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

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的遗传方面的了解。已有多项关于各种复杂疾病的全基因组关联研究发表，其中收集了大量单核苷酸多态(SNPs)的基因型数据，以研究这些SNPs与疾病之间的关联。这些GWA中的大多数仅限于单SNP关联分析。在这些GWA中，新的基因座被发现与不同的疾病相关，但通常它们很少解释这些疾病的遗传风险。由于GWAs在寻找小的主效应方面仍然能力不足，而且基因-基因相互作用可能会发挥作用，目前可能没有充分分析数据的潜力。在这项提案中，我们的目标是评估研究全球气候变化数据的替代方法。我们研究基于路径的方法，将先前的生物信息纳入分析，并试图检测路径(具有生物相关性的单核苷酸多态(SNPs)的集合)对疾病的影响，而不是专注于单个SNPs的影响。目前基于通路的方法大多局限于研究GWAs中通路的过度表达，主要集中在通路中SNPs的个体效应。这些方法试图避免在对一条路径内的大量SNP的影响进行联合建模时涉及的大量参数的估计。因此，这些方法大多没有考虑多个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，不可能估计和量化整个途径对疾病风险的影响，并评估其统计不确定性。在目标1中，这项建议提供了一系列新的统计方法以及一套用户友好的软件，以研究一条途径内的一组SNPs对复杂的多因素疾病的联合影响，纳入了SNPs之间相互作用的可能性。该模型还提供了一种数据约简策略，避免了与路径中大量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，这将使我们更好地理解这些方法的优势和局限性。在目标2中，我们在ARIC人群中调查通路对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。我们提出的基于途径的GWAS可能会揭开与2型糖尿病和这些数量性状相关的新的SNPs或途径，从而有助于深入了解2型糖尿病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，使用我们提出的方法进行基于路径的分析。该软件的使用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开发用于途径分析的统计方法。我们的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-基因和基因-环境的相互作用导致了复杂的疾病，而不是单基因。我们的方法和软件将提供另一种分析全球气候变化数据的方法，而不是只关注具有最高统计意义的SNPs。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。 公共卫生相关性：我们提出的方法和软件的潜力非常广泛，因为它们将极大地改进当前基于途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，不仅将识别通过单SNP分析发现的包括显著单核苷酸多态(SNP)的基因，而且还将检测新的基因，其中每个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。

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.