Quantitative Methods for Genetic Epidemiology

描述(由申请人提供)： 现代基因组流行病学已经迅速发展，超出了最初的预期，这主要是因为尖端的基因分析和下一代测序技术与大型、特征良好的研究相结合。然而，将基因组注释与测量的基因类型和表型相结合的新的统计分析方法已经落后，大多数已发表的全基因组关联研究(Gwas)集中在单标记(单核苷酸多态，SNPs)分析上。认识到大多数常见的遗传变异对性状的影响很小，而且有许多相关的变异，重新收集许多现有的GWAS数据集的时机已经成熟，许多数据集预计将在不久的将来通过将基因组注释与GWAS结果结合在一起。因此，我们建议开发新的统计和计算方法，以便使用GWASSNP数据和公共基因注释扫描所有可能的基因集。我们还计划开发惩罚回归模型，以同时模拟单个SNPs对一个性状的影响，基因对一个性状的影响，以及基因集对一个性状的影响。这将允许在可用时合并注释，但在注释不完整时不会丢失SNP或基因。罕见的变异很可能在复杂特征的病因学中发挥重要作用，下一代测序技术很快就能负担得起大规模研究的费用。我们提出了基于广义回归模型的一阶矩和二阶矩(以及删失生存模型)的新策略来筛选稀有变异与性状的关联。最后，将注释信息包括到统计模型中对于分析稀有变量特别重要，因为它们是稀疏的，并且有可能改进对常见SNP的分析，甚至将稀有和常见变量组合到模型中。为此，我们提出了基于核矩阵的新的统计方法，该方法提供了关于如何根据基于基因组注释的变体的相似性来融合回归系数的信息。 公共卫生相关性： 我们提出的为基因组流行病学开发改进的统计分析方法的计划可能会对许多不同的过去和正在进行的常见人类疾病和特征的遗传病因学研究产生很大影响。通过将我们的新分析方法应用于现有数据集或未来的研究，有望对疾病病因的遗传病因学或在药物基因组学研究中对治疗或毒性反应的遗传病因学有新的见解。这些见解应该为设计未来的后续研究提供基础，例如基于实验室的功能研究，以进一步完善对疾病病因的理解，或者如何最好地定制治疗方案，以获得最佳的治疗效果并减少副作用。因此，我们的研究计划具有广泛的公共卫生影响，从疾病筛查到诊断，再到预后和治疗。

DESCRIPTION (provided by applicant): Modern genomic epidemiology has rapidly evolved beyond initial expectations, primarily because of cutting- edge genetic assays and next-generation sequencing technologies combined with large well-characterized studies. Yet, novel statistical analysis methods that combine genomic annotation with measured genotypes and phenotypes have lagged behind, with most published genome wide association studies (GWAS) focused on single-marker (single nucleotide polymorphisms, SNPs) analyses. Recognizing that the majority of common genetic variants have small effects on traits, and that there are many associated variants, the time is ripe to re-harvest the many existing GWAS data sets, and many expected in the near future, by joining genomic annotation with GWAS results. Hence, we propose to develop new statistical and computational methods in order to scan all possible gene-sets using GWAS SNP data and public gene annotation. We also plan to develop penalized regression models to simultaneously model the effects of individual SNPs on a trait, the effects of genes on a trait, and the effects o gene-sets on a trait. This will allow incorporation of annotation when available, but not lose SNPs or genes when annotation is incomplete. Rare variants are likely to have a prominent role in the etiology of complex traits, and next-generation sequencing technologies will soon be affordable for large studies. We propose new strategies to screen for the association of rare variants with traits based on both the first- and second-moments of generalized regression models (as well as censored survival models). Finally, including annotation information into statistical models is particularly important for analyzing rare variants because they are sparse, and has potential to improve analyses for common SNPs, or even combining both rare and common variants into models. For this, we propose novel statistical methods based on kernel matrices that provide information on how regression coefficients should be "fused" according to similarities of variants based on genomic annotation. PUBLIC HEALTH RELEVANCE: Our proposed plans to develop improved statistical analysis methods for genomic epidemiology are likely to have high impact on the many different past and ongoing studies of the genetic etiology of common human diseases and traits. By applying our new analytic methods to existing data sets, or to future studies, new insights are expected regarding the genetic etiology of disease causation or - in pharmacogenomic studies- the genetic etiology of response to treatments or toxicities. These insights should provide the basis for designing future follow-up studies, such as laboratory-based functional studies to further refine understanding of disease causation, or how best to tailor treatments for optimal therapeutic benefits with reduced side-effects. Hence, our research plans have broad public health implications, ranging from disease screening, to diagnosis, to prognosis and treatment.