GWAS Using Integrated CNV and SNP Information

使用综合 CNV 和 SNP 信息的 GWAS

• 批准号: 8247786
• 负责人: Rui Feng
• 金额: $ 34.77万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-05-07 至 2014-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8247786
• 关键词: Adopted; Affect; Algorithms; Alleles; Archives; Blood Pressure; Characteristics; Communities; Complex; Computer software; Computing Methodologies; Copy Number Polymorphism; DNA copy number; Data; Data Set; Databases; Diagnosis; Diagnostic Procedure; Diet; Disease; Evolution; Family; Genes; Genetic; Genetic Polymorphism; Genetic Variation; Genotype; Haplotypes; Heart Diseases; Heritability; Human; Human Genetics; Human Genome; Hypertension; Joints; Lead; Left Ventricular Hypertrophy; Linkage Disequilibrium; Lipids; Malignant Neoplasms; Methods; Metric; Modeling; Nature; Non-Insulin-Dependent Diabetes Mellitus; Obesity; Output; Parkinson Disease; Pharmaceutical Preparations; Phenotype; Publishing; Role; Single Nucleotide Polymorphism; Statistical Methods; Testing; Triglycerides; Update; Variant; Weight; Work; base; density; disorder risk; genetic epidemiology; genetic variant; genome wide association study; human data; human disease; improved; novel; prevent; programs; public health relevance; response; simulation; success; tool; trait; user friendly software

DESCRIPTION (provided by applicant): Recently, genome-wide association studies using single nucleotide polymorphisms (SNPs) have gained some success in detecting genetic variants associated with diseases. Copy number variation (CNV) is another widespread characteristic of the human genome that has been shown to be related to various human phenotypes. The ongoing HapMap project that is constructing a database of validated CNVs will provide valuable information for studying associations of CNVs with disease risk, the effects of CNVs on response to drug treatment, and the role of structural variation in human evolution. However, limited by the available statistical methods, current practice in studies to detect associations between human diseases and genetic variants is separate calling of SNP genotypes and CNVs followed by separate analyses. Two studies published in Nature last year (Korn et al, 2008; McCaroll et al., 2008) have suggested that combining SNP allele and copy number information can lead to accurate inference of both copy numbers and genotypes and thus affect the results of the association studies. New methods are greatly needed for simultaneous inference of SNP and CNV and testing of their joint influences on complex diseases. We therefore propose to develop novel statistical and computational methods and software for whole-genome association studies using integrated CNV and SNP information. The specific aims of this project are (1) to develop calling algorithms for allele-specific copy numbers that integrate copy number and SNP allele information, (2) to develop single-locus and multi-locus methods for joint genotype and copy number association testing, (3) to develop haplotype association methods incorporating copy numbers information, and (4) to release a user-friendly software package in R. The proposed methods will be evaluated through simulations as well as with real data, which will include (but will not be limited to) the publicly available HapMap data and human data sets from our collaborators studying genetic effects on left ventricular hypertrophy, triglycerides, and blood pressure. The proposed methods will greatly facilitate the study of human genetic variations and their association with complex diseases. PUBLIC HEALTH RELEVANCE: The proposed methods will aid in the discovery of genetic variants responsible for complex human diseases, will help us to better understand these diseases, and finally will enhance our ability to prevent, diagnose, and treat these diseases.

描述（由申请人提供）：最近，使用单核苷酸多态性（SNP）的全基因组关联研究在检测与疾病相关的遗传变异方面取得了一些成功。拷贝数变异（CNV）是人类基因组的另一个普遍特征，已被证明与各种人类表型有关。正在进行的HapMap项目正在构建经验证的CNVs数据库，该项目将为研究CNVs与疾病风险的关联、CNVs对药物治疗反应的影响以及结构变异在人类进化中的作用提供有价值的信息。然而，受现有统计方法的限制，目前检测人类疾病与遗传变异之间关联的研究实践是分别调用SNP基因型和CNV，然后进行单独分析。去年发表在《自然》杂志上的两项研究（Korn等人，2008; McClam等人，2008）已经表明，组合SNP等位基因和拷贝数信息可以导致拷贝数和基因型的准确推断，从而影响关联研究的结果。因此，迫切需要新的方法来同时推断SNP和CNV，并测试它们对复杂疾病的联合影响。 因此，我们建议开发新的统计和计算方法和软件的全基因组关联研究，使用集成的CNV和SNP信息。本项目的具体目标是（1）开发整合拷贝数和SNP等位基因信息的等位基因特异性拷贝数的调用算法，（2）开发用于联合基因型和拷贝数关联检测的单位点和多位点方法，（3）开发整合拷贝数信息的单体型关联方法，（4）发布用户友好的R.所提出的方法将通过模拟以及真实的数据进行评估，这些数据将包括（但不限于）公开可用的HapMap数据和来自我们的合作者的人类数据集，这些合作者研究遗传对左心室肥大、甘油三酯和血压的影响。所提出的方法将极大地促进人类遗传变异及其与复杂疾病的关联的研究。 公共卫生关系：这些方法将有助于发现导致复杂人类疾病的遗传变异，帮助我们更好地了解这些疾病，最终提高我们预防、诊断和治疗这些疾病的能力。