Antimicrobial resistance is becoming a major threat to public health throughout the world. Researchers are attempting to contrast it by developing both new antibiotics and patient-specific treatments. In the second case, whole-genome sequencing has had a huge impact in two ways: first, it is becoming cheaper and faster to perform whole-genome sequencing, and this makes it competitive with respect to standard phenotypic tests; second, it is possible to statistically associate the phenotypic patterns of resistance to specific mutations in the genome. Therefore, it is now possible to develop catalogues of genomic variants associated with resistance to specific antibiotics, in order to improve prediction of resistance and suggest treatments. It is essential to have robust methods for identifying mutations associated to resistance and continuously updating the available catalogues. This work proposes a general method to study minimal inhibitory concentration (MIC) distributions and to identify clusters of strains showing different levels of resistance to antimicrobials. Once the clusters are identified and strains allocated to each of them, it is possible to perform regression method to identify with high statistical power the mutations associated with resistance. The method is applied to a new 96-well microtiter plate used for testing M. Tuberculosis.

翻译：抗菌药物耐药性正成为全球公共卫生的主要威胁。研究者们正通过开发新型抗生素和患者特异性治疗两种途径来应对这一挑战。在后者中，全基因组测序产生了巨大影响：首先，其成本日益降低且速度不断提升，使其在表型检测方面具有竞争力；其次，可统计地将耐药表型模式与基因组特定突变相关联。因此，目前可建立与特定抗生素耐药性相关的基因组变异目录，以改进耐药性预测并指导治疗决策。建立鉴定耐药相关突变的稳健方法并持续更新现有目录至关重要。本研究提出了一种通用方法，用于分析最低抑制浓度（MIC）分布并识别呈现不同抗微生物药物耐药水平的菌株簇。在确定菌株簇并将菌株分配到各簇后，可运用回归方法高统计功效地鉴定与耐药性相关的突变。该方法已应用于新型96孔微孔板检测结核分枝杆菌。