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中文摘要
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描述(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。他们能否成功地理清复杂疾病的遗传基础,在很大程度上将取决于有效处理这类研究带来的统计学挑战。将对数十万个SNPs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信息量转化为更大的统计能力。对于全基因组关联研究中计算的统计测试数量,处理多重测试问题的标准统计方法,如假发现率,过于保守,可能会冲淡任何真实的遗传信号。需要新的统计方法来处理这种规模的多重测试问题。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,如阿尔茨海默病和ADHD。公共卫生相关性:阿尔茨海默病和ADHD是美国的主要公共卫生问题。我们新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(遗传或环境诱导)。反过来,更好地了解这些疾病的遗传机制可以更好、更有效地照顾那些患有或最有可能患上这些疾病的人。
英文摘要
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.
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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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