Integrative statistical models for pathway analysis of GWAS data

GWAS 数据路径分析的综合统计模型

• 批准号: 9187527
• 负责人: Lily Wang
• 金额: $ 2.25万
• 依托单位: UNIVERSITY OF MIAMI SCHOOL OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-05-01 至 2016-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9187527
• 关键词: Integrative; statistical; models; pathway; analysis

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）已成为识别复杂疾病潜在遗传变异的流行方法。然而，迄今为止发现的变异，单独或组合，只占疾病风险遗传成分的一小部分。一个可能的原因是，复杂疾病很可能是由系统层面的变化引起的，例如生物网络或途径的变化，其中个体遗传变异对疾病风险的影响很小。在这个建议中，我们将结合联合收割机统计学，生物信息学和遗传学，以开发综合的方法，旨在了解复杂疾病的遗传结构。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异（CNVs）和单核苷酸多态性（SNPs），然后我们将整合先前的生物学知识，正式测试疾病与同一途径或更广泛的基因组中常见和罕见遗传变异（CNVs和SNPs）的联合作用的相关性。我们将应用我们的新方法来分析两个精神分裂症（GAIN和nonGAIN）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Affytron 6.0芯片生成的。我们的具体目标如下：（1）开发新的统计方法，通过利用不同类型的遗传变异（常见和罕见，CNV和SNP）的信息，识别GWAS中具有丰富关联信号的基因和通路（或基因集）。我们将使用随机基因效应以分层方式对通路中的所有基因、SNP和CNV进行建模，这提供了在同一通路中跨基因借用信息的能力。(2)应用所提出的模型，以两个复杂的疾病（精神分裂症和双相情感障碍），并开发一个用户友好的软件包，实现所提出的方法。目标1的成功完成将为我们当前和未来的GWA研究提供关键的统计工具。目标2的成功完成将大大提高我们对精神分裂症和双相情感障碍的遗传结构的理解，包括其共同的遗传成分，并将导致更有效的精神障碍治疗策略。