A gene-set approach for pathway analysis of genome-wide SNP data with application

一种用于全基因组 SNP 数据通路分析的基因集方法及其应用

• 批准号: 7961049
• 负责人: Saonli Basu
• 金额: $ 17.92万
• 依托单位: UNIVERSITY OF MINNESOTA
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-07-01 至 2012-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7961049
• 关键词: Accounting; Ally; Architecture; Area; Atherosclerosis; Biological; Biological Process; Biology; Caucasians; Caucasoid Race; Collection; Communities; Complex; Computer software; Data; Detection; Development; Diagnosis; Dimensions; Disease; Epidemiologist; Genes; Genetic; Genetic Risk; Genetic Structures; Genotype; Individual; Joints; Knowledge; Lead; Letters; Libraries; Link; Methods; Modeling; Molecular; Non-Insulin-Dependent Diabetes Mellitus; Pathway Analysis; Pathway interactions; Performance; Phenotype; Play; Polymorphism Analysis; Population; Predisposition; Probability; Programming Languages; Publishing; Regression Analysis; Reporting; Research Personnel; Risk; Role; Sample Size; Single Nucleotide Polymorphism; Statistical Methods; Testing; Time; Uncertainty; Work; base; cohort; disorder risk; gene environment interaction; gene interaction; genome wide association study; genome-wide; genome-wide analysis; genotyping technology; improved; insight; novel; public health relevance; research and development; simulation; trait; user friendly software

DESCRIPTION (provided by applicant): The rapid progress in genotyping technology has greatly facilitated our understanding of the genetic aspect of various diseases. Several genome-wide association studies (GWAS) have been published on various complex diseases, where genotype data on a large number of single nucleotide polymorphisms (SNPs) are collected to study the association between these SNPs and a disease. Most of these GWAS are limited to single SNP association analyses. New loci are found to be associated with different diseases in these GWAS, but generally they explain very little of the genetic risk for these diseases. As GWAS are still underpowered to find small main effects, and gene-gene interactions are likely to play a role, the data might currently not be analyzed to its full potential. In this proposal, we aim to evaluate alternative methods to study GWAS data. We investigate the pathway-based approaches that incorporate the prior biological information into the analysis and try to detect the effect of a pathway (a collection of single nucleotide polymorphisms (SNPs) with biological relevance) on a disease, instead of focusing on the effects of individual SNPs. Current pathway-based methods are mostly limited to investigating the overrepresentation of pathways in a GWAS, which mainly focus on the individual effects of the SNPs within a pathway. These methods try to avoid the estimation of a large number of parameters involved in the joint modeling of effects of a large number of SNPs within a pathway. Hence, most of these methods do not take into account the possibility of the interaction among multiple SNPs. Moreover, none of these approaches is likelihood-based. It is hence not possible to estimate and quantify the overall pathway effect on disease risk and assess its statistical uncertainty. In Aim 1, this proposal offers a collection of novel statistical methods as well as a suite of user-friendly software to study the joint effects of a group of SNPs within a pathway on a complex multifactorial disease, incorporating the possibility of interaction among the SNPs. The model also offers a data reduction strategy that avoids the issues associated with the estimation of a large number of parameter corresponding to a large number of SNPs within a pathway. We also propose to extensively compare the different existing approaches on pathway- based analysis through simulation studies, which would provide a better understanding of the advantages and limitations of these methods. In Aim 2, we investigate the effects of pathways on type 2 diabetes and related quantitative traits in the ARIC population. We will derive multiple pathways related to type 2 diabetes and will use our model as well as other existing approaches to compare the effects of these pathways. Our proposed pathway-based GWAS may unravel new SNPs or pathways associated with type 2 diabetes and these quantitative traits and thus facilitate to gain insight into the deep understanding of intricate networks of functionally related genes in type 2 diabetes. Finally, in Aim 3, we aim to provide a software to conduct pathway-based analyses using our proposed approach. The availability of the software would help the genetic epidemiologists to carry out more sophisticated pathway-based analyses, and in turn lead to further research and development of statistical methods for pathway analyses. The broader impact of our work lies in its ability to improve the understanding of the professionals engaged in unraveling the complex genetic architecture for complex disease. There is growing evidence that gene- gene and gene-environment interactions contribute to complex diseases rather than single genes. Instead of focusing only on the SNPs with the highest statistical significance, our approach and software will provide an alternative way of analyzing the GWAS data. Our findings will facilitate and improve the understanding of complex mechanisms of functionally related genes, thereby having far reaching beneficial effects on the diagnosis and treatment of complex diseases. PUBLIC HEALTH RELEVANCE: The potential of the methods and software we propose is exceedingly broad, since they will substantially improve the current pathway based genome-wide association studies (GWAS). We envision that our method would facilitate a new paradigm for GWAS, which not only will identify the genes that include significant single nucleotide polymorphisms (SNPs) found by single SNP analysis, but will also detect new genes in which each single SNP confer small disease risk, but their joint actions are implicated in the development of diseases. The pathway-based association analysis will improve the understanding of complex mechanisms of function- ally related genes, thereby having far reaching beneficial effects on the diagnosis and treatment of complex diseases.

描述（申请人提供）：基因分型技术的快速发展极大地促进了我们对各种疾病遗传方面的理解。已经发表了几项关于各种复杂疾病的全基因组关联研究（GWAS），其中收集了大量单核苷酸多态性（SNP）的基因型数据，以研究这些SNP与疾病之间的关联。这些GWAS中的大多数仅限于单个SNP关联分析。在这些GWAS中发现了与不同疾病相关的新基因座，但通常它们对这些疾病的遗传风险解释甚少。由于GWAS在发现小的主效应方面仍然动力不足，并且基因-基因相互作用可能会发挥作用，因此目前可能无法充分分析数据。在这项提案中，我们的目标是评估研究GWAS数据的替代方法。我们研究了基于通路的方法，将先前的生物信息纳入分析，并试图检测通路（具有生物相关性的单核苷酸多态性（SNP）的集合）对疾病的影响，而不是专注于单个SNP的影响。 目前基于通路的方法大多局限于研究GWAS中通路的过度代表性，其主要关注通路内SNP的个体效应。这些方法试图避免估计在通路内大量SNP的效应的联合建模中涉及的大量参数。因此，这些方法中的大多数没有考虑多个SNP之间相互作用的可能性。此外，这些方法都不是基于可能性的。因此，不可能估计和量化对疾病风险的总体途径效应，也不可能评估其统计不确定性。在目标1中，该提案提供了一系列新颖的统计方法以及一套用户友好的软件，以研究一组SNP在一个复杂的多因素疾病通路中的联合作用，并结合SNP之间相互作用的可能性。该模型还提供了一种数据简化策略，该策略避免了与估计对应于途径内大量SNP的大量参数相关的问题。我们还建议通过模拟研究广泛比较基于途径的分析的不同现有方法，这将提供对这些方法的优点和局限性的更好理解。在目标2中，我们研究了ARIC人群中通路对2型糖尿病和相关数量性状的影响。我们将推导出与2型糖尿病相关的多种途径，并将使用我们的模型以及其他现有的方法来比较这些途径的影响。我们提出的基于通路的GWAS可能会揭示与2型糖尿病和这些数量性状相关的新SNP或通路，从而有助于深入了解2型糖尿病中功能相关基因的复杂网络。最后，在目标3中，我们的目标是提供一个软件，使用我们提出的方法进行基于路径的分析。该软件的可用性将有助于遗传流行病学家进行更复杂的基于路径的分析，从而进一步研究和开发用于路径分析的统计方法。 我们工作的更广泛的影响在于它能够提高从事解开复杂疾病的复杂遗传结构的专业人员的理解。越来越多的证据表明，基因与基因以及基因与环境的相互作用会导致复杂的疾病，而不是单个基因。我们的方法和软件将提供一种分析GWAS数据的替代方法，而不是只关注具有最高统计学意义的SNP。我们的研究结果将促进和提高功能相关基因的复杂机制的理解，从而对复杂疾病的诊断和治疗产生深远的有益影响。 公共卫生相关性：我们提出的方法和软件的潜力是非常广泛的，因为它们将大大改善目前基于通路的全基因组关联研究（GWAS）。我们设想，我们的方法将促进GWAS的新范式，它不仅将识别包括通过单个SNP分析发现的显著单核苷酸多态性（SNP）的基因，而且还将检测新基因，其中每个SNP赋予小的疾病风险，但它们的联合作用与疾病的发展有关。基于通路的关联分析将有助于理解功能相关基因的复杂机制，从而对复杂疾病的诊断和治疗产生深远的影响。