结核分枝杆菌耐药相关单碱基突变的计算方法比较

Evalutation of three methods searching resistant-related mutations in mycobacterium tuberculosis genome

摘要:

[目的] 耐药结核分枝杆菌（drug-resistant Mycobacterium tuberculosis）的产生给结核病（tuberculosis）的治疗带来巨大困难。[方法] 使用基于全基因组测序的关联分析探究耐药强相关的单核苷酸多态性（single nucleotide polymorphism，SNP）突变，主要有GEMMA、phyc、plink。为了阐明其中最优的耐药相关SNP计算方法，本研究下载NCBI上已有的1504株结核分枝杆菌数据，并获取它们对于3种常见的一线抗结核治疗药物（isoniazid、rifampicin、ethambutol）的耐药性检验结果。并使用这3种耐药相关SNP计算方法计算与结核分枝杆菌耐药相关的SNP；并评估计算得到的耐药相关SNP在预测耐药表型的敏感性和特异性。[结果] 发现通过phyc可以预测到最多的已知耐药相关SNP和最少的耐药无关SNP，而且phyc预测的耐药相关SNP的敏感性和特异性恒定大于52.49%。[结论] phyc在预测结核分枝杆菌耐药相关SNP中结果最准确，但考虑到运行时间和表型数据的更新，GEMMA和plink的结果也应作为参考。

Abstract:

[Objective] The emergence of durg-resistant Mycobacterium tuberculosis brought a tough challenge to tuberculosis treatment. [Methods] Considering the lack of homologous recombination in Mycobacterium tuberculosis's genome, the mutation strongly related to resistance could be efficiently confirmed by genome-wide association analysis. However, many resistance-related mutations had yet been found by existing methods. To calculate resistant-related mutations, researchers commonly used some methods similar to genome-wide association analysis (GWAS), which mainly included genome-wide efficient mixed model association (GEMMA), phylogenetic convergence test (phyc), plink. To find the better method among the three methods when calculating resistant-related SNPs on non-mobile antibiotic resistance gene, the genomes of 1504 M. tuberculosis strains from Hunan province and National Center of Biotechnology Information was obtained with their phenotypes of three first-line antibiotics (isoniazid, rifampicin, ethambutol). The three methods were performed to calculate the association between phenotype and known or novel SNPs related to resistance, and their sensibility and specificity were evaluated by the resistant-related SNPs got by the three methods. [Results] Phyc was able to search more known resistance-related SNPs with the sensibility and specificity higher than 52.49%.[Conclusion] Phyc is the most accurate in predicting the SNP related to drug resistance of Mycobacterium tuberculosis, but considering the update of running time and phenotypic data, the results of GEMMA and plink should also be used as a reference.