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.

该子项目是利用该技术的众多研究子项目之一 资源由 NIH/NCRR 资助的中心拨款提供。子项目和 研究者 (PI) 可能已从 NIH 的另一个来源获得主要资金， 因此可以在其他 CRISP 条目中表示。列出的机构是 对于中心来说，它不一定是研究者的机构。 人们已经进行了大型全基因组关联研究（GWAS）来检测与常见疾病有关的常见遗传变异，但发现这种方式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导致常见疾病的性状变异。在这里，我们提出了两种设计，同胞对和无关病例设计，以在基于候选基因或全基因组关联分析中检测罕见的遗传变异。首先，我们表明，我们可以使用这些设计中的任何一种，使用相对较小的样本来检测和分类罕见的风险单倍型，然后在更大的病例对照样本中增强测试关联性的能力。该方法也可以应用于重新排序数据。接下来，我们将该方法应用于 Wellcome Trust 病例控制联盟 (WTCCC) 冠状动脉疾病 (CAD) 和高血压 (HT) 数据，后者是原始 WTCCC 研究中没有报告全基因组关联证据的唯一特征，并以 5% 的全基因组显着性水平鉴定出 1 个与 HT 相关的有趣基因和 4 个与 CAD 相关的基因。这些结果表明，寻找罕见的遗传变异是可行的，并且在当前的 GWAS、候选基因研究或重测序研究中可以取得丰硕成果。