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Machine Learning, Transcriptome, and Genotyping Chip Analyses www.nature.com/scientificreports OPEN Machine learning, transcriptome, and genotyping chip analyses provide insights into SNP markers identifying fower color in Platycodon grandiforus Go‑Eun Yu1,6, Younhee Shin2,6, Sathiyamoorthy Subramaniyam2, Sang‑Ho Kang1, Si‑Myung Lee1, Chuloh Cho3, Seung‑Sik Lee4,5 & Chang‑Kug Kim1* Bellfower is an edible ornamental gardening plant in Asia. For predicting the fower color in bellfower 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 classifcation 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 identifed using the Random forest based feature selection method from the Fluidigm chip dataset. Among six machines, the random forest (RF) model produced higher classifcation performance than the other models. The 9 SNP marker candidates selected for classifying the fower color classifcation were verifed 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. Te bellfower (Platycodon grandiforus) is a popular plant used as food, medicine, and ornamental plant in Asia1. P. g randif or u s is a monotypic species of the bellfower family (Campanulaceae). Due to its therapeutic efects, its root has been used in traditional medicine as a popular food additive for over 2000 years2. P. g randif or u s consists of 12 cultivars, which has received high commercial ratings in the ornamental fower market for their habitat and foral displays3. Its fower is characterized by an attractive colorful bud, a long fowering time, and an extended vase life 4,5. Genome-wide molecular markers-based genomic selections enhance plant breeding to produce the desired traits6. However, traditional molecular breeding techniques, such as molecular assisted selection, have been limited due to multiple gene variants for complex traits 7. Next-generation sequencing technologies have enabled large-scale genome-wide genotyping for heterogeneous phenotypes, which helped in precise genome selection associated with specifc phenotypes. Further, the reduced representation of genome-wide genotyping is tran- scriptome-wide association studies, which leverage the project cost for molecular breeding studies in the model and non-model plants 8–10. Tese massive genotyping eforts have recently been subject to machine learning (ML) methods to predict SNP associations with specifc traits. Population studies have demonstrated how ML based modeling can be used efectively to predict phenotype from genotype 11. ML facilitates pattern recognition of large biological datasets since ML algorithms are used widely in various biological felds, such as molecular marker identifcation, coding region recognition, pathway gene recognition, protein–protein interaction determina- tion, and metabolic network detection12. For instance, ML models have been constructed for genomic selection 1Genomics Division, National Institute of Agricultural Sciences, Jeonju 54874, Korea. 2Research and Development Center, Insilicogen Inc., Yongin-si 16954, Gyeonggi-do, Republic of Korea. 3Crop Foundation Research Division, National Institute of Crop Science, RDA, Wanju 55365, Korea. 4Advanced Radiation Technology Institute, Korea Atomic Energy Research Institute, 29 Geumgu-gil, Jeongeup 56212, Korea. 5Department of Radiation Science and Technology, University of Science and Technology, Daejeon 34113, Korea. 6These authors contributed equally: Go-Eun Yu and Younhee Shin. *email: [email protected] Scientifc Reports | (2021) 11:8019 | https://doi.org/10.1038/s41598-021-87281-0 1 Vol.:(0123456789) www.nature.com/scientificreports/ in wheat13, root genotype classifcation14, nut-size prediction in Castanea crenata10, and polyploidy associated SNPs identifcation in plants15. Several studies have been conducted in P. grandiforas to identify molecular markers such as simple sequence repeats16,17, microsatellites18, and cleaved amplifed polymorphic sequences 19. However, molecular markers that determine specifc fower colors are limited. Hence, we report in this study a newly designed transcriptome-wide approach as an efective method in identifying fower color in bellfower plants, where the selected SNP markers could be used to predict bellfower color during the plant breeding experiments. Methods Plant materials. P. g randif or u s 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 fower color was conformed, the petals were collected as a samples for experiments. All the plants used in this study were maintained at experi- mental feld located in Jeonju, Korea (N: 35° 49′; E: 127° 09′) National Institute of Agricultural Sciences/RDA living modifed organism (LMO) guidance. Accessions for three fower colours (i.e., Astra pink, Janbaek violet, and Jangbaek white) for RNA sequencing (RNA-Seq) were obtained from the RDA Genebank (http:// geneb ank. rda. go. kr/). For three colors (20 in each color), 60 samples were collected, and those sampling details are given in Supplementary Table S2. Te 480 genotyping samples for the Fluidigm chip array were collected from the mutant accessions created by the gamma irradiation of three diferent fower color accessions (Supplementary Table S3). Te leaf and fower components from each plant were collected individually, and the samples were frozen in liquid nitrogen and stored at -80℃ following DNA and RNA extractions. Gamma irradiation. A Co-60 gamma-irradiator (IR 222, MDS Nordion Inc., Kanata, Canada) was used for gamma irradiation. Dry seeds of P. g randif or u s were irradiated with 50, 100, 150, and 200 Gy of gamma radia- tion 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). P. grandiforas reference genome. Te reference genome for P. grandiforas was obtained from the P. g randif or u s genome project1 (http:// platy codon. thera genet ex. com/). Te gene model and other functional annotations were also obtained from the respective site. RNA sequencing and variant calling. Te 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 (Invitrogen, Termo Fisher Scientifc, Waltham, MA, USA). Total RNA was quan- titated using the NanoDrop spectrophotometer (Invitrogen, Termo Fisher Scientifc, Waltham, MA, USA), and quality was assessed using the RNA 6000 Nano assay kit and Bioanalyser2100 (Agilent Technologies, Santa Clara, CA, USA). Before variant calling, sequence reads were checked for bacterial contamination and adapters using Trimmo- matic v0.3620, as explained in shin et. al.,21 and mapped to the reference genome using bowtie2 22. 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) fles. Te command line includes the parameters (T:HaplotypeCaller; emitRefConfdence:GVCF, variant_index_type:LINEAR, vari- ent_index_parameter:128,000; nct:20; drf:DuplicateRead). Using flters such as the normalized quality score ≥ 20 and mapping quality ≥ 40, the high-quality SNPs were obtained. Te obtained SNPs were annotated using SnpEf v4.224 and the missing genotypes were imputed by Beagle v4.125 with the linkage disequilibrium (LD) score. Population stratifcation. For population stratifcation, SNPs were examined via principal component analysis (PCA) using the PLINK v1.9 tool26. Te commands include options such as assoc, adjust, fsher, model, logistic, hap-assoc, hap-impute. To reduce false positive predictions, stringent fltering cutofs such as genotyp- ing 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. Te sub-populations were estimated based on the number of clusters (i.e., K value), which was obtained using the STRU CTU RE v2.3.4 tool27. Te ad-hoc static ΔK (i.e., change rate of log probability between successive K values) was used to determine the uppermost hierarchical level of population structures. Structural analysis was performed following 20 replicated runs using the 100,000 iterations afer a burn-in period of 50,000 runs. Genome‑wide association. A genome-wide association study was conducted with categorical association (case vs. control). Here, there are three fower color categories tread as case vs control (example: white vs other two as controls). Te association between genotype and phenotype for the above model was conducted using PLINK-v.1.9 with the association mode (Fig. 1). Te signifcant SNPs were selected by applying the cut-of, p < 0.01. Genotyping chip construction. Te SNPs for Fluidigm chip manufacturing were selected in two steps from the color-associated SNPs obtained through GWAS. Firstly, 50% of SNPs were systematically selected
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