Journal 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 )

Bedir, Mehmet, Altaf, Muhammad Tanveer, Farid, Ruziyev, Karataş, Gülşah, Karık, Ünal, Alsaleh, Ahmad, Baloch, Faheem Shehzad, Aasim, Muhammad, Qureshi, Sarmad Ali, Nadeem, Muhammad Azhar, Liaqat, Waqas, Ali, Fawad, Ali, Amjad, Cavagnaro, Pablo Federico, Shahid, Muhammad Qasim, Ali, Syeid Amjad

2026

Overview

Laural ( ) 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 methanolic extracts against , , and , 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 showing the highest inhibition (Canakkale2: 24.5 mm), followed by (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 ( -Mean) ( = 1.10 × 10 , MarkerR = 0.1799, genetic variance = 9.41792) and marker 26584774 for BC-MEAN ( -Mean) ( = 8.89 × 10 , MarkerR = 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 (R = 0.999, RMSE = 0.434), while random forest (R = 0.984) demonstrated robust performance for both MIC and disc diffusion assays. LightGBM performed well for MIC prediction (R = 0.988) but showed limited accuracy for disc diffusion outcomes (R = 0.695). This study is the first to combine GWAS and ML for predicting antibacterial efficacy in , identifying specific genetic markers (e.g., 26557159, 26584774) and demonstrating that XGBoost achieves near-perfect MIC prediction (R = 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.

• Publisher: MDPI AG,Multidisciplinary Digital Publishing Institute (MDPI)
• Subject: Accuracy / Algorithms / Ammonium / Antibacterial activity / Artificial intelligence / Bacillus cereus / Datasets / E coli / Effectiveness / Escherichia coli / Flavonoids / Genetic diversity / Genetic markers / Genetic variance / Genome-wide association studies / Genomes / Germplasm / GWAS analysis / Laurus nobilis / Learning algorithms / Machine learning / Minimum inhibitory concentration / Pathogens / plant-derived bioactive compounds / Population genetics / Population structure / Predictions / Proteins / Provinces / Sustainable use / Variance