidence for the impact of CNV on phenotypes in pigs , 19. Forty acids . A previ in pigs , and is poptosis , and GAD muscles . The JSRl muscle . In addi quality , 31; howchickens . TherefoDOK7 gene, located on SSC8 (1.87 – 3.85 Mb) and the ARAP1 gene, located on SSC9 (7.16 – 7.64 Mb). The DOK7 gene encodes a protein that is essential for neuromuscular synaptogenesis and functions in aneural activation of muscle-specific receptor kinase, which is required for postsynaptic differentiation. The literature does not identify any link between LMA and the DOK7 gene, as it has rarely been investigated in muscle in pigs or other domestic animals. The ARAP1 gene plays a role in encoding ARF-GAP, Rho-GAP, ankyrin repeat and pleckstrin homology domain-containing protein 1 [ELMO2 and SLC13A3 genes, located on SSC17 (48.23 – 48.71 Mb). The protein encoded by the ELMO2 gene interacts with the dedicator of cytokinesis 1 protein. The ELMO2 gene plays a role in the phagocytosis of apoptotic cells and in cell migration and has been implicated in the regulation of Rac1 and Akt activation [SLC13A3 gene encodes a high-affinity protein that plays an important role in the processing of citrate by the kidneys. A previous study has shown that the ELMO2 and SLC13A3 genes may also play a role in type 2 diabetes. Although these genes have not been reported to be directly related to lean meat content, genes with pleiotropic effects on complex traits may be responsible in this case [KIT gene causes dominant white coat color in pigs [Furthermore, the wssGWAS for LMA, LMD and LMP traits detected 19 genomic windows. Six genes identified in common regions between the wssGWAS and CNVR analyses were highlighted as promising candidates according to their function. For LMA, we highlighted the rotein 1 . Furthertivation . The SLChis case . For ins in pigs . However in pigs . Loin muThis study investigated the CNVs of pigs and provided a high supplement density for the high-resolution map of copy number variation in the porcine genome by using NGS-based and SNP array data. Our results showed an overall validation rate of 68.5% for the CNV detection accuracy of chip data in comparison with that using NGS data. The functions of genes containing unique CNVRs are related to the carcass traits of pigs. From this, we have identified some candidate genes. Such a utilization of composite genetic strategy for complex traits in pigs provides valuable insights into elucidating the genetic architecture that may be influenced and regulated by multiple forms of variations.https://arriveguidelines.org).The animals and experimental methods used in this study are following the guidelines of the Ministry of Agriculture of China. The ethics committee of South China Agriculture University (SCAU)  approved this study . All methods are reported in accordance with ARRIVE guidelines . This machine employs a diagnostic ultrasound system and transducers to acquire high-resolution images. The LMD and LMA were obtained as described by the Canadian Centre for Swine Improvement (http://www.ccsi.ca/Reports/Reports_2007/Update_of_weight_adjustment_factors_for_fat_and_lean_depth.pdf) and our previous paper [The experimental animals used in this study consisted of 3941 American Duroc pigs  that were born from 2013 to 2017 and were raised on two farms of Wen’s Foodstuffs Group Co., Ltd. . Among the 3941 pigs, 3770 were genotyped and phenotyped, and 171 pigs were phenotyped but non-genotyped. All pigs sustained uniform feeding conditions and received fine fodder during the fattening period from 30 to 100 kg live weight, as previously described [us paper :\documenwhere BF is the backfat thickness and LMD is the loin muscle depth. Pedigree information was newly added in this study. The pedigree dataset contained genealogical information for all pigs and the completed pedigree of these individuals can be traced back to 4 generations, with 5679 pigs in the full pedigree. In this study, we reported the new information of the results of NGS data and GWAS with CNV compared with previous papers , and theSus scrofa genome 10.2 to build Sus scrofa genome 11.1. Quality control procedures were performed using PLINK v1.07 software [P > 10–6 for the Hardy–Weinberg equilibrium test. Pigs that met the criteria above remained in the study. SNPs located on the sex chromosomes and unmapped chromosomes were removed.Among the 3941 phenotyped Duroc pigs, 3770 animals  were genotyped using a GeneSeek Porcine SNP50 Bead chip, which contained 50,703 SNPs. The genotype dataset was converted from http://broadinstitute.github.io/picard/) were used for data sorting and duplicates marked, respectively.To further elucidation of the CNVs in Duroc pigs, we performed a detailed characterization of CNVs by investigating the whole genome sequencing data. In brief, a total of 21 samples were randomly selected from the mentioned above 131 pigs of this population and were sequenced on an Illumina HiSeq2000 platform at Novogene  with 150 bp paired-end reads and an average depth of ~ 10 × . Raw Illumina reads were processed to remove adapter and low-quality sequences. The paired-end reads were aligned to pig reference genome (Sus sacrofa 11.1) by BWA (version 0.7.15)  with defPennCNV software  was usedhttps://github.com/zeeev/mergeSVcallers), as described in previous study [To generate a maximally sensitive set of copy number variants (CNVs) in the Duroc samples, we carried out CNV calling for each sample using two read depth (RD)-based tools, namely, CNVnator (version 0.4.1) , Controlus study .GCG gene was selected as the reference locus because of its highly conserved among pigs and exists as a single copy in the reference genome.Among the 21 re-sequenced pigs, 20 samples were also genotyped by 50 K SNP array and one sample was not. Thus, this one sample was not used in the validation step. In order to determine an overall validation rate for the CNV detection accuracy of chip data, we extracted the CNVs detected in PennCNV for the same 20 re-sequenced (the average sequence coverage per sample was 12.1 ×) and compared them with the results of four whole genome sequencing (WGS)-based callers, successful validation was determined by at least one call in the WGS-based callers overlaps with the CNVs detected in PennCNVs (at least 1 bp overlap between them). Besides, six CNVRs identified in 50 K SNP array were randomly selected to conduct real-time quantitative polymerase chain reaction (qPCR) to validate the detection accuracy by PennCNV and the qPCR reaction was performed as described in our previous paper . The qPCA linear mixed model model was employed to conduct a CNVR-based association analysis for LMA, LMD and LMP using GEMMA software . GenomicW is a matrix of covariate ; α is a vector of corresponding coefficients that includes the intercept; X is the vector of CNVR marker genotypes; β represents the corresponding effect of the CNVR; u refers to an n × 1 vector of random effects, with u ~ MVNn, and ε is the vector of random residuals, with ε ~ MVNn. τ−1 is the variance of the residual errors; K is GRM and λ represents the ratio between the two variance components; and I is the identity matrix, and n refers to the number of pigs. Bonferroni correction was applied to determine the genome-wide significance thresholds, which were defined as 0.05/N, where N is the number of filtered CNVRs for the American Duroc pigs.where y is an n × 1 vector of phenotypes; The ssGBLUP framework proposed by Wang et al.  has beenW is the incidence matrix of fixed effects for relating phenotypes; Z refers to the incidence matrix of random effects, and where H is a blend of matrices that combined the pedigree and the genomic relationship matrix and I denotes the identity matrix. The inverse of matrix H is as follows:where G was constructed as previously described [where escribed :\documenM is an incidence matrix of the SNP genotype , D refers to a diagonal matrix of weights for SNP variance, m is the number of SNPs and th SNP.where G are calculated in an iterative way as described by Wang et al., [t as an iteration number and i as the ith SNP. The algorithm proceeds as follows:Initialization, set t = 1, Estimation of GEBV for all animals using ssGBLUP approach;Computation of SNP effects as Calculation of SNP weights for the next iteration by Normalization the SNP weights to keep the total genetic variance constant as Setting The variance components estimated by AI-REML were used in the BLUPF90 module to predict GEBVs. The postGSf90 module was used to perform the wssGWAS. Marker effects and weights for constructing  et al., , 52. DenThe procedure was run for three iterations as used in Wang et al., , which rwhere Sus scrofa genome  using the “biomaRt” package in R. The Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analyses were conducted using KOBAS 3.0 [P < 0.05 to explore the genes involved in pathways and biological processes. Furthermore, the GeneCards (http://www.genecards.org/) and NCBI (https://www.ncbi.nlm.nih.gov/) databases were used to query gene functions and determine promising candidates.Based on the length of the genome covered by significant CNVRs and 0.5 Mb windows (windows that explained > 1% of the additive genetic variance) associated with LMA, LMD and LMP, the genes within these regions were searched on the Ensembl genome database version 99 of the OBAS 3.0 . Fisher’Additional file 1: Table S1. Description of 695 CNVRs detected in the porcine genome.Additional file 2: Table S2. Chromosome distribution of all 695 CNVRs detected in the porcine genome (based on Sscrofa 11.1 reference genome assembly).Additional file 3: Table S3. Validation of CNVs detected in 50K SNP array using four different callers based on whole-genome resequencing data.Additional file 4: Table S4. Descriptive statistics and variance components of growth traits in Duroc pigs.Additional file 5: Table S5. The explained genetic variance of SNPs within significant windows for LMA.Additional file 6: Table S6. The explained genetic variance of SNPs within significant windows for LMD.Additional file 7: Table S7. The explained genetic variance of SNPs within significant windows for LMP.Additional file 8: Table S8. Significant KEGG pathways and GO terms associated with growth traits. (P < 0.05).Additional file 9: Table S9. Identified CNVRs compared with previous studies.Additional file 10: Table S10. Read depth statistics and bin size selection for CNVnator.Additional file 11: Table S11. qPCR primer and probe sequence information.


A common practice in the analysis of pathogens and their strains is using single-nucleotide polymorphisms (SNPs) to reconstruct their evolutionary history. However, genome-wide SNP-based phylogenetic trees are rarely analyzed without any further information. Including the underlying SNP data together with further metadata on the respective samples in the exploration process can facilitate linking the genomic and phenotypic properties of the samples.Treponema pallidum and Mycobacterium leprae.We introduce Efficient VIsual analytics tool for Data ENrichment in phylogenetic TreEs (Evidente), a web-application that provides an interactive visual analysis interface for the simultaneous interrogation of phylogenetic relationships, genome-wide SNP data and metadata for samples of an organism. Besides visualizing the phylogenetic tree, Evidente classifies SNPs as supporting or non-supporting of the tree structures and shows the distribution of both types of SNPs among samples and clades of interest. Furthermore, additional metadata can be included in the visualization. Lastly, Evidente includes an enrichment analysis to identify over-represented genomic features encoded by GO-terms within the clades of the tree. We demonstrate the usability of Evidente with the data of the pathogens https://evidente-tuevis.cs.uni-tuebingen.de/, it can also be run locally.Evidente is available at the TueVis visualization web server at Bioinformatics Advances online. SNPs are then often used e.g. to reconstruct the evolutionary relationship of the individuals. For this, a common procedure is to first map the NGS data to a common reference genome, followed by the SNP-calling step using a genotyping method. An alternative approach is to align sample genomes with a reference genome and to identify all positions where a sample differs by a mutation from the reference with respect to the coordinate system of the reference genome. The detected SNPs of all samples can be summarized into one SNP-table (see e.g. The rise of next-generation sequencing (NGS) techniques allows faster and more efficient procedures for the sequencing of genomes . Evidente enables a simultaneous visualization of genome-wide SNP data and phylogenetic data. Furthermore, Evidente allows to integrate additional metadata on the analyzed samples into the visualization. By providing an interactive visual interface, Evidente facilitates the identification of correlations between the phylogenetic clades, their respective SNPs and metadata. Moreover, Evidente allows the user to run an enrichment analysis on the GO . The columns of the SNP-table , which computes the distribution of the SNPs from the input files with respect to the lowest common ancestors (LCAs) that define a clade in the phylogenetic tree . The algIn this section, the main visual interface as well as design choices of Evidente is described. To illustrate this, we have chosen an artificial phylogeny and exemplary toy SNP and metadata. The corresponding input data can be found in the 2.3.1The key feature of Evidente is to interactively and simultaneously view and explore a phylogenetic tree and whole-genome SNP data as well as metadata of the samples of one organism. The central part of the graphical user interface of Evidente consists of three visualization components that are horizontally aligned: a horizontal dendrogram and two heat map-like visualizations of SNP and metadata see . In addiFor the visualization of the phylogenetic tree, we chose a horizontal linear dendrogram. It is created using a modified version of the D3-based library Phylotree . T3).Interactive functionalities of Evidente are offered both in the dendrogram itself as well as in the sidebar see . InitialT2) or to simplify the visualization by summarizing clades with similar characteristics into one element depending on their data type. Numerical values are summarized into horizontal box plots, while SNP, categorical and ordinal data are transformed into bar plots. All aggregated plots are scaled to the maximal value within each column to facilitate the comparison of aggregated data. An example for aggregation can be seen in Besides the extraction of SNPs for specific clades, further interactions, such as the aggregation and filtering of the data are possible. Any internal node can be collapsed and afterwards labeled as the user desires. This functionality is helpful to get an overview of the characteristics within a summarized clade . Before running an enrichment analysis, each SNP that is found within a gene needs to be linked with the GO-terms corresponding to the gene. This is achieved by using the annotation GFF file together with the file containing the GO-terms relationships. Since GO-terms are modeled in a directed acyclic graph  as a structural framework and the library D3 (Evidente is a client–server application that uses established libraries in the backend and the frontend. The frontend of Evidente is written in JavaScript using React (brary D3  for datahttps://evidente-tuevis.cs.uni-tuebingen.de/), but can also be run locally. The stand-alone version requires NodeJS (https://www.npmjs.com/) to be installed.Evidente is available on the TueVis server , yaws (subspecies TPE) or bejel (subspecies TEN) disease types. The collapsing allows a detailed inspection of the most ancient clade of M. leprae. One of the findings of Schuenemann et al. was that the ancient M.leprae strains are not present in one common clade, but that they can be found in several branches. T2.The first use-case was performed on the data of the study by T1 (identifying clade-specific SNPs). For the root of the clade B2, Evidente returns that no SNP is supporting this split. This speaks in favor of the decision of splitting this branch into three different subbranches.Next, a specific branch (labeled B2) has been analyzed in detail see . The resF to identify SNPs supporting this branch that surprisingly consists of two modern Ethiopian samples and many ancient samples. Evidente lists only two supporting SNPs, which underlines the heterogeneity of the strains. These two SNPs could be analyzed in detail to identify their location in the respective genomes and analyze their possible effects.Furthermore, T. pallidum samples with a total of 2550 different SNP positions , since they could refer to any allele at the position. When visualizing the T4, the clade SS14 of T.pallidum was analyzed to identify enriched GO-terms within the clade. The GFF annotation file of the Nichols strain (RefSeq NC_021490) was used, since this reference genome was used to compute the SNP-table (http://facop.molgenrug.nl/). This analysis returned 61 significantly enriched GO-terms within the SS14 clade. The four most significant results were related to the cell’s membrane . Evidente also allows the user to visualize the distribution of (supporting or non-supporting) SNPs found within genes labeled with the GO-term of interest. membrane organization (GO: 0061024). However, the SNP distribution hints that the over-representation is not unique for clade SS14, since a second clade (with the TPE/TEN samples) also shows SNPs in genes associated with this GO-term.Finally, to demonstrate how Evidente can be used to fulfill task Evidente is designed to simultaneously visualize the phylogenetic information, genome-wide SNP data and further aspects of an evolutionary reconstruction of different samples or strains from a single species. As a true visual analytics tool, it does not only provide a visual link by aligning the SNPs to the dendrogram, but it also links them computationally with its module CLASSICO, which adds the structural information ‘supporting of the tree structure’ and ‘non-supporting of the structure’ to the SNPs. Moreover, this type of tree visualization is one of the most commonly used in related work and therefore is easy to interpret for users. Another layer of information is added with the GO-term enrichment analysis, which aims to find significantly enriched functions for genes affected by SNPs in a clade of interest. Further features, such as filtering, aggregation and selection, facilitate the interaction with the tool.M. leprae were analyzed, we showed that Evidente can effectively be used to summarize metadata by collapsing clades. Moreover, we analyzed how strongly a certain clade is supported by the SNPs. This distinction helps finding SNPs that are unique to a clade, which might be candidates for a closer inspection by a domain expert.With the two case studies, we showed that the simultaneous visualization of SNP data and metadata, and the integration of an enrichment analysis provided in Evidente supports the identification of interesting patterns, within analyses focused on the spread of a pathogen or in antibiotic resistance development. In the first case study, where modern and ancient samples of the T.pallidum. Such correlations can, e.g. indicate convergent evolution, where strains in different clades in the tree develop similar mutation due to a shared environmental factor. Furthermore, we were able to show how SNPs in genes associated with a specific GO-term are distributed in the tree to find GO-terms specific for the genes affected by SNPs in two of the three main clades in the phylogenetic tree of T. pallidum.In the second case study, an exploration process was shown for finding SNPs that are non-supporting for the tree structure but correlate with metadata for samples of In both case studies, phylogenies of bacterial strains were used. While this data source is suitable since SNP-based phylogenetic trees are often used for the analysis of the evolution of  pathogens and their strains, the interest on eukaryotic pangenomes has started to increase in the last years . Due to In our use-cases, the phylogenetic tree was based on the SNPs themselves, but it might also be interesting to use the tool for analyzing a tree that is not SNP-based to investigate how well, for instance, a tree reconstructed using the whole genome is supported by SNPs. Currently, the usage of Evidente is restricted to the analysis of samples with a common genomic coordinate system. Hence, insertions and deletions are not considered.In the current implementation, only SNPs that are found uniquely in clades, i.e. monophyletic groups are considered supporting in order to ensure clade-conformation. However, a further category, which could be considered supporting are SNPs found within a paraphyletic group. Such a third category of SNPs will be a future work for Evidente’s preprocessing module CLASSICO.M. leprae. An overview of the data that consists of 159 samples is provided due to the possibility of collapsing clades. To facilitate the visualization of even larger trees, we plan to work on enhancing the tree scalability. For such trees, one future implementation could provide automatic collapsing of clades to reduce the number of nodes shown. With this, the user is first shown an overview of the data, before having the possibility of decollapsing clades to zoom in and interact with more detailed data, thus implementing the visual information seeking mantra by M. leprae’s use-case, around 100 SNPs were studied simultaneously. Further SNPs can be explored by horizontally dragging the SNP heat map.Presently, Evidente provides a user-friendly interaction with phylogenetic trees up to a couple of hundred genomes, like the examples shown here on a laptop screen. The largest phylogenetic tree visualized so far in Evidente is the one of With the inclusion of GO-terms in the analysis, Evidente offers already the exploration of a wide range of functions. Further future work will expand the statistical computations of Evidente, such as e.g. by integrating KEGG pathways as another resource for an enrichment analysis. Moreover, we will provide further methods for statistical correction. In addition, we plan to include the metadata into the computational analysis, by either conducting an enrichment based on the metadata, or by automatically identifying SNP patterns that correlate with metadata.We have presented Evidente, a web-application that enables an interactive and simultaneous visualization of phylogenetic, genome-wide SNP data and further metadata. The linked dendrogram of the phylogenetic tree with the two heat map-like table visualizations make the visual assessment of clade-specific or non-clade-specific SNPs and the connection of SNPs with specific metadata as well as enriched GO-terms straight-forward. With these visualization approaches and the functionalities implemented in Evidente, the identified tasks required for the interaction with the underlying data could be addressed successfully as demonstrated in the case studies. Hence, Evidente allows the exploration and identification of possible associations that may not be immediately evident in the data.vbac075_Supplementary_DataClick here for additional data file.


Of these, 12 stable MTAs were identified in multiple environments, and six superior alleles showed positive effects on KL, KP, and KDR. Four potential candidate genes underlying the associated SNP markers were predicted for encoding ML protein, F-box protein, ethylene-responsive transcription factor, and 1,4-α-glucan branching enzyme. These genes were strongly expressed in grain development at different growth stages. The results will provide new insights into the genetic basis of kernel traits in wheat. The associated SNP markers and predicted candidate genes will facilitate marker-assisted selection in wheat breeding.Kernel size and weight are crucial components of grain yield in wheat. Deciphering their genetic basis is essential for improving yield potential in wheat breeding. In this study, five kernel traits, including kernel length (KL), kernel width (KW), kernel diameter ratio (KDR), kernel perimeter (KP), and thousand-kernel weight (TKW), were evaluated in a panel consisting of 198 wheat accessions under six environments. Wheat accessions were genotyped using the 35K SNP iSelect chip array, resulting in a set of 13,228 polymorphic SNP markers that were used for genome-wide association study (GWAS). A total of 146 significant marker-trait associations (MTAs) were identified for five kernel traits on 21 chromosomes [–log Triticum aestivum L., 2n = 6 × = 42, BBAADD) is one of the most important cereals worldwide, providing calories and proteins for ∼40% of the world population (Bread wheat (pulation . Over thpulation . Given tpulation . Thousanpulation . TherefoOryza sativa L.) (Zea mays L.) (Gossypium spp.)  , maize (mays L.) , cotton um spp.) , 2020a, um spp.)  and qualum spp.) . Pang etum spp.) . HoweverA diverse set of 198 bread wheat accessions was used for this study. Of these, 183 varieties were from eight provinces in China, including 114 from Gansu, 33 from Shanxi, 11 from Hebei, ten from Beijing, six from Shaanxi, six from Shangdong, two from Tianjin, and one from Henan. The remaining 15 cultivars were from America . All culAt grain maturity, five individual plants of each cultivar were randomly selected for threshing, and incomplete seeds were removed. Kernel length (KL), kernel width (KW), kernel diameter ratio (KDR), kernel perimeter (KP), and thousand kernel weight (TKW) were measured by image analysis using the SC-G software . All trait measurements were repeated three times.h2B) was calculated by the following equation (h2B = σ2G/(σ2G + σ2G × E/l + σ2e/rl), where σ2G is the genetic variance, σ2G × E is the genotype-environment variance, σ2e is the residual variance, l is the number of environments, and r is the number of replications. Descriptive statistics, ANOVA, and Pearson’s correlation coefficient were calculated using IBM SPSS Statistics V.25.The best linear unbiased prediction (BLUP) of kernel traits for wheat was calculated by using the R package “lme4.” The broad-sense heritability , which contained 35,143 SNP markers . The marK value set as 2–10. The optimal number of clusters was determined according to the cross-validation error (CV error) rate, and the K-value with the minimum CV error rate corresponds to the optimal number of subpopulations  ≥ 3] was regarded as the threshold using a liberal approach to reduce ignoring any significantly associated SNP marker for the kernel traits , where population structure (Q) and kinship (K) matrices were applied to avoid spurious associations caused by population structure and control false positives simultaneously. To declare the significant MTAs, l traits . Manhatt2 Subsequently, JBrowse was used to examine candidate genes that overlapped with the flanking region of the SNPs . Three biological replications were used for quantitative real-time PCR (qRT-PCR) analysis in each treatment. TaActin was used as an endogenous reference, and the 2–ΔΔC(t) method was used to determine relative gene expression levels. Primer sequences used for qRT-PCR are listed in After the significant MTAs were identified in multiple environments, the flanking sequence spanning 1 kb upstream and downstream of the significant SNP position was used to query against the Chinese Spring wheat reference genome IWGSC RefSeq v1.1.the SNPs . Functio SNPs , whereas KL had the highest h2B value (0.85)  .P < 0.001). KL, KW, KDR, and KP showed positive correlations with TKW (r = 0.24–0.83). KW showed a negative correlation with KDR (r = –0.37), whereas the other traits were positively correlated with each other. The highest correlation was between KL and KP (r = 0.98).Pearson’s correlation coefficients based on BLUP for the kernel traits are shown in A total of 35,143 SNPs were genotyped for 198 wheat accessions. After filtering, 13,228 SNPs were polymorphic and were used for subsequent analysis . Of thesK = 4, indicating that the association population was structured into four subpopulations. Each of them contained 48, 60, 44, and 46 accessions, respectively  for the kernel traits were identified on all 21 chromosomes, explaining phenotypic variation (R2) ranging from 5.91 to 15.02%  was slightly higher than that of the inferior alleles (AA and GG) for both markers AX-94409249 and AX-95629937.The allelic effect was estimated from the MTAs repeatedly identified in multiple environments. Significant differences between SNP alleles were found for six MTAs except 95629937 . The allThe average values for phenotypes of wheat accessions carrying different numbers of superior alleles (0–2) are shown in TraesCS3B02G139500 on chromosome 3B, TraesCS5B02G428200 on chromosome 5B, TraesCS6B02G323800LC on chromosome 6B, and TraesCS7D02G535400 on chromosome 7D. Those genes encoded a rotundifolia-like protein, an MD-2-related lipid recognition domain-containing protein, an F-box protein, and a 1,4-α-glucan branching enzyme, respectively. On chromosome 5D, two candidate genes, namely, TraesCS5D02G011900 and TraesCS5D02G657200LC, correlated with KL and KW, and they encoded hydroxysteroid dehydrogenase and chromosomal replication initiator protein DnaA, respectively. Of the other three candidate genes, which were involved in two different traits, the gene TraesCS5D02G223200 on chromosome 5D for KL and KP encoded serine protease HtrA-like and the gene TraesCS5D02G223700 on chromosome 5D for KL and KP encoded ethylene-responsive transcription factor. TraesCS1A02G135300 on chromosome 1A for KW and TKW encoded a protein of the superfamily of nucleoside triphosphate hydrolases with P-loop. The expression levels of the candidate genes were examined in various tissues, including roots, stems, leaves, developing spikes, spikes at anthesis, and developing grains at 5, 10, and 20 DAA. Among the nine candidate genes identified, the CDS sequences of TraesCS5D02G657200LC and TraesCS5B02G428200 were short, and it was difficult to design suitable specific primers. Therefore, we only presented the qRT-PCR results for the other seven candidate genes  of a TKW QTL (QTkw.ncl-1A.1) reported by QTkw-1A.1) for TKW reported by AX-95629937 and AX-94578940 that were associated with KDR co-localized with QTkw-5B.1 on chromosome 5B for KL and QTkw-7D.2 on chromosome 7D for TKW, respectively, as reported by AX-95248961 on chromosome 5D was near to a QTL (QTkw-7D.2) for hundred grain weight reported in Some studies have performed QTL mapping for wheat kernel traits using different molecular markers . In thisAX-94400331 and CC for AX-94509671 simultaneously showed highly positive effects on KL and KP , which was also conjugated to ubiquitin pathway with a critical role in controlling seed size in plants , which encodes an F-box protein and is involved in the regulation of cytokinin levels, can improve rice yield by regulating panicle structure.The gene rocesses . Li et aAX-94509671 was associated with KL in all environments except E1 and E6 and with KP in E2 and BLUP. A potential candidate gene TraesCS5D02G223700 encoding ethylene-responsive transcription factor (ERF) was found near the physical map position of AX-94509671 on chromosome 5D. ERF is a plant-specific transcription factor involved in plant growth and development processes (OsERF interacts with a transcription factor (OsNF-YB1) specifically expressed in the aleurone layer of the endosperm. This process affected kernel filling and endosperm development, regulating rice kernel size and weight. ZmEREB94 regulates several starch-synthetic genes, further affecting endosperm development.The marker rocesses . Xu et aAX-94578940 associated with KDR in the E3 environment and BLUP corresponded to a candidate gene TraesCS7D02G535400 (7D) encoding the 1,4-α-glucan branching enzyme (GBE). GBE is the key enzyme that catalyzes the formation of α-1,6-linked branch in starch and belongs to the glycoside hydrolase 13 (GH13) family (The marker ) family . In tapi) family . We specIn this study, GWAS for kernel traits was performed on a diverse panel of 198 bread wheat accessions, genotyped with the 35K SNP iSelect chip array. As a result, 146 significant MTAs were identified for five kernel traits. Of these, 12 stable significant MTAs were identified in different environments. Six superior alleles of nine major SNP markers showed positive effects on KL, KP, and KDR. Four potential candidate genes were predicted to be highly expressed in the developing grain at different growth stages. The results provide not only new insights into the genetic basis of kernel traits in wheat but also diagnostic makers that can be used for marker-assisted selection in wheat breeding.The original contributions presented in this study are included in the article/PW and DY conceived the study. PW, TT, and JM performed the phenotypic data measurement and data analysis. YL, PZ, TC, and FS provided scientific feedback and revised the content. PW wrote the first draft of the manuscript. DY and FS revised and edited the manuscript. All authors have read and agreed to the published version of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Malus genepools. Fruit samples were phenotyped for 34 metabolites, including a stable vitamin C glycoside “ascorbic acid 2-β-glucoside” (AA-2βG), and the accessions were genotyped using the Apple 20 K SNP Array. Several fruit quality traits, including red skin over-colour (OCOL), were also assessed. Wild Malus accessions showed at least 2-fold higher average content of several metabolites  than Malus domestica accessions. Several new genomic regions and potential candidate genes underpinning the genetic diversity of apple phytochemicals were identified. The percentage of phenotypic variance explained by the best SNP ranged between 3% and 21% for the different metabolites. Novel association signals for OCOL in the syntenic regions on chromosomes 13 and 16 suggested that whole genome duplication has played a role in the evolution of apple red skin colour. Genetic correlations between phytochemicals and sensory traits were moderate. This study will assist in the selection of Malus accessions with specific phytochemical profiles to establish innovative genomics-based breeding strategies for the development of apple cultivars with enhanced nutritional value.Understanding the genetic architecture of apple phytochemicals, and their interplay with conventional selection traits, is critical for the development of new apple cultivars with enhanced health benefits. Apple accessions (n = 344) used for this genome-wide association study (GWAS) represented the wide diversity of metabolic profiles in the domesticated and wild  Apple pet al. [Malus domestica and wild Malus species and reported that modern apples have drastically reduced polyphenol content compared with the ancestral heritage and germline cultivars. Davies et al. [Malus sieversii), and suggested that selection against internal flesh browning and bitterness could have contributed to the lower phenolic content in commercial cultivars. There is an opportunity to utilise the diversity of wild apples to enhance the nutritional content of future cultivars.The visual characteristics (e.g. skin characteristics), eating quality (e.g. texture and flavour), and storability are among the main fruit quality traits being targeted in apple breeding programmes, but the enhancement of phytochemicals is now gaining traction to select “bio-fortified” apple cultivars. Farneti et al.  screeneds et al.  found thM. domestica, have identified the upper region of chromosome 16 as a potential hotspot for apple phytochemicals [M. domestica accessions, McClure et al. [M. domestica and Malus spp. identified marker-trait associations for phytochemicals on each linkage group [Malus species offers opportunities to develop new cultivars with all the associated health benefits of vitamin C [QTL mapping studies, using relatively small bi-parental populations ca. 150) of hemicals , 8. In ae et al.  reportedge group . Apples 0 of hemiM. domestica and wild Malus species accessions to assess the diversity and genetic architecture of main bioactive compounds of apple fruit. Marker-trait association analyses were conducted, and the relationships between sensory traits and phytochemicals were also investigated to guide the development of bio-fortified apple cultivars.Studies on apple phytochemicals have reported on the genetic variation , 11 and M. domestica and wild species. Seasonal variation was observed for most phytochemicals for the 15 accessions assessed in both years, with between-year correlations ranging from 0.22 for fatty acids to 0.96 for dihydrochalcones, with an average of 0.65 across all 34 compounds.The targeted LC–MS analyses of fruit samples revealed the presence of 34 compounds in the accessions of p < 0.01) different between Malus spp. and M. domestica accessions  content (2746 μg/g FW) in Malus spp. accessions was about 2.5-fold higher than the maximum of 1104 μg/g FW in cultivated apples. The average content of triterpenoids  in M. domestica accessions was 11, 8, 60 and 21 μg/g FW, respectively, which did not differ significantly from the wild species . The average content of malic acid (MA) in M. domestica accessions was 8682 μg/g FW, which was significantly lower compared with wild accessions (11504.3 μg/g FW)  and Malus sieboldii “Snowbright” (29.9 μg/g FW) was about four times higher than in the M. trilobata accession (7.93 μg/g FW). Trilobatin was not considered for GWA analysis due to the low detection frequency. The phytochemicals-based clustering  . Trilobaustering  of domesustering  suggesteh2) estimates suggested that most of the phytochemicals in this study have high heritability (h2 < 0.20) traits, while quercetin glycosides, chlorogenic acid, phloridzin and epicatechin were among the highly heritable (h2 > 0.50) traits. AA-2βG was more heritable than AsA (0.43 versus 0.20). Flavour intensity (FINT) was the least heritable (h2 = 0.14) and the OCOL was the most heritable (h2 = 0.82) trait among the fruit quality traits (Genomic heritability (tability . Pomaceiy traits .p < 0.001) positive correlations among epicatechin and the procyanidins were observed. The correlations among the six quercetin compounds varied between 0.40 and 0.90 (p < 0.001). Although several pairs of compounds were negatively correlated, only few pairs  were found to be significant (p < 0.001). Maslinic and corosolic acids showed strong positive correlation with several other compounds (e.g. annurcoic acid and 3-oxo-hydroxy-urs-12-en-28-oic acid_1). The correlation (ca. 0.40) between AsA and AA-2βG was significant, and both of these compounds were also significantly correlated with MA (quercetin compounds) clustered separately. Similarly, several triterpene acids, such as pomaceic, annurcoic, euscaphic, pomolic, maslinic, betulinic and ursolic acids also clustered together .The degree of the correlations among all pairs of phytochemicals are illustrated in  with MA . The clur = −0.30, p = 8.6E-09), and between trans-4-p-coumaroyl quinic acid and eating quality . Not surprisingly, MA content was positively correlated with sourness  and flavour intensity . Fruit skin colour (OCOL) was significantly positively correlated with ursolic acid content . Among all the phytochemicals assessed in this study, corosolic acid showed the highest positive correlation with the fruit texture traits   .Malus species and M. domestica, while the PC2 revealed the variability within each of the two genepools  between SNPs separated by 100 bp was 0.17, and decayed rapidly to 0.07 and 0.05 for SNPs separated by 100 kb and 500 kb, respectively  distinguishes the accessions belonging to wild enepools . Furthercessions , which hene pool . The aveectively . LD decaistances .There were multiple genomic regions significantly associated with various phytochemicals on all 17 chromosomes, and the number of significant genomic regions was the highest on chromosome 9 and the lowest on chromosomes 8 and 14 . The perNine triterpenoid acids  were investigated in this study, but a strong association signal was found only for euscaphic acid . The mosO-xyloglucoside) were located on almost all chromosomes  for 3-hydroxyphloridzin and phloretin was about 18 kb from the 3-dehydroquinate synthase (DHQS) gene (MD02G1126200). A MYB protein (MdMYB52: MD06G1235300) is within 100 kb of the second most significant SNP (chr6: 36724439) associated with phloridzin. A SNP associated with 3-hydroxyphloridzin and phloridzin on chromosome 15 (chr15:24335063) is surrounded by a cluster of CC-NBS-LRR disease resistance proteins . A SNP at the distal end of chromosome 15 (chr15:51077031) associated with phloridzin is within 20 kb of MYB protein 3 (MdMYB3: MD15G1411200). A GWAS signal for 3-hydroxyphloridzin on chromosome 16 (chr16:8480114) occurred about 80 kb downstream of a caffeoyl-CoA 3-O-methyltransferase (CCOAMT) gene (MD16G1118200). Another significant signal for this phenotype in the upper region of chromosome 17 (chr17:2146770) occurred slightly more than 200 kb upstream of MYB24 . An association for 3-hydroxyphloridzin on chromosome 17 (chr17:10280548) is within 100 kb of a cluster of cinnamyl-alcohol dehydrogenase (CAD) genes .A hydroxycinnamoyl-CoA shikimate/quinate hydroxycinnamoyl transferase (HCT/HQT) gene (MD17G1225100) resided about 250 kb upstream of a SNP (chr17:27602614) associated with 3-hydroxyphloridzin, and a 4-coumarate:CoA ligase (4CL) gene (MD17G1229400) was located about 140 kb downstream. In addition, at about 300 kb downstream of this SNP, there is a cluster of senescence-associated gene 12 .Seven compounds including flavanol monomers (catechin and epicatechin) and flavanol polymers  were observed in this study, and the GWA signals were observed on almost all chromosomes, but the most prominent association signals were detected on chromosome 9 . A SNP (SNPs associated with one or more flavanol compounds in the upper region of chromosome 10 (Chr10:7887052 and Chr10:9437064) were flanked by a B-box zinc finger protein (MD10G1066000). A SNP (Chr2:10212596) associated with four of the seven flavanol compounds was within 20 kb of the DHQS gene MD02G1126200, and a signal in the upper region of chromosome 17 (chr17:109495) was within 18 kb of a WRKY transcription factor (WRKY75: MD17G1001500).O-glucoside and quercetin 3-O-rutinoside was within 200 kb of several UGT proteins   is flanked by genes involved in regulating senescence (MD09G1239100) and acyl-CoA oxidase (ACOX) (MD09G1241300). There is a polyketide synthase protein (MD17G1158100) in the vicinity of the signal on chromosome 17 (chr17:15775967). Polyketide synthase genes refer to a more general description for the CHS and CHS-like genes involved in flavonol biosynthesis.A SNP (chr3:32741919) associated with quercetin 3-1241400) . For queO-rhamnoside were on chromosome 1 (chr1:26506469 and chr1:27465802), which flanked several gene functions including, a chalcone-flavanone isomerase (CHI) gene (MD01G1167300), laccase proteins (MD01G1158900 and MD01G1159400), and a cluster of disease resistance LRR family proteins . The third significant SNP for quercetin 3-O-rhamnoside (chr1:22717183) is within 500 kb of a chalcone-flavanone isomerase (CHI) gene cluster .The most significant SNPs associated with quercetin 3-O-rhamnosidein the upper region of chromosome 16 (chr16:8480114) is about 80 kb from a cluster of caffeoyl-CoA 3-O-methyltransferase (CCOAOMT) genes .Another association signal for quercetin 3-The two significant SNPs (chr2:16706831 and chr2:18670719) associated with CGA flanked the 1-aminocyclopropane-1-carboxylate synthase (ACS) gene (MD02G1190900), while the signal on chromosome 9 (chr9:17335476) is within 650 kb of a MYB domain protein 26 (MYB26: MD09G1191100) . The assAn association signal for MA on chromosome2 (chr2:7978884) is within 2 kb of a SAUR-like protein (MD02G1100200) and about 18 kb from pectin methylesterase 2 protein (MdPME2: MD02G1100400). Methionine sulfoxide reductase (MSR) B5 genes  were located about 80 kb downstream from a SNP (chr13:14686218) is marginally associated with AsA .Significant marker-trait associations were detected for five  out of 11 fruit quality traits in this study . There wThree main genomic regions associated with OCOL were identified on chromosomes 13, 16 and 17 . The twoM. domestica and wild Malus species accessions. Compared with the other metabolite groups, differences in organic acids and DHC content between M. domestica and Malus spp. were more prominent. The fruit from wild accessions also showed significantly higher content of AsA and AA-2βG than M. domestica accessions.Similar to earlier studies , 12, ourh2 of AA-2βG was considerably higher than that of AsA (0.42 vs 0.20), and the between-season correlation for AA-2βG was also higher compared with AsA (0.95 vs 0.70). These findings would suggest that AA-2βG could be a better selection trait than AsA for improving Vitamin C content of future apple cultivars, but further research is needed on the stability of AA-2βG in the human gut and whether this will increase vitamin C bioavailability. Dihydrochalcones (e.g. phloridzin), flavanols  and hydroxycinnamic acids (e.g. chlorogenic acid), which are associated with several health benefits [The genomic benefits , tended Trilobatin has shown strong antioxidant, anti-inflammatory, and α-glucosidase inhibition activities for the management of diabetes . In our Several ripening and senescence-associated gene functions  were located close to the significant SNPs, supporting earlier studies showing linkage between the ripening and phytochemical biosynthesis pathways [Malus species [The occurrence of several UGT proteins in close proximity to the DHC-associated regions in this study is in agreement with earlier reports on the role of UGTs that convert phloretin to phloridzin in  species . The DHC species –9. Chalc species , 8. Over species .FLS1 gene expression in Arabidopsis thaliana [, and MYB24 also plays a role in flavonoid regulation in grape berries [The MYB transcription factor MYB101 (MD09G1109900), underpinning a significant region, was shown to play role in chalcone synthase (CHS) leading to flavonoid biosynthesis in Lotus japonicus [Rosa rugosa [This is the first study to identify the upper region (1.80–3.60 Mb) of Chr9 as a major hotspot for flavanol regulation. This region harbours MYB transcription factors MYB24 (MD09G1030700) and MYB16 (MD09G1054000). Transgenic assays showed that MYB16 affects the regulation of anthocyanin and flavanol in apples , 21, andthaliana , and MYBaponicus  and Rosaa rugosa .The occurrence of MYB52 (MD06G1235300) and MYB3 (MD15G1411200) within genomic hotspots for phloridzin and CGA is supported by previous reports. For example, overexpression of MdMYB3 in tobacco resulted in upregulation of several flavonoid pathway genes, including CHS, CHI, UFGT and FLS . The expThe HCT/HQT and 4CL genes (MD17G1225100 and MD17G1229400) resided in the DHC (3-hydroxyphloridzin) associated genomic regions, and these gene functions were hypothesised to play a role in the regulation of CGA , and a cOur GWA results suggest that a CCOAMT gene (MD16G1118200), CAD genes  and DHQS gene (MD02G1126200) are potential candidates underpinning DHC-associated genomic regions. These gene functions were also proposed as candidate genes for the biosynthesis of hydroxycinnamic acids (CGA) , which gMdMYB1, MdANS) and flavonoid pathway genes  [Overexpression of B-box (BBX) genes in apple callus led to the higher expression of anthocyanin biosynthesis-related genes (d MdDFR) , which sd MdDFR) .Arabidopsis thaliana also reported that flavonol synthase requires 2OG and ferrous iron (Fe) for catalytic function [Clusters of 2-oxoglutarate (2OG) and Fe(II)-dependent oxygenase proteins occurred within the flavonols-linked regions on Chr1 and Chr16. 2OG genes are involved in the synthesis of anthocyanin in crabapples , and it function . The confunction , and intWe identified a novel texture-associated signal on Chr9 in this study, and the MYB6 (MD09G1261200), located within 50 kb of the significant SNP, has been shown to play a role in ripening and storability of strawberry . Some otOur study identified novel genomic regions associated with OCOL variation in apple germplasm. Among other gene functions, paralogs of MYB15 and ERF1B underpinned the association signals for OCOL in the syntenic regions on Chr13 and Chr16 – suggesting that whole genome duplication has played a role in the evolution of apple red skin colour. The levels of anthocyanin significantly increased in MdERF1B-overexpressing callus , 41, whiThere were two WRKY transcription factors  in the OCOL-associated regions on Chr17. Both of these WRKY transcription factors have been reported being involved in the regulation of anthocyanin biosynthesis , 43. It The content of several key phytochemicals was significantly higher in wild apple accessions, suggesting that these accessions could be used as parents for introgression breeding to develop phytonutrient-rich apple cultivars. In this germplasm study, the correlations between eating quality traits and phytochemicals were moderate, which suggested that it would be feasible to develop tailored breeding strategies for the simultaneous improvement of these two groups of traits. Studies using segregating populations would be helpful to further understand the genetic correlations between eating quality and phytochemicals, which would guide future cultivar development strategies. The complex polygenic architecture of most metabolites in this study suggested that genomic selection would be a better option to reduce the generation interval for introgression breeding .Malus germplasm collection maintained at the Hawke’s Bay site (39°40’S 176°53′E) of The New Zealand Institute for Plant and Food Research Limited was established as grafted trees in late 1950s when budwood from heritage cultivars and crab-apples was imported mainly from the United Kingdom. In 2013, each accession was re-propagated onto “M9” rootstock and maintained in the orchard following standard commercial practices. This study was performed on 344 accessions, comprising 313 M. domestica accessions and 31 accessions of wild Malus species . The response was calibrated using AsA and AA-2αG, therefore AA-2βG was quantified as AA-2αG equivalents. It is noteworthy that AsA is unstable and can degrade during the analytical process. The sample preparation methods were not optimised specifically for AsA, so the measurements of AsA in this study are estimates only. In contrast, AA-2βG is a very stable form of Vitamin C [Immediately after extraction, a portion of the extract was diluted 1:1 with 2 mM tris(2-carboxyethyl) phosphine hydrochloride (TCEP.HCl)  in MeCN/formic acid/water 90:5:5) to preserve the AsA. The actual volume used was 200 μL extract +200 μL 2 mM TCEP in MeCN/formic acid/water (90:5:5) directly into LC vials. The UHPLC system Dionex Ultimate® 3000 Rapid Separation LC was composed of a pump, autosampler, column oven, and Photodiode Array (PDA) detector. The LC column was a Luna HILIC 150 mm x 4.6 mm, 3 μm (Phenomenex) and was maintained at 40°C. The flow was 1000 μL min:5 to pre−1. The solvents were A = 0.2% formic acid and B = 100% acetonitrile. The solvent gradient was: initial composition 90% A 10% B, 0–0.5 min; linear gradient to 60% A 40% B, 0.5–7 min; linear gradient to 5% A 95% B, 5–12 min; composition held at 5% A 95% B, 12–15 min; linear gradient to 90% A 10% B, 15–15.2 min; to return to the initial conditions before another sample injection at 18 min. The injection volume for samples and standards was 1 μL. The micrOTOF QII parameters were same as those described earlier [Each sample extract was diluted 1:1 with methanol  and then analysed for polyphenols by Liquid Chromatography – High Resolution Accurate Mass – Mass Spectrometry (LC-HRAM-MS) as described earlier . LC–MS get al. [et al. [Malus species accessions were very small and/or had an unpleasant taste, such accessions were assessed only for AVFW and OCOL.Thin wedges of cortical tissue were cut from each of the two apples from each accession for sensory evaluation by one trained assessor. Following Kumar et al. , several [et al. . As fruiLeaf DNA from all 344 accessions were genotyped using the 20 K Infinium SNP array , and SNPt-test was used to test whether the population means of two groups  are equal or not. The genetic structure of the 344 accessions was estimated using PCA, Neighbour Joining (NJ) tree, and model-based Bayesian clustering using ADMIXTURE software. Estimates of linkage disequilibrium (LD: r2) were calculated between SNPs located on the same linkage group (LG) using R package “LDcorSV”. The estimates of pair-wise LD between SNPs were averaged in the increments of one kilobase (kb) distance, and the LD decay curve was fitted using a standard logarithmic function.Exploratory analyses, including trait distribution, correlation between traits, and principal component analysis (PCA) were conducted using the R software . The two-sample α = 0.01 was used to identify significant genotype–phenotype associations. The putative candidate genes within 500 kb upstream and downstream of the most significant SNPs were retrieved using GDDH13 genome v.1.1 Jbrowser (https://www.rosaceae.org/jbrowse/). Genomic best linear unbiased prediction (GBLUP) of the additive genetic value of all accessions were obtained for each trait, and then the product–moment correlations between GBLUP for different traits were used as estimates of genetic correlations. Following Kumar et al. [h2) were also calculated for each trait using R package GAPIT.The best linear unbiased estimates (BLUEs), adjusted for year effect, were used as phenotypes for GWAS. A mixed linear model (MLM) approach, accounting for cryptic relatedness (K-matrix) and population stratification (Q-matrix), implemented in R package GAPIT , was user et al. , the estWeb_Material_uhac218Click here for additional data file.