Quantitative Methods for Genetic Epidemiology

最近的人类基因组测序[L，2]提供了一个独特的机会来改变我们理解常见人类疾病的方式，并最终改善疾病的诊断、预后和治疗[3]。导致常见疾病的复杂遗传机制构成了重大挑战，必须以生物学和统计学原理的坚实理论基础为基础，采用新的分析方法加以应对。我们拟议的研究的潜在好处是巨大的，当从公共健康的角度衡量时，预期在发现和评估常见复杂人类疾病的遗传机制方面有所改进。我们的总体目标是通过开发可供生物医学研究人员使用的创新统计方法和软件来促进复杂遗传机制的分析，如我们的四个具体目标所述：目标1.开发和评估单倍型的概率模型，以提高我们对人类群体中单倍型的复杂结构的理解，并提供解释由于二倍体表型的未知阶段而无法直接观察到的模糊单倍型的方法。目的2.病例对照研究的单倍型和其他复杂遗传机制：建立统计遗传模型，通过标准病例对照研究设计评估复杂遗传机制(单倍型和代谢途径)和环境危险因素对疾病的相对贡献。目的3.家族性研究的单倍型和其他复杂遗传机制：AIMS 1和2中开发的方法将扩展到家族性研究设计，包括结合病例对照设计和家族性设计的混合设计，增加检测小效应基因的能力。目标4.开发用户友好的软件，实现我们的方法，并向生物医学研究人员广泛提供这些方法，包括记录良好的程序和使用它们的例子。

The recent sequencing of the human genome [l, 2] provides a unique opportunity to change the way we understand common human diseases, and ultimately improve diagnosis, prognosis, and treatment of disease [3]. Complex genetic mechanisms that contribute to common diseases pose a significant challenge that must be met with novel analytic methods based on a sound theoretical foundation of biological and statistical principles. The potential benefits of our proposed research are great when measured in terms of public health, with anticipated improvements in the way that genetic mechanisms are discovered and evaluated for common complex human diseases. Our overall objectives are to facilitate analyses of complex genetic mechanisms by developing innovative statistical methods and software that can be used by biomedical researchers as outlined in our four specific aims: Aim 1. Develop and evaluate probability models for haplotypes in order to improve our understanding of the complex structure of haplotypes in human populations and provide methods to account for ambiguous haplotypes when they are not directly observed due to unknown phase of diploid phenotypes. Aim 2. Haplotypes and other complex genetic mechanisms for case- control studies: Build statistical genetic models to evaluate the relative contribution of complex genetic mechanisms (haplotypes and metabolic pathways) and environmental risk factors to disease, as evaluated by standard case-control study designs. Aim 3. Haplotypes and other complex genetic mechanisms for family- based studies: The methods developed in Aims 1 & 2 will be extended to family-based study designs, including a hybrid design that combines the strengths of case-control and family-based designs, increasing power to detect genes of small effects. Aim 4. Develop user-friendly software that implements our methods and make them widely available to biomedical researchers, including well-documented procedures and examples on their usage.