Statistical Methods in Genetic Epidemiology Research

描述(由申请人提供)：这项研究的长期目标是开发强大的统计方法来分析遗传流行病学研究的数据。虽然由于人类基因组计划和高通量基因分型技术的快速进步，大量数据变得可用，但在最终成功识别易感基因变异及其环境修饰物方面，需要强大的统计方法。该项目的重点是开发统计方法，用于分析围产期或早期疾病的遗传相关性研究。这些研究经常采用回溯性病例对照设计，但它们有一个明显的特点，即也包括母亲病例/对照的子女(用于围产期疾病)或子女病例/对照的父母(用于早期疾病)。因此，这些研究既有关于不相关的病例对照比较的信息，也有关于家庭内基因/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内是独立的。在另一种假设下，这种独立性在病例人群中不成立，这一事实提供了有关这种联系的进一步信息，超出了标准的病例对照比较。这些研究通常试图评估母子两种基因型/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。在已有的病例对照关联研究和病例-父母三联体分析方法的基础上，我们提出了新的有效的估计和检验方法，该方法可以解释回溯性病例对照设计，并将关于基因/单倍型传播的家系信息和结构纳入协变量分布。经典Logistic回归用于病例对照研究的大部分分析，但由于忽略了家庭信息和协变量结构，效率较低。用于分析病例-亲本三联体的传递/不平衡类型检验或基于似然的方法丢弃了对照和/或其亲本，并且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。这个项目是由宾夕法尼亚大学正在进行的科学研究推动的，并将应用于PI正在进行的科学研究，这些研究的表型包括早产、先兆子痫、尿道下裂和哮喘。我们的方法对围产期和早期疾病以外的表型研究也有广泛的意义。我们将为所提出的方法发展大样本理论，通过模拟研究来评估它们的有限样本性能，并使用真实数据来证明它们的有效性。将使用免费提供的统计包R提供用于实施这些方法供公众使用的完整文件软件。

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.