Genome-Wide Statistical Methods for Detecting Deletions in Case-Control Studies

病例对照研究中检测缺失的全基因组统计方法

• 批准号: 8104084
• 负责人: Chih-Chieh Wu
• 金额: $ 7.66万
• 依托单位: UNIVERSITY OF TX MD ANDERSON CAN CTR
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-01 至 2013-05-13
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8104084
• 关键词: Autistic Disorder; Cancer Control; Case-Control Studies; Chromosome Mapping; Complex; Copy Number Polymorphism; Data; Detection; Development; Disease; Evolution; Funding; Genes; Genetic; Genetic Variation; Genome; Genomics; Genotype; Goals; Head and Neck Squamous Cell Carcinoma; Hereditary Disease; Human Genome; Incidence; Individual; Length; Linkage Disequilibrium; Malignant Neoplasms; Malignant neoplasm of lung; Mental disorders; Methods; Parents; Patients; Penetrance; Play; Population; Predisposition; Prevalence; Research; Resources; Role; Scanning; Schizophrenia; Single Nucleotide Polymorphism; Statistical Methods; Statistical sensitivity; Structure; Syndrome; Testing; base; cancer prevention; cancer risk; density; design; genetic risk factor; genome wide association study; genome-wide; human disease; interstitial; microdeletion; novel; offspring; simulation; trait

DESCRIPTION (provided by applicant): Recent genetic studies have increasingly shown that interstitial deletions are common in patients with cancers, such as lung cancer and head and neck squamous cell carcinoma, and psychiatric disorders, such as autism and schizophrenia, suggesting that genomic deletions play an important role in the genetic basis of complex traits in the human genome. However, the association between genomic deletions and common, complex diseases has not yet been systematically investigated in gene mapping studies. Whole-genome studies of genomic deletions have been performed extensively over the past few years. Many of these studies focus on investigating genetic variations in non-diseased individuals and can provide fundamental resource of baseline information for the study of human disease and genomic evolution. However, assessing these effects and associating them with susceptibility to common, complex diseases remain challenging. The central theme of this proposal is to develop statistical approaches to be used to perform genome-wide deletion scans in case-control studies. Our proposed methods are designed to be used with high-density SNP genotypes to detect deletions in large-scale or whole-genome genetic studies. As more and more high-density SNP genotype data on a variety of common, complex diseases will be available from genome-wide association studies, development of sophisticated statistical approaches is especially relevant and novel. Two SNP-based statistical approaches will be developed. The first method is used to test the presence of deletions associated with disease on each of contiguous SNP loci along a chromosomal region for SNP-by-SNP analyses. The second is designed to utilize evidence from multiple adjacent SNPs combined to assess the statistical significance of disease-associated deletions in cases compared with in controls using cluster-based approaches. We propose to use simulation-based approaches to quantitatively determine the statistical sensitivity and power of the proposed methods, adjusting for deletion length, deletion prevalence, deletion penetrance, SNP density, and magnitude of linkage disequilibrium. The newly developed statistical approaches will be used to perform genome-wide detection of deletions in associated with lung cancer. The NCI-funded project "The Ecogenetics Study of Lung Cancer" (R01 CA 55769, PI: MR Spitz) provides the SNP genotype data from a recently completed genome-wide association study of lung cancer.

描述（由申请人提供）： 最近的遗传研究越来越多地表明，癌症患者（例如肺癌，头颈部鳞状细胞癌）以及精神疾病（例如自闭症和精神分裂症）很常见，这表明基因组缺失在人类基因组中复杂特征的遗传特征中起着重要作用。然而，在基因映射研究中，基因组缺失与常见的复杂疾病之间的关联尚未系统地研究。在过去的几年中，对基因组缺失的全基因组研究进行了广泛的进行。这些研究中的许多研究着重于研究非疾病个体的遗传变异，并可以为人类疾病和基因组进化的研究提供基线信息的基本资源。但是，评估这些影响并将它们与对常见的复杂疾病敏感的敏感性相关联仍然具有挑战性。该提案的中心主题是开发统计方法，用于在病例对照研究中进行全基因组缺失扫描。我们提出的方法旨在与高密度SNP基因型一起使用，以检测大规模或全基因组遗传研究中的缺失。随着越来越多的高密度SNP基因型数据数据可从全基因组关联研究中获得各种常见的复杂疾病，因此，复杂的统计方法的发展尤其相关且新颖。将开发两种基于SNP的统计方法。第一种方法用于测试与疾病相关的缺失，沿着染色体区域的每个连续SNP基因座进行SNP-SNP分析。第二个旨在利用来自多个相邻SNP的证据合并来评估与使用基于群集的方法在对照组中相比，病例中与疾病相关的缺失的统计显着性。我们建议使用基于仿真的方法定量确定所提出方法的统计灵敏度和功率，以调整缺失长度，缺失率，缺失渗透率，SNP密度和链接不平衡的幅度。新开发的统计方法将用于对与肺癌有关的缺失进行全基因组检测。 NCI资助的项目“肺癌的生态基因研究”（R01 CA 55769，PI：Spitz先生）提供了来自最近完成的全基因组肺癌基因组关联研究的SNP基因型数据。