Mutation Profile Analysis of HIV Genetic Heterogeneity

描述(由申请人提供)：临床试验证明 联合治疗减少艾滋病毒病毒数量(病毒载量)的能力 在血浆中。具有讽刺意味的是，使用这种疗法可能会促进 病毒耐药基因型别，患者几乎没有选择 后续治疗。可选择的、基于基因类型的策略，利用 HIV基因变异与某些表型的关系 正在研究病毒药物敏感性等治疗反应 指导治疗选择。这种关系很难研究，部分原因是 关于艾滋病毒的高维度、准物种性质以及 其他经常嵌入其中的关系，如果被忽视，可能会导致 错误的解释和处理。例如，因为一种已知的病毒 负载(或其他协变量)对观察到的HIV基因分型和潜在的影响 对于病毒载量影响所考虑的表型，它的关系 艾滋病毒携带者的基因异质性可能会被病毒载量混淆 表型间的异质性。需要检验的主要假设是 除了艾滋病毒基因以外的异质性来源可以在 表型群及其对遗传关联的贡献可以是 估计。提出了从其他基因推断HIV基因的新方法 与表型相关的异质性来源，基于非参数 使用复合(序列对)度量的(无分布)方法 遗传距离。与其他方法相比，建议的方法是 更稳健，因为经验分布，而不是分析建模的分布 进行了比较和协变量调整。这些方法的开发将基于 轮廓分析-类型设置，其中一系列相关的假设 考察了利益关系的构建。具体目标是： (1)构建模拟准物种的测度论框架 使用无分布方法与协变量相关联的异质性， 包括基于距离的方法；(2)开发无需分发的方法 确定改变HIV基因之间关系的协变量 异质性和表型，并调整这种关系 协变量；(3)扩展目标1和目标2中的方法，以解决 HIV基因区域和时间趋势的多变量分析 与表型相关的异质性，在存在 依赖于这些连续体；(4)表征HIV基因的异质性 通过开发翻译复合体的方法与表型相关联 遗传距离量度地区、时间和地点效应；(5) 评估目标1-3中开发的方法对以下类型的敏感性 表型群体的遗传组成；(6) 将AIMS 1-4中的方法电脑化为免费的、可上网的软件； 以及(7)向临床医生/病毒学家传达统计测试 将它们的意义转化为遗传背景的范例。

DESCRIPTION (provided by applicant): Clinical trials have demonstrated the ability of combination therapy to curtail the amount of HIV virus (viral load) in plasma. Ironically, the use of such therapy may promote proliferation of viral drug resistant genotypes, leaving patients with few options for subsequent treatment. Alternative, genotypic-based strategies that make use of the relationship between HIV genetic alterations with some phenotypic treatment response like viral drug susceptibility are being researched to guide treatment choice. This relationship is difficult to study, due in part to its high-dimension, the quasi-species nature of HIV, and the presence of other relationships often imbedded in it, which if neglected, may lead to incorrect interpretations and treatment. For example, because of a known viral load (or other covariate) effect on observed HIV genotypes and the potential for viral load to affect the phenotype under consideration, its relationship with HIV genetic heterogeneity is likely to be confounded by viral load heterogeneity among phenotypes. The primary hypothesis to be examined is that sources of heterogeneity other than HIV genetic can be identified between phenotypic groups and their contributions to genetic associations can be estimated. New methods are proposed for extrapolating HIV genetic from other heterogeneity sources associated with phenotype, based on a non-parametric (distribution-free) approach that uses a composite (sequence pair) measure of genetic distance. In comparison to other approaches, the proposed methods are more robust, since empirical, rather than analytically modeled distributions are compared and covariate-adjusted. The methods will be developed based on a profile analysis-type setting in which a series of related hypotheses for examining the relationship of interest are constructed. The specific aims are: (1) to construct a measure-theoretic framework for modeling quasi-species heterogeneity associated with covariates using distribution-free approaches, including distance-based; (2) to develop distribution-free approaches for identifying covariates that alter the relationship between HIV genetic heterogeneity and phenotype and to adjust this relationship for such covariates; (3) to extend the approaches in Aims 1 and 2 to address multivariate analysis of gene regions and temporal trends in HIV genetic heterogeneity associated with phenotype, in the presence of covariates that depend upon these continuums; (4) to characterize HIV genetic heterogeneity associated with phenotype by developing methods that translate composite genetic distance measures into region, time and location effects; (5) to assess the sensitivity of the methods developed in Aims 1-3 to the type of phenotypic populations with respect to their genetic compositions; (6) to computerize the methods in Aims 1-4 into a free, internet-accessible software; and (7) to communicate statistical tests to clinicians/virologists using paradigms that translate their meaning into genetic settings.