2026/01/22 by Catalina Riquelme-Zamora, Camila Concha-Toro, Héctor Pérez-Muñoz +2 · 1 voice
Agricultural and Biological Sciences · Biochemistry, Genetics and Molecular Biology · #Fermentation and Sensory Analysis #Gene expression and cancer classification #Genetic Mapping and Diversity in Plants and Animals
paper · pdf · doi:10.1080/03610470.2025.2595368
openalex publication_date 2026/01/22 · openalex created_date 2026/01/23 · openalex updated_date 2026/07/22
Saccharomyces cerevisiae is a model organism with key industrial relevance in wine fermentation and other biotechnological processes. Identifying single-nucleotide polymorphisms (SNPs) linked to quantitative traits is essential for understanding the genetic basis of strain performance. Most previous studies have frequently focused on lineage-based comparisons, limiting the resolution of genotype–phenotype associations. To deal with this constraint, we applied regression and machine-learning models to characterise SNPs and genes associated with continuous oenological traits in wine strains. Four phenotypes were evaluated: proliferation efficiency, proliferation rate, lag phase, and area under the growth curve. Genome-wide SNP data were encoded and used to train predictive models based on Ridge regression, Support Vector Machines, K-nearest Neighbours, and Random Forest. Model performance was evaluated through cross-validation, and key SNPs were prioritised using feature-importance metrics. Functional enrichment of the associated genes was performed using curated biological databases to provide a mechanistic context. This integrative approach identified genomic regions consistently linked to fermentation traits and revealed candidate genes involved in stress adaptation and metabolic regulation. The results refine genotype–phenotype associations in wine yeasts and establish a reproducible framework for future experimental validation of functionally relevant variants.