Genotype to Phenotype Prediction
One-Sentence Definition
Genotype-to-phenotype prediction infers observable microbial traits — resistance, virulence, host range, metabolism — from genome sequence.
Simple Explanation
Read the genome, predict the behaviour. It works well for some traits and poorly for others, and knowing which is which is the skill.
Detailed Scientific Explanation
Two strategies:
- Rules/catalogue-based — curated determinant → phenotype mapping; transparent, auditable, limited to known mechanisms (AMR Gene Databases)
- Statistical/ML — learn from labeled genome–phenotype pairs; can capture epistasis and unknown determinants, but risks confounding by lineage (Machine Learning Basics for Microbiology)
Where prediction is strong:
- Mycobacterium tuberculosis resistance to several first-line drugs (mechanisms few and well catalogued)
- Acquired resistance genes with clean phenotype links (e.g., mecA → methicillin resistance in Staphylococcus aureus)
- Species and serotype/serovar assignment
Where prediction is weak:
- Expression-dependent mechanisms — efflux upregulation, porin loss, inducible AmpC (Microbial Transcriptomics)
- Borderline MICs near breakpoints; heteroresistance; tolerance/persistence
- Traits shaped by host and environment (biofilm behaviour in vivo, virulence severity)
Additional layers: metabolic phenotype prediction via genome-scale metabolic models (flux balance analysis), growth requirements, and phage susceptibility for phage therapy matching.
Mechanism
Map determinants (genes, alleles, SNPs, copy number, truncations) to phenotype either by curated rule or learned function, ideally with confidence and a “no call” option.
Clinical Importance
- Genotype-first reporting only works where evidence supports high negative predictive value; otherwise phenotypic testing remains mandatory
Research Importance
- Systematic discovery of missing determinants when prediction fails
Diagnostic Relevance
- Basis for sequencing-based susceptibility reporting policies
AMR Relevance
Core aim of clinical microbial genomics; performance judged with very-major-error rules (Model Evaluation in Clinical Microbiology).
Related MOCs
- MOC - Bioinformatics in Microbiology · MOC - AI in Microbiology · MOC - Antimicrobial Resistance (AMR)
Active Recall Questions
- Name two traits predicted well and two predicted poorly from genome sequence.
- Rules-based vs ML prediction — trade-offs?
- Why is heteroresistance a problem for genotype-based reporting?