Penalized likelihood methods for estimation and testing with genomic data

 描述(申请人提供)：近年来，科学界已经获得了大量的基因组数据，这得益于发现疾病和表型变异的遗传和调控基础的承诺。这一承诺尚未完全实现，部分原因是我们目前的统计和计算工具的局限性。研究与序列变体的表型关联的问题现在已经被很好地研究了，但利用非序列数据类型和集成多个数据源的工具还不是很成熟。许多非序列数据类型具有关于遗传位置的空间相关性--我们可能期望这些数据遵循沿染色体的位置的平滑函数。在这项研究提案中，我们将开发适应性方法来同时估计这些功能或基因组图谱，并发现它们与临床或生物相关结果相关的区域。这些方法诞生于称为联合自适应差分估计(JADE)的单一惩罚回归框架，并将在高效、可扩展的算法中实现。JADE方法套件将包括二进制和数量性状分析、自适应空间变化聚类和显著性检验。这些广泛而灵活的能力为将空间结构的基因组数据类型与生物学或临床结果或其他二进制或定量基因组信息(如基因表达水平)相关联提供了许多可能性。在我们的应用中，我们将专注于各种健康组织类型的DNA甲基化数据，这些数据来自DNA元素百科全书(ENCODE)联盟和DNase I数据，我们将与一个实验室合作，探索与环境变化相关的体内基因调控的变化。

 DESCRIPTION (provided by applicant): In recent years the scientific community has acquired vast amounts of genomic data fueled by the promise of discovering the genetic and regulatory foundations of disease and phenotypic variation. This promise has not yet been fully realized, in part due to the limitations of our current statistical and computational tools. The problem of studying phenotypic associations with sequence variants is now well studied, but tools utilizing non-sequence data types and integrating multiple data sources are less well established. Many non-sequence data types possess spatial correlation with respect to genetic position - we might expect these data to follow a smooth function of position along the chromosome. In this research proposal we will develop adaptive methods for simultaneously estimating these functions or genomic profiles and discovering regions in which they are associated with clinically or biologically relevant outcomes. These methods are born out of a single penalized regression framework called Joint Adaptive Differential Estimation (JADE) and will be implemented in efficient, scalable algorithms. The suite of JADE methods will include binary and quantitative trait analysis, adaptive spatially varying clustering, and significance testing. These broad, flexible capabilities offer many possibilities for relating spatially structured genomic data typesto biological or clinical outcomes, or to other binary or quantitative genomic information such as gene expression levels. In our applications we will focus on DNA methylation data in a variety of healthy tissue types available from the Encyclopedia of DNA Elements (ENCODE) consortium and DNase I data in collaboration with a lab exploring in vivo changes in gene regulation associated with environmental changes.