Markov chain Monte Carlo and expectation maximization approaches for estimation of haplotype frequencies for multiply infected human blood samples.
Markov chain Monte Carlo and expectation maximization approaches for estimation of haplotype frequencies for multiply infected human blood samples.
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DOI：
10.1186/s12936-016-1473-5
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发表时间：
2016-08-25
期刊：
影响因子：
3
通讯作者：
Hastings IM
中科院分区：
文献类型：
--
作者：
Ken-Dror G;Hastings IM
单倍型在抗疟疾药物抗性中是重要的，因为编码药物抗性的基因可能在同一基因中的几个密码子处积累突变，每个突变增加药物抗性的水平，并且可能降低先前突变的代谢成本。患者的血液样本中通常有两种或更多种单倍型，这可能使得不可能准确地识别他们携带的单倍型，从而无法测量疟疾人群中耐药单倍型的类型和频率。本研究提出两种新的统计方法，期望最大化（EM）和马尔可夫链蒙特卡罗（MCMC）算法来研究这个问题。该算法的性能进行评估的模拟数据集组成的患者血液的特点是他们的感染复数（MOI）和疟疾基因型。使用每个单核苷酸多态性（SNP）处的不同抗性等位基因频率（RAF）以及SNP和MOI的不同检测限（LoD）生成数据集。EM和MCMC算法经过验证，与之前的相关统计方法相比，更准确、更快速，受SNP LoD和MOI的影响略小。EM和MCMC算法在分析从受感染的人类血液样本获得的疟疾遗传数据时表现良好。结果对LoD引起的基因分型错误具有稳健性，即使在缺乏个体患者MOI数据的情况下也能很好地发挥作用。本文的在线版本（doi：10.1186/s12936-016-1473-5）包含补充材料，可供授权用户使用。
Haplotypes are important in anti-malarial drug resistance because genes encoding drug resistance may accumulate mutations at several codons in the same gene, each mutation increasing the level of drug resistance and, possibly, reducing the metabolic costs of previous mutation. Patients often have two or more haplotypes in their blood sample which may make it impossible to identify exactly which haplotypes they carry, and hence to measure the type and frequency of resistant haplotypes in the malaria population. This study presents two novel statistical methods expectation–maximization (EM) and Markov chain Monte Carlo (MCMC) algorithms to investigate this issue. The performance of the algorithms is evaluated on simulated datasets consisting of patient blood characterized by their multiplicity of infection (MOI) and malaria genotype. The datasets are generated using different resistance allele frequencies (RAF) at each single nucleotide polymorphisms (SNPs) and different limit of detection (LoD) of the SNPs and the MOI. The EM and the MCMC algorithm are validated and appear more accurate, faster and slightly less affected by LoD of the SNPs and the MOI compared to previous related statistical approaches. The EM and the MCMC algorithms perform well when analysing malaria genetic data obtained from infected human blood samples. The results are robust to genotyping errors caused by LoDs and function well even in the absence of MOI data on individual patients. The online version of this article (doi:10.1186/s12936-016-1473-5) contains supplementary material, which is available to authorized users.
影响因子：
2.9
作者：
Adkins RM
通讯作者：
Adkins RM