Estimating Cancer Risks of Rare Genetic Variants

描述（由申请人提供）：现在已经确定许多基因影响癌症的风险。对于已知影响风险的主要基因，一项重要的任务是确定个体变异所赋予的风险。遗传学家认为，如果变异被证明与家族中的疾病分离，那么变异就会带来风险，但越来越多的证据将来自基于人群的关联研究，在这些研究中，经验证据是根据所有观察到的变异的病例和对照频率获得的，其中许多变异在研究中必然很少发生，也许只有一次。此外，许多这些变异在以前易患癌症的家族中是观察不到的。分层建模提供了一种自然的策略，可以利用这些具有稀疏数据的罕见变体的集体证据。当变体能够在与风险预测相关的表征变体的功能特性的更高水平协变量的基础上有效地分组时，这可以实现。在本应用中，我们建议详细研究用于此目的的可用分层建模技术的特性，以及对这些技术的适当修改，以期建立有效的分析策略，以获得罕见变异的相对风险估计。我们将使用模拟来评估来自分层模型的罕见变体相对风险的伪似然估计的小样本特性。模拟将处理单个估计器的偏差和覆盖年龄概率，它们与普通逻辑回归相比的相对效率，较高水平协变量的预测性的影响，模型错误规范的影响，样本量的影响，缺失数据对较高水平协变量的影响，以及使用已解释的变化作为衡量较高水平协变量解释风险变化程度的措施。我们还将研究各种假设下伪似然估计的渐近性质：正确指定的层次模型；不正确指定的层次模型；在这个设定中，变量的数量可以无限增加，但关于单个变量的数据仍然很少。这些研究解决了在设计和分析主要癌症基因的关联（病例对照）研究中具有实际重要性的不同问题。公共卫生相关性：许多主要基因已被确定强烈影响癌症的风险。然而，基因中通常有许多不同的突变，每种突变都可能或可能不会增加风险。确定哪些基因突变是有害的，哪些是无害的是至关重要的，这样那些从基因检测中得知自己有突变的人就可以得到适当的咨询。这是一项具有挑战性的任务，因为新的突变不断被发现，而关于每个突变的证据通常相对较少。在这个提议中，我们计划研究新的统计技术，这些技术有可能以更高的准确性识别有害的突变。这项研究将涉及分层统计模型，这是一种汇集大量罕见突变证据的技术，以提高预测每种突变单独影响的能力。

DESCRIPTION (provided by applicant): It is now well established that many genes influence the risk of cancer. For major genes known to affect risk, an important task is to determine the risks conferred by individual variants. Geneticists consider variants to confer risk if they have been shown to segregate with disease in families, but increasingly the evidence will accrue from population-based association studies, where empirical evidence is obtained on the basis of case and control frequencies for all observed variants, many of which will necessarily occur very infrequently, perhaps only once, in the study. Furthermore, many of these variants will not have been observed in previous cancer-prone families. Hierarchical modeling offers a natural strategy to leverage the collective evidence from these rare variants with sparse data. This can be accomplished when the variants can be effectively grouped on the basis of higher- level covariates that characterize the functional properties of the variants that are relevant to risk prediction. In this application we propose to study in detail the properties of available hierarchical modeling techniques for this purpose, and suitable modifications of these techniques, with a view to establishing valid analytic strategies for obtaining relative risk estimates for rare variants. We will use simulations to evaluate the small sample properties of pseudo-likelihood estimation of the relative risks of rare variants from a hierarchical model. The simulations will address bias and cover- age probabilities of the individual estimators, their relative efficiency compared to ordinary logistic regression, the influence of the predictiveness of the higher-level covariates, the impact of model misspecification, the influence of sample size, the impact of missing data on higher-level covariates, and the use of explained variation as a measure of extent to which the higher-level covariates explain the risk variation. We will also examine the asymptotic properties of pseudo-likelihood estimation under various assumptions: a correctly specified hierarchical model; an incorrectly specified hierarchical model; and a setting in which the number of variants is allowed to increase indefinitely, but data on the individual variants remains sparse. These investigations address distinct questions of practical importance in the design and analysis of association (case-control) studies of major cancer genes. PUBLIC HEALTH RELEVANCE: Many major genes have been identified that strongly in0uence the risk of cancer. However, there are typically many different mutations in the gene, each of which may or may not confer increased risk. It is critical to identify which genetic mutations are harmful, and which ones are harmless, so that individuals who learn from genetic testing that they have a mutation can be appropriately counseled. This is a challenging task, since new mutations are continually being identified, and there is typically relatively little evidence available about each individual mutation. In this proposal we plan to examine new statistical techniques that have the potential to identify the mutations that are harmful with much greater accuracy. The research will involve hierarchical statistical modeling, a technique that aggregates the evidence about lots of rare mutations to increase the ability to predict the effects of each mutation individually.