Statistical Methods in Genetic Epidemiology Research

描述（由申请人提供）：本研究的长期目标是为遗传流行病学研究的数据分析开发强大的统计方法。虽然由于人类基因组计划和高通量基因分型技术的快速发展，大量数据变得可用，但要最终成功地识别易感遗传变异及其环境修饰因子，还需要强大的统计方法。这个项目的重点是发展统计方法来分析围产期或生命早期疾病的遗传关联研究。这些研究通常采用回顾性病例-对照设计，但它们有一个明显的特点，即也招募了母亲病例/对照（围产期疾病）或后代病例/对照（早期疾病）的父母。因此，这些研究提供了不相关的病例对照比较和家庭内基因型/单倍型传播的信息。这些研究的另一个重要特征是，研究人群中的协变量分布是结构化的，因此遗传和环境变量通常在家庭中是独立的。这种独立性在替代假设下的病例群体中不成立，这一事实提供了标准病例对照比较之外的进一步信息。这些研究通常试图评估母亲和后代基因型/单倍型的影响，它们的相互作用，以及基因与环境的相互作用。在现有病例-对照关联研究和病例-亲本三联分析方法的基础上，我们提出了新的有效的估计和检验方法，可以解释回顾性病例-对照设计，并结合基因型/单倍型传播和协变量分布结构的家庭信息。病例对照研究的经典逻辑回归适用于大多数分析，但由于忽略了家庭信息和协变量结构，效率较低。传播/不平衡型检验或基于似然的方法用于分析病例-亲本三联症，抛弃了对照组和/或其亲本，不能估计所有感兴趣的参数（例如，环境暴露的主要影响）。我们的方法范围从轮廓似然方法和基于估计函数的方法到基于情况三元组和伪似然的条件似然的混合方法。该项目受到宾夕法尼亚大学正在进行的科学研究的推动，并将应用于PI合作进行的科学研究，其表型包括早产、先兆子痫、尿道下裂和哮喘。我们的方法对除围产期和早期疾病外的其他表型的研究也有广泛的意义。我们将为所提出的方法开发大样本理论，通过模拟研究评估其有限样本性能，并使用实际数据证明其实用性。将使用免费的统计软件包R提供实现这些方法以供公众使用的完整文档软件。

DESCRIPTION (provided by applicant): The long-term objective of this research is to develop powerful statistical methods for the analysis of data from genetic epidemiology studies. While voluminous data are becoming available owing to the Human Genome Project and rapid advancement of high throughput genotyping technology, powerful statistical methods are needed for ultimate success in identifying predisposing genetic variants and their environmental modifiers. This project focuses on developing statistical methods for analyzing genetic association studies on perinatal or early-life diseases. These studies very often adopt a retrospective case-control design, but they have a distinct feature in that offspring of mother cases/controls (for perinatal diseases) or parents of offspring cases/controls (for early-life diseases) are also recruited. Thus these studies have information on both unrelated case-control comparisons and genotype/haplotype transmissions within families. Another important feature of these studies is that the covariate distribution in the study population is structured so that genetic and environmental variables are usually independent within families. The fact that such independence does not hold in the case population under the alternative hypothesis provides further information on the association beyond standard case-control comparison. These studies usually seek to evaluate effects of both maternal and offspring genotypes/haplotypes, their interactions, and gene-environment interactions. Building on currently available approaches for analysis of case-control association studies and case-parent triads, we propose novel efficient estimation and testing methods that can account for the retrospective case-control design and incorporate the family information on the genotype/haplotype transmission and the structure in the covariate distribution. Classical logistic regression for case-control studies applies for most of the analysis but is less efficient due to the ignorance of family information and covariate structure. The Transmission/Disequilibrium type test or likelihood-based methods for analyzing case-parent triads discard the controls and/or their parents and cannot estimate all parameters of interest (e.g., main effects of environmental exposures). Our methods range from profile-likelihood methods and estimating-function based methods to hybrid methods based on the conditional likelihood for case triads and pseudo-likelihoods. This project is motivated by and will be applied to ongoing scientific studies at the University of Pennsylvania on which the PI is collaborating, and the phenotypes include pre-term birth, preeclampsia, hypospadias, and asthma. Our methods also have broad implications to the study of phenotypes other than perinatal and early-life diseases. We will develop large sample theories for the proposed methods, evaluate their finite sample performance by simulation studies, and demonstrate their usefulness using real data. Fully documented software to implement these methods for public use will be provided using freely available statistical package R.