"Statistical Methods for Genetic Epidemiology"

描述（由申请人提供）：对于患有和没有疾病的个体的大样本的高维单核苷酸（SNP）数据的可用性为遗传流行病学家提供了前所未有的机会，但也面临许多统计挑战。我们实现这些机会的能力取决于发展和应用健全和创新的统计方法。特别需要的领域包括处理多重假设检验问题的方法（多重比较问题），以及合并来自多种来源的数据以实现更清晰推论的方法（数据综合问题）。这项研究的目标是开发新的或改进的方法来解决这些问题，并将其应用于乳腺癌，卵巢癌和前列腺癌的数据。为了实现这些目标，研究人员将在过去15年多的工作基础上再接再厉。具体来说，1988年，国家癌症研究所（NCI）向主要研究者颁发了杰出研究者补助金（OIG），用于开发和应用流行病学研究中使用的新的和改进的统计方法。1995年，该补助金被延长，直到NCI于2001年终止该方案。该研究由R 01 CA 94069资助，从2002年1月到2006年12月。这一相互竞争的延期申请要求提供资金，以继续开展这项工作，并将其应用于新出现的重要领域。具体目标有四个方面：1）评价病例对照全基因组关联（GWA）研究中控制混杂和尾计数变异的新方法和现有方法; 2）评价与已确定疾病相关性的基因中未知意义的错义突变相关的风险; 3）结合联合收割机多个独立数据来源评估基因组的联合致癌效应;和4）整合关于肿瘤和患者特征的数据以改进表型特异性病因学的评估。我们将通过模拟评估新方法，并将其应用于乳腺癌，卵巢癌和前列腺癌的现有数据，以及GWA乳腺癌和前列腺癌研究的新兴数据。现有数据由研究者和该项目的顾问收集，或作为研究者参与的三个合作的一部分：乳腺癌家族登记（Breast-CFR），卵巢癌协会联盟（OCAC）和国际前列腺癌遗传学联盟（ICPCG）。总而言之，我们需要健全、可靠的方法来分析来自患有和没有特定类型癌症的个体的大量新兴遗传数据。这项研究的目标是开发这样的方法，从而帮助产生对癌症预防和治疗有用的新知识。

DESCRIPTION (provided by applicant): The availability of high-dimensional single-nucleotide (SNP) data for large samples of individuals with and without disease presents unprecedented opportunities for genetic epidemiologists, but also many statistical challenges. Our ability to bring these opportunities to fruition depends on the development and application of sound and innovative statistical methods. Areas of particularly great need include methods for dealing with multiple hypothesis-testing problems (multiple comparison issues), and methods for combining data from multiple sources to achieve sharper inferences (data synthesis issues). The goals of this research are to develop new or improved methods for addressing these issues, and to apply them to data on cancers of the breast, ovary and prostate. To accomplish these goals, the investigators will build on more than 15 years of previous work. Specifically, in 1988 the National Cancer Institute (NCI) awarded an Outstanding Investigator Grant (OIG) to the Principle 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 program in 2001. The research was then funded by R01 CA94069 for the period January 2002 to December 2006. This competing renewal application requests funds to continue this work and to apply it to new areas of emerging importance. The specific aims are fourfold: 1) to evaluate new and existing methods for controlling confounding and tail count variability in case-control genome-wide association (GWA) studies; 2) to evaluate risks associated with missense mutations of unknown significance in genes of established disease relevance; 3) to combine multiple independent sources of data in assessing the joint carcinogenic effects of groups of genes; and 4) to integrate data on tumor and patient characteristics for improved assessment of phenotype-specific etiology. We will evaluate the new methods by simulations, and we will illustrate them by application to existing data on cancers of the breast, ovary and prostate, and emerging data from GWA studies of breast and prostate cancer. The existing data have been collected either by the investigators and the consultants to this project, or as part of three collaborations in which the investigators participate: the Breast Cancer Family Registry (Breast-CFR), the Ovarian Cancer Association Consortium (OCAC), and the International Consortium for Prostate Cancer Genetics (ICPCG). In summary, we need sound, reliable methods for analyzing the vast amounts of emerging genetic data from individuals with and without a given type of cancer. The goals of this research are to develop such methods, and thereby to help generate new knowledge useful for cancer prevention and treatment.