GENETIC EPIDEMIOLOGY MODELS AND COMPUTER SOFTWARE

遗传流行病学模型和计算机软件

• 批准号: 6143882
• 负责人: George Bonney
• 金额: $ 51.89万
• 依托单位: HOWARD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 1998
• 资助国家: 美国
• 起止时间: 1998-09-30 至 2001-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6143882
• 关键词: computer data analysis; computer program /software; disease /disorder onset; epidemiology; family genetics; gene expression; genetic disorder; genetic markers; genetic models; genetic susceptibility; human data; linkage mapping; mathematical model; model design /development

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.

描述（改编自研究者摘要）：目的 这个资助申请是为了继续开发一个分析框架， 家庭数据分析的回归模型， 将这种新的生物统计学方法转移到遗传学上， 流行病学通过软件的发展：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。这种家庭研究包括评估 考虑到特定生物学因素的衡量风险因素的作用 关系，分布和家庭关系的确定 发病年龄隔离和连锁的相关性，以确定 基因可能参与该疾病的病因。期望的 统计框架包括依赖性二元和生存结局 回归变量，但范围应包括建模 不依赖或与家族遗传基因无关的依赖 根据遗传学的规律。 分析框架是回归模型， 通过指定人的 表型和一组解释变量，包括他的基因型， 关于特定基因座、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， 它同时提供了重要的 基因和那些由复杂模式的残余家族性 相关性，包括兄弟姐妹，配偶配偶和父母后代 表型相关性，没有或参考明确的遗传或 环境因果机制。知识机制由以下方面组成： 选择合适的参数化。 拟议的发展包括广泛适用的分布和 分类和回归树（CART）方法筛选大型 标记物数量和其他风险因素。它们被改造成 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 发病年龄的相关性;分离和连锁分析。 这些方法将在软件包G.E.M.S.中实施，这是 设计为用户友好型，可跨通用计算环境进行移植 平台