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

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中复杂性状的遗传基础中起着重要作用。然而，基因组缺失和常见的，复杂的疾病之间的关联尚未在基因定位研究中进行系统研究。基因组缺失的全基因组研究在过去的几年中已经广泛进行。这些研究主要集中在非疾病个体的遗传变异研究上，可以为人类疾病和基因组进化研究提供基础信息资源。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。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。我们提出的方法被设计用于高密度SNP基因型，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基因型数据的各种常见的，复杂的疾病将可从全基因组关联研究，复杂的统计方法的发展是特别相关和新颖的。将开发两种基于SNP的统计方法。第一种方法用于测试沿着染色体区域的每个连续SNP基因座上与疾病相关的缺失的存在，用于SNP逐个SNP分析。第二个目的是利用多个相邻的SNPs的证据相结合，以评估疾病相关的缺失的统计学意义的情况下，与对照组相比，使用基于聚类的方法。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灵敏度和功率，调整缺失长度，缺失患病率，缺失突变率，SNP密度和连锁不平衡的大小。新开发的统计方法将用于进行全基因组检测与肺癌相关的缺失。NCI资助的项目“肺癌的生态遗传学研究”（R01 CA 55769，PI：MR Spitz）提供了最近完成的肺癌全基因组关联研究的SNP基因型数据。

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.