Family Based Tests of Association for Complex Diseases

复杂疾病关联的家庭测试

• 批准号: 7201914
• 负责人: NAN MCKENZIE LAIRD
• 金额: $ 31.37万
• 依托单位: HARVARD SCHOOL OF PUBLIC HEALTH
• 依托单位国家: 美国
• 财政年份: 1998
• 资助国家: 美国
• 起止时间: 1998-09-30 至 2010-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7201914
• 关键词: Affect; Algorithms; Alzheimer' s Disease; Area; Asthma; Attention deficit hyperactivity disorder; Australia; Bipolar Disorder; Boston; Chromosomes; Chronic Obstructive Airway Disease; Chronic Obstructive Asthma; Cohort Studies; Collaborations; Collection; Complex; Computer software; Data; Development; Disease; Disease susceptibility; Education; Family; France; Funding; Gene Expression; Genes; Genetic; Genetic Models; Genetic Polymorphism; Genomics; Genotype; Guidelines; Haplotypes; Human Genome Project; Measures; Mental Health; Methodology; Methods; Mexico; Micro Array Data; Modeling; Nicotine Dependence; Nuclear Family; Nucleotides; Outcome; Parents; Participant; Phase; Phenotype; Population; Relative (related person); Research; Sample Size; Screening procedure; Single Nucleotide Polymorphism; Standards of Weights and Measures; Suggestion; Sweden; Testing; Time; Validation; Work; X Chromosome; analytical method; base; case control; conditioning; density; design; family structure; gene environment interaction; gene interaction; genetic association; genetic pedigree; genome wide association study; genotyping technology; method development; proband; response; success; trait

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.

描述（由申请人提供）：该应用程序将开发用于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疾病。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，彻底改变了我们理解复杂疾病遗传基础的能力。单核苷酸多态性 (SNP) 的广泛可用性意味着基因组区域可以充满数千个足够密度的 SNP，因此，通过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且功效良好。我们的工作重点是基于家庭的遗传关联测试，因为它们可以防止存在群体亚结构时可能出现的潜在虚假结果。我们之前开发了一种分析家庭数据的通用方法，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批评是我们不使用“非信息性”家庭，或者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，该算法使我们能够非常有效地处理多重比较问题。在本应用中，我们计划开发用于基于家族的关联测试的附加方法，以及以下领域的配套软件：基因-基因和基因-环境相互作用的测试、涉及二分结果的全基因组扫描测试、使用谱系中的基因表达数据识别顺式和反式作用基因的复杂网络的方法，以及与x染色体上的基因关联的测试。这些方法的开发和实施将利用我们合作者在双相情感障碍、尼古丁成瘾、注意力缺陷多动障碍、阿尔茨海默病、哮喘、慢性阻塞性肺病等方面的真实数据。