A phylogenetic method to perform genome-wide association studies in microbes that accounts for population structure and recombination.
A phylogenetic method to perform genome-wide association studies in microbes that accounts for population structure and recombination.
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DOI：
10.1371/journal.pcbi.1005958
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发表时间：
2018-03
影响因子：
4.3
通讯作者：
Didelot X
中科院分区：
文献类型：
--
作者：
Collins C;Didelot X
微生物的全基因组关联研究（GWAS）有可能极大地改善我们理解、管理和治疗传染病的方式。然而，迄今为止建立的微生物GWAS方法仍然不足以利用日益丰富的细菌和病毒基因序列数据。面对克隆群体结构和同源重组，现有的GWAS方法很难达到拒绝虚假发现所需的精度和检测微生物关联所需的能力。在本文中，我们介绍了一种新的系统发育的方法，已量身定制的微生物GWAS，这是适用于生物体范围从纯克隆频繁重组，二元和连续的表型。我们的方法是强大的人口结构和重组的混杂效应，同时保持高的统计能力来检测协会。通过应用到模拟数据的彻底测试为我们的方法的能力和特异性提供了强有力的支持，并展示了替代基于聚类和降维方法的优势。脑膜炎奈瑟氏球菌的两个应用说明了我们方法的多功能性和潜力，证实了先前鉴定的青霉素耐药位点，并导致鉴定了侵袭性疾病的良好特征和新型驱动因素。我们的方法是作为一个名为treeWAS的开源R包实现的，它可以在https://github.com/caitiecollins/treeWAS上免费获得。可测量的差异通常存在于微生物种群中，具有重要的生态或流行病学后果。例子包括生长速率，宿主范围，传播性，抗菌药物耐药性，毒力等的差异。了解这些表型特性中涉及的遗传因素是微生物基因组学的一个重要目标。这样做的一个基本方法是进行全基因组关联研究（GWAS），其中比较基因组以搜索与感兴趣的属性系统相关的遗传标记。如果这种策略在微生物中天真地实施，由于群体结构和重组的混杂效应，它可能导致虚假的结果。在这里，我们提出了treeWAS，一种新的系统发育方法来执行微生物GWAS，避免这些陷阱。我们使用模拟数据集表明，treeWAS能够区分真正与感兴趣的属性相关的遗传标记和那些不相关的遗传标记。此外，我们证明了treeWAS在灵敏度和特异性方面优于其他基于聚类和降维技术。我们还展示了treeWAS在两个应用程序中的真实的数据集从N。脑膜炎我们已经在R环境中开发了一个易于使用的treeWAS实现，这对微生物基因组学的广泛研究人员来说应该是有用的。
Genome-Wide Association Studies (GWAS) in microbial organisms have the potential to vastly improve the way we understand, manage, and treat infectious diseases. Yet, microbial GWAS methods established thus far remain insufficiently able to capitalise on the growing wealth of bacterial and viral genetic sequence data. Facing clonal population structure and homologous recombination, existing GWAS methods struggle to achieve both the precision necessary to reject spurious findings and the power required to detect associations in microbes. In this paper, we introduce a novel phylogenetic approach that has been tailor-made for microbial GWAS, which is applicable to organisms ranging from purely clonal to frequently recombining, and to both binary and continuous phenotypes. Our approach is robust to the confounding effects of both population structure and recombination, while maintaining high statistical power to detect associations. Thorough testing via application to simulated data provides strong support for the power and specificity of our approach and demonstrates the advantages offered over alternative cluster-based and dimension-reduction methods. Two applications to Neisseria meningitidis illustrate the versatility and potential of our method, confirming previously-identified penicillin resistance loci and resulting in the identification of both well-characterised and novel drivers of invasive disease. Our method is implemented as an open-source R package called treeWAS which is freely available at https://github.com/caitiecollins/treeWAS. Measurable differences often exist within a microbial population, with important ecological or epidemiological consequences. Examples include differences in growth rates, host range, transmissibility, antimicrobial resistance, virulence, etc. Understanding the genetic factors involved in these phenotypic properties is a crucial aim in microbial genomics. A fundamental approach for doing so is to perform a Genome-Wide Association Study (GWAS), where genomes are compared to search for genetic markers systematically correlated with the property of interest. If this strategy were implemented naively in microbes, it could lead to spurious results due to the confounding effects of population structure and recombination. Here we present treeWAS, a new phylogenetic method to perform microbial GWAS that avoids these pitfalls. We show, using simulated datasets, that treeWAS is able to distinguish between genetic markers that are truly associated with the property of interest and those that are not. Furthermore, we demonstrate that treeWAS offers advantages in both sensitivity and specificity over alternative cluster-based and dimension-reduction techniques. We also showcase treeWAS in two applications to real datasets from N. meningitidis. We have developed an easy-to-use implementation of treeWAS in the R environment, which should be useful to a wide range of researchers in microbial genomics.
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纳达（Nada），奈瑟氏菌脑膜炎的新型疫苗候选者。
影响因子：
15.3
作者：
Comanducci, Maurizio;Bambini, Stefania;Brunelli, Brunella;Adu-Bobie, Jeannette;Arico, Beatrice;Capecchi, Barbara;Giuliani, Marzia Monica;Masignani, Vega;Santini, Laura;Savino, Silvana;Granoff, Dan M;Caugant, Dominique A;Pizza, Mariagrazia;Rappuoli, Rino;Mora, Marirosa
通讯作者：
Mora, Marirosa
影响因子：
3.3
作者：
Ansari MA;Didelot X
通讯作者：
Didelot X
影响因子：
4.5
作者：
Chewapreecha C;Marttinen P;Croucher NJ;Salter SJ;Harris SR;Mather AE;Hanage WP;Goldblatt D;Nosten FH;Turner C;Turner P;Bentley SD;Parkhill J
通讯作者：
Parkhill J
DOI：
10.1093/bioinformatics/bts396
发表时间：
2012-09-15
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Cohen O;Ashkenazy H;Burstein D;Pupko T
通讯作者：
Pupko T
影响因子：
3.3
作者：
Didelot, Xavier;Falush, Daniel
通讯作者：
Falush, Daniel