Revealing Genetic Diversity and Population Structure in Türkiye’s Wheat Germplasm Using iPBS-Retrotransposon Markers
2024, Demirel, Fatih, Yıldırım, Bünyamin, Eren, Barış, Demirel, Serap, Türkoğlu, Aras, Haliloğlu, Kamil, Nowosad, Kamila, Bujak, Henryk, Bocianowski, Jan
Investigating the genetic diversity and population structure of wheat germplasm is crucial for understanding the underlying variability essential for breeding programs and germplasm preservation. This research aims to contribute novel insights with respect to the genetic makeup and relationships among these wheat genotypes, shedding light on the diversity present within the Turkish wheat germplasm. In this study, iPBS-retrotransposon markers were employed to analyze 58 wheat genotypes, encompassing 54 landraces and 4 cultivars sourced from Türkiye. These markers serve as genetic indicators that can be used to evaluate genetic variation, build genealogical trees, and comprehend evolutionary connections. The PCR products were visualized on agarose gel, and bands were scored as present/absent. The ten iPBS primers collectively yielded an average of 16.3 alleles, generating a total of 163 polymorphic bands. The number of alleles produced by individual markers ranged from 4 (iPBS-2386) to 29 (iPBS-2219). The genetic parameters were calculated using the popgen and powermarker programs. The genetic relationships and population structures were assessed using the ntsys and structure programs. Polymorphism information content (PIC) per marker varied from 0.13 (iPBS-2390) to 0.29 (iPBS-2386), with an average value of 0.22. Shannon’s information index (I) was calculated as 1.48, while the number of effective alleles (Ne) and Nei’s genetic diversity (H) were determined to be 0.26 and 0.31, respectively. Genotype numbers 3 (Triticum dicoccum) and 10 (Triticum monococcum) exhibited the maximum genetic distance of 0.1292, signifying the highest genetic disparity. Population structure analysis revealed the segregation of genotypes into three distinct subpopulations. Notably, a substantial portion of genotypes clustered within populations correlated with the wheat species. This population structure result was consistent with the categorization of genotypes based on wheat species. The comprehensive assessment revealed noteworthy insights with respect to allele distribution, polymorphism content, and population differentiation, offering valuable implications for wheat breeding strategies and germplasm conservation efforts. In addition, the iPBS markers and wheat genotypes employed in this study hold significant potential for applications in wheat breeding research and germplasm preservation.
Prediction of Grain Yield in Wheat by CHAID and MARS Algorithms Analyses
2023, Demirel, Fatih, Eren, Baris, Yilmaz, Abdurrahim, Türkoğlu, Aras, Haliloğlu, Kamil, Niedbała, Gniewko, Bujak, Henryk, Jamshidi, Bita, Pour-Aboughadareh, Alireza, Bocianowski, Jan, Nowosad, Kamila
Genetic information obtained from ancestral species of wheat and other registered wheat has brought about critical research, especially in wheat breeding, and shown great potential for the development of advanced breeding techniques. The purpose of this study was to determine correlations between some morphological traits of various wheat (Triticum spp.) species and to demonstrate the application of MARS and CHAID algorithms to wheat-derived data sets. Relationships among several morphological traits of wheat were investigated using a total of 26 different wheat genotypes. MARS and CHAID data mining methods were compared for grain yield prediction from different traits using cross-validation. In addition, an optimal CHAID tree structure with minimum RMSE was obtained and cross-validated with nine terminal nodes. Based on the smallest RMSE of the cross-validation, the eight-element MARS model was found to be the best model for grain yield prediction. The MARS algorithm proved superior to CHAID in grain yield prediction and accounted for 95.7% of the variation in grain yield among wheats. CHAID and MARS analyses on wheat grain yield were performed for the first time in this research. In this context, we showed how MARS and CHAID algorithms can help wheat breeders describe complex interaction effects more precisely. With the data mining methodology demonstrated in this study, breeders can predict which wheat traits are beneficial for increasing grain yield. The adaption of MARS and CHAID algorithms should benefit breeding research.
Investigation of the Influence of Polyamines on Mature Embryo Culture and DNA Methylation of Wheat (Triticum aestivum L.) Using the Machine Learning Algorithm Method
2023, Eren, Barış, Türkoğlu, Aras, Haliloğlu, Kamil, Demirel, Fatih, Nowosad, Kamila, Özkan, Güller, Niedbała, Gniewko, Pour-Aboughadareh, Alireza, Bujak, Henryk, Bocianowski, Jan
Numerous factors can impact the efficiency of callus formation and in vitro regeneration in wheat cultures through the introduction of exogenous polyamines (PAs). The present study aimed to investigate in vitro plant regeneration and DNA methylation patterns utilizing the inter-primer binding site (iPBS) retrotransposon and coupled restriction enzyme digestion–iPBS (CRED–iPBS) methods in wheat. This investigation involved the application of distinct types of PAs (Put: putrescine, Spd: spermidine, and Spm: spermine) at varying concentrations (0, 0.5, 1, and 1.5 mM). The subsequent outcomes were subjected to predictive modeling using diverse machine learning (ML) algorithms. Based on the specific polyamine type and concentration utilized, the results indicated that 1 mM Put and Spd were the most favorable PAs for supporting endosperm-associated mature embryos. Employing an epigenetic approach, Put at concentrations of 0.5 and 1.5 mM exhibited the highest levels of genomic template stability (GTS) (73.9%). Elevated Spd levels correlated with DNA hypermethylation while reduced Spm levels were linked to DNA hypomethylation. The in vitro and epigenetic characteristics were predicted using ML techniques such as the support vector machine (SVM), extreme gradient boosting (XGBoost), and random forest (RF) models. These models were employed to establish relationships between input variables (PAs, concentration, GTS rates, Msp I polymorphism, and Hpa II polymorphism) and output parameters (in vitro measurements). This comparative analysis aimed to evaluate the performance of the models and interpret the generated data. The outcomes demonstrated that the XGBoost method exhibited the highest performance scores for callus induction (CI%), regeneration efficiency (RE), and the number of plantlets (NP), with R2 scores explaining 38.3%, 73.8%, and 85.3% of the variances, respectively. Additionally, the RF algorithm explained 41.5% of the total variance and showcased superior efficacy in terms of embryogenic callus induction (ECI%). Furthermore, the SVM model, which provided the most robust statistics for responding embryogenic calluses (RECs%), yielded an R2 value of 84.1%, signifying its ability to account for a substantial portion of the total variance present in the data. In summary, this study exemplifies the application of diverse ML models to the cultivation of mature wheat embryos in the presence of various exogenous PAs and concentrations. Additionally, it explores the impact of polymorphic variations in the CRED–iPBS profile and DNA methylation on epigenetic changes, thereby contributing to a comprehensive understanding of these regulatory mechanisms.
Mammalian Sex Hormones as Steroid-Structured Compounds in Wheat Seedling: Template of the Cytosine Methylation Alteration and Retrotransposon Polymorphisms with iPBS and CRED-iBPS Techniques
2023-08-23, Demirel, Fatih, Türkoğlu, Aras, Haliloğlu, Kamil, Eren, Barış, Özkan, Güller, Uysal, Pinar, Pour-Aboughadareh, Alireza, Leśniewska-Bocianowska, Agnieszka, Jamshidi, Bita, Bocianowski, Jan
Phytohormones are chemical compounds found naturally in plants that have a significant effect on their growth and development. The increase in research on the occurrence of mammalian sex hormones (MSHs) in plants has prompted the need to investigate the functions performed by these hormones in plant biology. In the present study, we investigated the effects of MSHs on DNA damage and DNA methylation of wheat (Triticum aestivum L.) during the seedling growth stage, using the CRED-iPBS (coupled restriction enzyme digestion/inter primer binding site) assay and iPBS analysis to determine DNA methylation status. Exogenous treatment with four MSHs (17-β-estradiol, estrogen, progesterone, and testosterone) was carried out at four different concentrations (0, 0.05, 0.5, and 5 µM). The highest genomic template stability (GTS) value (80%) was observed for 5 µM 17-β-estradiol, 0.5 µM testosterone, and 0.05 µM estrogen, while the lowest value (70.7%) was observed for 5 µM progesterone and 0.5 µM estrogen. The results of the CRED-iPBS analysis conducted on MspI indicate that the 0.05 µM estrogen-treated group had the highest polymorphism value of 40%, while the 5 µM progesterone-treated group had the lowest value of 20%. For HpaII, treatment with 0.5 µM 17-β-estradiol had the highest polymorphism value of 33.3%, while the group treated with 0.05 µM 17-β-estradiol and 0.05 µM progesterone had the lowest value of 19.4%. In conclusion, MSH treatments altered the stability of the genomic template of wheat plants and affected the cytosine methylation status at the seedling growth stage. Upon comprehensive examination of the results, it was seen that the employed methodology successfully detected alterations in cytosine methylation of genomic DNA (gDNA), as well as changes in the pattern of genomic instability.
Agro-morphological characterization and machine learning-based prediction of genetic diversity in six-row barley genotypes from Türkiye
2025, Akdogan, Guray, Benlioglu, Berk, Ahmed, Hussein Abdullah Ahmed, Bilir, Melih, Ergun, Namuk, Aydogan, Sinan, Türkoğlu, Aras, Demirel, Fatih, Nowosad, Kamila, Bocianowski, Jan
Abstract The restricted genetic diversity observed in modern barley represents a significant obstacle to enhancing productivity. This study addresses this issue by characterising 445 six-row barley genotypes from the Osman Tosun Gene Bank in Türkiye. A comprehensive analysis of 22 agro-morphological traits, comprising 11 qualitative and 11 quantitative traits, was conducted to explore morphological, growth and phenological diversity. Principal component analysis identified four principal components, which collectively explained 72.86% of the total variance. Of these, the first two components accounted for 52.45%. Based on agro-morphological similarities, the genotypes were grouped into seven clusters. Clusters 5, 6, and 7 contained genotypes with high-yield traits, including early maturity, increased grain per spike, and higher thousand grain weight. The findings contribute directly to the expansion of the barley gene pool. Moreover, this study provides a comprehensive characterisation of the hitherto overlooked six-row barley germplasm present in Türkiye. This offers invaluable genetic resources for future breeding and molecular studies. Furthermore, the study compares the performance of three machine learning models (XGBoost, MARS, and Gaussian Processes) in predicting the harvest index from various traits. The XGBoost model demonstrated superior predictive ability, with the lowest RMSE (0.137), MAPE (0.222), and MAD (0.101) values, and was able to explain 99.8% of the barley variation. This research highlights the potential of machine learning algorithms in enhancing barley breeding by accurately predicting beneficial traits for yield improvement.