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COIL: a methodology for evaluating malarial complexity of infection using likelihood from single nucleotide polymorphism data

机译:COIL:一种利用单核苷酸多态性数据的可能性评估感染疟疾复杂性的方法

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Background Complex malaria infections are defined as those containing more than one genetically distinct lineage of Plasmodium parasite. Complexity of infection (COI) is a useful parameter to estimate from patient blood samples because it is associated with clinical outcome, epidemiology and disease transmission rate. This manuscript describes a method for estimating COI using likelihood, called COIL, from a panel of bi-allelic genotyping assays. Methods COIL assumes that distinct parasite lineages in complex infections are unrelated and that genotyped loci do not exhibit significant linkage disequilibrium. Using the population minor allele frequency (MAF) of the genotyped loci, COIL uses the binomial distribution to estimate the likelihood of a COI level given the prevalence of observed monomorphic or polymorphic genotypes within each sample. Results COIL reliably estimates COI up to a level of three or five with at least 24 or 96 unlinked genotyped loci, respectively, as determined by in silico simulation and empirical validation. Evaluation of COI levels greater than five in patient samples may require a very large collection of genotype data, making sequencing a more cost-effective approach for evaluating COI under conditions when disease transmission is extremely high. Performance of the method is positively correlated with the MAF of the genotyped loci. COI estimates from existing SNP genotype datasets create a more detailed portrait of disease than analyses based simply on the number of polymorphic genotypes observed within samples. Conclusions The capacity to reliably estimate COI from a genome-wide panel of SNP genotypes provides a potentially more accurate alternative to methods relying on PCR amplification of a small number of loci for estimating COI. This approach will also increase the number of applications of SNP genotype data, providing additional motivation to employ SNP barcodes for studies of disease epidemiology or control measure efficacy. The COIL program is available for download from GitHub, and users may also upload their SNP genotype data to a web interface for simple and efficient determination of sample COI.
机译:背景复杂的疟疾感染被定义为含有超过一种遗传上不同的疟原虫寄生虫的感染。感染的复杂性(COI)是从患者血液样本中估算的有用参数,因为它与临床结果,流行病学和疾病传播率相关。该手稿描述了一种从双等位基因分型分析方法中使用可能性估计COI的方法,称为COIL。方法COIL假定复杂感染中不同的寄生虫谱系无关,并且基因分型的基因座未显示明显的连锁不平衡。考虑到每个样本中观察到的单态或多态基因型的普遍性,COIL使用基因型基因座的人群次要等位基因频率(MAF),使用二项分布来估计COI水平的可能性。结果通过计算机模拟和经验验证,COIL分别用至少24或96个未连锁的基因型基因座可靠地估计了高达3或5的水平的COI。评估患者样本中COI大于5可能​​需要大量的基因型数据收集,这使得测序成为一种在疾病传播极高的条件下评估COI的更具成本效益的方法。该方法的性能与基因型基因座的MAF呈正相关。现有的SNP基因型数据集的COI估计值比仅基于样本中观察到的多态性基因型的数量进行分析所产生的疾病肖像更为详尽。结论从SNP基因型的全基因组面板可靠地估计COI的能力为依赖PCR扩增少量基因座来估计COI的方法提供了一种可能更准确的替代方法。这种方法还将增加SNP基因型数据的应用数量,从而为采用SNP条码研究疾病流行病学或控制措施的有效性提供了额外的动力。可以从GitHub下载COIL程序,用户也可以将其SNP基因型数据上传到Web界面,以简单有效地确定样本COI。

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