Quantitative Methods for Genetic Epidemiology

最近的人类基因组测序[1,2]提供了一个独特的机会来改变我们了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。导致常见疾病的复杂遗传机制提出了重大挑战，必须采用基于生物学和统计学原理的良好理论基础的新型分析方法。从公共卫生的角度来看，我们提出的研究的潜在好处是巨大的，预计会改善发现和评估常见复杂人类疾病的遗传机制的方式。我们的总体目标是通过开发生物医学研究人员可以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复杂结构的理解，并提供方法来解释由于二倍体表型的未知阶段而不能直接观察到的模糊单倍型。目标2。单倍型和其他复杂遗传机制用于病例对照研究：建立统计遗传模型，评估复杂遗传机制（单倍型和代谢途径）和环境风险因素对疾病的相对贡献，通过标准病例对照研究设计进行评估。目标3。单倍型和其他复杂的遗传机制用于基于家庭的研究：目标1和目标2中开发的方法将扩展到基于家庭的研究设计，包括结合病例对照和基于家庭的设计优势的混合设计，增加检测小影响基因的能力。目标4。开发用户友好的软件，实现我们的方法，并使其广泛提供给生物医学研究人员，包括良好记录的程序和使用示例。

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.