Statistical Methods for Genetic Epidemiology

描述（申请人提供）：连锁分析和经典关联 对病例对照数据的分析已经确定了许多疾病的易感性 基因，这些发现将带来重要的公共卫生效益。 然而，最近的证据表明， 这些方法用于进一步的基因发现。也有相当多的 不确定性，目前关于最佳设计的特点的影响， 疾病易感基因这项研究的目的是开发新的， 改进了在复杂情况下鉴定和表征这些基因的方法， 并根据患病风险对个体进行分类。完成 这些目标，调查人员将建立在十多年以前的基础上， 工作1988年，美国国家癌症研究所（NCI） 杰出研究者补助金（OIG），主要研究者为 发展和应用新的和改进的统计方法， 流行病学研究。1995年，这一补助金延长到NCI终止为止 2001年的OIG计划。本申请要求继续提供资金 这一统计研究。它的目标是开发更好的方法来设计 并分析遗传易感性和生活方式特征的研究， 导致位点特异性癌症家族聚集的因素，特别是 卵巢癌乳腺癌和前列腺癌具体目标有两个方面。一 是在四个问题领域开发和评估改进的方法：a） 估计所鉴定基因的突变的突变率和这种突变的修饰， 非遗传因素引起的遗传易感性; B）评估家族中的遗传关联 数据; c）在存在遗传异质性的情况下鉴定基因;以及d） 估计遗传混杂的个体指数。第二个具体目标是 通过应用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腺癌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），以及来自三项前列腺癌研究的现有数据。主推力 的工作将是开发用户友好的软件， 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.