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)： 最近的遗传学研究越来越多地表明，间质缺失在癌症患者中很常见，如肺癌和头颈部鳞状细胞癌，以及精神疾病，如自闭症和精神分裂症，这表明基因组缺失在人类基因组复杂特征的遗传基础中发挥着重要作用。然而，在基因定位研究中，基因组缺失与常见的复杂疾病之间的联系还没有得到系统的研究。在过去的几年里，基因组缺失的全基因组研究得到了广泛的开展。其中许多研究侧重于研究非疾病个体的遗传变异，可以为人类疾病和基因组进化的研究提供基本的基线信息来源。然而，评估这些影响并将其与常见、复杂疾病的易感性联系起来仍然具有挑战性。这项建议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。我们提出的方法被设计用于检测大规模或全基因组遗传学研究中的高密度SNP基因类型。随着越来越多关于各种常见、复杂疾病的高密度SNP基因型数据将从全基因组关联研究中获得，开发复杂的统计方法尤其相关和新颖。将制定两种基于SNP的统计方法。第一种方法用于检测与疾病相关的每个SNP基因座上是否存在与疾病相关的缺失，用于对染色体区域进行逐个SNP分析。第二项研究旨在利用来自多个相邻SNPs的证据，结合使用基于聚类的方法，评估与疾病相关缺失的病例与对照相比的统计学意义。我们建议使用基于模拟的方法来定量确定所提出的方法的统计敏感性和能力，调整缺失长度、缺失流行率、缺失外显率、SNP密度和连锁不平衡的大小。新开发的统计学方法将用于在全基因组范围内检测与肺癌相关的基因缺失。美国国立卫生研究院资助的项目“肺癌的生态遗传学研究”(R01 CA 55769，PI：MR Spitz)提供了最近完成的一项肺癌全基因组关联研究的SNP基因数据。