DETECTING RARE VARIANTS FOR COMPLEX TRAITS USING FAMILY AND UNRELATED DATA

使用家庭和不相关的数据检测复杂性状的罕见变异

• 批准号: 8171726
• 负责人: XIAOFENG ZHU
• 金额: $ 0.99万
• 依托单位: CASE WESTERN RESERVE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-08-01 至 2011-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8171726
• 关键词: Accounting; Candidate Disease Gene; Complex; Computer Retrieval of Information on Scientific Projects Database; Coronary Arteriosclerosis; DNA Resequencing; Data; Disease; Family; Funding; Genes; Grant; Haplotypes; Hypertension; Institution; Methods; Reporting; Research; Research Personnel; Resources; Risk; Sampling; Source; Testing; Trust; United States National Institutes of Health; Variant; base; case control; design; genetic variant; genome wide association study; genome-wide; interest; trait

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Large genome-wide association studies (GWAS) have been performed to detect common genetic variants involved in common diseases, but most of the variants found this way account for only a small portion of the trait variance. Furthermore, candidate gene-based resequencing suggests that many rare genetic variants contribute to the trait variance of common diseases. Here we propose two designs, sibpair and unrelated-case designs, to detect rare genetic variants in either a candidate gene-based or genome-wide association analysis. First we show that we can detect and classify together rare risk haplotypes using a relatively small sample with either of these designs, and then have increased power to test association in a larger case-control sample. This method can also be applied to resequencing data. Next we apply the method to the Wellcome Trust Case Control Consortium (WTCCC) coronary artery disease (CAD) and hypertension (HT) data, the latter being the only trait for which no genome-wide association evidence was reported in the original WTCCC study, and identify one interesting gene associated with HT and four associated with CAD at a genome-wide significance level of 5%. These results suggest that searching for rare genetic variants is feasible and can be fruitful in current GWAS, candidate gene studies or resequencing studies.

这个子项目是许多利用 由NIH/NCRR资助的中心赠款提供的资源。子项目和 研究者（PI）可能从另一个NIH来源获得了主要资金， 因此可以在其他CRISP条目中表示。所列机构为 研究中心，而研究中心不一定是研究者所在的机构。 已经进行了大的全基因组关联研究（GWAS）来检测常见疾病中涉及的常见遗传变异，但以这种方式发现的大多数变异仅占性状变异的一小部分。此外，基于候选基因的重测序表明，许多罕见的遗传变异有助于常见疾病的性状变异。在这里，我们提出了两种设计，sibpair和不相关的情况下的设计，以检测罕见的遗传变异的候选基因为基础的或全基因组关联分析。首先，我们表明，我们可以检测和分类罕见的风险单倍型使用一个相对较小的样本与这些设计中的任何一个，然后有更大的权力来测试关联在一个较大的病例对照样本。该方法也可以应用于重排序数据。接下来，我们将该方法应用于Wellcome Trust病例对照协会（WTCCC）冠状动脉疾病（CAD）和高血压（HT）数据，后者是唯一的性状，在原始WTCCC研究中没有全基因组关联证据，并确定一个有趣的基因与HT相关，四个与CAD相关，全基因组显著性水平为5%。这些结果表明，寻找罕见的遗传变异是可行的，可以在目前的GWAS，候选基因研究或重测序研究成果。