Unified Statistical Methods for Sequence-Based Association Studies

说明书(申请人提供)：基于序列的联想研究的统一统计方法。快速和经济的下一代测序(NGS)技术将产生前所未有的海量(数千个个体)和高维(1000万个)基因组和表观基因组变异数据，这些数据允许几乎完整地评估基因组和表观基因组变异，包括常见和罕见的变异、RNA-SEQ、mRNA-SEQ和甲基化-SEQ数据。因此，这些基因组变异数据是如此密集地分布在基因组中，以至于遗传变异可以被认为是在连续统中变化的基因组变异观察。NGS技术的出现不仅改变了我们对基因组学的看法，从独立分离的离散模型向混合(离散和连续)模型转变，而且导致基因组和表观基因组分析的分析方法发生了巨大变化，从标准的多变量数据分析到功能数据分析，从独立抽样到相依抽样，从低维数据分析到高维数据分析，从单一基因组或表观基因组变异分析到基因组和表观基因组的综合分析。为了应对我们在NGS数据分析中面临的巨大挑战，这项建议的目标是开发新的和强大的统计方法，用于基于序列的关联研究和QTL(EQTL)分析，利用高维数据简化、因果推理和功能数据分析技术来识别基因组中常见和罕见的风险变异，通过中间表型和表达来研究它们的功能，估计总效应(干预效应)和变异对表型的直接影响，并统一基于家庭和群体的设计。我们将通过模拟数据集和真实数据集来评估这些方法的性能。

DESCRIPTION (provided by applicant): Unified Statistical Methods for Sequence-based Association Studies. Fast and economic next generation sequencing (NGS) technologies will generate unprecedentedly massive (thousands of individuals) and high-dimensional (ten millions) genomic and epigenomic variation data that allow nearly complete evaluation of genomic and epigenomic variation including common and rare variants, RNA-seq, mRNA-seq and methylation-seq data. As a consequence, these genomic variation data are so densely distributed across the genome that the genetic variants can be considered as genomic variation observations varying over a continuum. The emergence of NGS technologies is not only changing our view of genomics from independently segregating discrete model to hybrid (both discrete and continuous) models, but also causing great changing in analytic methods for genomic and epigenomic analysis from standard multivariate data analysis to functional data analysis, from independent sampling to dependent sampling, from low dimensional data analysis to high dimensional data analysis, from single genomic or epigenomic variant analysis to integrated genomic and epigenomic analysis. To address the great challenges we are facing in NGS data analysis, the goals of this proposal are to develop novel and powerful statistical methods for sequence-based association studies and QTL (eQTL) analysis which leverage high dimensional data reduction, causal inference and functional data analysis techniques to identify both common and rare risk variants across the genome, investigate their function via intermediate phenotypes and expressions, estimate the total effects (intervention effects) and direct effects of variants on the phenotypes, and unify family and population-based designs. We will evaluate the performance of these methods by simulated and real datasets.