Article

Genetic Diversity, Population Structure, Integration of Genome-Wide Association Studies and Machine Learning for Antibacterial Trait Analysis in the Mediterranean Spice Laurel (Laurus nobilis)

Gülşah KARATAŞDepartment of Food Engineering, Tokat Gaziosmanpaşa University, Tokat 60250, TürkiyeAmjad AliFaculty of Agricultural Sciences and Technologies, Sivas University of Science and Technology, Sivas 58000, TürkiyeÜnal KarıkAegean Agricultural Research InstituteMuhammad Azhar NadeemDepartment of Biotechnology, Faculty of Science, Mersin University, Yenişehir, Mersin 33110, TürkiyeMuhammad AasımDepartment of Precision Agriculture and Agricultural Robots, Faculty of Agricultural Sciences and Technologies, Sivas University of Science and Technology, Sivas 58000, TürkiyeMehmet BedirFaculty of Agricultural Sciences and Technologies, Sivas University of Science and Technology, Sivas 58000, TürkiyeMuhammad Tanveer AltafDepartment of Field Crops, Faculty of Agriculture, Recep Tayyip Erdoğan University, Pazar, Rize 53300, TürkiyeWaqas LiaqatDepartment of Field Crops, Faculty of Agriculture, Recep Tayyip Erdoğan University, Pazar, Rize 53300, TürkiyeQureshiFaculty of Agricultural Sciences and Technologies, Sivas University of Science and Technology, Sivas 58000, TürkiyeFawad AliDepartment of Plant Resources and Environment, Jeju National University, 102 Jejudaehak-ro, Jeju 63243, Republic of KoreaFarid RuziyevDepartment of Genetics, Institute of Biochemistry, Sh. Rashidov Samarkand State University, Samarkand 140104, UzbekistanPablo F. CavagnaroConsejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Ciudad Autónoma de Buenos Aires C1425FQB, ArgentinaMuhammad Qasim ShahidDepartment of Genetics, Institute of Biochemistry, Sh. Rashidov Samarkand State University, Samarkand 140104, UzbekistanSyed Amjad AliDepartment of Information Systems and Technologies, Bilkent University, Ankara 06800, TürkiyeAhmad AlsalehDepartment of Agriculture and Food, Hemp Research Institute, Yozgat Bozok University, Yozgat 66200, TürkiyeFaheem Shehzad BalochDepartment of Genetics, Institute of Biochemistry, Sh. Rashidov Samarkand State University, Samarkand 140104, Uzbekistan

2026en

ABI

Abstract

Laural (Laurus nobilis) is a Mediterranean plant with reported antibacterial properties, yet the genetic basis of its antibacterial efficacy remains largely unexplored. This study evaluated the antibacterial activity of Laurus nobilis methanolic extracts against Escherichia coli, Staphylococcus aureus, and Bacillus cereus, combined with genome-wide association studies (GWAS) and machine learning (ML) approaches to identify genetic markers and predict antibacterial efficacy in 92 plant samples. Antibacterial tests revealed significant variability in inhibition zones, with E. coli showing the highest inhibition (Canakkale2: 24.5 mm), followed by S. aureus (Aydin2: 26.0 mm). Minimum inhibitory concentration (MIC) analysis demonstrated notable regional differences; extracts from Mersin3 showed the highest efficacy (MIC = 6.25 mg/mL), while Aydin1 exhibited the lowest activity (MIC = 100 mg/mL). Population structure and neighbor joining tree analysis split the germplasm into two groups. GWAS identified significant genetic markers associated with antibacterial traits, including marker 26557159 for EC-MEAN (Escherichia coli-Mean) (p = 1.10 × 10−4, MarkerR2 = 0.1799, genetic variance = 9.41792) and marker 26584774 for BC-MEAN (Bacillus cereus-Mean) (p = 8.89 × 10−5, MarkerR2 = 0.18512, genetic variance = 12.48948). Protein–protein interaction network of loci associated with marker trait association (MTA) marker (26557159) indicated involvement in high-affinity secondary active ammonium transmembrane transporter activity, providing insights into genetic regions influencing antibacterial properties. ML models predicted antibacterial activity with high accuracy. XGBoost achieved the best performance for MIC predictions (R2 = 0.999, RMSE = 0.434), while random forest (R2 = 0.984) demonstrated robust performance for both MIC and disc diffusion assays. LightGBM performed well for MIC prediction (R2 = 0.988) but showed limited accuracy for disc diffusion outcomes (R2 = 0.695). This study is the first to combine GWAS and ML for predicting antibacterial efficacy in L. nobilis, identifying specific genetic markers (e.g., 26557159, 26584774) and demonstrating that XGBoost achieves near-perfect MIC prediction (R2 = 0.999). These findings provide a genomic and computational foundation for marker-assisted breeding of laurel with enhanced antibacterial properties and support the sustainable use of plant-derived anti-microbials.

Identifiers

DOI: 10.3390/plants15131997

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