Statistical Methods for Genetic Epidemiology

描述(申请人提供)：连锁分析和经典联想 对病例对照数据的分析发现了许多疾病的易感性 基因，而这些发现将带来重要的公共健康利益。 然而，最近的证据表明，使用这些药物有很大的障碍 这些方法为进一步发现基因奠定了基础。也有相当大的 目前关于表征生物多样性影响的最优设计的不确定性 疾病易感基因。这项研究的目标是开发新的和 改进了在复杂情况下识别和表征这些基因的方法， 并根据个人的疾病风险对其进行分类。要完成 这些目标，调查人员将在十多年前的基础上再接再厉 工作。具体地说，1988年，国家癌症研究所(NCI)授予 杰出调查员补助金(OIG)授予首席调查员 新的和改进的统计方法的开发和应用 流行病学研究。1995年，这项拨款被续期，直到NCI终止 2001年的OIG计划。本申请要求继续提供资金 这项统计研究。它的目标是开发更好的设计方法 并分析了关于遗传易感性和生活方式特征的研究 特定部位癌症的家族聚集的贡献者，特别是 卵巢癌、乳腺癌和前列腺癌。具体目标有两个。一 是在四个问题领域开发和评估改进的方法：a) 估计已识别基因突变的外显率及其修改 非遗传因素的外显性；b)评估家庭中的遗传联系 数据；c)在存在遗传异质性的情况下识别基因；和d) 估计遗传混合体的个体指数。第二个具体目标是 通过将这些方法应用于流行病学研究的数据来验证这些方法。 这些数据包括来自乳房合作家庭登记处的现有数据 癌症研究(CFRBCS)，来自卵巢癌家族登记的新数据 (FROC)，以及三项前列腺癌研究的现有数据。一大推力 的工作将是开发用户友好的软件，以允许 流行病学家将这些方法应用于他们的数据。

DESCRIPTION (provided by applicant): Linkage analysis and classical association analysis of case-control data have identified many disease susceptibility genes, and these discoveries will lead to important public health benefits. However, recent evidence suggests that there are substantial barriers to using these methods for further gene discovery. There also is considerable uncertainty at present about optimal designs for characterizing the effects of disease susceptibility genes. The goals of this research are to develop new and improved ways to identify and characterize such genes in complex situations, and to classify individuals with respect to their disease risk. To accomplish these goals, the investigators will build on more than ten years of previous work. Specifically, in 1988 the National Cancer Institute (NCI) awarded an Outstanding Investigator Grant (OIG) to the Principal Investigator for the development and application of new and improved statistical methods for use in epidemiological research. In 1995 this grant was renewed until NCI terminated the OIG Program in 2001. The present application requests funding to continue this statistical research. Its objectives are to develop better ways to design and analyze studies of genetic predisposition and lifestyle characteristics as contributors to familial aggregation of site-specific cancers, particularly cancers of the ovary, breast and prostate. The specific aims are twofold. One is to develop and evaluate improved methods in four problem areas: a) estimating penetrance of mutations of identified genes and modification of such penetrance by nongenetic factors; b) assessing genetic association in family data; c) identifying genes in the presence of genetic heterogeneity; and d) estimating individual indices of genetic admixture. A second specific aim is to validate these methods by application to data from epidemiological studies. These include existing data from the Cooperative Family Registry for Breast Cancer Studies (CFRBCS), new data from the Familial Registry for Ovarian Cancer (FROC), and existing data from three studies of prostate cancer. A major thrust of the work will be the development of user-friendly software to allow epidemiologists to apply the methods to their data.