From Genotype to Phenotype in Antimicrobial Resistance: Genomic Representation, Population Generalisation, and Incremental Value of Gene Expression

Whole-genome sequencing (WGS) enables genomic prediction of antimicrobial resistance (AMR) by linking bacterial genetic features to resistance phenotypes. Although genomic prediction can complement standard antimicrobial susceptibility testing, its performance depends on how genomic information is represented and how models are evaluated. Resistance-associated genes and genomic variants help with accurate prediction. Prediction performance can change due to several factors, including genomic representation, genetic background, population structure, sampling, and validation strategy. These limitations raise the question of whether gene expression provides predictive information beyond that recoverable from genomic sequence. This review explores the genome-to-phenotype antimicrobial resistance prediction issue through three primary aspects. The review examines three primary aspects: genomic representation (how genetic information is structured), population generalisation (how findings apply across groups), and biological state. There are diverse ways of encoding the same sequence information that can change predictions. Determinant-based, pangenome-wide, biologically constrained, and interaction-aware methods show this clearly. Lineage-held-out and external validation further show that within-population performance does not establish transferability. These findings indicate that poor prediction does not, by itself, establish that genomic information is insufficient. Gene expression provides a more direct test of this distinction. In Pseudomonas aeruginosa, transcriptomic profiles can predict AMR. Genomic sequences also help predict transcriptional changes. These observations do not establish whether measured gene expression provides predictive information beyond that recoverable from genomic sequence. A direct comparison of genome-only, genome-plus-genome-predicted-expression, and genome-plus-measured-expression models under matched population-aware and independent-cohort validation would help distinguish information recoverable from sequence from information contributed by measured biological state. This framework would help identify where predictive information is lost along the genome-to-phenotype pathway and determine whether measured expression contributes information beyond that recoverable from genomic sequence. The findings could inform the evaluation of genomic AMR prediction and surveillance, including in African populations.