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风险。对于已知影响风险的主要基因，一项重要任务是确定单个变异所带来的风险。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，其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，其中许多变异必然会非常罕见，也许只有一次，在研究中。此外，这些变异中的许多变异在以前的癌症易感家族中没有观察到。分层建模提供了一种自然的策略来利用来自这些具有稀疏数据的罕见变体的集体证据。当可以基于表征与风险预测相关的变体的功能特性的更高水平的协变量对变体进行有效分组时，可以实现这一点。在本申请中，我们建议详细研究可用的分层建模技术的属性，并对这些技术进行适当的修改，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异的相对风险的伪似然估计的小样本性质。模拟将讨论个体估计量的偏倚和覆盖概率、与普通逻辑回归相比的相对效率、较高水平协变量预测性的影响、模型误设的影响、样本量的影响、缺失数据对较高水平协变量的影响，以及使用解释的变异作为更高水平协变量解释风险变异的程度的度量。我们还将在各种假设下研究伪似然估计的渐近性质：一个正确指定的分层模型;一个错误指定的分层模型;以及一个允许变量数量无限增加但个体变量数据仍然稀疏的设置。这些调查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，每一种突变可能会或可能不会增加风险。关键是要确定哪些基因突变是有害的，哪些是无害的，以便从基因检测中得知他们有突变的人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识别，并且通常关于每个个体突变的证据相对较少。在这项提案中，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。该研究将涉及分层统计建模，这是一种汇总有关大量罕见突变的证据的技术，以提高单独预测每个突变影响的能力。

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.