STATISTICAL ANALYSIS OF FAMILY DATA IN CANCER RESEARCH

这项赠款申请的目的是开发一个全面的 分析框架，家庭数据分析的回归模型， 并促进将这一新的生物统计学方法转移到 癌症遗传流行病学。 许多癌症的遗传流行病学研究都是以家族为基础的。 两代或更多代的。这类家庭研究包括评估 测量的危险因素在考虑特定生物因素时的作用 关系、分布和家族关系的确定 发病年龄、分离连锁和多效性分析中的相关性 目的：确定主效基因可能参与本病的发病机制。 癌症(S)，以及一些模型适用性的确定 致癌的可能性。所需的统计框架包含依赖项 带有回归变量的二元和生存结果，但范围 应包括在不考虑或参考主要因素的情况下对依赖关系进行建模 根据遗传学规律在家族中传递的基因。 分析框架是考虑家庭因素的回归模型 通过指定一个人的 表型和一组解释变量，包括他的基因和 尊重特定的基因座，年长亲属的表型，以及 环境和生活方式的协变量。这种方法的吸引力在于 它同时规定了由重要的 基因(S)和复杂残存家族模式的基因 相互关系，包括兄弟姐妹、配偶-配偶和父母-子女 表型相关性，没有或参照显性遗传或 环境因果机制。已知的致癌机制是 通过选择适当的参数化进行合并。 在具体目标一中，模型的提法扩大到包括 广泛适用的分布和推论方法。然后这些就是 在具体目标2中作了调整，以完成以下工作： 癌症家族聚集性及其危险因素的建模与分析； 发病年龄的分布和家族相关性； 偏析分析； 连锁性和多效性分析。 为便利技术转让(具体目标3)，适当 将开发软件并组织培训讲习班。

The purpose of this grant application is to develop a comprehensive analytic framework, the regressive models for the analysis of family data, and to facilitate the transfer of this new biostatistical methodology to cancer genetic epidemiology. Many studies on the genetic epidemiology of cancer are based on families of two or more generations. Such family studies include the assessment of the role of measured risk factors taking into account specific biologic relationships, the determination of the distribution and familial correlations in age-of-onset, segregation linkage and pleiotropy analyses to determine the possible involvement of major genes in the etiology of the cancer(s), and the determination of the applicability of some models of carcinogenesis. The desired statistical framework encompasses dependent binary and survival outcomes with regression variables, but the scope should include modeling the dependence without or with reference to major genes transmitted in families according to the laws of genetics. The analytic framework is the regressive models which account for familial correlations by specifying a regression relationship between a person's phenotype and a set of explanatory variables including his genotype with respect to specific loci, the phenotypes of older relatives, and environmental and life-style covariates. The appeal of this method is that it simultaneously provides for the effects resulting from important gene(s) and those resulting from complex patterns of residual familial correlations, including sib-sib, spouse-spouse, and parent-offspring phenotypic correlations, without or with reference to explicit genetic or environmental causal mechanisms. Known mechanisms of carcinogenesis are incorporated by choosing suitable parametrization. In specific aim I, the formulation of the models is extended to include broadly applicable distributions and methods of inference. These are then adapted in specific aim 2 to do the following: Modeling and analysis of familial aggregation of cancer and risk factors; Distribution and familial correlations in age-of-onset; Segregation analysis; Linkage and pleiotropy analysis. To facilitate the technology transfer (specific aim 3), appropriate software will be developed and training workshops will be organized.