Penalized likelihood methods for estimation and testing with genomic data

 描述（由申请人提供）：近年来，科学界已经获得了大量的基因组数据，这些数据被发现疾病和表型变异的遗传和调控基础的承诺所推动。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。研究表型与序列变异的关联的问题现在已经得到了很好的研究，但是利用非序列数据类型和集成多个数据源的工具还没有很好地建立起来。许多非序列数据类型具有相对于遗传位置的空间相关性-我们可能期望这些数据遵循沿着染色体沿着位置的平滑函数。在这项研究中，我们将开发自适应方法，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学相关结果相关的区域。这些方法诞生于一个单一的惩罚回归框架，称为联合自适应差分估计（JADE），并将在高效，可扩展的算法中实现。该套JADE方法将包括二元和数量性状分析，自适应空间变化聚类和显著性检验。这些广泛、灵活的功能为将空间结构化的基因组数据类型与生物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。在我们的应用程序中，我们将专注于从DNA元素百科全书（ENCODE）财团和DNase I数据与实验室合作，探索与环境变化相关的基因调控的体内变化的各种健康组织类型中的DNA甲基化数据。

 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.