P < 0.0001) MTAs were identified for eight different traits, including five for wrinkled vining pea yield on chr2LG1, chr4LG4, chr7LG7, and scaffolds (two), and six for adult plant resistance to downy mildew on chr1LG6, chr3LG5 (two), chr6LG2, and chr7LG7 (two). We reported several novel MTAs for different crucial traits with agronomic importance in wrinkled vining pea production for the first time, and these candidate markers could be easily validated and integrated into the active breeding programs for marker-assisted selection.Estimating the allelic variation and exploring the genetic basis of quantitatively inherited complex traits are the two foremost breeding scenarios for sustainable crop production. The current study utilized 188 wrinkled vining pea genotypes comprising historical varieties and breeding lines to evaluate the existing genetic diversity and to detect molecular markers associated with traits relevant to vining pea production, such as wrinkled vining pea yield (YTM100), plant height (PH), earliness (ERL), adult plant resistance to downy mildew (DM), pod length (PDL), numbers of pods per plant (PDP), number of peas per pod (PPD), and percent of small wrinkled vining peas (PSP). Marker-trait associations (MTAs) were conducted using 6902 quality single nucleotide polymorphism (SNP) markers generated from the diversity arrays technology sequencing (DArTseq) and Genotyping-by-sequencing (GBS) sequencing methods. The best linear unbiased prediction (BLUP) values were estimated from the two-decades-long (1999–2020) unbalanced phenotypic data sets recorded from two private breeding programs, the Findus and the Birds eye, now owned by Nomad Foods. Analysis of variance revealed a highly significant variation between genotypes and genotype-by-environment interactions for the ten traits. The genetic diversity and population structure analyses estimated an intermediate level of genetic variation with two optimal sub-groups within the current panel. A total of 48 significant ( Pisum sativum L., 2n = 14) is the second most essential legume crop next to common bean globally with an annual production of 14.2 million tons of dry pea and 21.6 million tons of vining pea in 20191. Wrinkled vining pea for the freezing industry is harvested as immature pea seeds (green peas) and consumed as a vegetable. Green peas have a nutritionally favorable composition with respect to macronutrients  and micronutrients  and have a low glycemic index contributing to the health benefits for consumers (Rhizobacteria) that makes the plant a good source of nitrogen fertilizer, thereby playing a role in alleviating the greenhouse gas emissions  and genomic selection (GS). Estimating the existing genetic variation within particular germplasm is a critical component of plant genetics, breeding, and evolution  digestion followed by high-throughput sequencing (The GBS is a robust one-step method for simultaneous genotyping and SNP discovery . The GBSquencing . Subseququencing . The DArquencing  and is squencing . The DArquencing . The GBSquencing .A genome-wide association study (GWAS) has been widely used for the last couple of decades as a primary tool in detecting the genetic background of polygenic traits, and this technique has been converted from a promising new tool to a robust, ubiquitous technique for understanding complex traits in various crops . Indepenvia the GWAS analysis. As a result, the historically recorded unbalanced phenotypic datasets found in breeding programs have been successfully used to detect relevant QTLs and markers in different crops associated with traits such as yield, quality, and disease resistance  to estimate the existing genetic variation, population structure, and linkage disequilibrium, and (II) to detect the SNP markers associated with economically relevant traits related to wrinkled vining pea yield and downy mildew resistance. To accomplish this, the best linear unbiased predictions were estimated from the breeding programs derived two-decades-long historical unbalanced data sets from 1999 to 2020, for ten different traits. Genotyping of the current wrinkled vining pea panel was done with both DArTseq and GBS sequencing methods.Aphanomyces euteiches root rot (1 genotype). The other nine genotypes were originated from Birds Eye (now part of Nomad Foods) in the United Kingdom, which included six breeding lines and three varieties.A total of 188 wrinkled vining pea varieties and lines were assembled for this study . The pan2, excluding roots, while the plant height was taken from 10 plants/plot average values (cm). The ERL or relative maturity is expressed in days, earlier (−) or later (+) to the control variety “Cabree.” The yield (YTM100) was defined as the wrinkled vining pea yield at a 100-tenderometer value (t/ha). The percent of small wrinkled vining peas was calculated from the mass of small wrinkled vining peas divided by the total wrinkled vining pea yield. The small peas are those with less than 8.7 mm in diameter and are considered as the higher quality standard in wrinkled vining pea production. The number of pods with developed wrinkled vining peas per plant was calculated from 10 plants/plot aggregate values. The number of pods per node was counted from the aggregate value of the second node of 10 plants per plot. The pod length (mm) was measured from the plant’s second node, and the average score was taken from 10 plants per plot. The number of wrinkled vining peas per pod was measured from the second node and taken as the average values of 10 plants/plot. Adult plant resistance to DM was measured as the ratio of pods that were free of DM from the total number of infected pods per plant and were taken from the average of 10 plants/plot.The phenotypic data of 10 different agronomic traits related to wrinkled vining pea yield, ERL, and adult plant resistance to DM were extracted from the unbalanced historical data recorded for the past 2 decades (1999–2020) in Nomad Foods breeding programs. The genotypes were tested in various locations of southern Sweden, with a total of 192 year-by-location combinations (environments hereafter). The phenotypic data from these trails were included for the GWAS analysis. The number of trials in a year ranged from three in 2019 to eleven in 2001, with an average of eight locations per year. The number of genotypes per year varied in a range of 17 in 2007 to 77 in 2020, with an average of 30 genotypes. Individual genotypes were tested for a minimum of two environments per year to a maximum of 15 environments, with an average of seven environments. The trials were connected by the ten used checks across all experiments to account for the environmental effects, followed by mean adjustment including the widely known pea varieties, “Bikini” and “Avola.” The field trial experimental design was based on the incomplete block design with two replications. The following agronomic and disease resistance traits were included in the current GWAS analysis: Above-ground biomass (BM), PH, ERL, YTM100, PSP, number of pods per plant (PDP), number of pods per node (PDN), Pod length (PDL), number of peas per pod (PPD), and adult plant resistance to DM. The above-ground biomass (kg) was recorded from the total biomass of plants from the harvested area of 10 m2. Briefly, the DNA samples were subjected for complexity reduction by digestion/ligation reactions following the procedure by PstI-compatible adaptor with two adaptors corresponded for the two different restriction enzymes (PstI and MseI) overhangs. The PstI-compatible adapter was designed to include Illumina flowcell attachment sequence, sequencing primer sequence, and barcode region, while the reverse adapter contained flowcell attachment region and MseI-compatible sequence. The only “mixed fragments” (PstI-MseI) were effectively amplified in PCR for 30 rounds using the most optimal reaction conditions as follows: 94°C for 1 min , 30 cycles each with 94°C for 20 s (denaturation), 58°C for 30 s , 72°C for 45 s for extension, and 72°C for 7 min final extension. Subsequently, each sample’s equimolar amounts of amplification products were bulked and sequenced with the HiSeq 2000  running the single read for 77 cycles. The generated sequences were then processed using proprietary DArT analytical pipelines. Approximately 2,500,000 sequences per barcode/sample were used in the marker call. Identical sequences were collapsed into fastqcall files followed by SNPs calling using the proprietary algorithm DArTsoft14. Finally, the SNP markers were mapped with the pea reference genome sequence assembly  with 75 base pair (bp) single read. Demultiplexing of samples with barcode adapters ligated to individual DNA samples and verification of restriction site was processed using the in-house Illumina bcl2fastq v2 software package. The barcode sequences were removed from the read sequences, and the reads were trimmed using the in-house trimming software, the Trimmomatic-0.33. The SNP polymorphism discovery of filtered reads was made by mapping to the pea reference genome assembly using the sequence alignment tool, the BWA-MEM version 0.7. The filtering of variants and the minimum read depth was applied according to the GBS-specific ruleset .The leaf samples of the panel were outsourced to the LGC Genomics GmbH  for GBS SNP discovery. The GBS genotyping was conducted following the procedure by https://itol.embl.de/ , Nei’s gene diversity, heterozygosity, and MAF of SNP markers generated from the DArTseq and GBS genotyping platforms were calculated separately using the Power Marker v 3.25 . In addiembl.de/ .via the web-based Structure Harvester ver. 0.6.94 http://taylor0.biology.ucla.edu/structureHarvester/  ≥4] and Bonferroni threshold adjusted for multiple marker tests at P ≤ 0.05 were used to report significant marker-trait associations. The Bonferroni test was estimated with the formula: −log10 (α/m), where α is the overall false positive threshold (0.05) and m is the number of markers used for the GWAS analysis . Thus, the Bonferroni threshold was set at −log10  = 5.14, which corresponded to a p-value of 7.24e-6. The cryptic relatedness matrix (K) of genotypes based on the SNP markers was included in the GWAS analysis to control the familial kinship. Including equal numbers of principal components (PCs) across the studied traits created either inflation  or overcorrection  of the GWAS results based on the produced quantile-quantile (Q-Q) plots. Because of this, Q-Q plots were first generated with different numbers of PCs and were used to select the appropriate number in the GWAS model based on the trait’s performance. A range of 1–3 PCs sufficiently captured the confounding effects of the population structure without overcorrections during the analysis.The pair-wise LD between SNP markers from DArTseq and GBS was calculated as er pairs . The maxgenetics . The GWAgenetics  availablgenetics . The expAfter outlier testing and initial data cleaning for any possible recording errors, BLUP and other variance components were estimated from the historical unbalanced data sets recorded for different phenotypic traits using the package ASReml v. 4 , fittingijk is the phenotypic value for ith genotype in kth replicate at jth environment; μ is the overall mean effect; Geni and Envj are the effects from the ith genotype and jth environment, respectively; Repk (Envi) and Geni × Envj are the effects of kth replicate at the jth environment, and the genotype by environment interaction, respectively; and εijk is the effect of the error associated with the ith genotype, jth environment, and kth replicate. Both genotypes and environments were treated as random effects. The significance level of the genetic and G × E interaction variance components was computed from the log-likelihood ratio test based on the full and reduced models. Broad-sense heritability was estimated from the genotypic, G × E interaction and error variances using the following formula:where Y2g is the genotypic variance, σ2e is the error variance, σ2ge is the G × E interaction variance, and nRep and nEnv are the numbers of replicates and environments, respectively. Pearson’s correlation between BLUP estimated phenotypic values of traits was computed using cor function in R environment.where σThe DArTseq and GBS high-throughput genotyping methods were implemented to generate SNP markers from the current 188 wrinkled vining pea genotypes. A total of 9,306 SNP markers were produced from DArTseq, of which 61.9%  reached the threshold applied for quality checking . From GBS, the overall SNP markers discovered across all samples and with a minimum read count of eight/locus were 13,528 and 5,784, respectively. However, only 1,138 (8.4%) markers reached the same quality threshold applied to the earlier sequencing method. Hence, the number of quality SNP markers generated from DArTseq was higher than GBS by more than five-fold. Notwithstanding, the SNPs discovered from the two genotyping approaches appeared to have a similar distribution pattern across chromosomes . For insThe two sequencing approaches similarly estimated the existing genetic variation within the wrinkled vining pea genotypes. The small numbers of SNP markers generated from GBS equally quantified the existing allelic variation with the DArTseq markers .The SNPs from the two sequencing methods were pooled together for the structure and clustering analysis. The Bayesian model clustering approach conducted in STRUCTURE identified two optimal sub-groups in the current panel . CongrueThe analysis of variance for the two-decades-long recorded field data revealed a highly significant variation between genotypes and the genotype by environment (G × E) interaction for all agronomic and downy mildew resistance traits included in the current study . The bror2 value ranging from 0.05 in chr4LG4 to 0.09 in chr6LG2, in which 29 and 25% of the pairwise LD comparisons were significant at P < 0.01, respectively  was calculated from the whole set of SNPs pairwise comparison. However, the genome-wide LD was estimated from 6,401,500 pairwise comparisons created from 6,902 SNP markers included in 1,000 sliding window sizes. The LD was varied across chromosomes with a mean ectively . The mea at 0.22 . The gen at 0.22 .P < 0.0001), of which 26 are with the Bonferroni threshold (P < 7.24e-6), were identified for all traits except for biomass and the number of pods per node  on chr6LG2 significantly increased the days to maturity by 10.25, while the second topmost significant marker 3549425| F| 0-57:T > C-57:T > C (MAF = 0.053) on chr4LG4 increased the plant’s maturity by 2.8 days (S3LG5_7947727 (MAF = 0.07) marker on chr3LG5 significantly increased the average numbers of pods per plant by one compared to the alternative allele. The favorable allele of marker 5938535| F| 0-18:G > T-18:G > T (MAF = 0.10) on chr2LG1 increased the pod length by 2.18 mm.The average values of individuals with the favorable allele of the topmost significant SNP marker 2.8 days . The favGenetic diversity of crops is an essential part of breeding for the successful selection and development of new varieties that are advanced with certain characteristics of agronomic importance. A gene pool with rich genetic variation commonly leads to novel allele combinations with an enormous potential to enhance crop yield and to endure the recurrent biotic and abiotic stress due to climate change, thereby safeguarding food security. Nonetheless, a gene pool with narrow genetic diversity is susceptible to emerging pathogens and other restraints that lead to loss of productivity and declining adaptation to particular agroecology .The current panel comprises 188 wrinkled vining pea genotypes including varieties, breeding lines, and individuals with different resistance levels to DM and root rot. The panel was subjected to SNP genotyping with two different approaches (DArTseq and GBS). The result indicated that DArTseq was more efficient than GBS for high-throughput SNP discovery in the current wrinkled vining pea panel. The DArTseq sequencing method was discovered more than five-fold higher quality SNP markers than GBS. This is due to the higher sequence depth and usage of strict filtering criterion, which the earlier method applied that ultimately lead to generating higher numbers of quality SNPs with less missing data compared to the latter approach . The SNPConsidering the increments in the affordability of genotyping costs coupled with the availability of reference genomes, resequencing crop plants and exploring QTLs of quantitatively inherited traits are imperative for marker-assisted selection. However, the limited population size and the nature of unbalanced data sets have hindered the application of GWAS analysis in the widely applicable breeding-program-derived phenotypic data . NonethePeronospora viciae f. sp. pisi, is one of the most important foliar diseases leading to a significant reduction in yield and quality of pea  is far from the currently discovered marker (7.95 Mbp). Nonetheless, the authors reported other MTA on chr2LG1 (402.0 Mbp) with close proximity to the marker S2LG1_389058024 (389.1 Mbp) identified in our study. The four MTAs detected for PPD were on chromosomes chr1LG6 (two), chr5LG3, and chr7LG7. Similarly, Numbers of pods per plant (PDP), pod length (PDL), and wrinkled vining number of peas per pod (PPD), were the three pod-related yield component traits included in the current GWAS study. Three significant MTAs were spotted for PDP, of which two were on chr2LG1 and the other one on chr3LG5. The GBS SNP marker The maturity of vining pea and tenderness is usually determined by a tenderometer that measures the shearing force needed to press immature pea seed (green peas) samples through a standard grid . The tenThis study applied the GWAS approach to uncover candidate genetic variants or MTAs connected to the traits of economic importance for wrinkled vining pea production. Validations of the identified MTAs and further functional studies are prospects for the successful employment of marker-assisted selection. Furthermore, the identified SNP markers linked to the traits of interest would assist in the improvement of genomic selection models’ accuracy in the estimation of genomic estimated breeding values (GEBVs) of candidate progenies or breeding lines.via GWAS for eight traits related to agronomic and DM resistance using the wrinkled vining pea breeding-program-derived unbalanced phenotypic data sets recorded for the past two decades. We reported several novel MTAs for different economically relevant traits in wrinkled vining pea production for the first time, and these candidate markers could be validated and easily integrated for marker-assisted selection in the active breeding programs.The current study exploited 188 wrinkled vining pea genotypes to estimate the existing genetic variation and to detect the markers associated with various economically relevant traits in wrinkled vining pea production. Genotypes were sequenced for SNPs with both DArTseq and GBS approaches. The pattern of SNP distribution from these two sequencing platforms was uniform across chromosomes, though the number of SNPs generated from DArTseq was more than five times higher than the GBS. The two sequencing platforms similarly estimated the existing genetic variation. However, the higher quality-checked SNP markers generated from DArTseq were more powerful in the LD estimation and GWAS analysis. Hence, a higher number of SNP markers from DArTseq was identified with significant association to the studied traits. The current study detected several valuable MTAs The genotypic data used for the findings of this study are available from authors upon reasonable request.AC and AB conceived and designed the study. AA carried out the data analysis, curation, and first draft manuscript write-up. All authors contributed to the data interpretation, manuscript revision, and approval of the final version.AB was employed by Nomad Foods Ltd. The remaining 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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TMEM175 and GAPDHP25. These results demonstrate that interaction-based machine learning models can improve genetic prediction models and might provide an answer to the missing heritability problem.Polygenic risk scores (PRS) aggregating results from genome-wide association studies are the state of the art in the prediction of susceptibility to complex traits or diseases, yet their predictive performance is limited for various reasons, not least of which is their failure to incorporate the effects of gene-gene interactions. Novel machine learning algorithms that use large amounts of data promise to find gene-gene interactions in order to build models with better predictive performance than PRS. Here, we present a data preprocessing step by using data-mining of contextual information to reduce the number of features, enabling machine learning algorithms to identify gene-gene interactions. We applied our approach to the Parkinson’s Progression Markers Initiative (PPMI) dataset, an observational clinical study of 471 genotyped subjects (368 cases and 152 controls). With an AUC of 0.85 , the interaction-based prediction model outperforms the PRS ). Furthermore, feature importance analysis of the model provided insights into the mechanism of Parkinson’s disease. For instance, the model revealed an interaction of previously described drug target candidate genes  The need to understand how to predict phenotypes from genetic data is becoming ever-more important for the prediction of disease risk for individuals and for plant and animal breeding as well as for genome editing. Polygenic risk scores (PRS), simple additive models, are the state of the art in the investigation of the genetic architecture of complex traits or diseases, and, more importantly, in the prediction of disease susceptibility. . A Polyg(1) Due to the current limited sample size of discovery GWAS datasets , biologically relevant rare variants with small effect sizes cannot be detected. Additionally, the limited sample sizes of discovery GWAS can lead to biased PRS models that might not perform well in populations with ancestry different to that of the discovery dataset. .(2) It has been shown that statistically significant outcome-associated SNPs are not automatically good predictors of that outcome. .missing heritability problem in GWAS.  It has been reported that genetic effects discovered in genome-wide association studies do not sum to the estimate of the heritability of the trait derived from twin studies. . This hain GWAS. . BesidesImportantly, PRS models are not optimized for predictive performance. . There aThe concept of epistasis was first described more than 100 years ago. . StatistMachine learning algorithms that improve automatically through the use of data represent an opportunity to find gene-gene interactions in order to build models with better predictive performance than PRS. Nevertheless, in a recent study, a PRS model outperformed five machine learning algorithms  that were used to build predictive models for coronary artery disease status. .https://www.ppmi-info.org) as this dataset has been intensively analyzed and is broadly available for replication studies. We explore two machine learning approaches in particular, which complement those applied by Gola et al.: deep learning and interaction-based feature selection. The first approach, deep learning, employs artificial neural networks to discover automatically from raw data the representations needed for classification. Despite not being widely used in the field of genomics, there is work on applying deep learning to GWAS: Here we revisit the potential of machine learning algorithms to predict disease status compared to a PRS model. For this purpose, we adopt the Parkinson’s Progression Markers Initiative (PPMI) dataset , 2018 (hn = 367) for training predictive models; a validation set (n = 33) for so-called hyperparameter tuning, and a test set (n = 36) for model evaluation. Training and validation are described in further detail below for each approach as appropriate. In all cases, evaluation metrics were calculated on the basis of bootstrap resampling with 104 iterations.For all 471 subjects in the PPMI database (368 cases and 152 controls) subject genotyping information was collected from two complementary genotyping chips (NeuroX and ImmunoChip). After careful quality control and harmonization, we merged that information into a single dataset with 369,036 variants and 436 individuals (296 cases and 140 controls). The data was then split into three disjoint sets: a training set  analysis was performed on the training data. The Manhattan plot of the han 10–4 .p-value thresholds  for the subjects in the training, validation and test set were used. The PRS of the subjects in the training set were then used to train a separate logistic regression classifier for each p-value threshold. Receiver operating characteristics (ROC) curves were used to evaluate classifier performance relative to the validation data. The classifier with the highest mean area under the curve (AUC) was that which had been trained on the PRS resulting from the 0.05 p-value threshold, comprising the weighted sum of 57 different SNPs. This classifier was finally evaluated relative to the test data set, where the mean AUC was 0.58 with a 95% confidence interval from 0.42 to 0.81 To calculate the PRS, seven different et al.‘s approach to the PPMI dataset, again using the training data set to train competing networks with distinct hyperparameter settings and the validation data set to select between these networks. When evaluated relative to the test data set, the mean AUC of the final deep learning model was 0.67  and the optimal Youden index corresponding to the accuracy, sensitivity, and specificity measures reported in We applied Romero A set of less than 100 polygenic hypotheses were generated using the interaction-based feature selection approach applied to the training data, as described in the Methods section below.  and rs17022,452_GG (GAPDHP25) split the individuals into cases and controls  contains 471 subjects (368 cases and 152 controls), and for each subject, genotyping information collected from two complementary chips (NeuroX and ImmunoChip) is available. (The Parkinson’s Progression Marker Initiative (PPMI) dataset ; as a result, 6,084 variants and 22 people were removed. SNP filtering was performed before individual filtering.With high missing call rates filtered, all variants not on autosomal chromosomes were removed . This was followed by the identification and removal of variants deviating from Hardy-Weinberg equilibrium, which can indicate genotyping errors. These variants were identified in a two-stage process whereby we first applied a threshold of 1e-6 exclusively to controls, followed by a threshold of 1e-10 applied to all samples, leading to the removal of 0 and 202 variants, respectively.Next, individuals were filtered based on their heterozygosity rates, which can indicate sample contamination. Individuals deviating by more than 3 standard deviations from the mean of the rate of all samples  were removed. To assess the heterozygosity rate per sample, variants in linkage disequilibrium were first extracted, scanning the genome at a window size of 50 variants, a step size of 5, and a pairwise correlation threshold of 0.2.Finally, relatedness between individuals was ascertained through the calculation and assessment of their respective identity by descent coefficients (IBD). Only one individual in a related pair would be kept, although in this case, no related individuals were identified and so none were removed.The final quality-controlled dataset contained 369,036 variants and 436 individuals passing the various filters.As a preliminary step, a genome-wide association (GWA) analysis was performed with the R package SAIGE  to test https://choishingwan.github.io/PRS-Tutorial/plink/). The clumping cut-off of r2 was 0.1. For all subjects in the training, validation and test sets, seven distinct risk scores were calculated, corresponding to seven potential p-value thresholds . The seven risk scores for the subjects in the training set were then used to train seven separate logistic regression classifiers  using the glm function in R (www.R-project.org). These classifiers were evaluated relative to the validation data set, leading to the selection of the classifier based on the PRS calculated using the p-value threshold of 0.05. The predictions of this final classifier were then evaluated relative to the test set.The PRS was constructed by using PLINK  followinhttps://github.com/adri-romsor/DietNetworks.The deep learning prediction model was built using a Diet Network according to the procedure described by Romero et al.  The modeglm function in R (www.R-project.org). If an interaction predicts disease status well, the graph is incentivized to ‘fine-tune’ the hypothesis by comparing a set of very similar hypotheses. If a hypothesis has little or no predictive power, the graph is not incentivized to explore it or similar hypotheses further and will instead propose hypotheses containing different variants.  into a hierarchical knowledge graph, which generates disease-specific hypotheses based on interactions of genetic variants . Each inariants. . This leariants. .https://glmnet.stanford.edu/index.html) for R (www.R-project.org) and its function cv.glmnet with five-fold cross-validation in order to avoid overfitting. (LASSO (least absolute shrinkage and selection operator) regression models were computed by using the glmnet package (fitting. .


Global efforts are underway to develop cassava with enhanced levels of provitamin A carotenoids to sustainably meet increasing demands for food and nutrition where the crop is a major staple. Herein, we tested the effectiveness of genomic selection (GS) for rapid improvement of cassava for total carotenoids content and associated traits. We evaluated 632 clones from Uganda’s provitamin A cassava breeding pipeline and 648 West African introductions. At harvest, each clone was assessed for level of total carotenoids, dry matter content, and resistance to cassava brown streak disease (CBSD). All clones were genotyped with diversity array technology and imputed to a set of 23,431 single nucleotide polymorphic markers. We assessed predictive ability of four genomic prediction methods in scenarios of cross-validation, across population prediction, and inclusion of quantitative trait loci markers. Cross-validations produced the highest mean prediction ability for total carotenoids content (0.52) and the lowest for CBSD resistance (0.20), with G-BLUP outperforming other models tested. Across population, predictions showed low ability of Ugandan population to predict the performance of West African clones, with the highest predictive ability recorded for total carotenoids content (0.34) and the lowest for CBSD resistance (0.12) using G-BLUP. By incorporating chromosome 1 markers associated with carotenoids content as independent kernel in the G-BLUP model of a cross-validation scenario, prediction ability slightly improved from 0.52 to 0.58. These results reinforce ongoing efforts aimed at integrating GS into cassava breeding and demonstrate the utility of this tool for rapid genetic improvement. Manihot esculenta Crantz), grown on approximately 18 million hectares in Africa, offers great potential to end extreme hunger, achieve food security and improve nutrition, if proven varieties are unconditionally accessed by producers and consumers (−1), of which >90% is constituted by ß-carotene, a provitamin A carotenoid, reported in large collection (>2400 clones) of cassava accessions (Cassava (onsumers  as a cosIpomoviruses: Ugandan cassava brown streak virus and Cassava brown streak virus (A major drawback in our endeavors to breed cassava varieties with elevated provitamin A carotenoids has been the undesirable tendency of low dry matter content (DMC) in most provitamin A cassava clones  technologies have enabled the use of genome-wide markers for implementing genomic selection (GS), a tool that can significantly enhance the efficiency of crop breeding . ConsequA broad review of GS in plant breeding, detailing requirements for training population and features of prediction models, has been elegantly provided by Indeed, results from both simulation and empirical studies have illustrated the efficacy and limitations of GS for several crops, including maize , rice , Uganda. This panel constituted the training population, hereafter referred to as Cycle 0. Second, a subset of 50 Cycle 0 clones with the highest breeding values for carotenoid content was identified  as progenitors and planted in a crossing nursery at Namulonge to generate cycle one (Cycle 1) population, during the period 2016/2017. Following seed germination and preliminary screening at nursery and seedling stages, 352 Cycle 1 genotypes were cloned and evaluated in 2018. Third was a set of 648 clones derived from 80 progenitors introduced from West Africa (WA) through International Institute for Tropical Agriculture (IITA); this population segregated for carotenoids and DMC.The Cycle 0 population was evaluated at two locations, Namulonge  and at Serere  during the 2017/2018 cropping season in Uganda. Both locations are characterized by high prevalence of CBSD causing viruses and high whitefly vector populations (At harvest (12 months after planting), all plants in a plot were uprooted. Total carotenoid content was assessed by visually scoring the intensity of pigmentation of the root parenchyma on a qualitative scale of 1–8 (http://www.diversityarrays.com/) for DNA extraction and genotyping, respectively. Briefly, DArTseq technology relies on a complexity reduction method to enrich genomic representations with single copy sequences and subsequently perform next-generation sequencing using HiSeq 2500 . Further detail of the DArTseq genotyping process has been described by Leaf tissues were collected from all test clones evaluated and sent to Intertek and Diversity Array Technology Pty Ltd.  markers were selected using the following quality control parameters  such thatb with its corresponding incidence matrix block(l)Z represented random effect for blocks nested in locations (l) such that l and incidence matrix g.lZ represented random effect of genotype-environment interaction; and Phenotypic dataset for each trial was considered independent and analyzed separately using a two-step genomic prediction approach. In the first step, we fitted linear mixed models accounting for each trial’s design and extracted the best linear unbiased predictions (BLUPs) of the clone effects for TCC, DMC, and CBSD root necrosis using the H) as:mixed.solve function in R to fit a single-stage genomic best linear unbiased prediction (G-BLUP) model, with the grand mean and location as fixed effects and clone effects treated randomly. Eventually, SNP-based heritability  from SNP data. Finally, principal component analysis (PCA) was done on the genomic relationship matrix, using the prcomp function in R. The first two principal components (PC1 and PC2) were used to visualize population structure.To assess population structure, we used 23,431 polymorphic DArTSeq markers filtered to keep only SNPs with minor allele frequency (MAF) ≥ 0.01. Using the  package , we consA.mat function in the R package rrBLUP was initially used to construct a genomic realized relationship matrix from SNP marker dosages and GEBVs obtained after fitting a linear mixed model using the mixed.solve function in the same package. Cross-validations for Bayesian models were computed with the BGLR package  genomic BLUP (G-BLUP), (ii) BayesA, (iii) BayesB, and (iv) Bayesian Lasso. The main features of these genome-wide prediction models have been reviewed by  package , with mon = 280), 224 genotypes were used as training set while the remaining 56 individuals were used as validation set. This process was repeated for each of the five folds across 10 replications. Prediction abilities were computed as Pearson’s correlation coefficients (r) between GEBVs predicted for the test set and the corresponding BLUPs obtained from the first step of the analysis.To achieve cross-validation for each fold in a replication, the total number of genotypes in a population was divided into five equal proportions such that four groups at a time formed the training set to build the prediction model, while the fifth group was the test set. For example, when cross-validation was performed for Cycle 0 , using 632 individuals (constituted by Cycle 0 and Cycle 1) and 23,431 polymorphic markers filtered at MAF >0.05. To avoid any potential upward bias of prediction abilities, we performed GWAS in the training set of each fold across 10 replications of the cross-validation scheme, such that kernel-based predictions were done in the test set of the respective fold. For example, for each fold, ∼506 genotypes (training set) representing 80% of the 632 individuals were used for GWAS and the remaining ∼126 (test set) used for kernel-based predictions.rrBLUP following the modified methodology described by qqman . Subsequently, we designed four GWAS-guided cross-validation schemes for TCC: (a) single-kernel with significant QTL markers on chromosome 1, (b) single-kernel with all chromosome 1 markers, (c) single-kernel excluding all markers on chromosome 1, and (d) multi-kernel model that included the significant QTL markers and the rest of the markers fitted as independent kernels in G-BLUP model . Finallyhttps://doi.org/10.6084/m9.figshare.12752405.All the raw phenotypic and genotypic data are available at the link provided for references. Supplementary material is available at figshare: Total carotenoids content had the highest broad-sense, varying between 0.68 and 0.73, while CBSDrs had the lowest estimates (0.36–0.50) . We did PCA was used to describe population structure in the genetic materials analyzed. Grouping clones by the first two PCs (PC1 and PC2) showed no clear-cut differentiation among the three populations , with thn = 280 genotypes), (b) Cycle 0 combined with Cycle 1 (n = 632 genotypes), and (c) the West African population (n = 648 genotypes). Distribution of predictive abilities across the five folds and ten replications is presented in We performed cross-validation under three scenarios: within (a) Cycle 0  recorded for Bayesian Lasso (Supplementary Table S1). In this scenario, the highest mean prediction ability of 0.52 was recorded for TCC with G-BLUP while the lowest value of 0.39 was noted for the trait with BayesB. Similarly, increase in training population size resulted into an increase of prediction ability for CBSDrs, except for BayesB that had no appreciable (0%) change.Cross validation within the West African population showed similar trend, with the highest predictive ability 0.47 recorded for TCC with G-BLUP. For this population, predictive abilities for CBSDrs were remarkably low compared to values recorded in Cycle 0 and with Cycle 1.We predicted each of the three traits across the four models under the following scenarios: Cycle 0 to predict performance of Cycle 1 and West African (WA) clones and a combination of Cycle 0 and with Cycle 1 to predict WA clones. First, we noted generally higher cross-population predictive ability for TCC than for other traits across all models, with the highest values recorded with G-BLUP. For example, the highest predictive ability of 0.43 was recorded for TCC when using the Cycle 0 to predict the performance of Cycle 1 . HoweverPredictive abilities for CBSDrs were particularly low in all scenarios. For instance, the highest predictive ability (0.12) was only recorded when Cycle 0 was used to predict CBSDrs in Cycle 1 with G-BLUP; other models had negligible predictions for CBSDrs . In facti.e., without segmentation of chromosome markers, with the exception where single kernel excluding all chromosome 1 markers was used. Narrow-sense heritability estimates were proportionately higher for scenarios of predicting with markers on chromosome 1 and the multi-kernel approach than for the case of excluding chromosome 1 (All marker-trait associations for DMC and CBSDrs were nonsignificant (data not shown). GWAS for each training set per fold consistently revealed one QTL for TCC on chromosome 1, with ∼49 markers meeting the Bonferroni threshold for genome-wide association signal  for TCC and DMC, implying the possibility of making meaningful genetic gains for both traits through phenotypic recurrent selection  were a mWe found relatively low broad-sense heritability (0.36–0.50) for severity of CBSD root necrosis, which relates to the trait’s quantitative nature . This coBased on the cross-validation results, predictive abilities were largely consistent across traits and prediction models. First, we noted a downward pattern for predictive ability TCC > DMC > CBSDrs across all models, and for both scenarios of small and increased size of training set. TCC is largely a qualitative trait . When we explored prospects of cross-population predictions, we noted higher predictive ability across Uganda’s populations than in situations of predicting performance of WA clones. In evaluating the potential of East African training populations for genomic prediction of CBSD resistance in West African cassava germplasm, Manes.01G124200.1, a gene which encodes enzyme phytoene synthase known to catalyze accumulation of ß-carotene in plant tissues (Motivated by success attained by selection programs that exploit GWAS-aided prediction ( tissues . Li et aTaken together, the moderate to high predictive ability achieved for DMC and TCC in this study underpin the transformative ability of GS when adapted and integrated as a cassava breeding tool. We remark that GS offers a promise for rapid improvement of cassava for provitamin A carotenoids and DMC, especially when prediction models are properly chosen and tuned. In parallel, concerted efforts are required for further enrichment of provitamin A cassava breeding population with CBSD resistance alleles to make genomic prediction an effective tool for increasing gains for the trait. Results presented in this study complement ongoing efforts aimed at integrating the use of GS in cassava breeding programs.


Hundreds of genome-wide association studies (GWAS) of human traits are performed each year. Theresults of GWAS are often published in the form of summary statistics. Information from summary statistics canbe used for multiple purposes – from fundamental research in biology and genetics to the search for potentialbiomarkers and therapeutic targets. While the amount of GWAS summary statistics collected by the scientific community is rapidly increasing, the use of this data is limited by the lack of generally accepted standards. In particular,the researchers who would like to use GWAS summary statistics in their studies have to become aware that the dataare scattered across multiple websites, are presented in a variety of formats, and, often, were not quality controlled.Moreover, each available summary statistics analysis tools will ask for data to be presented in their own internalformat. To address these issues, we developed GWAS-MAP, a high-throughput platform for aggregating, storing,analyzing, visualizing and providing access to a database of big data that result from region- and genome-wideassociation studies. The database currently contains information on more than 70 billion associations betweengenetic variants and human diseases, quantitative traits, and “omics” traits. The GWAS-MAP platform and databasecan be used for studying the etiology of human diseases, building predictive risk models and finding potential biomarkers and therapeutic interventions. In order to demonstrate a typical application of the platform as an approachfor extracting new biological knowledge and establishing mechanistic hypotheses, we analyzed varicose veins, adisease affecting on average every third adult in Russia. The results of analysis confirmed known epidemiologic associations for this disease and led us to propose a hypothesis that increased levels of MICB and CD209 proteins inhuman plasma may increase susceptibility to varicose veins. Genome-wide association studies (GWAS) are one of themain approaches for identifying associations between geneticvariants and traits . One of the mostimportant advantages of this approach is that it is agnosticto the molecular mechanisms or biochemical nature of thetraits or diseases under study, thus allowing fundamentallynew knowledge to be obtained. Based on the functions of thegenes mapped by a GWAS, researchers aim to discover newmolecular mechanisms underlying the development of traitsand pathologies under consideration.GWAS are performed on large samples of genotyped andphenotyped individuals to identify statistically significant associations between single-nucleotide polymorphisms(SNPs)and traits . SNPs are located relativelyhomogeneously and with sufficient density, consequently,functional variants occurring at high frequency in the population are detected with a high probability, either becausethe causative allele is being tested directly or because it isin linkage disequilibrium with genotyped markers. A specialcase of GWAS is a regional genetic study of associations ora region-wide association study (RWAS), where the analysisis applied to SNPs in a particular region instead of the wholegenome. RWAS is used, for example, to find cis-SNPs associated with the expression of a certain gene .The GWAS approach has become very popular over thepast decade. Since 2007 the number of GWAS has increasedexponentially and hundreds of original genome-wide studiesare published every year. The earliest GWAS addressed theassociations between a single trait and several hundreds ofthousands SNPs, using samples of several hundreds or thousands individuals .Currently, both the number of analyzed traits and the genomic coverage of GWAS have increased by many orders ofmagnitude . This has become possibledue to the advent of new sequencing and genotyping technologies and the improvement of existing ones, as well as othermethods for studying biological objects, leading to an increasein the resolution of sequencing, genotyping and phenotyping.Modern GWAS normally assess associations with millions of SNPs and in some cases the sample size exceeds one millionpeople . The number of phenotypesstudied can go to hundreds , thousands  and even tens of thousands, e. g. for “-omics” traits. Thesame trait is analyzed in multiple studies, often with progressively increasing sample sizes, as well as in new populations,offering increased power and generalizabilityThe direct results of GWAS/RWAS consist of files withsummary statistics. These files can include up to ten of millions rows, where each row contains information about theassociation between a given SNP and the investigated trait.Taking into account the number of GWAS studies and the sizeof the files with results, GWAS results qualify as Big Data. Importantly,not only does this body of data grow, but so do the rates ofdata acquisition. GWAS results can be used to address a large number ofproblems ranging from fundamental biology and genetics tothe search for biomarkers and targets for therapeutic interventions. Currently, a range of methods has been developed thatimplement the solution of these problems based on summarystatistics data.In particular, methods have been developed to define sets ofSNPs that are most likely to contain the true functional variantat loci suggested by GWAS . For example, this problem isaddressed by the PAINOR  softwareand the conditional and joint analysis as implemented in theGCTA tool .Also, identification of causal genes influencing a trait of interest is possible through the use of summary statistics. By regulating the expression of those genes or bymanipulating their products through the use of, for example,pharmacological interventions, the trait of interest can beaddressed in a targeted manner. Several instruments implement these methods, for example, the SMR (Summary-levelMendelian Randomization) tool . The samemethods can often be used to study pleiotropic effects . The results of studies ofpleiotropy can be used for drug repositioning, for predicting possible side effects of gene editing, and for prediction ofpossible side effects of pharmacological manipulation of theproducts of these genesOver the past decade, the number of studies using Mendelian randomization methods has increased substantially,providing important new information about disease etiology. Mendelian randomization methodscombined with the use of summary statistics and multipleinstrumental variables  can help to reconstruct the theoretical hierarchy of causeand effect relationships between traits and, in practice, havethe potential to be used for the identification of traits that canbe targeted by therapeutic interventions.Methods for studying genetic correlations  can be particularly useful inaddressing fundamental questions related to the genetic architecture of complex traits. One of these methods is implementedin a popular LDsr (Linkage Disequilibrium score regression)python package .Finally, summary statistics from GWAS can be used inmethods to develop models for the prediction of quantitativetraits and disease risks for a given individual or group .The simplest of these models use effects of the most significantindependent SNPs . If a GWAS involvesa large number of cases and controls, powerful predictorscan be developed for some traits even with simple models,breast cancer being a well-known example . Methods allowing the researcher to manage information about millions of SNPs and whole-genome LD structurewhile developing a prediction model have recently becomepopular . Such models were usedfor predicting the risk of ischemic heart disease , type 2 diabetes , and obesity.Although the amount of GWAS results obtained by thescientific community is constantly growing, as are the numberof methods for their analysis, they have currently found onlylimited use. The problems researchers face when workingwith these data are multiple. First, summary statistics filesfrom GWAS are large (more than tens of terabytes), and sotheir storage and processing require dedicated infrastructure.Secondly, data are produced by different laboratories usingdifferent protocols, and consequently, quality control and aharmonization procedure for storing such data in a commonformat are required. Thirdly, the existing tools for analyses ofsummary statistics data from GWAS are implemented usingdifferent languages, hosted at different repositories and websites and require custom input data formats. Finally, large-scaleadoption of these methods and data require user interfaces forresearchers without specialized bioinformatics skills.The existing solutions are incomplete or partial. On theone hand, resources such as GWAS Central  or GWAS Catalog (https://www.ebi.ac.uk/gwas/) can do as much , but originally they were intendedfor handling “small data” , and so their architecture does not scalewell enough to handle big data and the requirements of newmethods for processing GWAS results. On the other hand, most software applications , GCTA , LDsr ) are intended for analyzing data rather than foraggregating, storing or providing access to them. Finally,the portals MR-Base (http://www.mrbase.org/) and LD Hub(http://ldsc.broadinstitute.org/) can aggregate and store GWASresults and allow the user to conduct specify types of analysis;however, these portals do not offer anything for other methodsof analysis, and software solutions for data aggregation andstorage are not available.To help address these issues, we developed the GWASMAP platform for aggregating, storing, analyzing, visualizing and providing access to big data obtained from GWAS.The name GWAS-MAP means both a map between phenotypes and genotypes, but is also an abbreviation of MultipleAnalyses Platform. Using GWAS-MAP we collected GWASMAP|homo database of GWAS and RWAS results for humantraits. Currently, the database contains more than 70 billionassociations between SNPs and human traits. GWAS-MAPprovides an opportunity to carry out research that will contribute to the search for new biomarkers that has a bearing onthe development of high-efficacy drugs and also reveal sideeffects in existing drugs. We have performed a genetic analysisof varicose veins to demonstrate how the platform works.GWAS-MAP software architectureThe GWAS-MAP platform consists of two data processingmodules  and a database (DB) module .Data integration starts with the conversion of summary statistics files collected from various sources into a universal dataformat. After conversion, we perform quality control (QC)and if the summary statistics pass, they are uploaded to thedatabases.The DB module is the part responsible for setting up thedatabases and tables structure required for the GWAS-MAPplatform. The DB module consists of two components, eachcontrolled by a separate open source database managementsystem (DBMS). One of the components is used to store theGWAS summary statistics; for this component the ClickHouseDBMS version 19.16.2. revision 54427 (https://clickhouse.tech/) is used. Arecord in this system contains the parametersof association between certain SNP and a trait. The other component contains (1) meta-data that gives particular informationabout the summary statistics collected from articles, studyweb-sites or other sources, and (2) the results of analyses; forthis component the PostgreSQL DBMS version 10.6 (https://www.postgresql.org/) is used.With the analysis module a user can run various analyses onthe GWAS/RWAS summary statistics using the integrated analytical tools written in Python, which are accessible throughcommand-line utilities.Integration and quality control of GWAS/RWAS resultsThe platform offers users the option to upload GWAS summary statistics files of their own original research. Becausethese data were generated using different protocols, the resulting summary statistics files may appear in different formats. To address this, GWAS-MAP provides an integration moduleconverting summary statistics files to a common format andperforming QC on the data.To ensure data consistency within the DB, informationabout a SNP’s identifier, position in the genome, and allelesand allele frequencies are compared with reference data. Thereference is a list of SNPs with their main characteristics:the identifier (rsID), chromosome, position, alleles and allelefrequencies. At present, the reference is based on the 503 genomes of European-ancestry individuals from the “1000 Genomes” project phase 3 version 5 .In general, summary statistics contain all fields requiredfor unification in a universal format. If some fields are absent,then the missing information is added from the reference  or calculated from the informationin the input file. For example, it is possible to recover thestandard error of the effect size based on the effect size andp-value.Before uploading GWAS/RWAS data to the DB, it is absolutely necessary to have them passed through QC. QC isindispensable not only for meta-analyses of GWAS/RWASresults, but also for verifying separate studies, because seemingly insignificant data errors may lead to heavily biasedresults later on.We have developed a QC module which spots outlyingSNPs and assesses the overall quality of the input data. More specifically, QC includes (1) a comparison of the frequencies ofalleles from the input data with those from the reference set,a comparison of the p-values provided in the study and thosecalculated from the Z-statistics (if present), (2) an analysisof the distribution of estimates of the allele effect sizes,(3) calculation of the trait variance and (4) genomic controlfactor (λGC). SNPs whose characteristics depart by more thana threshold value from those expected are labeled as outliersand can be filtered out by the user. If the summary statisticsfrom GWAS have more than 5 % outliers, or the effect size distribution is not symmetric, this data will be not recommended for upload, although the final decision is up to a user.We should notice that all current data in DB have passed theabove described criteria.Analysis methods using GWAS/RWAS summary statisticsimplemented in GWAS-MAPGWAS-MAP incorporates several widely used methods forthe analysis of GWAS/RWAS summary statistics with specialemphasis on the identification of genes, molecules, traits andfunctional SNPs that appear as potential targets of therapeuticinterventions. In particular, data processing can be carried outusing the following methods.Linkage disequilibrium score regression is a method to assess the heritability of a trait and to calculate genetic correlations between two traits . Thismethod was implemented in Python 2 by Bulik-Sullivanand co-authors (2015). We have re-written it in Python 3because it is the main programming language used forGWAS-MAP and because Python 2 has been deprecatedsince January 1, 2020. This also allowed us to optimize itfor working with our DBs.Mendelian randomization methods – a set of tests that allow to infer causal relationships between two traits . Hemani and colleagues provided an opensource R package, which includes such methods. To this,we added a module for reading summary statistics from theGWAS-MAP DB in the required formatSummary-level Mendelian randomization (SMR) and heterogeneity in dependent instruments (HEIDI) are the teststo ascertain whether two different traits are associated withthe same locus (SMR) and whether this association canbe explained by the null hypothesis of pleiotropy or byan alternative hypothesis that each trait is associated withdifferent SNPs in linkage disequilibrium (LD) (HEIDI). We implemented the SMR-HEIDI testsourselves for the GWAS-MAP platform. The rationale behind this was mainly that the SMR tool developed by Zhu and colleagues (2016) specializes in testing pleiotropy between the level of gene expression (RWAS) and a complextrait (GWAS), but not between two sets of GWAS resultssummary statistics.We also implemented the θ metric defined by Momozawaet al., which assesses the similarity between associationprofiles using only summary statistics and is an alternative to the HEIDI test. This method is preferable when theLD information of the population used in a GWAS is lacking or unreliable . The θ metricas implemented in GWAS-MAP is based on the equationsprovided in the article .Finally, the GWAS-MAP platform implements severalstandard methods for meta-analysis which can be applied toa pool of GWAS results of the same trait in order to obtainenhance power . GWAS-MAP has amodule for checking the quality of the GWAS results tobe used in meta-analyses and a module for meta-analysis.We have implemented two methods for meta-analysis:inverse-variance weighting and Z-score .GWAS-MAP|homo database contentTo allow the researcher to filter GWAS/RWAS according tocertain criteria, the platform offers key information includingthe publication data, reference set used for imputations, thename/type of the DNA microarray  or whole-genome sequencingused in each study. Currently, the DB contains more than70 billion associations between SNPs and traits, collected from7281 GWAS and more than a million RWAS (see the Table).To give a reader an idea of the context, such popular databasesas “GWAS central”  provides informationon 71 million of associations, while Phenoscanner  – 65 billion.The GWAS and RWAS in the DBs are assigned to the following domains: complex traits (including diseases), metabolites (mQTL), proteins (pQTL), glycans and gene expressiondata. Additionally, the GWAS and RWAS results coming fromthe same study are pooled in a collection. The presence ofGWAS traits from different domains enables the researcherto conduct a comprehensive study of the trait of interest, toidentify ways of how the trait of interest is influenced by theexpression levels of genes, proteins and metabolites, and tolook for associations with other diseases or quantitative traits.Varicose veins (VV) is a widely prevalent disease affecting onaverage every third adult in Russia .The genetic basis of this pathology has long been poorlystudied. Shadrina and co-authors have performed the firstlarge-scale study of its genetic architecture using a range ofmodern methods in bioinformatics as implemented in GWASMAP The study used UK Biobank (http://www.ukbiobank.ac.uk/)data on 408,455 individuals of European descent. GWASsummary statistics of VV were retrieved from the open accessdatabases Gene ATLAS  and theNeale Lab website . Shadrina and co-authorsidentified 12 genetic loci associated with VV which accountfor 13.4 % of the SNP-based heritability. A gene or a groupof genes most probably involved in VV pathogenesis wasprioritized for each locus. The SMR-HEIDI implementationin GWAS-MAP was used as one of the prioritization methods.With SMR-HEIDI, we searched for the genes for which theexpression levels are associated with SNPs affecting VV risk. Theanalysis relied on data from the eQTL (expression quantitativetrait loci) domain, namely data of 44 tissues in the GTEx_v7  and blood_eQTL  collections. Colocalization was demonstratedfor the following loci: rs3101725 (associated with the expression level of the long non-coding RNA LINC01184 in9 tissues), rs2241173 (associated with the expression levelof the non-coding RNA AC005152.3 in the lower extremityskin) and rs2861819 (associated with expression levels of thePPP3R1 gene in blood). Because the functions of LINC01184and AC005152.3 are not yet known, we may only speculateabout the role of these RNAs in VV. As far as PPP3R1 isconcerned, its association with VV appears to be more sound.Its product is involved in the inflammatory response in thevascular wall, stimulating the production of the chemokineMCP-1 , which is consistent with themodern view of the pathogenesis of chronic venous disease. Additionally,Smetanina and co-workers demonstrated enhanced PPP3R1expression in VV specimens compared to unaffected veins.In addition to gene prioritization, SMR-HEIDI was used tosearch for traits associated with VV-related functional variants.The analysis involved 2219 traits, including various diseases,levels of metabolites and proteins in blood, and revealed32 traits associated with 6 loci. The traits can conventionallybe divided into three main groups: one associated with bodyweight and the total metabolic rate; a second with blood testresults, and a last one with all others.The GWAS-MAP platform was also used for the analysisof genetic correlations between VV and 861 traits, the summary statistics of which were obtained by analyzing more than10 thousand individuals. The analysis showed the presenceof common genetic variance between VV cases and 62 traits.Some of these traits were already known from previous epidemiological studies: overweight, standing and heavy physicalwork, deep venous thrombosis, gonarthrosis, and pain in thelegs when walking. Other traits that, at the genetic level, correlate with VV, such as intellect, memory, educational attainment, or whole-body pain, have not previously been reportedas associated with VVFinally, Shadrina and co-workers used Mendelian randomization for the analysis of causal relationships between various traits and VV. Analysis results showed that the followingtraits directly influence the risk for VV: height (irrespective ofweight), body weight; waist and hip circumferences, and theblood levels of two proteins, MICB and CD209 . Curiously, the risk for VV increased with anincrease of both body fat and fat-free mass. Data on height asa risk factor for VV are consistent with the Edinburgh VeinStudy results . MICB and CD209 participatein the innate and the adaptive immune response. Becausepresented work is the first to propose that these proteins haveroles in VV pathogenesis, we think it reasonable to repeatthe analysis with an independent dataset. If the Mendelianrandomization results are confirmed, these proteins can be regarded as promising candidates for further in vivo and in vitrostudies aimed at finding therapeutic targets.The GWAS-MAP platform offers a broad range of opportunities for comprehensive analysis of GWAS results. We expect that GWAS-MAP will be helpful both for bioinformaticsstudies and as a reference source for medical researchers. Forexample, given a trait of interest, it is possible to compute whatother traits it is genetically associated with, i. e., is controlledby overlapping sets of genetic variants. More generally, notonly correlations between traits can be calculated, but alsoall pairwise correlations between the traits in the DB. Theresults can be used to cluster the traits and/or to build a network connecting traits that have a shared genetic basis . Furthermore,the Mendelian randomization methods implemented in theplatform will help to elucidate which of these associationsare causal. Thus, it is possible to build a directed graph forinteractions between traits. By considering a particular vertex,for example, “disease”, it is possible to infer what metabolites,glycans and/or proteins can be used as its biomarkers.If a researcher’s interest lies with a locus or loci associated with a certain GWAS of interest, it is also interesting toconsider colocalization. With SMR-HEIDI and the θ metric,it is possible to understand with the expression of what genesthe GWAS loci are associated. Additionally, by analysis ofRWAS results for the genes of interest and GWAS resultsin the domains for metabolites, proteins and/or glycans, itis possible to infer what biological processes are associatedwith changes in the expression of these genes. A large-scaleanalysis of colocalization will help to build networks of associations between traits in the DB and genes. These networkswill be helpful in developing medications. Not only will theyshow what genes can be targeted, but also what implicationsand side effects of manipulations with the gene may entail.However, it should be kept in mind that these analyses aredone in silico and therefore, and experimental validation isabsolutely required.A large number of methods have already been implementedin the platform – however, there are certainly more to come.Our short-term plans include the addition of new analysismethods: Depict , CoJo ,and SbayesR . We are planning todevelop a web-interface to allow external users to access ourDBs and perform analyses. Such a web-interface will guidethe user through the search for information about the association between a SNP and traits and will be convenient fore. g. medical researchers. We continue adding new data to thedatabase and we are working on making GWAS-MAP usefulfor human populations other than Europeans.We have developed the GWAS-MAP platform for aggregating,storing, analyzing, visualizing and providing access to summary statistics from GWAS and RWAS. Using the platform wecollected GWAS-MAP|homo DB which contains over 70 billion associations between SNPs and traits. The GWAS-MAPuser interface offers a universal workspace for operating onpublic and private data, and allows for rapid implementation ofnew analysis methods in the platform. The user communicateswith the platform through command-line utilities, allowinghim to upload data to the platform and run analyses.The analysis of the genetic basis of varicose veins demonstrates the power of the platform for generating new biologicalhypotheses such as, for example, ours postulating a causal relationship between the levels of the proteins MICB andCD209 in blood and the risk for this disease.GWAS-MAP is a powerful platform for the analysis ofsummary statistics from GWAS and RWAS, it is activelyused in research work and can be useful to a broad range ofscientists. The platform evolves continuously through constantacquisition of more functionalities, and the DBs are updatedwith the actual data from GWAS and RWAS.The authors declare no conflict of interest.Beck T., Shorter T., Brookes A.J. GWAS Central: a comprehensive resource for the discovery and comparison of genotype and phenotype data from genome-wide association studies. Nucleic Acids Res.2020;8(48):D933-D940. 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We performed GWAS using the PorcineSNP60K BeadChip and a linear mixed model. Four genome-wide significant QTL regions in SSC8, SSC12, SSC14, and SSC16 were detected (p < 2.53 × 10−7). Several co-localizations of QTLs in SSC12 for oleic acid, linoleic acid, arachidonic acid, monounsaturated FAs, polyunsaturated FAs, and the polyunsaturated/saturated FA ratio were observed. To refine the QTL region in SSC12, a linkage and linkage disequilibrium analysis was applied and could narrow down the critical region to a 0.749 Mb region. Of the genes in this region, GAS7, MYH2, and MYH3 were identified as strong novel candidate genes based on further conditional association analyses. These findings provide a novel insight into the genetic basis of FA composition in pork and could contribute to the improvement of pork quality.Fatty acid (FA) composition is one of the most important parameters for the assessment of meat quality in pigs. The FA composition in pork can also affect human health. Our aim was to identify quantitative trait loci (QTLs) and positional candidate genes affecting the FA profile of the  FA composition plays an important role in the evaluation of pork quality because it can contribute to sensorial, nutritional, and functional factors. For example, FA composition controls the oxidative stability of pork, which in turn affects meat color and flavor . Linolei2 intercross between Landrace pigs and KNPs, detailed studies on the individual major genes that are responsible for the FA profile of KNP are still limited. Hence, we conducted a GWAS in combination with post-GWAS high-resolution mapping analyses to reveal individual positional candidate gene(s) for the FA profile traits in the longissimus dorsi muscle using an F2 intercross between Landrace and KNP pigs.Despite their slow growth rate, Korean native black pigs (KNPs), which inhabit Jeju Island, are renowned for their meat quality, which fulfills current consumer requirements, such as reddish meat color, white-colored fat, solid fat structure, and good marbling ,12. ConcAll experimental procedures were conducted according to national and institutional guidelines and approved by the Ethical Committee of the National Institute of Animal Science, Republic of Korea : 2014-095 (6 August 2014)).2 resource population  were established by crossing purebred Landrace pigs and purebred KNPs (LK cross). Nineteen KNPs were crossed with 17 Landrace pigs, generating 91 animals in the F1 generation. Subsequently, F1 progeny were intercrossed to generate F2 offspring. All experimental procedures for breeding, feeding, and phenotyping took place at the same research farm in the Subtropical Livestock Research Institute of the National Institute of Animal Science, RDA, which is located on Jeju Island. All pigs were managed with the same procedure at farrowing. Male pigs were not castrated. Male and female piglets were weaned at 21 days of age. All F2 experimental animals were slaughtered in the same commercial abattoir. This population has previously been used in linkage and association studies for mapping QTLs affecting growth ..GAS7:g.1p = 0.0004), no additional significant association signals were detected , MYH2: c.−22675 T>C , and MYH3−1805_−1810delCAGTCC ) in the critical region for the six FA traits (r) values were >0.996 between the three SNPs and the key marker  on the FA traits.To explore the possibility of dissociating association signals in the high LD region, we performed a conditional association analysis by conditioning the key SNP at each of the remaining SNP markers in the 749.1 kb critical region. Except for one SNP ) showing marginal significance for C18:1 , was strongly and positively correlated with overall palatability, including flavor, juiciness, and tenderness in beef . Among UGAS7 transgenic (Tg) mice and reported decreased adiposity and unesterified cholesterol in a Tg mouse model. They also suggested a putative involvement of the GAS7 gene in free FA metabolism on the basis of pathway and network analyses. In the case of genes encoding myosin heavy chain (MYH) isoforms, Lim et al. reported effects of SNPs in the MYH2 locus on muscle fiber characteristics and meat quality including IMF content [MYH3 is causatively associated with muscle fiber-type composition and IMF content in the same population used in this study [The GAS7 gene encodes growth arrest-specific protein 7, which was initially known for its important roles in neuronal development and is implicated in the regulation of lipid metabolism ,48. Yang content . Recentlis study . Myoblasis study . Therefois study , our reslongissimus dorsi muscle in an intercross between Landrace pigs and KNPs. For this purpose, we conducted a GWAS in combination with post-GWAS high-resolution mapping analyses. The GWAS approaches identified significant QTLs in SSC8, SSC12, SSC14, and SSC16 at the genome-wide level. Relevant concordance was revealed between the QTLs detected in this study and previously reported QTLs for muscle FA traits, mainly in SSC8, SSC14, and SSC16. The QTL in SSC12 has not been well documented for FA traits. The post-GWAS high-resolution mapping analyses of the QTL in SSC12 identified a 749.1 kb critical region, and the GAS7, MYH2, and MYH3 genes were identified as strong candidate genes for FA traits. Furthermore, although we could not elucidate the individual contribution of the three genes to the phenotypic variation of the FA traits because of the extremely high LD, these findings provide new insight into the genetic basis of FA composition in pork and could contribute to the genetic improvement of pork quality.In this study, we aimed to identify individual positional candidate gene(s) in the FA profile of the