A New Approach to Mental Health Phenotypes in Family Genomewide Association

家庭全基因组关联中心理健康表型的新方法

• 批准号: 8496967
• 负责人: CHRISTOPH LANGE
• 金额: $ 7.08万
• 依托单位: HARVARD SCHOOL OF PUBLIC HEALTH
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-12-01 至 2012-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8496967
• 关键词: Address; Admixture; Advocate; Alzheimer' s Disease; Architecture; Attention deficit hyperactivity disorder; Caring; Communities; Complex; Computer software; Copy Number Polymorphism; Data; Development; Disadvantaged; Disease; Disease susceptibility; Family; Genes; Genetic; Genetic Risk; Genotype; Height; Heritability; Heterogeneity; Individual; Literature; Logistic Regressions; Measures; Mental Health; Meta-Analysis; Methodology; Methods; Obesity; Phenotype; Population; Public Health; Regression Analysis; Research Personnel; Sampling; Staging; Techniques; Testing; Time; Training; United States; Variant; base; design; disease phenotype; genome wide association study; genome-wide; genome-wide analysis; improved; insight; novel; novel strategies; population based; public health relevance; statistics; tool; trait

DESCRIPTION (provided by investigator): Genome-wide association studies (GWAs) have led to the discovery of novel, robust associations for numerous complex diseases and phenotypes. While the new findings can be replicated reliably in other studies, the amount of phenotypic variation that is explained by the new association findings is small compared to the estimated total heritability of most diseases/traits. This suggests that the current GWAs are not able to identify most of the disease loci. Potential reasons are the study heterogeneity/confounding and the lack of sufficient statistical power to address the inherent multiple testing problem. For family-based designs, we will develop novel statistical methodology that achieves higher power levels than the currently used methodology and, at the same time, are completely robust again confounding. The application of the new methods to genome-wide association studies for Alzheimer's' Disease and Attention Deficit Hyperactivity Disorder will provide new insights that will help the scientific community to identify new genes for these diseases which are major public health problems in the United States. PUBLIC HEALTH RELEVANCE: Alzheimer's disease and Attention Deficit Hyperactivity Disorder are major public health problems in the United States. The proposed statistical methodology will provide new analysis approaches that will enable researchers and clinicians to identify genetic risk loci for these diseases and other complex disease and phenotypes. In turn, an improved understanding of the genetic architecture of these conditions will result in a better and more efficient care for those who suffer from these diseases.

描述（由研究者提供）：全基因组关联研究 (GWA) 发现了许多复杂疾病和表型的新颖、强有力的关联。虽然新的发现可以在其他研究中可靠地复制，但与大多数疾病/性状的估计总遗传力相比，新的关联发现所解释的表型变异量很小。这表明当前的 GWA 无法识别大多数疾病位点。潜在的原因是研究的异质性/混杂性以及缺乏足够的统计能力来解决固有的多重测试问题。对于基于系列的设计，我们将开发新颖的统计方法，该方法可以实现比当前使用的方法更高的功率水平，同时完全稳健且抗混杂。将新方法应用于阿尔茨海默病和注意力缺陷多动障碍的全基因组关联研究将提供新的见解，帮助科学界识别这些疾病的新基因，这些疾病是美国的主要公共卫生问题。 公共卫生相关性：阿尔茨海默病和注意力缺陷多动障碍是美国的主要公共卫生问题。拟议的统计方法将提供新的分析方法，使研究人员和临床医生能够识别这些疾病和其他复杂疾病和表型的遗传风险位点。反过来，更好地了解这些疾病的遗传结构将为这些疾病患者提供更好、更有效的护理。