Novel Statistical Approaches to Mental Health Phenotype Analysis in GWA Studies

GWA 研究中心理健康表型分析的新统计方法

基本信息

批准号：
7893048
负责人：
CHRISTOPH LANGE
金额：
$ 41.54万
依托单位：
HARVARD SCHOOL OF PUBLIC HEALTH
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：
2009-07-14 至 2012-02-28
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/7893048
关键词：
Address Affect African American Algorithms Alzheimer&apos s Disease Attention deficit hyperactivity disorder Budgets Candidate Disease Gene Caring Case-Control Studies Communities Complex Computer software Data Data Set Development Diabetes Mellitus Disadvantaged Disease Family Framingham Heart Study Genes Genetic Genotype Goals Heterogeneity Literature Mental Health Methodology Methods Phase Phenotype Population Public Health Publishing Replication-Associated Process Research Personnel Risk Sampling Scanning Screening procedure Selection Criteria Signal Transduction Staging Statistical Methods Step Tests Subgroup Testing Training Translating United States base case control design genetic risk factor genome wide association study genome-wide improved novel novel strategies population based proband psychogenetics public health relevance success tool trait

项目摘要

DESCRIPTION (provided by applicant): Recent scientific and technological developments have made genome-wide association studies a reality. Their success in disentangling the genetic basis of complex diseases will depend largely on the efficient handling of the statistical challenges posed by such studies. Several hundred thousand SNPs will be genotyped and examined for the potential associations with phenotypes. Genome-wide association studies must translate the markedly increased amount of SNP-information into increased statistical power. For the number of statistical tests computed in a genome-wide association study, standard statistical methods for handling the multiple testing problem, such as false-discovery rate, are too conservative and are likely to dilute any true genetic signals. Novel statistical methodology is required to handle the multiple testing problems at this scale. We will develop novel statistical methodology to solve these major hurdles in genome-wide association studies. Our novel statistical methodology will enable researchers to examine the underlying genetic mechanism of complex diseases, such ad Alzheimer Disease and ADHD. PUBLIC HEALTH RELEVANCE: Alzheimer Disease and ADHD are major public health problems in the United States. Our novel statistical methodology will provide a set of tools, which researchers and clinicians can use to identify factors (inherited or environmentally-induced) that affect the development of these diseases. In turn, a better understanding of the genetic mechanisms of these conditions can result in better and more efficient care of those who suffer from - or are most at risk for the development of -these diseases.

描述（由申请人提供）：最近的科学技术发展使全基因组关联研究成为现实。他们能否成功解开复杂疾病的遗传基础，很大程度上取决于能否有效应对此类研究带来的统计挑战。将对数十万个 SNP 进行基因分型并检查其与表型的潜在关联。全基因组关联研究必须将显着增加的 SNP 信息量转化为更高的统计功效。对于全基因组关联研究中计算的统计测试数量，处理多重测试问题（例如错误发现率）的标准统计方法过于保守，并且可能会淡化任何真实的遗传信号。需要新颖的统计方法来处理这种规模的多个测试问题。我们将开发新的统计方法来解决全基因组关联研究中的这些主要障碍。我们新颖的统计方法将使研究人员能够检查复杂疾病的潜在遗传机制，例如阿尔茨海默病和多动症。公共卫生相关性：阿尔茨海默病和多动症是美国的主要公共卫生问题。我们新颖的统计方法将提供一套工具，研究人员和临床医生可以使用这些工具来识别影响这些疾病发展的因素（遗传的或环境诱发的）。反过来，更好地了解这些疾病的遗传机制可以为那些患有这些疾病或最有可能患上这些疾病的人提供更好、更有效的护理。