Together, our findings show that Taiwan black pigs had a unique genotype distribution, whereas Landrace and Yorkshire had a more similar genotype distribution. Thus, an understanding of the genetic uniqueness of each breed could help to identify functionally important SNPs in immunoregulation.Enhancing resistance and tolerance to pathogens remains an important selection objective in the production of livestock animals. Single nucleotide polymorphisms (SNPs) vary gene expression at the transcriptional level, influencing an individual’s immune regulation and susceptibility to diseases. In this study, we investigated the distribution of SNP sites in immune-related genes and their correlations with cell surface markers of immune cells within purebred  and crossbred (Landrace-Yorkshire) pigs. Thirty-nine SNPs of immune-related genes, including 11 cytokines, 5 chemokines and 23 Toll-like receptors (TLRs) , tumor necrosis factor-α (TNF-α), granulocyte-macrophage colony-stimulating factor (GM-CSF), Monocyte chemoattractant protein-1 (MCP-1) and TLR3, TLR4, TLR7, TLR8, and TLR9) were selected, and the percentages of positive cells with five cell surface markers of CD4, CD8, CD80/86, MHCI, and MHCII were analyzed. There were 28 SNPs that were significantly different among breeds, particularly between Landrace and Taiwan black. For instance, the frequency of SNP1 IFN-α -235A/G in Taiwan black and Landrace was 11.11% and 96.15%, respectively. In addition, 18 SNPs significantly correlated with the expression of cell surface markers, including CD4, CD8, CD80/86, and MHCII. The percentage of CD4+ (39.27%) in SNP33 TLR-8 543C/C was significantly higher than those in A/C (24.34%), at  In the past three decades, swine have become important in agricultural and biomedical research. Genetic improvements have been widely focused on production traits, such as growth and meat quality , or suscTo date, several TLRs have been linked to antiviral immunity, particularly TLR3, TLR7, TLR8, and TLR9. The initial engagement of TLRs with viral pathogen-associated molecular patterns  is essential for the induction of the interferon (IFN)-mediated antiviral and inflammatory cytokine responses , whereasSingle-nucleotide polymorphisms (SNPs) are genetic variations of single base-pairs occurring in the genomic sequence impacting gene expression activities, amino acid substitution, and dysfunction. SNPs occur in three different regions, in the promotor, extron and intron, and have been shown to impact gene expression at various degrees . It was a) enhance robustness, (b) reduce the use of antibiotics, (c) enhance vaccine responsiveness and (d) reduce economic losses to disease. The genetic analysis between Taiwan black, Duroc, Yorkshire and Landrace has already been defined [An understanding of the genetic repertoire of each breed and their possible association with the immunophenotypes could help in identifying functionally important SNPs that regulate immune response. This could also be beneficial in establishing high pathogen-resistant breeding parameters which , Duroc (n = 27), Landrace (n = 30), Yorkshire (n = 26), and Landrace–Yorkshire hybrid (n = 77), from three unrelated pig farms located in Pingtung, Taiwan. Samples were collected from the external jugular vein with a 21G needle in EDTA vacutainer tubes , Franklin Lakes, NJ, USA) and were brought back to the laboratory on ice for immediate analysis. All procedures were conducted with approval from the National Pingtung University of Science and Technology Institutional Animal Care and Use Community (IACUC) following AAALAC guidelines (IACUC protocol number NPUST-104-068).Blood was collected from 187 four-week-old piglets of the breeds Taiwan black (a) restriction enzyme (RE) digestion  for SNP sites located at certain restriction sites, and (b) amplification-refractory mutation system PCR (ARMS-PCR) for SNP sites without any restriction sites. Established for different purposes, 3′ end primers were designed for ARMS-PCR and two primers for RE digestion were used to identify the SNP sites. The locations of primer pairs are in the promoter or first exon region . All extractions were performed using the manufacturer’s protocol. DNA quality and concentration were quantified by a spectrophotometer with an A260/280 ratio of 1.8–2.0.To detect mutations involving single base changes in immune-related genes, ARMS-PCR and restriction enzyme (RE) digestion were used. ARMS-PCR is based on using sequence-specific PCR primers to amplify target DNA that the nucleotide sequences contained in the sample. Following ARMS, the presence or absence of PCR products amplified from genomic DNA indicate single nucleotide variations . In parag for 30 min. The PBMC layer was removed and transferred into ammonium-chloride-potassium (ACK) lysing buffer (Gibco) for the lysis of red blood cells. PBMCs were washed twice with RPMI-1640 medium , the cells were adjusted to 1 × 106 cells/mL with PBS and separated into 9 light-protected 1.5 mL microcentrifuge tubes, each containing 1 × 105 cells/mL. The tubes were centrifuged, the supernatant was discarded, and the pellet was resuspended with 2% bovine serum albumin (BSA) in PBS. The cells were incubated for 30 min at 4 °C with mAb against surface molecules (see below). After staining, the cells were washed twice with 1mL of PBS and fixed with 4% paraformaldehyde for 30 min at 4 °C. Lastly, the cells were resuspended with 1% BSA/PBS and stored at 4 °C for later testing with an EPICS® XL-MCL flow cytometer (Beckman Coulter). The flow cytometry gates were set on singlets, following lymphocytes and live cells (SSC-H and FSC-H). All gating strategies for immune cells are shown in Peripheral blood mononuclear cells (PBMCs) were isolated from 10 mL of whole blood, and the samples were diluted at a ratio of 1:2 with phosphate-buffered saline  containing 2% fetal bovine serum , overlaid on Lymphoprep , and centrifuged at 800× Specific primary and isotype control antibodies were used in this study. Isotype-matched unspecific antibodies served as a negative control. Fluorochrome-conjugated primary mAbs included: mouse CD4-fluorescein (FITC) , mouse CD8-fluorescein (FITC) , CD80/86 (fusion protein Human CTLA-4)-phycoerythrin (PE) , mouse anti-pig MHCI-fluorescein (FITC)  and mouse anti-pig MHCII-fluorescein (FITC) . Unspecific binding of primary antibodies that were not directly conjugated with fluorochromes was evaluated by means of the use of mouse IgG1-fluorescein (FITC) (Bio-Rad), mouse IgG2a-fluorescein (FITC) , mouse IgG2b-phycoerythrin (PE)  and mouse IgG2b-fluorescein (FITC) (Bio-Rad).p-values less than 0.05 indicated that the results were significantly different.Flow cytometric data were analyzed with FlowJo software . Data were analyzed using SAS software’s  GLM procedures. Tukey’s honest significant difference test was used for comparisons if a significant F static was detected by ANOVA. Observed genotypes were used to estimate allele frequencies for each SNP by chi-square distribution, and departures from Hardy–Weinberg equilibrium were assessed using Pearson’s goodness-of-fit test. p < 0.0001). SNP1: IFN-α-235A/G polymorphism was associated with a higher CD4:CD8 ratio and a higher percentage of MHCII-positive cells. A predominantly higher frequency of SNP2: IFN-γ 382C/T genotype was noted in the Taiwan black and Landrace–Yorkshire breeds (p < 0.0001), and this variant showed a positive correlation with a higher expression of CD4+ cells. Among the pig breeds, Taiwan black had the highest distribution in the SNP6: TNF-α 1219A/A genotype and showed a correlation with increased expression of CD8+ and MHCII cells. Moreover, the SNP8: GM-CSF 245C/T showed a correlation with increased CD4+ and CD8+; interestingly, this genotype was predominant in the Taiwan black, Landrace, and Landrace–Yorkshire breeds.There were eleven SNP sites of cytokine genes analyzed in this study; all indicated significant differences among breeds except for SNP9: GM-CSF (741). The genotype distribution and allele frequencies of the eleven SNPs of cytokine genes were investigated in relation to different pig breeds  and theip < 0.0001). In addition, the SNP15: MCP-1 360C/T gene polymorphism was associated with higher levels of CD4+, CD8+, and MHCII cells. Furthermore, there was a more predominant frequency of the SNP16: MCP 383A/A genotype observed in the Taiwan black, Landrace, Yorkshire, and Landrace–Yorkshire pig breeds.There were five SNP sites in the first intron of MCP-1 gene analyzed in this study, three SNP12: MCP-1 (273), SNP13: MCP-1 (336), and SNP14: MCP-1 (351) were not significantly different among the pig breeds. The genotype distribution and allele frequencies of the two SNPs of chemokine genes were investigated in relation to different pig breeds  and theip < 0.0001); interestingly, the SNP17: TLR3 95G/G polymorphism was associated with a higher CD4:CD8 ratio. Additionally, 357G/G, 1413T/T, and 2034A/A TLR7 gene polymorphism were more predominant in Taiwan black. A predominantly higher frequency of SNP34: TLR8 534C/C genotype was noted in Landrace–Yorkshire (p < 0.0001), and this variant showed a correlation with a higher expression of CD4+ and CD8+ cells. Additionally, SNP39: TLR9 1186C/T genotype was observed in all pig breeds.Ten of the twenty-three SNPs in the Toll-like receptor genes analyzed in this study indicated an association between the pig breeds. The genotype distribution and allele frequencies of the ten SNPs of Toll-like receptor genes were investigated in relation to different pig breeds  and theiThis study investigated 39 SNPs of immune-related gene variation between common pig breeds in Taiwan and sought to determine whether there were any correlations between the genotype and phenotype. The 39 SNPs’ genotype distribution was analyzed for 187 pigs via ARMS-PCR with or without restrictive enzyme digestion, and the phenotypes were analyzed by flow cytometry to observe CD4-, CD8-, CD80/86-, MHCI-, and MHCII-positive cell surface markers. In a previous study by Wilkie et al., it was indicated that immune response varies within different pig breeds . Methodsp > 0.05), except for SNP1:IFN-α(-235) and SNP2:IFN-γ (382). In a study by Huang et al., they analyzed eight IFN-γ SNP regions in humans, and suggested that the promotor region of the −764G/C IFN-γ gene is functionally important in determining viral clearance and treatment response in hepatitis C virus-infected patients [Our results indicate that Taiwan black had distinctive genotype variations compared to the other pig breeds. The frequency of five cytokine SNP genes—the SNP1:IFN-α-235G/G, SNP2:IFN-γ 382C/T, SNP5:TNF-α 755T/T, SNP6:TNF-α 1219A/A, and SNP11:GM-CSF 782C/C genotypes—were all significantly higher in Taiwan black pig breeds; these genes were also associated with a higher CD4+ and CD8+ T cell population. Interestingly, the genotype distribution for all SNPs of cytokine genes in Taiwan black were consistent with the Hardy–Weinberg equilibrium , and this variant showed a correlation with a higher expression of CD4+ and CD8+ cells. Ligation of multiple TLRs concurrently or in sequence, particularly TLR3 or TLR4 together with TLR7, TLR8, or TLR9, has been shown to induce a synergistic increase in the production of multiple cytokines produced by dendritic cells [It is worth noting that TLR gene polymorphisms were more consistent throughout all five pig breeds. Additionally, the Duroc, Landrace, and Yorkshire breeds exerted a similar pattern in genotype distribution. The variations of five TLR genes, SNP17:TLR3 95A/A, SNP20: TLR3 800T/T, SNP27:TLR7 2034A/A, SNP35 TLR8 570A/A, and SNP39:TLR9 1186C/C, were all significantly higher in Taiwan black, whereas in the other breeds they had a fairly similar genotype distribution. We did not see any association between the TLR genotypes and their immunophenotypes. Morozumi and Shinkai et al. noted that in pigs, the nonsynonymous RNA-sensing TLR genes such as TLR3, 7, 8, and 9 presented fewer polymorphisms causing amino acid changes in cell-surface genes ,41; thisic cells .Disease association can be seen in functional single nucleotide polymorphisms (SNPs) . Our results demonstrate the SNP distribution that exists in common pig breeds in Taiwan, and these include cytokines, chemokines, and TLR genes. Numerous polymorphisms were identified within the coding and non-coding regions of cytokines and several disease-associated studies based on these variants. Bidwell et al. summarized several reviews regarding these associated studies and created an online database ,43,44. AFurthermore, we noted that Landrace and Yorkshire had a more similar SNP distribution, especially in IFN-α, IFN-γ, and TNF-α cytokine genes. Interestingly, the SNP analyses in TLR genes suggest that Duroc, Landrace, and Yorkshire had similar genotype distribution and were quite different from Taiwan black. Several observations have indicated connections between polymorphisms in cytokines, chemokines, or pattern recognition receptor-related genes and disease susceptibility ,26,46,47Ultimately, there is still insufficient research conducted in pigs which demonstrate SNPs’ association with diseases and the host’s susceptibility to infectious disease. We noted that a higher frequency of mutations occurred in cytokine and chemokine polymorphisms, and that TLR genes indicated fewer genetic variations within the different breeds. More importantly, SNP genetic variations of immune-related genes did occur within common pig breeds in Taiwan. Our findings are an example of applying candidate gene polymorphism approaches to identifying functionally important mutations that may affect immunophenotype expressions which could influence host susceptibility to infectious pathogens, but further studies are required to better understand the underlying correlation between these genotypes and immunophenotypes, and could potentially be used as an indirect method for measuring immune traits. If true, SNPs in immune-related genes could potentially to be used for breed selection of pigs which are more resistant to infectious pathogens. In future studies, we aim to expand our breed and sample count, investigate whether these SNPs follow a genetic hitchhiking pattern in related pigs, and integrate different detection methods such as real-time PCR. However, despite advances in SNP genotyping with next-generation sequencing (NGS) , ARMS-PC


Bellflower is an edible ornamental gardening plant in Asia. For predicting the flower color in bellflower plants, a transcriptome-wide approach based on machine learning, transcriptome, and genotyping chip analyses was used to identify SNP markers. Six machine learning methods were deployed to explore the classification potential of the selected SNPs as features in two datasets, namely training (60 RNA-Seq samples) and validation (480 Fluidigm chip samples). SNP selection was performed in sequential order. Firstly, 96 SNPs were selected from the transcriptome-wide SNPs using the principal compound analysis (PCA). Then, 9 among 96 SNPs were later identified using the Random forest based feature selection method from the Fluidigm chip dataset. Among six machines, the random forest (RF) model produced higher classification performance than the other models. The 9 SNP marker candidates selected for classifying the flower color classification were verified using the genomic DNA PCR with Sanger sequencing. Our results suggest that this methodology could be used for future selection of breeding traits even though the plant accessions are highly heterogeneous. Platycodon grandiflorus) is a popular plant used as food, medicine, and ornamental plant in Asia1. P. grandiflorus is a monotypic species of the bellflower family (Campanulaceae). Due to its therapeutic effects, its root has been used in traditional medicine as a popular food additive for over 2000 years2. P. grandiflorus consists of 12 cultivars, which has received high commercial ratings in the ornamental flower market for their habitat and floral displays3. Its flower is characterized by an attractive colorful bud, a long flowering time, and an extended vase life5.The bellflower  methods to predict SNP associations with specific traits. Population studies have demonstrated how ML based modeling can be used effectively to predict phenotype from genotype11. ML facilitates pattern recognition of large biological datasets since ML algorithms are used widely in various biological fields, such as molecular marker identification, coding region recognition, pathway gene recognition, protein–protein interaction determination, and metabolic network detection12. For instance, ML models have been constructed for genomic selection in wheat13, root genotype classification14, nut-size prediction in Castanea crenata10, and polyploidy associated SNPs identification in plants15.Genome-wide molecular markers-based genomic selections enhance plant breeding to produce the desired traitsP. grandifloras to identify molecular markers such as simple sequence repeats17, microsatellites18, and cleaved amplified polymorphic sequences19. However, molecular markers that determine specific flower colors are limited. Hence, we report in this study a newly designed transcriptome-wide approach as an effective method in identifying flower color in bellflower plants, where the selected SNP markers could be used to predict bellflower color during the plant breeding experiments.Several studies have been conducted in P. grandiflorus plant seeds germinated at 60-hole pots for 30 days and transferred to 18.3 CM PI-pots for 90 days in green house setup under 25 °C temperature. Once the flower color was conformed, the petals were collected as a samples for experiments. All the plants used in this study were maintained at experimental field located in Jeonju, Korea  National Institute of Agricultural Sciences/RDA living modified organism (LMO) guidance. Accessions for three flower colours  for RNA sequencing (RNA-Seq) were obtained from the RDA Genebank (http://genebank.rda.go.kr/). For three colors (20 in each color), 60 samples were collected, and those sampling details are given in Supplementary Table P. grandiflorus were irradiated with 50, 100, 150, and 200 Gy of gamma radiation at dose rates of 25, 50, 75, and 100 Gy h–1 for 2 h using a Co-60 gamma-irradiator at the Advanced Radiation Technology Institute (ARTI), Korea Atomic Energy Research Institute (KAERI).A Co-60 gamma-irradiator  was used for gamma irradiation. Dry seeds of P. grandifloras was obtained from the P. grandiflorus genome project1 (http://platycodon.theragenetex.com/). The gene model and other functional annotations were also obtained from the respective site.The reference genome for The 60 transcriptome sequence libraries were subjected to a high-throughput Illumina NovaSeq sequencing system for RNA sequencing. RNA was extracted from individual samples using TRIzol reagent . Total RNA was quantitated using the NanoDrop spectrophotometer , and quality was assessed using the RNA 6000 Nano assay kit and Bioanalyser2100 .20, as explained in shin et. al.,21 and mapped to the reference genome using bowtie222. To optimize the small insertion and deletion artifacts, the reads were re-mapped to the reference genome using the GATK v3.5 tool23 and the variants for individual samples were stored in the variant call format (VCF) files. The command line includes the parameters . Using filters such as the normalized quality score ≥ 20 and mapping quality ≥ 40, the high-quality SNPs were obtained. The obtained SNPs were annotated using SnpEff v4.224 and the missing genotypes were imputed by Beagle v4.125 with the linkage disequilibrium (LD) score.Before variant calling, sequence reads were checked for bacterial contamination and adapters using Trimmomatic v0.3626. The commands include options such as assoc, adjust, fisher, model, logistic, hap-assoc, hap-impute. To reduce false positive predictions, stringent filtering cutoffs such as genotyping rate ≥ 90%, mapping quality ≥ 40, minor allele frequency (MAF) > 5%, and Hardly-Weinberg equilibrium (HWE) < 0.001were applied to the scores calculated by the PLINK tool. The sub-populations were estimated based on the number of clusters , which was obtained using the STRUCTURE v2.3.4 tool27. The ad-hoc static ΔK  was used to determine the uppermost hierarchical level of population structures. Structural analysis was performed following 20 replicated runs using the 100,000 iterations after a burn-in period of 50,000 runs.For population stratification, SNPs were examined via principal component analysis (PCA) using the PLINK v1.9 toolp < 0.01.A genome-wide association study was conducted with categorical association (case vs. control). Here, there are three flower color categories tread as case vs control (example: white vs other two as controls). The association between genotype and phenotype for the above model was conducted using PLINK-v.1.9 with the association mode  ≥ 2 among the color-specific sets with others, and false discovery rate (FDR) < 0.05, and ± 5 Kb of flanking regions. Another 50% were manually selected by the reported flower color-associated genes. The SNPs were identified from the reported anthocyanin pathway and gene expression profile of the P. grandiflorus tissues. Finally, a Fluidigm genotyping chip was designed using 96 SNPs . These SNPs belong to the 75 genes present in the reference genome  ≥ 0.3, Read counts ≥ 5), differentially expressed genes/transcripts . DNA integrity was first assessed by spectrophotometry at 260/280 nm and quantification of DNA concentration (ng/ul) using a NanoDrop spectrophotometer . One Fluidigm SNP chip contains allele-specific primer 1, 2, locus-specific primer, and specific target amplification primer. Furthermore, the probe for individual SNPs was prepared as per the manufacturer’s instructions and the bi-allele fluorescence signals were captured using a Fluidigm chip-compatible instrument.k-neural network (k-NN), random forest (RF), C5.0 decision tree (C5.0), partial least square (PLS), and gradient boosting (GBM). The training population in each dataset was divided into a training dataset and a validation dataset at a 7:3 ratios for the prediction models. The best model was selected automatically by the caret package28. Here we used the caret R package to train  and predict (type = "prob") functions with default values other than custom parameters used in brackets. For the feature prioritization we used varImp function only with random forest method. To compare the prediction methods, we determined sensitivity, specificity, and accuracy, using the following equations: Sensitivity = [TP/(TP + FN)]; Specificity = [TN/(TN + FP)]; and Accuracy = [(TP + TN)/ (TP + FP + TN + FN)]; where TP was the number of true positives, TN was the number of true negatives, FP was the number of false positives, and FN was the number of false negatives. The performances of the prediction models were assessed using ROC curves, plotting the sensitivity as a function (1-specificity) for different decision thresholds. Further, to quantitatively compare the ROC curves, we computed the AUC, and significant differences between two ROCs were assessed using a two-tailed Student’s t-test. These evaluation metrics were calculated as explained by Kang e.al10. To calculate the ROC and the AUC, we used the plotROC package29. The six ML models for flower color prediction were estimated using two datasets. Dataset 1 consisted of training using 60 RNA-Seq data and validation with 480 Fluidigm data. Dataset 2 consisted of training using RNA-Seq + 40% Fluidigm data (n = 252) and validation with 60% Fluidigm data (n = 288). For the training stage, 40% of samples were randomly selected from all Fluidigm samples , P. grandiflorus30. Genomic DNA was extracted from the leaves of P. grandiflorus plants with pink, violet, and white flowers using the general cetyltrimethylammonium bromide (CTAB) method. Genomic DNA PCR was performed using the following conditions: 95 °C for 5 min; followed by 35 cycles of 95 °C for 30 s, 55 °C for 30 s, and 72 °C for 1 min; and a final extension at 72 °C for 7 min. Afterward, PCR amplicons were separated by 1.5% agarose gel electrophoresis and visualized by staining with EtBr solution. Representative PCR amplicons for each of the three flower color plants were selected and subjected to ABI Sanger sequencing using an ABI 3730xl System . The nucleotide sequences of PCR amplicons were assembled using SeqMan pro  with default parameters.Genomic sequences, including forward and reverse 500-bp flanking regions of the candidate SNPs, were extracted from the reference genome of P. grandiflorus reference genome .A total of 411 Gb of RNA sequence data was produced from 60 tissue samples of leaf and flower components in three accessions from plants with pink, violet, and white flowers . The selected SNPs were tested for their associated relationship with the flower color using PCA and heat-map analysis. The results were clustered efficiently into three flower color groups with minor allelic frequency , population stratification demonstrated that each of the three groups included different flower color sub-groups  in two steps, namely training and validation process. For the first dataset stage , which consists of 96 SNPs, 60 transcriptome data of the three flower color types were used to train six ML models, and 480 chip data were validated using the same models. A total of 540 samples were evaluated using the ML model. The gamma irradiation mutants produced 480 verification samples. These mutant plants with distinct molecular properties make it possible to gain further insight into the relationship between the main regulatory processesThe prediction accuracy of the six models was low with an average value of 0.601 Table . Hence, To improve the prediction accuracy, the feature importance , the value was predicted using the random forest (RF) model algorithm. The feature importance value indicated the ability to distinguish the three flower color groups, and these values generally appeared in a slightly decreasing pattern  using the 9 SNPs, 252 training samples were tested, and 288 genotyping chip samples were validated using the same models. The prediction accuracy had an average value of 0.771, which is an improvement over that of 96 SNPs  better than the 96 SNPs, even though two SNP groups were well-classified for flower color based on the transcriptome and chip data Fig. . EvaluatP. grandiflorus’s flower color. The 9 selected SNPs correlated strongly with the flower color in P. grandifloras and were associated with some secondary metabolite genes, including four pigmentation-related genes, chorismate synthase, and three MYB-like family genes , k-neural network (k-NN), random forest (RF), C5.0 decision tree (C5.0), partial least square (PLS), and gradient boosting (GBM). The result shows that the RF-based model achieved the best performance among the six classifiers employed, indicating that RF can capture the hidden relationship between positive and negative samples efficiently compared to other classifiers. When 96 SNPs were validated using dataset 1, the validation efficiency of flower color identification was low, while training accuracy was high. This overfitting problem was improved by the new dataset using the feature importance value of the ML algorithm36. Therefore, 9 SNPs were newly identified that improved the overfitting problem and validated using dataset 2.ML algorithms are widely used in molecular biology to systematically elucidate specific molecular markers with associated functions and phenotype data37. We found 9 transcriptome-wide SNPs in the coding regions associated with flower color  is actively involved in the biosynthesis of anthocyanin, a precursor for various secondary metabolites40, and is characterized by turmeric leaves variegation41. A glycosyl hydrolase family gene  activates carbohydrate changes in the secondary metabolite gene42. Three MYB-like family genes  have been reported previously to be involved in pigmentation metabolism in carrot43, cherry44, and soybean45.A transcriptome-based SNP approach can be a cost/time-saving method for identifying large-scale markersor Table . Flower 46. Some common SNP genotyping methods used in genetic studies are AS-PCR , CAPS (cleaved amplified polymorphic sequence), and dCAPS (derived CAPS)47. An approach for identifying SNP markers using PCR fragments and targeted region sequencing has been reported previously to provide highly accurate SNP genotypes48. The 9 SNP sequences were used to search for chromosomal gene location and genomic DNA PCR (Table P. grandiflorus. Results from our study could help select the best trait in edible urban gardening when used in combination with advanced breeding systems such as genome selection, even though plant accessions are highly heterogeneous. Finally, this methodology can be applied to different characteristics which have additional phenotype and genotype datasets.SNP markers are the method of choice for plant and animal genetic analysesCR Table . The 9 SCR Table . These 9Supplementary Information.


The transcription factor Nurr1 is a member of the steroid hormone nuclear receptor superfamily. Ablation of Nurr1 expression arrests mesencephalic dopamine neuron differentiation while attenuation of Nurr1 in the subiculum and hippocampus impairs learning and memory. Additionally, reduced Nurr1 expression has been reported in patients with Parkinson’s disease and Alzheimer’s disease. In order to better understand the overall function of Nurr1 in the brain, quantitative immunohistochemistry was used to measure cellular Nurr1 protein expression, across Nurr1 immunoreactive neuronal populations. Additionally, neuronal Nurr1 expression levels were compared between different brain regions in wild-type mice (+/+) and Nurr1 heterozygous mice (+/−). Regional Nurr1 protein was also investigated at various time points after a seizure induced by pentylenetetrazol (PTZ). Nurr1 protein is expressed in various regions throughout the brain, however, a wide range of Nurr1 expression levels were observed among various neuronal populations. Neurons in the parietal and temporal cortex  had the highest relative Nurr1 expression (100%) followed closely by the claustrum/dorsal endopiriform cortex (85%) and then subiculum (76%). Lower Nurr1 protein levels were found in neurons in the substantia nigra pars compacta and ventral tegmental area (39%) followed by CA1 (25%) and CA3 (19%) of the hippocampus. Additionally, in the parietal and temporal cortex, two distinct populations of high and medium Nurr1 expressing neurons were observed. Comparisons between +/− and +/+ mice revealed Nurr1 protein was reduced in +/− mice by 27% in the parietal/temporal cortex, 49% in the claustrum/dorsal endopiriform cortex, 25% in the subiculum, 33% in substantia nigra pars compacta, 22% in ventral tegmental area, and 21% in CA1 region of the hippocampus. Based on these data, regional mechanisms appear to exist which can compensate for a loss of a Nurr1 allele. Following a single PTZ-induced seizure, Nurr1 protein in the dentate gyrus peaked around 2 h and returned to baseline by 8 h. Since altered Nurr1 expression has been implicated in neurologic disorders and Nurr1 agonists have showed protective effects, understanding regional protein expression of Nurr1, therefore, is necessary to understand how changes in Nurr1 expression can alter brain function. Nurr1 can function as monomer or form dimers or a heterodimer with other nuclear receptors, such as Nur77, NOR-1, retinoid X receptor (RXR), or retinoic acid receptor (RAR), to bind to DNA response elements, such as the Nur binding response element (NBRE) and Nur response elements (NurRE), to regulate target gene expression . The disea (VTA) , glutamaea (VTA) , dorsal ea (VTA) , 2019, aea (VTA) . Within pression . Expresspression  and hipppression .The first functional role discovered for Nurr1 was the necessity of Nurr1 for terminal differentiation of midbrain dopamine neurons . SubsequAnother important aspect to Nurr1 function is the regulation of its expression levels. Altered expression and function of Nurr1 has also been implicated in several diseases, including addiction, schizophrenia, Parkinson’s disease, and Alzheimer’s disease. Patients with substance use disorders  were shown to have altered Nurr1 expression within the dopaminergic system . SchizopBecause of the widespread distribution of Nurr1 and the importance of Nurr1 expression levels with regard to function, the current study investigated the cellular expression levels of Nurr1 protein across various brain regions. Although the distribution of Nurr1 mRNA and protein has been described, the relative expression levels of Nurr1 across various neuronal populations have not been reported. Popular protein quantification methods like western blot and ELISA are not yet sensitive enough for quantification in an individual neuron. Here we use a quantitative immunohistochemistry (Q-IHC) approach to identify Nurr1 protein expressing cells as well as get a quantitative value for Nurr1 expression. The main advantage of this method is that we can identify and quantify nuclear Nurr1 protein of individual cells. Potential observer bias is greatly reduced, as compared to a qualitative assessment, when specific cell regions are being examined with Q-IHC.In this study, a global assessment of Nurr1 protein expression throughout the brain in mice is described and compared to published data on Nurr1 mRNA expression. Additionally, we used Q-IHC to study the Nurr1 protein levels across populations of Nurr1 expressing neurons in subiculum, parietal and temporal cortex, the deep temporal nuclei which included the claustrum and dorsal endopiriform nucleus, CA1 and CA3 areas of the hippocampus, SNpc, and VTA. Our results demonstrate a wide range of Nurr1 expression levels across regions, with lowest expression in the SNpc/VTA and the hippocampus. Additionally, reductions in Nurr1 protein expression in the Nurr1 +/− genotype across regions was not uniform, with the greatest reduction in the parietal/temporal cortex. Additionally, following a single pentylenetetrazol (PTZ)-induced seizure, we demonstrate a peak in Nurr1 protein expression in the dentate gyrus at around 2 h and a return to baseline by 8 h. These data are significant because they provide a better understanding of Nurr1 function in the brain and important insight into how alterations in Nurr1 expression and/or function could affect the brain.ad libitum. They were weaned at the age of 19–21 days and housed 3–5 per cage. Genotyping was done using tail DNA to differentiate between +/− and +/+ as described previously by 2 asphyxiation and decapitated. The brains were removed, frozen on dry ice, and stored at −80°C. For global Nurr1 expression on free floating sections, brains were emersion fixed in 4% paraformaldehyde for either 24 h, cryoprotected in 30% sucrose, sectioned on a cryostat and processed for free floating immunohistochemistry. Paraformaldehyde fixed brains were serially sectioned at 30 μm thickness and collected in PBS as free floating sections. Fresh frozen brains from +/+ and +/− mice were serially sectioned on a Microm HM 560 cryostat into 10 μm sections onto silanized slides. Nurr1 +/− mice were breed together and embryos at approximately embryonic day 14 were obtained. Tissue from embryos were genotyped to determine embryos homozygous for the Nurr1 knockout allele. Male mice aged 90–120 days of age were used in these studies. For Nurr1 distribution studies, sections from four mice were assessed. Serial fresh frozen section from 1 +/+ mouse was used for antibody concentration measurements. Three wild-type and three heterozygous mice were used to quantify Nurr1 protein expression across regions and genotype. For PTZ treatments one mouse was used at each time point.The Nurr1 +/− and +/+ mice used for this study were from a colony bred at Mississippi State University and East Carolina University. They were maintained at 18–22°C with food and water 2O2 in PBS for 30 min then washed in PBS three times. Section were then blocked with 10% rabbit normal serum in 1% BSA-0.1% Triton-X100-PBS for 45 min. After blocking the sections were incubated with goat anti-Nurr1 primary antibody (R&D Systems AF2156) diluted to a concentration of 10 nM (1:133 from stock of 200 mg/ml) in PBS-BSA-Triton overnight at 4°C. Sections were then washed three times in PBS and incubated with biotinylated rabbit anti-goat (1:200) secondary antibody in PBS-BSA-Triton for 2 h. The sections were then washed three times in PBS and incubated with ABC reagent (made 30 min before to equilibrate) for 2 h followed by three washes in PBS. Sections were incubating for 5–10 min in 0.5X of 10 × 3,3′-Diaminobenzidine (DAB) Liquid Substrate in PBS with 0.003% hydrogen peroxide (Sigma-Aldrich) or in Tyramide-488 or 594 in incubation buffer (Biotium). Sections were rinsed in PBS for three times, mounted on silane prep slides and air dried at room temperature for 30 min. DAB labeled sections were then dehydrated in graded ethanol, cover-slipped using Permount and air dried overnight at room temperature. Tyramide labeled sections were mounted on slides, labeled with DAPI then coverslipped with EverBriteTM Hardset mounting medium (Biotium). For duel Nurr1-tyrosine hydroxylase immunohistochemistry, following Nurr1 immunohistochemistry, sections were washed three times and placed into a 4% goat serum  in PBS-1% BSA-0.1% TritonX-100 for 1 h. Sections were then incubated in a rabbit anti-TH primary antibody  diluted 1:1000 in PBS-1% BSA-0.1% TritonX-100 overnight at 4°C. Sections were then washed five times in PBS-0.1% BSA and placed into goat-Anti-Rabbit IgG secondary antibody conjugated to AlexaFluor594  diluted 1:500 in PBS-1% BSA-0.1% TritonX-100 for 2 h. Sections then underwent five PBS washes, mounted on silanized slides, DAPI stained, and coverslipped using EverBrite  mounting media. Photomicrographs were taken using the using Olympus BX51 microscope or the Celldiscoverer 7. These sections were used to demonstrate regional expression of Nurr1.In preliminary studies using this antibody, we found that antibody recognition was very sensitive to fixation. In fresh frozen section, 1 h of fixation is both necessary and sufficient for antibody signal. Longer fixation diminished the immunohistochemistry signal. Whole brain tissue emersion fixed in 4% paraformaldehyde for 24 h, however, required antigen retrieval. For antigen retrieval, these sections (30 mm) were incubated in 10 mM citrate buffer for 20 min at 95°C and then washed in PBS three times. Sections were incubated with 1% H2O2 in PBS for 30 min. Sections were washed in PBS three times followed by blocking in 10% rabbit serum in PBS-BSA-Triton X100 for 45 min. Sections were then dipped in PBS once and incubated overnight at 4°C with a goat anti- Nurr1 primary antibody (R&D Systems AF2156) diluted in PBS-BSA-Triton at the following varying concentrations: 3 nM (1:444), 10 nM (1:133), 20 nM (1:66.5), 30 nM (1:44), or 60 nM (1:22). Sections were then washed five times in PBS containing 0.1% BSA, rinsed once in PBS and incubated for 2 h with biotinylated rabbit anti-goat secondary antibody (Vector Laboratories) at a dilution of 1:200 in PBS-BSA-Triton. This was followed by washing three times in PBS and incubating with ABC reagent (Vector Laboratories) for 2 h, then followed by three washes in PBS. Tissue was then incubated for 5 min in 0.5X of 10 × 3,3′-Diaminobenzidine (DAB) Liquid Substrate in PBS with 0.003% hydrogen peroxide (Sigma-Aldrich). Sections were then washed in PBS, dehydrated in graded ethanol followed by xylene and coverslipped using Permount. A concentration of 20 nM Nurr1 primary antibody was used to compare regional Nurr1 expression in Nurr1 +/− and +/− mice. The specificity of Nurr1 primary antibody was tested using brain tissue from an approximately 14 day old embryo lacking the Nurr1 gene and compared to a wild-type embryo.Fresh frozen section mounted on silanized slides were fixed in 4% paraformaldehyde for 1 h at room temperature, washed three times in phosphate buffered saline (PBS), and then incubated with 1%HNurr1 immunohistochemistry on fresh frozen sections was used in this experiment to measure the amount of Nurr1 protein expression in six different regions of the brain, comparing +/+ and +/− mice, and after a PTZ seizure. The six different regions were the subiculum, parietal/temporal cortex, the claustrum/dorsal endopiriform cortex, CA1 area of the hippocampus, SNpc, and VTA. Different concentrations of Nurr1 antibody were also tested to ensure that Nurr1 expressing cells were adequately stained in regions of low and high expression. The different concentrations used were 3 nM, 10 nM, 30 nM, and 60 nM. Based on this data, the concentration of 20 nM was then used to further study the intensity of Nurr1 expression in frozen sections.Photomicrographs of these six Nurr1 expressing regions were obtained using Olympus BX51 microscope, which has a fluorescent lamp and fluorescent filters with a CCD camera and a motorized Z-stage, connected to a computer with Stereo Investigator Stereology Software from MicroBrightField Inc. Using Stereo Investigator software, the height and width of the digital image frame were set at 268.5 mm and 174.0 mm, respectively, for the 40x field. Each individual region was outlined at 10x magnification. Stereo Investigator software was used to obtain unbiased fields and digital images were acquired at 40x magnification with an oil immersion objective. For each region, 3–5 sections were samples with 4–6 frames captures and approximately 40–80 nuclei were measured see .After procuring digital images, each Jpeg image obtained was opened in Photoshop . The marquee tool was used to select a 30 × 30 pixel square in the nucleus of neurons in the images . The NurMatlab software was used to determine the pixel energy of the labeled nuclei using the program described by in situ hybridization reported strong Nurr1 mRNA expression.Based on immunofluorescence for Nurr1 on free floating sections, the densest population of Nurr1 protein expressing cells was found in the claustrum, dorsal endopiriform cortex, and the subiculum , 2. AddiFor Q-IHC, 10 mm fresh frozen sections processed for Nurr1 immunohistochemistry were used because it provided very good labeling with low background. Using brain tissue for a Nurr1 knockout embryo we tested the specificity of the Nurr1 antibody. Specific labeling was observed in the Nurr1 wild-type tissue in the cortex and brainstem that was clearly absent in the knockout tissue . This daConsiderable regional differences in Nurr1 protein expression was observed based on intensity of staining. Based on chromogen intensity, at a 10 nM antibody concentration, neurons in the parietal/temporal cortex had the highest relative Nurr1 expression (100%) followed closely by the, claustrum/dorsal endopiriform cortex (85%), and subicullum (76%). Lower Nurr1 levels were found in the dopamine neurons in the SNpc and VTA (39%) followed by CA1 region (25%) and CA3 region (19%) of the hippocampus. In the subiculum, claustrum/dorsal endopiriform cortex, CA regions of the hippocampus, SNpc, and the intensity of immunoreactivity was more uniform with a narrow range of intensities . In contSince difference in regional intensity of Nurr1 immunoreactivity was observed, the next set of experiments investigated intensity of Nurr1 immunoreactivity across brain regions in Nurr1 +/− mice. Nurr1 nuclei in +/− mice, as compared to +/+ mice, had reduced Nurr1 protein expression. Based on chromogen intensity calculated using Matlab and compared to the average intensity in wild-type mice, Nurr1 immunoreactive nuclei of the parietal/temporal cortex in +/− mice had a 27% reduction of Nurr1 protein. In the claustrum/dorsal endopiriform cortex, 49% less Nurr1 protein was found in +/− mice as compared to the +/+ mice. There was 25% less Nurr1 in subiculum, 33% less in SNpc and 22% less in VTA, and 21% less in CA1 region of the hippocampus in +/− than in +/+ Nurr1 mice .In preliminary studies, Nurr1 protein expression was examined at various times after a PTZ-induced seizure, and compared to a vehicle-injected control, using one mouse at each time point. In all mice injected with PTZ, a seizure consisting of facial and forelimb clonus followed rapidly by generalized tonic forelimb extension was induced. Mice were euthanized 30 min, 120 min, 240 min, and 480 min following the seizure. A mouse that was injected with saline and did not receive PTZ was used as control. Based on Nurr1 immunohistochemistry, changes in the distribution of Nurr1 protein was not noticeably alter by PTZ seizure with the exception of the dentate gyrus of the hippocampus . Therefoin situ hybridization. To our knowledge, however, an evaluation of Nurr1 protein expression throughout the brain has not be previously reported. Using this specific method, along with sampling regions in an unbiased manor, we were able to quantify the relative amount of Nurr1 protein in individual neurons as an average across these neuronal populations.The current study investigated the distribution and quantification of Nurr1 protein in six different regions of the brain, namely parietal/temporal cortex , subiculum, claustrum/dorsal endopiriform cortex, CA1/CA3 regions of the hippocampus, and SNpc/VTA using Q-IHC. In contrast to other immunoassays like Western blot and ELISA that determine total region protein levels, Q-IHC enables protein measurements in individual neurons. Here we used Q-IHC as a method to measure expression of Nurr1 protein in neurons across different anatomical locations, in a manner similar to quantification of RNA with in situ hybridization data to demonstrate the distribution of Nurr1 mRNA expression throughout the brain (in situ hybridization data showed a strong signal for Nurr1 mRNA in the cerebellar cortex (Several published studies, including images available on the Allen Brain Atlas, have used he brain . Althougr cortex . HoweverHere we confirm the expression pattern of Nurr1 protein but also, based on our data using different concentrations of Nurr1 antibody, we were able to demonstrate a wide range of Nurr1 expression levels. In contrast, a previous publication using a qualitative assessment of expression level, reported the same moderate expression across the cortex, subiculum, hippocampus and SNpc/VTA . Our datAlthough little is known about the function of Nurr1 in areas outside of the mesencephalic dopamine neurons and in the hippocampus, some anatomical data about these Nurr1 immunoreactive neuron populations have been reported. The claustrum is located in the deep temporal/insular cortex region. Functionally, the claustrum is thought to coordinate activation across cortical areas as these neurons received and project back to multiple cortical regions . Within The neurons with the highest expression levels of Nurr1 protein were located in the parietal and temporal cortex, which included secondary somatosensory cortex, insular cortex, auditory cortex, and temporal association cortex. In addition to this high Nurr1 expressing population, a separate populations of dopamine neurons in this region were observed with a lower level of Nurr1 expression. Based on available data, Nurr1 in the parietal/temporal cortex appears to be expressed in glutamate neurons. It remains to be determined if the high and medium Nurr1 expressing neurons represent different anatomical or functional populations of cortical neurons. To better characterize the distribution of Nurr1 expression in the brain, future studies will focus on identifying these specific populations of neurons in the temporal and parietal cortex. Additionally, Nurr1 immunoreactive neurons were observed in the deep layers of frontal and parietal cortex. In the deep layers of the rest of the cortex, Nurr1 has been shown to be a marker for neurons in layer VIb . CurrentPrevious studies have reported reduced Nurr1 protein expression in the Nurr1 +/− mice . Using tAs Nurr1 is an immediate early gene, expression of Nurr1 appears to be important for regulating its function . PreviouBecause of this rapid turnover of Nurr1 protein, maintenance of Nurr1 expression appears to require environmental stimuli and pathological processes can disrupt this regulation. Alterations in Nurr1, particularly reduced Nurr1 expression, have been reported in neuropsychiatric and neurologic diseases . Within Using Q-IHC to measure Nurr1 protein expression revealed a wide range of expression levels across various neuronal populations. Additionally, it appears that the Nurr1 +/− genotype differentially affects Nurr1 protein expression in a region-specific way with expression in most region is considerably less than the expected 50%. Because Nurr1 expression can be induced rapidly and the protein appears to have a relatively rapid turnover of an estimated 4 h, it is important to understand the mechanisms that maintain Nurr1 expression in various neuronal types. These data are important for understanding the regional regulation of Nurr1 protein expression and how Nurr1 functions across various neuronal populations.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The animal study was reviewed and approved by the Mississippi State University and East Carolina University IACUC.JE and EK designed the experiments and wrote the manuscript. EK, SG-R, JN, and HP conducted the experimental procedures. All authors have read and approved 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.


Recent growth in crop genomic and trait data have opened opportunities for the application of novel approaches to accelerate crop improvement. Machine learning and deep learning are at the forefront of prediction-based data analysis. However, few approaches for genotype to phenotype prediction compare machine learning with deep learning and further interpret the models that support the predictions. This study uses genome wide molecular markers and traits across 1110 soybean individuals to develop accurate prediction models. For 13/14 sets of predictions, XGBoost or random forest outperformed deep learning models in prediction performance. Top ranked SNPs by F-score were identified from XGBoost, and with further investigation found overlap with significantly associated loci identified from GWAS and previous literature. Feature importance rankings were used to reduce marker input by up to 90%, and subsequent models maintained or improved their prediction performance. These findings support interpretable machine learning as an approach for genomic based prediction of traits in soybean and other crops.The online version contains supplementary material available at 10.1186/s12870-022-03559-z. Glycine max) has a variety of uses including human consumption, livestock and aquaculture feed, and biofuel production [Soybean  can improve prediction accuracy in GS –11. The The underlying mechanisms of genotype to phenotype predictions remain unclear. Adding associated SNPs from Genome Wide Association Studies (GWAS) into linear prediction models has shown varying results , 15. ForFor genotype to phenotype predictions, it is appropriate to test a range of ML/DL algorithms as each algorithm has its own underlying assumptions and biases, and no algorithm provides the best performance for all traits . StudiesIn this study we build robust prediction models for seven agronomic traits in soybean and compare the suitability of prediction models for use in crop breeding. Our results suggest that XGBoost has an affinity for genotype to phenotype prediction and should be considered in future model development. The trait associated regions identified by XGBoost overlap with regions of significantly associated loci from GWAS, and XGBoost can independently identify these regions. Furthermore, reducing the input data to a targeted selection of SNPs based upon initial regions of importance to XGBoost tree building can provide equal or better results using far fewer SNPs.http://broadinstitute.github.io/picard/). Reads were realigned using GATK [https://npgsweb.ars-grin.gov/) for the accessions that had available observation data. The range of phenotype data is summarised in Table SA total of 1110 diverse soybean accessions were selected from the USDA Soybean Germplasm Collection for SNP calling . The seqing GATK  v3.8-1-0ing GATK , . Jupyter notebooks were connected to the server, and are available at rn v0.21  was usedFor multiclass classification problems, XGBClassifier was initiated with objective = ‘multiclass’ and num_classes set to the number of trait classes. For other regression and classification problems, XGRegressor and XGBClassifier were initiated with default settings, all of which were loaded in with the XGBoost package v1.1.1. XGBoost model parameters were optimised using the BayesSearchCV object from scikit optimisation package v0.8.1. The setting space was follows: learning_rate(0.01 - 1.0), min_child_weight(0 - 10), max_depth(0 - 50), max_delta_step(0 - 20), subsample(0.01 - 1.0), colsample_bytree(0.01 - 1.0), colsample_bylevel(0.01 - 1.0), reg_lambda(1e^9 - 1000), reg_alpha(1e^9 - 1.0), gamma(1e-9 – 0.5), min_child_weight(0 - 5), n_estimators(50 – 200) and scale_pos_weight(1e^6 - 500). The optimum XGBoost parameters were then used to fit the XGBoost models using stratified k-fold cross validation for classification tasks, and standard k-fold cross validation (k = 10) from the scikit learn model selection package. The best performing model from cross validation was saved using the inbuilt pickle python package (protocol version 5) and used for prediction on the holdout dataset.The functions ‘get_booster’ and ‘get_scores’ was used to generate dictionaries of importance scores for each feature used by the XGBoost model. The top 20 scores were retrieved and ranked. Original SNP names were retrieved from a stored list of headers, and allele values were retrieved by a custom inverse one hot encoding function.Random forest objects for classification (RandomForestClassifier), and continuous (RandomForestRegressor) traits were loaded from the scikit learn ensemble python package. Both the random forest classifier object and random forest regressor object were initialised with n = estimators = 100, max_features = “sqrt” and the random state set to a random integer between 0 and 5000. To ensure an optimised and fitted model could be selected for use on the holdout dataset, the random forest objects were fitted using stratified k-fold cross validation for classification tasks, and standard k-fold cross validation (k = 10) from the scikit learn model selection package. The best performing model from all folds during cross validation was selected and subsequently saved using the inbuilt pickle python package and used for prediction on the holdout dataset.https://github.com/keras-team/keras) for the Tensorflow v2.1.0 [The Keras 2.4.3 interface (w v2.1.0  python lw v2.1.0  as a basw v2.1.0 . The finThe CNN models used three 1D convolution layers using Rectified Linear Unit (ReLU) activation, with a 20% dropout between layer one and two, and a 10% dropout between layer two and three. The convolution layers had 12, 10 and 8 filters and a kernel size of 14, 10 and 8 respectively. The convolution layers were followed by a 1D max pooling layer of size 2 and batch normalisation before being flattened and fed into three dense layers. These dense layers had 48, 32 and 16 nodes, each with ReLU activation and were followed by a batch normalisation layer.The DNN models for this study were a fully connected feed forward multilayer perceptron network consisting of five dense layers with ReLU activation functions, a dropout layer of 3, 2 and 1% after the first three layers respectively, and a batch normalisation layer after the final dense layer. The layers consisted of 200, 100, 64, 32 and 16 nodes each. For both CNN’s and DNN’s, the output layer had one node with linear activation for continuous traits, 1 node with sigmoid activation for binary categorical traits and x nodes with softmax activation for multiclass traits, where x = the number of possible classes. The optimiser used was Adamax with a learning rate of 0.003. The batch size was 1/50th of the total amount of samples to ensure memory could be managed when running through the neural network.XGBoost, random forest, CNN and DNN models were trained and evaluated on SNP input data uniformly distributed across the genome to predict each of the following traits: flower colour, pod colour, pubescence density, seed coat colour, seed oil content, seed protein content and seed weight Table , Fig. 1.To compare the results and assess the potential to reduce input data we interpreted the XGBoost models and found SNPs that were important for prediction. For each trait, the XGBoost input SNPs were ranked by their F-score, measured in gain, which is a measure of the relative contribution of each SNP. For flower colour, seed coat colour, pubescence density and seed weight, the 20 highest ranking SNPs by XGBoost F-score included subsets of at least 3 SNPs within close proximity. Each SNP in a subset was separated by a maximum of 100kbp from their nearest neighbouring SNP within a given subset. The genetic regions that these subsets of SNPs spanned were defined as regions of importance (ROI), of which 6 in total were identified across 4 traits, and are summarised in Table We selected a subset of targeted SNPs for each trait based on the XGBoost interpretation Figs. S. Input dWhen using the reduced SNP input data to evaluate the ML and DL architectures Table , XGBoostAcross all traits with the complete SNP data, XGBoost was the best performing model for 4/7 traits, random forest for 2/7 traits, and a CNN for 1/7 traits. With reduced SNP data, XGBoost was the best performing model for 4/7 traits, and random forest for 3/7 traits. For both datasets XGBoost was the best predictor of flower colour, pod colour and seed colour whilst random forest was the best predictor of seed protein content. Overall, XGBoost was the best performer 8 of 14 times, and remained robust after feature reduction.Here we demonstrate the performance of XGBoost and random forest for genotype to phenotype predictions in comparison to widely used DL architectures. From the 14 sets of models, DL outperformed the ML models on only one occasion. The models in this study were all non-linear, which has the advantage of being able to include non-additive variances , 35. OurBuilding and training effective DL models requires abundant high-quality training data , and forThe importance of marker density for prediction varies between datasets, with some evidence demonstrating increasing SNP density can increase accuracy and is positively correlated with heritability , whereasXGBoost’s inbuilt methods were used to interpret the model and guide the selection of dense areas of SNPs for further model building. Using model interpretability to guide feature selection was effective, however a potential limitation of the study is that for some traits in the initial genome-wide SNP input data models, XGBoost was not the best performing model. As an alternative, model-agnostic local explanation methods such as SHAP  could beOne concern with explaining models that were built without the intention of interpretability is that it can lead to issues of validation , and expThe loci identified from GWAS for flower colour on chromosome 13, seed coat colour on chromosome 8 and pod colour on chromosome 19 are supported by previous research , 49. In This study demonstrated that XGBoost and random forest models have the ability to outperform DL architectures for genotype to phenotype prediction problems. XGBoost models and GWAS identified overlapping genomic regions for two traits. For other traits, XGBoost identified genomic regions hosting multiple SNPs that may help define new associated regions. Finally, this study used the feature importance results as a guide to reduce the number of SNPs required. These results demonstrate the feasibility of feature reduction for genotype to phenotype predictions and showcase the importance of an appropriate representation of input.Additional file 1: Supplementary Figure 1.P-value of each SNPs association for a) flower colour b) seed coat colour c) pod colour in the soybean VCF. SNPs coloured red have been determined as significantly associated for the given trait as they have a p-value less than the -log10(8) significance threshold for this GWAS. Supplementary Figure 2. Graphs ranking the top 20 most input SNPs by gain as identified by XGBoost models for trait predictions for traits with regions of importance identified from XGBoost. Blue bars are region of importance, whereas other colours represent collections of important SNPs on the same chromosome. Black bars represent left over SNPs with no relation to other SNPs in the ranking. SNP rankings for genome wide SNP input for A) flower colour B) seed coat colour C) pubescence density D) seed weight. Supplementary Figure 3. Top 20 ranked SNPs for XGBoost Seed Oil Prediction. Supplementary Figure 4. Top 20 ranked SNPs for XGBoost Pod Colour Prediction. Supplementary Figure 5. Top 20 ranked SNPs for XGBoost Seed Protein Prediction. Supplementary Table 1. Targeted Regions of SNPs for Reduced Input Models. Supplementary Table 2. List of soybean germplasm in the pangenome with the sequence coverage. . Supplementary Table 3. Trait Data Types.


Zea mays L.) production is constrained by drought and heat stresses. The combination of these two stresses is likely to be more detrimental. To breed for maize cultivars tolerant of these stresses, 162 tropical maize inbred lines were evaluated under combined heat and drought (CHD) and terminal drought (TD) conditions. The mixed linear model was employed for the genome-wide association study using 7834 SNP markers and several phenotypic data including, days to 50% anthesis (AD) and silking (SD), husk cover (HUSKC), and grain yield (GY). In total, 66, 27, and 24 SNPs were associated with the traits evaluated under CHD, TD, and their combined effects, respectively. Of these, four single nucleotide polymorphism (SNP) markers  had pleiotropic effects on both AD and SD under CHD conditions. Four SNPs (SNP_138825271 (Chr03), SNP_244895453 (Chr04), SNP_168561609 (Chr05), and SNP_62970998 (Chr06)) were associated with AD, SD, and HUSKC under TD. Twelve candidate genes containing phytohormone cis-acting regulating elements were implicated in the regulation of plant responses to multiple stress conditions including heat and drought. The SNPs and candidate genes identified in the study will provide invaluable information for breeding climate smart maize varieties under tropical conditions following validation of the SNP markers.Maize ( The world’s population is estimated to reach 10 billion by 2050 ,4. AdditHeat stress is reported to prolong the anthesis–silking interval, reduce kernel set ,10,19,20The majority of traits which are of agronomic importance in maize are quantitatively inherited, thus, they are controlled by multiple genes/loci with both major and minor effects, and are largely influenced by the environment ,36. Two Several QTLs have been identified via GWAS in maize including QTLs associated with drought or heat tolerance ,45,46,47Breeding for heat stress as well as drought tolerance in maize is crucial to the effort to overcome the challenges associated with these two key threats to sustainable maize production. Therefore, a total of 162 tropical maize inbred lines developed by the IITA were evaluated under two contrasting environments: heat combined with drought (CHD), and terminal drought (TD) conditions. Furthermore, the inbred lines were genotyped using the genotyping-by-sequencing (GBS) with the Diversity Array Technology (DArT) sequencing (DArTseq) ,28,29. TIn total, 162 early maturing inbred lines obtained from IITA, Ibadan, Nigeria were used for this study. The lines were evaluated at the Manga Station of the Savannah Agricultural Research Institute, Ghana  and Kadawa, Nigeria  in 2018 and 2019 under CHD and TD conditions . DetaileH2) was calculated following the formula by . Th. Th54]. n et al.  while thhttps://qteller.maizegdb.org/, accessed on 14 December 2021) [https://phytozome-next.jgi.doe.gov/, accessed on 14 December 2021) [http://gsds.gao-lab.org/, accessed on 14 December 2021).Model genes within ±120 kb of stable SNP positions (SNPs detected under either CHD and TD or across (combined) effects  were dower 2021) . The exper 2021)  and, coner 2021) , and theer 2021) . The funer 2021) , proteiner 2021) , and Kyoer 2021)  of the mer 2021) . Promoteer 2021) . Based oThe rainfall and temperature recorded during the experiments are presented in p < 0.05, p < 0.01, p < 0.001) were observed between AD and SD (r = 0.97), HUSKC and PLASP (r = 0.59), and PLASP and EASP (r = 0.54) under CHD (Positive and significant correlations (nder CHD . Similarnder CHD . These rnder CHD . Averagenditions . These rP and phenotypic variation explained (R2) ranging from 3.89 to 15.55% and 8.37–49.23%, respectively. Among these, the highest number of 13 and 10 SNPs was found on chromosomes (Chr) 1 and 4, respectively. This indicates the major role of Chr01 in regulating the traits evaluated under CHD conditions in maize  were used with the 7834 SNPs taking into consideration the population structure and relatedness earlier published . Sixty-sin maize a. In allP and R2 ranges of 3.90–24.11% and 12.53–64.79%, respectively, across the chromosomes except Chr10 (P (24.11) and R2 (64.11%) together with the other two SNPs (SNP_104170418 and SNP_162101608) on Chr02 were linked to GY  were used for MTA mapping. A total of 24 SNPs with a mean number of ≈2.8 SNPs, minimum = 1 SNP (on either Chr07 or Chr08), and maximum = 5 SNPs (on Chr04) were associated with the 11 traits . These SCumulatively, 117 SNPs were detected among the traits under the three environmental conditions, CHD, TD, and across (combined) mapping . No SNP https://qteller.maizegdb.org/, accessed on 15 December 2021) [Zm00001d041124 located at 99054551–99065304 bp on Chr03, the remaining 11 genes have annotations related to either CHD/TD or both conditions (Zm00001d048531 located 117.36 kb (downstream) from SNP_158056460 encodes RNA helicase which has been implicated in improving abiotic stress tolerance in crop plants [Zm0001d002374 possessed 1 exon with no intron while the Zm00001d046434 possessed 17 exons and 16 introns  . In all,er 2021)  or heat er 2021)   ≥ 3.89) were found across the 10 chromosomes in the maize genome  and R2 (64.11%) plus two other SNPs (SNP_104170418 and SNP_162101608) on chromosome 2 were linked to YIELD  predicted in the present study is reported to play a significant role under drought or heat and combined heat and drought stress in Arabidopsis [In an effort to select potential candidate genes for cloning and functional verification using gene overexpression and CRISPR/Cas technology, a number of candidate genes were predicted based on propelling evidence from in silico and could be validated to unravel their actual regulatory role in the studied traits. For instance, recent evidence suggest that Auxin induced-like protein (bidopsis ,80. Circbidopsis . It is obidopsis , therefoMultiple traits from 162 inbred lines and 7834 high quality SNPs were used to conduct MTAs. In all, 117 SNPs associated with the measured traits were evaluated in the present study. Specifically, 66, 27, and 24 SNPs associated with the traits were evaluated under CHD, TD, and combined CHD and TD conditions, respectively. Of these, three SNPs were repetitive under CHD and combined CHD and TD conditions and nine were found concurrently between TD and combined conditions. The highest number of SNPs detected under CHD conditions elucidated the complex nature of the genetic factors that regulated the maize responses under the test conditions. The stable SNPs and those linked to more than one trait could be targeted for MAS. Twelve candidate genes as well as their CAREs, gene structure, and expression profiles were predicted by bioinformatics analyses. The findings from this study offer useful clues about the genetic architecture of these traits under multiple abiotic stresses. The results of this study provided essential information that could be used to improve maize breeding programs in SSA.


We proposed several novel methods to find the optimal synthetic marker to represent the SNP bin. We also proposed methods to evaluate the information loss or information conservation between using the original genome-wide markers and using dimension-reduced synthetic markers. Our performance assessments on the real-life SNP data from a rice recombinant inbred line (RIL) population and a rice HapMap project show that the new methods produce satisfactory results. We implemented these functional modules in C/C++ and streamlined them into a web-based pipeline named PIP-SNP (https://bioinfo.noble.org/PIP_SNP/) for processing SNP data.Genome-wide association study data analyses often face two significant challenges: (i) high dimensionality of single-nucleotide polymorphism (SNP) genotypes and (ii) imputation of missing values. SNPs are not independent due to physical linkage and natural selection. The correlation of nearby SNPs is known as linkage disequilibrium (LD), which can be used for LD conceptual SNP bin mapping, missing genotype inferencing and SNP dimension reduction. We used a stochastic process to describe the SNP signals and proposed two types of autocorrelations to measure nearby SNPs’ information redundancy. Based on the calculated autocorrelation coefficients, we constructed LD bins. We adopted a  Due to the great success in identifying causal genetic markers conferring complex traits and diseases , genome-It is well known that increasing marker density and sample sizes can further increase the resolution of QTL mapping . Next-geP values for interaction genetic marker pairs require much higher computing capacity , which can be used for SNPs’ dimension reduction. LD exists because of shared ancestry resulting in haplotype patterns, a particular combination of alleles along the contemporary chromosomes . Studieset al.   , from whDue to linkage disequilibrium, a genome can be mapped into haplotype blocks. We can select only informative SNPs or synthetic markers representing the original block structures in the genome for genome-wide association studies ,20. The Autocorrelation is a type of serial correlation, which has been used in stochastic signal processing to measure the similarity of a signal with a delayed copy of itself as a function of delay . Due to A simple way to measure the relationship between two neighbor signals entclass1pt{minimaFurther, the similarity profile of one fix SNP signal with its continuous neighbor SNP signals is given in Equation , which eThe correlation between two neighbor SNPs and the autocorrelation profile measuring the LD decay in a local range of the genome can be described graphically. Figures The haplotype block pattern as LD changes is demonstrated as a right triangle in the 2D Pearson correlation of SNP pairs Figure , or a flThe NGS technology provides high-dimensional SNP markers but also suffers from more missing values. However, the downstream association analysis requires the genotype completeness for all SNPs. Therefore, imputation is a critical step in GWAS analysis, which essentially is to infer the most optimal substitute to fill the missing values. Of all the imputing methods and tools, there are two distinct categories. One is based on a phasing procedure that maps the ordered SNPs to the high quality reference genome or genotype panels, e.g. humans  and cattnt} SNPs . BecauseIn this study, we modified the LD-kNNi method and applied it to impute the missing values in each detected LD conceptual bins . For eacA high-dimensional SNP marker can statistically improve the QTL mapping resolution, but it has reached a plateau  in epistSupposing one bin containing Figure n Figure . Figure Regarding the binary genotype coding such as 0, 1, 2, the integrated marker will become continuous float format not following the original binary format. In addition, the synthetic marker comprehensively integrates the genetic information of the whole bin, but the resolution of the marker’s position in the chromosome will decrease into a bin. Comparably, the tag SNP still follows the same binary format and conserves the resolution of the marker’s position.We investigated two distinct SNP data sets from a rice RIL population and a diverse rice HapMap population. We found that the autocorrelation characteristics for the two types of SNP data are quite different Figures  and 2. Ihesizing . SupplemIn general, we aimed to develop a platform to solve the two great challenges to GWAS technology: high-dimensional SNP data and the incompleteness of genotype data. Through the proposed methods rooted in the correlation analysis of SNPs, the biological Haplotype block can be mapped, the LD conceptual bins can be detected, the missing genotype values can be imputed, and the high dimensional SNP marker can be reduced to an acceptable number. Figure Most of the numerical SNP data are coded as the count of minor alleles. The biallelic SNPs have three combinational genotypes numerically as 0 , 1 (heterozygous) and 2 , respectively. As previously mentioned, there may be many missing values that need to be imputed before conducting a GWAS analysis. To simplify, we specifically coded all the missing genotype as -1 in this study.Along with the function implementation, ease of use also is a very important criterion. Users prefer to choose a data processing tool or platform with a friendly user interface and painless learning curve. Considering this, we developed the platform as a web pipeline, which can naturally avoid the tool’s installation and updating. Additionally, we need to consider the different scenarios in real application, which can increase the flexibility.Correlation analysis of the neighbor SNPs is the backbone of this study. Based on the correlation and autocorrelation analysis of one SNP and its neighbor, LD conceptual bins can be detected and the whole genome can be mapped. After finishing the LD bin mapping, the missing values in each detected bin will be imputed by the kNN method with the specific Considering the high dimension of SNP variant, the computational efficiency should be seriously considered. Therefore, we chose C/C++ to implement each function module in Open-Source IDE Code:Blocks. All the source codes are compiled into executable command lines in Linux. To run this command line, users only need to provide the raw SNP data at the specific format together with the configured parameters.Web interfaces usually can provide user-friendly convenience by maximally avoiding the mistaken parameter configuration. This motivated us to develop a web-based pipeline PIP-SNP. Generally, PIP-SNP includes a server part for the computation dense analysis and a remote-client part for a user's job submission and results downloading. We realized that the original genotyped SNP data can reach up to several GBs with millions of SNP markers and uploading such a large text file from a remote client side to the PIP-SNP’s web server can be exceedingly difficult. Therefore, we technically developed a module that can work in an HTML5 browser and implement the resumable multithreading chunked data uploading. Additionally, the original genotyped SNP data can be stored in a remote cloud server, such as Google Drive. PIP-SNP provides the options to allow the user to provide the shared URL.LD bin mapping is the most crucial part of the process, but it is also very subtle, which will affect the whole implementation. Using the existing LD bin mapping information with higher accuracy usually is the top choice. Therefore, when we designed PIP-SNP, we considered fully the two practical application scenarios to directly detect the LD bins from the raw SNP data and to use the existing LD bin mapping information. In the web client part, we populated the two scenarios separately as PIP_SNP_Venue1 and PIP_SNP_Venue2. We investigated the correlation analysis of SNP data from the HapMap population and found that there may be more acute spike autocorrelation patterns. This phenomenon indicates that several types of SNPs may be closely entangled in a local region. We have developed a two-phase procedure, including a shallow- and deep-synthesizing step, to clump the SNPs into groups. Each group corresponds to a conceptual LD bin. However, the groups can overlap each other to some degree. If using deep synthesizing, we can achieve a higher SNP marker compression ratio, which will be defined and discussed in later sections. When we designed the architecture of PIP-SNP, we left to the user the option whether to choose one phase or two phases to generate the synthesized marker. When implementing this function module, we built and compiled a special project for processing the deep synthesizing. To suit the two application scenarios, we needed to seam the three executable command lines into an integrative pipeline. In addition to developing the three executable command lines, we also developed some python scripts to seam the three executable command lines and parse the returned configured parameters from a remote client user.P-values. A comprehensive comparison of the results of these three aspects can answer this question.In this study, we have proposed a series of methods and developed a web-based pipeline PIP-SNP to preprocess the SNP data, including the LD conceptual bin mapping, missing value imputing and LD bin markers synthetizing. It was also important to address how much the genetic information has been conserved or lost due to the SNP markers being processed and greatly reduced. To address this question, we needed to go to the very nature of statistical genetic itself. Essentially, the genetic variants and the phenotypic values of quantitative traits can be connected through a kind of LMM. Statistically solving the proposed LMMs mainly include three procedures: kinship matrices calculation, genetic variance component analysis and statistical testing of We developed a more complex LMMs and GWAS tool, PATOWAS, which can outperform the existing LMM and GWAS tools by delivering a specific broad-sense heritability, the marker's additive effect results and the marker pairs’ interaction effect results . Based oy is an N × 1 vector of a quantitative phenotypic trait, and Z is an M × N marker matrix for M SNP markers and N individual samples. ith column of matrix Z, and ia is the ith marker’s additive effect on the trait; i and marker j; e is an N × 1 vector of residual error.The LMM that incorporates the markers’ additive effects and marker pairs’ interaction effects can be simply represented as PATOWAS . Based oPATOWAS also output the testing results as p values measuring how likely the putative trait associated with genetic variants as SNP markers or SNP marker pairs is due to random chance.Using PIP-SNP, we can configure different parameters to get a dimension reduced synthetic marker. Compared with the original high-dimensional SNP markers, the marker compression ratio (MCR) as an analogy of compression ratio in image processing can be defined asWe submitted the two typical SNP datasets, including a RIL population and a HapMap population, to PIP-SNP and generated a series of dimension-reduced synthesized SNPs or markers. Table We then submitted the marker data, together with the phenotype trait data, to PATOWAS for the genetic performance evaluation. Kinship matrix measures the relatedness between individuals, and its accuracy will affect the following p value testing. Epistatic GWAS needs to calculate two kinds of kinship matrices mentclasspt{minimaectively . Therefoentclass1pt{minimaentclass1pt{minimaA kinship matrix essentially measures the relatedness of individuals. Considering its symmetric feature, a kinship matrix can be represented as a lower triangular matrix. Therefore, the additive and interaction effect kinship matrix The two kinship matrices If we set a correlation cutoff threshold The representative marker for each bin can be the optimal tag SNP or the integration of all SNPs. Based on the PATOWAS analysis result for the RIL population SNP data, Figure 6 Figure , E and FComparably, the integration method considers all the SNP information in one detected LD bin. Therefore, this method conserves the more genetic information and reports the higher kinship matrix similarity. The mathematical bases that prove this characteristic have been detailed and deduced in Based on the PATOWAS result, we can use formula 8 to calculate the broad-sense heritability. Both the narrow and broad sense heritability can be used to measure the portion of the phenotypic variation that can be biologically explained by the considered causal genetic variants. Comparably, the broad-sense heritability delivers more explainable genetic components because the canonical narrow-sense heritability considers only the marker's additive effect.The two calculated broad-sense heritability using the original high dimensional SNP data and dimension reduced synthetic marker are represented as To a specific trait, the defined RH is can be greatly affected by the dimension reduced SNP markers. Based on the analysis result for RIL data, we generated Figure P-value points. In this study, we developed pre-processing methods and platforms to group and synthesize SNP markers, which will cause the marker number to be reduced and make it easier to be manipulated in the GWAS analysis. However, biologists and breeders may show more concern regarding whether the significant QTL patterns can be conserved.GWAS analysis needs the genotype and phenotype data as inputs, and the GWAS analysis usually delivers the Based on the PATOWAS analysis result for the moderate-scale RIL data, we generated a series of aligned line plots of Q–Q plots at different correlation cutoff threshold We then submitted the dimension-reduced tag SNP markers to both TASSEL and PATWOAS. TASSEL has the option to accept user-defined kinship matrix. Therefore, we have two types of TASSEL results corresponding to the kinship matrix from the full SNP marker set or from dimension reduced markers. The 1D GWAS result, including the aligned Manhattan plots and the This phenomenon can be explained by the theoretical basis of the Beavis effect describing the relationship between independent QTL number and the sample size .Due to the existence of LD and haplotype block patterns, the SNPs are not independent and the whole genome can be mapped into block structures. SNP data face two obvious challenges: the huge computing burden due to its high dimensionality and the more missing values affecting the biological completeness. SNP data need to be processed before conducting the downstream GWAS analysis. In this study, we used stochastic processes to describe the SNP signals and proposed two kinds of autocorrelation to measure the information redundancy of nearby SNPs. Based on the autocorrelation measures, we proposed novel methods to detect the LD conceptual bins. Further, we treated each detected bin independently and used the kNN method to infer the missing values. Finally, one representative marker per LD conceptual bin can be synthesized either as the optimal tag SNP or the integrative marker using the Euclidean norm of all SNPs.The dimension-reduced synthetic markers will inevitably cause genetic information loss, yet the marker dimension reduction is necessary for epistatic GWAS analysis. To address how much the genetic information is conserved or lost due to the preprocessing of SNP markers, we used our in-house association tool, namely PATOWAS, to evaluate the resulted relative heritability, kinship matrix and the canonical 1D Manhattan plots. The defined relative broad sense heritability includes two biological components for additive effect and interaction effect. Therefore, it is possible that the relative broad sense heritability does not reduce so much, but the 1D Manhattan plot becomes noisy, and the QTL patterns become less evident.We analyzed two kinds of typical SNP data, including a moderate-scale SNP dataset from a RIL rice population and a high-dimensional SNP dataset from a rice HapMap population. We found that it is more challenging to describe the random SNP data from a HapMap population. The autocorrelation criteria spreading across the neighboring two SNPs is very limited to grouping similar SNPs and, therefore, it cannot achieve a satisfactory SNP marker compression ratio. However, the very acute spike autocorrelation patterns indicate that several kind of SNPs can be closely entangled together in a local region, which inspire us to jump out off the neighbor-joining SNPs and consider its second and even third neighbor SNPs. Based on these thinking, we developed a specific function module called deep synthesizing, which is more aggressive in grouping the more neighbor SNPs and distinguishes from the shallow synthesizing method by spreading consideration of only the two neighbor-joining SNPs.To the detected LD bins, we propose two options to generate the representative synthetic marker. One is to find an optimal tag SNP, and the other is to calculate the Euclidean norm of all SNPs to get an integrative marker. The first option can keep the same genetic variant format and the marker resolution but will lose the genetic information from other SNPs. Comparably, the second option considers the integrative genetic information of all the SNPs at the same LD bin. Our performance evaluation results also support that more genetic information has been retained. However, the genetic variant data format will be changed into float, and the marker resolution will be degraded from a single SNP into a LD conceptual bin.et al., 2015).We realized that LD bin mapping is the most important part compared with other modules. The actual bin mapping should match well to the real haplotype block structure and reflect the genetic recombination. However, the actual genetic recombination event is difficult to know and different haplotype block partitioning algorithms produce a varied range in the bin number, size and coverage, which make it difficult to propose a golden standard to define the haplotype block and then conduct a fair performance evaluation . As suchAlthough methods have been developed to perform haplotype block mapping, imputing or tag SNP selection. However, most of them were independently developed for a specific aim. It's difficult to integrate them into a pipeline for high dimensional SNP data manipulation . To the https://bioinfo.noble.org/PIP_SNP/. We are committed to maintaining and improving the specific function modules per user comments and suggestions. Additionally, we have made the source code open and deposited them in Git-Hub https://github.com/noble-research-institute/PIP_SNP.The pipeline PIP-SNP, source codes for three project packages PIP_SNP_Venue1, PIP_SNP_Venue2 and Deep Synthesizing, test data, including a RIL rice data, HapMap rice data and phenotypic traits, are freely available at The current version of PIP-SNP only accepts biallelic SNP data in pure text format. The SNP data must be stored as lqab060_Supplemental_FilesClick here for additional data file.