Integrative statistical models for pathway analysis of GWAS data

GWAS 数据路径分析的综合统计模型

基本信息

批准号：
8654353
负责人：
Lily Wang
金额：
$ 17.46万
依托单位：
VANDERBILT UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2013
资助国家：
美国
起止时间：
2013-05-01 至 2015-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8654353
关键词：
Accounting Architecture Bioinformatics Biological Biological Process Bipolar Disorder Communities Complex Data Data Set Development Diagnosis Disease Disease Association Disease susceptibility Etiology Frequencies Future Genes Genetic Genome Genotype Heritability Individual Inherited Joints Knowledge Lead Light Maps Mental disorders Methodology Methods Mining Modeling Mutation Overlapping Genes Pathway Analysis Pathway interactions Pattern Predisposition Prevention Property Residual state Resources Risk Schizophrenia Series Signal Transduction Single Nucleotide Polymorphism Statistical Methods Statistical Models Susceptibility Gene System Technology Testing Uncertainty Variant Weight Work computerized tools database of Genotypes and Phenotypes density disorder risk effective therapy genetic variant genome wide association study genome-wide improved insight novel public health relevance rare variant statistics tool trait treatment strategy user friendly software

项目摘要

DESCRIPTION (provided by applicant): Genome-Wide Association Studies (GWAS) have become a popular approach for identifying genetic variants underlying complex diseases. However, the variants identified so far, individually or in combination, account for only a small proportion of the inherited component of disease risk. One possible reason is that complex diseases are likely to be caused by changes at the systems level, such as in a biological network or pathway, in which individual genetic variants only have weak marginal effects on disease risk. In this proposal, we will combine statistics, bioinformatics, and genetics to develop integrative approaches aimed at understanding the genetic architecture underlying complex diseases. We will examine copy number variants (CNVs) and single nucleotide polymorphisms (SNPs) detected by the recently developed high density genotyping arrays, and then we will integrate prior biological knowledge to formally test disease association with joint effects of groups of common and rare genetic variants (CNVs and SNPs) in the same pathway, or more broadly, gene set. We will apply our novel methods to analyze two schizophrenia (GAIN and nonGAIN) and one bipolar disorder (GAIN) GWAS datasets, all of which were generated using Affymetrix 6.0 chips. Our Specific Aims are as follows: (1) Develop novel statistical method to identify genes and pathways (or gene sets) with enriched association signals in GWAS by leveraging information from different types of genetic variants: common and rare, CNVs and SNPs. We will model all the genes, SNPs, and CNVs within a pathway in a hierarchical fashion using random gene effects, which provide the ability to borrow information across genes in the same pathway. (2) Apply the proposed model to two complex diseases (schizophrenia and bipolar disorder) and develop a user friendly software package that implements the proposed methodology. The successful completion of Aim 1 will provide us with critical statistical tools for current and future GWA studies. The successful completion of Aim 2 will significantly enhance our understandings of the genetic architecture underlying schizophrenia and bipolar disorder, including their common genetic components, and will lead to more effective treatment strategies for mental disorders.

描述（由申请人提供）：全基因组关联研究（GWAS）已成为鉴定复杂疾病潜在的遗传变异的流行方法。但是，到目前为止，单独或组合确定的变体仅占疾病风险遗传成分的一小部分。一个可能的原因是，复杂疾病可能是由于系统级别的变化引起的，例如在生物网络或途径中，其中单个遗传变异仅对疾病风险的边际影响较弱。在此提案中，我们将结合统计，生物信息学和遗传学，以开发旨在了解遗传结构复杂疾病的综合方法。我们将检查最近开发的高密度基因分型阵列检测到的拷贝数变体（CNV）和单核苷酸多态性（SNP），然后我们将先前的生物学知识与常见和稀有遗传变异组的关节效应正式测试疾病的关联（CNV和SNP）在相同的途径或更广泛的基因集中。我们将采用新的方法来分析两种精神分裂症（增益和非核心）和一个双相情感障碍（GAIN）GWAS数据集，所有这些数据集都是使用Affymetrix 6.0芯片生成的。我们的具体目的如下：（1）开发新的统计方法，通过利用来自不同类型的遗传变异的信息来识别GWAS中具有丰富关联信号的基因和途径（或基因集）：常见和稀有，CNV和SNP。我们将使用随机基因效应以层次方式在途径中对所有基因，SNP和CNV进行建模，从而提供了在同一途径中借用跨基因信息的能力。（2）将提出的模型应用于两种复杂疾病（精神分裂症和双相情感障碍），并开发出实现拟议方法的用户友好软件包。 AIM 1的成功完成将为我们提供当前和将来的GWA研究的关键统计工具。 AIM 2的成功完成将显着增强我们对精神分裂症和躁郁症基础遗传结构的理解，包括它们的常见遗传成分，并将导致更有效的精神疾病治疗策略。