GENETIC EPIDEMIOLOGY MODELS AND COMPUTER SOFTWARE

说明(改编自《调查员摘要》)：目的 这项赠款申请是为了继续开发一个分析框架， 该回归模型用于分析家庭数据，并便于 将这一新的生物统计学方法转移到遗传学 流行病学通过开发的软件：G.E.M.S. 癌症遗传流行病学的许多研究都是基于家族的。 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、隔离和连锁的相关性决定了 基因可能参与了这种疾病的病因学。所需的 统计框架包括相依的二元和生存结果 使用回归变量，但范围应该包括对 没有或与家族遗传的基因有关的依赖 根据遗传学定律。 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 环境和生活方式是相互影响的。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。已知机制通过以下方式合并 选择合适的参数化法。 建议的发展包括广泛适用的分发版本和 大样本筛选的分类回归树(CART)方法 标记物数量和其他危险因素。然后，这些功能被调整为 疾病家族聚集性的建模与分析 和危险因素，包括遗传标记；分布和家族性 发病年龄的相关性；隔离和连锁分析。 这些方法将在软件包G.E.M.S.中实现，该软件包是 设计为用户友好，可跨通用计算进行移植 站台。

DESCRIPTION (Adapted from the Investigator's Abstract): The purpose of this grant application is to continue to develop an analytic framework, the regression models for the analysis of family data, and to facilitate the transfer of this new biostatistical methodology to genetic epidemiology through the development of the software: G.E.M.S. Many studies in 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 and linkage to determine the possible involvement of genes in the etiology of the disease. 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 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 lifestyle co-variates. 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. Know mechanisms are incorporated by choosing suitable parameterization. Proposed developments include broadly applicable distributions and the classifications and regression trees (CART) method for screening large numbers of markers and other risk factors. These are then adapted to do the following: Modeling and analysis of familial aggregation of disease and risk factors including genetic markers; Distribution and familial correlations in age-of-onset; Segregation and linkage analyses. The methods will be implemented in the software package G.E.M.S., which is designed to be user-friendly and portable across common computing platforms.