Combining epidemiologic designs to model genetic risks for psychiatric disorders

结合流行病学设计来模拟精神疾病的遗传风险

基本信息

批准号：
7649215
负责人：
VERONICA J. VIELAND
金额：
$ 116.42万
依托单位：
RESEARCH INST NATIONWIDE CHILDREN'S HOSP
依托单位国家：
美国
项目类别：
财政年份：
2009
资助国家：
美国
起止时间：
2009-06-05 至 2011-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/7649215
关键词：
American Architecture Back Behavioral Biology Bipolar Disorder Case-Control Studies Collection Complex Computer software Data Data Set Databases Deposition Development Diagnostic Disease Environment Epidemiologic Methods Epidemiologic Studies Epigenetic Process Equilibrium Family Family Study Foundations Genes Genetic Genetic Models Genetic Predisposition to Disease Genetic Risk Genome Scan Genomics Human Genetics Information Networks Investments Journals Knowledge Links List Maps Mental disorders Methods Microsatellite Repeats Mitochondria Modeling Modification National Institute of Mental Health Neurobiology Paper Pattern Peer Review Phenotype Public Domains Publications Publishing Research Design Research Personnel Resources Risk Factors Sampling Schizophrenia Techniques United States National Institutes of Health Vacuum X Chromosome base case control design flexibility gene function genetic association genetic linkage analysis genetic pedigree genetic risk factor genome wide association study genome-wide imprint insight novel repository segregation tool trait

项目摘要

Combining epidemiologic designs to model genetic risks for psychiatric disorders It is now widely recognized that genetic predispositions constitute major risk factors for psychiatric disorders such as schizophrenia (SZ) and biopolar disorder (BP). Yet we lag behind in epidemiologic methods for illuminating complex genetic architecture, or the ways in which mutational patterns at the genotypic level combine with environmental and other factors to produce specific behavioral phenotypes. One obstacle is the difficulty of combining information across different genetic epidemiologic study designs, for instance, exploiting both family studies and case-control studies in simultaneous analyses. As a result, it is difficult to construct models which adequately allow for the genetic and epidemiologic background in which given genes function. We have developed a powerful statistical approach (based on the “PPL” framework) and built a specialized computational platform (KELVIN) for modeling genetic architecture. We now propose to apply KELVIN to data contained in repositories established by the NIH, leveraging publicly available data sets in order to formulate more comprehensive models of the genetic architecture of SZ and BP. Specifically, we will make use of the large collection of multiplex pedigrees with microsatellite genome scan data assembled by the NIMH Human Genetics Initiative (HGI); and the extensive case-control samples with genome-wide SNP data assembled by the Genetic Association Information Network (GAIN). By applying our methods for combining information across genetic epidemiologic study designs to data already in the public domain, we can leverage earlier public investments to perform powerful modeling of genetic risk factors for major psychiatric disorders.

将流行病学设计相结合，以建模精神疾病的遗传风险现在，人们广泛认识到，遗传倾向构成精神疾病（例如精神分裂症）（SZ）和生物极性疾病（BP）等精神疾病的主要危险因素。然而，我们落后于流行病学方法来照亮复杂的遗传结构，或者基因型水平的突变模式与环境和其他因素结合产生特定行为表型的方式。一个障碍是在简单分析中使用不同遗传流行病学研究设计的信息结合到不同遗传流行病学研究设计和病例对照研究的困难。结果，很难构建适当允许给定基因功能的遗传和流行病学背景的模型。我们已经开发了一种强大的统计方法（基于“ PPL”框架），并建立了用于建模遗传体系结构的专业计算平台（Kelvin）。现在，我们建议将开尔文应用于NIH建立的存储库中包含的数据，利用公开可用的数据集，以制定SZ和BP遗传结构的更全面的模型。具体而言，我们将利用大量的多重谱系，以及由NIMH人类遗传学计划（HGI）组装的微卫星基因组扫描数据；以及由遗传关联信息网络（GAN）组装的广泛的病例对照样品，具有全基因组SNP数据。通过应用我们的方法将跨遗传流行病学研究设计的信息组合到已经在公共领域中的数据，我们可以利用早期的公众投资对主要精神疾病的遗传危险因素进行强大的建模。