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Gene-set pathway analysis of GWAS data for T2DM and related quantitative traits

T2DM 和相关数量性状的 GWAS 数据的基因组通路分析

基本信息

批准号：
8097238
负责人：
Saonli Basu
金额：
$ 21.45万
依托单位：
UNIVERSITY OF MINNESOTA
依托单位国家：
美国
项目类别：
财政年份：
2010
资助国家：
美国
起止时间：
2010-07-01 至 2014-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8097238
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 data reduction 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

项目摘要

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进步极大地促进了我们对各种疾病遗传方面的理解。已经发表了一些关于各种复杂疾病的全基因组关联研究（GWAS），其中收集大量单核苷酸多态性（SNP）的基因型数据来研究这些SNP与疾病之间的关联。大多数 GWAS 仅限于单个 SNP 关联分析。在这些 GWAS 中发现新的基因座与不同的疾病相关，但通常它们很少解释这些疾病的遗传风险。由于 GWAS 仍然不足以发现小的主效应，并且基因与基因的相互作用可能发挥作用，因此目前可能无法充分分析数据的潜力。在本提案中，我们的目标是评估研究 GWAS 数据的替代方法。我们研究基于通路的方法，将先前的生物学信息纳入分析中，并尝试检测通路（具有生物学相关性的单核苷酸多态性 (SNP) 的集合）对疾病的影响，而不是关注单个 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。这些方法试图避免对通路内大量 SNP 效应的联合建模中涉及的大量参数进行估计。因此，大多数这些方法没有考虑多个 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疾病风险的整体途径影响并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，用于研究通路中一组 SNP 对复杂多因素疾病的联合影响，并纳入 SNP 之间相互作用的可能性。该模型还提供了一种数据缩减策略，避免了与路径内大量 SNP 相对应的大量参数估计相关的问题。我们还建议通过模拟研究广泛比较现有的基于路径的分析的不同方法，这将有助于更好地理解这些方法的优点和局限性。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。我们将得出与 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方法来比较这些途径的效果。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特征相关的新 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。该软件的可用性将帮助遗传流行病学家进行更复杂的基于路径的分析，进而促进路径分析统计方法的进一步研究和开发。我们工作的更广泛影响在于它能够提高致力于揭示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另一种分析 GWAS 数据的方法，而不是只关注具有最高统计显着性的 SNP。我们的研究结果将促进和提高对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非常广泛，因为它们将大大改善当前基于路径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，它不仅将识别包含通过单个 SNP 分析发现的显着单核苷酸多态性 (SNP) 的基因，而且还将检测新基因，其中每个单个 SNP 赋予较小的疾病风险，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提高对功能相关基因复杂机制的理解，从而对复杂疾病的诊断和治疗产生深远的有益影响。