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中文摘要
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描述(由研究者提供):全基因组关联研究(GWAs)已经发现了许多复杂疾病和表型之间新的、强大的关联。虽然新的发现可以在其他研究中可靠地重复,但与大多数疾病/性状的估计总遗传力相比,由新的关联发现解释的表型变异的数量很小。这表明目前的GWAs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检验问题。对于基于家庭的设计,我们将开发新的统计方法,以实现比目前使用的方法更高的功率水平,同时,完全稳健的再次混淆。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,有助于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。
英文摘要
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.
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Preparing Association Analysis Software Tools for Next Generation Sequencing Data
  • 批准号:
    9080392
  • 项目类别:
  • 资助金额:
    $36.4万
  • 财政年份:
    2016
  • 负责人:
    CHRISTOPH LANGE
  • 依托单位:
Biostatistics and Bioinformatics
  • 批准号:
    9982411
  • 项目类别:
  • 资助金额:
    $28.53万
  • 财政年份:
    2016
  • 负责人:
    CHRISTOPH LANGE
  • 依托单位:
Novel Statistical Approaches to Mental Health Phenotype Analysis in GWA Studies
  • 批准号:
    8647000
  • 项目类别:
  • 资助金额:
    $37.43万
  • 财政年份:
    2009
  • 负责人:
    CHRISTOPH LANGE
  • 依托单位:
A New Approach to Mental Health Phenotypes in Family Genomewide Association
  • 批准号:
    7764864
  • 项目类别:
  • 资助金额:
    $40.3万
  • 财政年份:
    2009
  • 负责人:
    CHRISTOPH LANGE
  • 依托单位:
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