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

描述（由申请人提供）：基因分型技术的快速发展极大地促进了我们对各种疾病的遗传方面的理解。一些关于各种复杂疾病的全基因组关联研究（GWAS）已经发表，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大多局限于单SNP关联分析。在这些GWAS中发现了与不同疾病相关的新位点，但通常它们对这些疾病的遗传风险解释得很少。由于GWAS在发现小的主效应方面的能力仍然不足，基因-基因相互作用可能起作用，因此目前可能无法对数据进行充分的分析。在本提案中，我们旨在评估研究GWAS数据的替代方法。我们研究了基于途径的方法，将先前的生物学信息纳入分析，并试图检测途径（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，主要关注通路内snp的个体效应。这些方法试图避免在对一条通路内大量snp效应的联合建模中涉及的大量参数的估计。因此，这些方法大多没有考虑到多个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，并评估其统计不确定性。在Aim 1中，该建议提供了一系列新颖的统计方法以及一套用户友好的软件来研究一组snp在复杂多因子疾病通路中的联合效应，包括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来广泛比较现有的基于路径分析的不同方法，从而更好地了解这些方法的优点和局限性。在Aim 2中，我们研究了这些通路对ARIC人群中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影响。我们提出的基于通路的GWAS可能揭示与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型糖尿病中复杂的功能相关基因网络。最后，在目标3中，我们的目标是提供一个软件来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途径的分析，进而导致进一步研究和发展途径分析的统计方法。我们的工作更广泛的影响在于它能够提高专业人员对复杂疾病复杂遗传结构的理解。越来越多的证据表明，基因-基因和基因-环境的相互作用是导致复杂疾病的原因，而不是单一基因。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显著性的snp。我们的发现将促进和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。

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.