Many antibacterial drugs have multiple mechanisms of resistance, which are often represented simultaneously by a mixture of resistance mutations (some more frequent than others) in a clinical population. This presents a challenge for Genome-Wide Association Studies (GWAS) methods, making it difficult to detect less prevalent resistance mechanisms purely through (weak) statistical associations. Homoplasy, or the occurrence of multiple independent mutations at the same site, is often observed with drug resistance mutations and can be a strong indicator of positive selection. However, traditional GWAS methods, such as those based on allele counting or linear regression, are not designed to take homoplasy into account. In this article, we present a new method, called ECAT (for Evolutionary Cluster-based Association Test), that extends traditional regression-based GWAS methods with the ability to take advantage of homoplasy. This is achieved through a preprocessing step which identifies hypervariable regions in the genome exhibiting statistically significant clusters of distinct evolutionary changes, to which association testing by a linear mixed model (LMM) is applied using GEMMA (a well-established LMM-based GWAS tool). Thus, the approach can be viewed as extending GEMMA from the usual site- or gene-level analysis to focusing on clustered regions of mutations. This approach was evaluated on a large collection of more than 600 clinical isolates of multidrug-resistant (MDR) Mycobacterium tuberculosis from Lima, Peru. We show that ECAT does a better job of detecting known resistance mutations for several antitubercular drugs (including less prevalent mutations with weaker associations), compared with (site- or gene-based) GEMMA, as representative of existing GWAS methods. The power of the multiphase approach in ECAT comes from focusing association testing on the hypervariable regions of the genome, which reduces complexity in the model and increases statistical power.

许多抗菌药物具有多种耐药机制，在临床人群中通常同时表现为耐药突变的混合物（某些突变比其他突变更频繁）。这对全基因组关联研究（GWAS）方法提出了挑战，使得难以仅通过（弱）统计关联来检测较少普遍存在的耐药机制。经常在药物耐药性突变中观察到同质性或在同一位点发生多个独立突变，这可能是阳性选择的有力指标。但是，传统的GWAS方法（例如基于等位基因计数或线性回归的方法）并未考虑同质性。在本文中，我们提出了一种称为ECAT（用于基于进化聚类的关联测试）的新方法，扩展了传统的基于回归的GWAS方法并具有利用同质性的能力。这是通过预处理步骤实现的，该步骤可识别基因组中表现出统计学上显着不同进化变化簇的高变区，并使用GEMMA（成熟的基于LMM的GWAS工具）通过线性混合模型（LMM）进行关联测试。 。因此，该方法可以看作是将GEMMA从通常的位点或基因水平分析扩展到关注突变的聚类区域。对大量超过600种多药耐药（MDR）临床分离株进行了评估 这是通过预处理步骤实现的，该步骤可识别基因组中表现出统计学上显着不同进化变化簇的高变区，并使用GEMMA（成熟的基于LMM的GWAS工具）通过线性混合模型（LMM）进行关联测试。 。因此，该方法可以看作是将GEMMA从通常的位点或基因水平分析扩展到关注突变的聚类区域。对大量超过600种多药耐药（MDR）临床分离株进行了评估 这是通过预处理步骤实现的，该步骤可识别基因组中表现出统计学上显着不同进化变化簇的高变区，并使用GEMMA（成熟的基于LMM的GWAS工具）通过线性混合模型（LMM）进行关联测试。 。因此，该方法可以看作是将GEMMA从通常的位点或基因水平分析扩展到关注突变的聚类区域。对大量超过600种多药耐药（MDR）临床分离株进行了评估 该方法可以看作是将GEMMA从通常的位点或基因水平分析扩展到关注突变的聚类区域。对大量超过600种多药耐药（MDR）临床分离株进行了评估 该方法可以看作是将GEMMA从通常的位点或基因水平分析扩展到关注突变的聚类区域。对大量超过600种多药耐药（MDR）临床分离株进行了评估秘鲁利马的结核分枝杆菌。我们证明，与（基于位点或基因的）GEMMA相比，ECAT在检测几种抗结核药物的已知抗药性突变方面表现更好（包括关联性较弱的较少普遍性突变），可作为现有GWAS方法的代表。ECAT中多阶段方法的强大之处在于将关联测试集中在基因组的高变区上，从而降低了模型的复杂性并提高了统计能力。