Family Based Tests of Association for Complex Diseases

描述（由申请人提供）：该申请将开发遗传关联研究的统计方法和软件，特别强调心理健康中的复杂障碍。人类基因组计划和相关工作的成功，以及新的基因分型技术，已经彻底改变了我们理解复杂疾病的遗传基础的能力。单核苷酸多态性（snp）的广泛存在意味着基因组区域可以被数千个具有足够密度的snp所饱和，因此，有足够的样本量和处理多重比较问题的适当方法，我们可以使用普通的关联研究来定位疾病易感性位点。传统的病例/对照和病例/队列研究通常用于这种情况，因为它们相对容易收集和具有良好的效力。我们的工作重点是基于家庭的遗传关联测试，因为它们可以防止在存在群体亚结构时可能出现的潜在虚假结果。我们之前已经开发了一种家庭数据分析的一般方法，该方法在各种非标准设计中保持稳健性，包括缺失父母和测量或发病时间表型。对我们的方法的一个潜在的批评是，我们没有使用“非信息性”的家庭，或家庭中不包含有关关联的信息的家庭。我们通过开发一种独特的“筛选”算法，将这种潜在的批评转化为优势，使我们能够相当有效地处理多重比较问题。在这个应用程序中，我们计划为基于家族的关联测试开发额外的方法，并在以下领域开发配套的软件：基因-基因和基因-环境相互作用的测试，涉及二分类结果的全基因组扫描测试，使用系谱中的基因表达数据识别顺式和反式作用基因复杂网络的方法，以及与x染色体上基因的关联测试。该方法的开发和实施将利用我们在双相情感障碍、尼古丁成瘾、注意缺陷多动障碍、阿尔茨海默病、哮喘、慢性阻塞性肺病等领域的合作者的真实数据。

DESCRIPTION (provided by applicant): This application will develop statistical methodology and software for genetic association studies with special emphasis on complex disorders in mental health. The success of the Human Genome Project and related efforts, as well as the new genotyping technologies, has revolutionized our ability to understand the genetic underpinnings of complex disorders. The widespread availability of Single Nucleotide Polymorphisms (SNPs) means that genomic regions can be saturated with thousands of SNPs with sufficient density so that, with sufficient sample sizes and appropriate methods for handling the multiple comparisons problem, we can locate Disease Susceptibility Loci using ordinary association studies. Conventional case/control and case/cohort studies are often used in this setting because of their relative ease of collection and good power. Our work has focused on family based genetic association tests because they can protect against potentially spurious results that can arise when there is population substructure. We have previously developed a general approach to the analysis of family data which maintains robustness in a variety of non-standard designs, including missing parents and measured or time-to-onset phenotypes. A potential criticism of our approach is that we do not use 'non- informative' families, or families which do not contain within family information about association. We have turned this potential criticism to an advantage by developing a unique 'screening' algorithm which enables us to handle the multiple comparisons problem quite effectively. In this application we plan to develop additional methodology for family based association tests, and accompanying software in the following areas: tests for gene-gene and gene-environment interaction, tests for whole genome scans involving dichotomous outcomes, methods for identification of complex networks of cis- and trans- acting genes using gene expression data in pedigrees, and tests for association with genes on the x- chromosome. The methods development and implementation will utilize real data from our collaborators in Bipolar Disorder, Nicotine Addiction, Attention Deficit Hyperactivity Disorder, Alzheimer's disease, Asthma, Chronic Obstructive Pulmonary Disease, among others.