QUANTITATIVE ASSOCIATION METHODS FOR GENE MAPPING

确定常见复杂人类疾病和特征的原因， 受到多种遗传和环境因素的影响， 公共卫生福利，从预防到早期发现， 治疗 然而，目前用于分析的定量方法 复杂的疾病在它们的力量，它们的灵活性， 多种遗传和环境因素， 对偏离有时无法检验的假设的鲁棒性。 的 这项研究计划的总体目标是促进 “复杂人类疾病遗传学研究的未来”， 创新的统计方法和软件，可用于 生物医学研究人员设计和分析以家庭为基础的协会 利用连锁和连锁不平衡的研究。 这项拟议研究计划的四个具体目标包括： 开发必要的量化工具： 1. 发展稳健的半参数统计方法， 用于分析基于家族的遗传关联研究， 解释了许多以家庭为基础的研究的复杂性， 疾病，例如不同类型的性状（例如，二元的，多分支的， 有序、审查的年龄相关和数量性状）、环境 风险因素、基因-基因和基因-环境相互作用，以及 家系成员间的相关性; 2. 开发统计 方法和模拟程序，将用于确定 遗传关联研究的设计; 3.开发便于使用的 执行这些程序的计算机软件; 4. 发展 描述所实施的统计方法的文件， 如何使用开发的软件，以及如何解释结果， 的分析。 这项研究的优势在于， 目前用于鉴定和表征复杂疾病的基因， 它将提供一种更强大和灵活的方法来找到这些 基因的类型。

Determining the causes of common complex human diseases and traits that are influenced by several genetic and environmental factors has immense public health benefits, ranging from prevention to earlier detection and treatment. However, the current quantitative methods used to analyze complex diseases are limited in their power, their flexibility to account for multiple genetic and environmental factors, and their robustness to departures from sometimes untestable assumptions. The overall objectives of this proposed research program are to facilitate "The Future of Genetic Studies of Complex Human Diseases" by developing innovative statistical methods and software that can be used by biomedical researchers to design and analyze family-based association studies that take advantage of both linkage and linkage disequilibrium. The four specific aims of this proposed research program encompass the development of the necessary quantitative tools: 1. Development of robust semi-parametric statistical methods that will be used to analyze family-based genetic association studies, and which account for many of the complexities of family-based studies of common diseases, such as different types of traits (e.g., binary, polytomous, ordinal, censored age-dependent, and quantitative traits), environmental risk factors, gene-gene and gene-environment interactions, and residual correlations among pedigree members; 2. Development of statistical methods and simulation routines that will be used to determine the design of genetic association studies; 3. Development of user-friendly computer software that implements these procedures; 4. Development of documentation that describes the implemented statistical methodology, how to use the developed software, and how to interpret the results from the analyses. The advantages of this proposed research, over those currently used to identify and characterize genes of complex diseases, are that it will offer a more powerful and flexible method to find these types of genes.