Mechanisms underlying complex trait human disease

描述(申请人提供)：现在有一个新的基因组规模数据爆炸式增长，将人类群体内的遗传变异与表型，特别是与常见疾病联系起来。微阵列技术已经确定了100个基因座，其中特定变异的存在与许多常见疾病的风险改变有关；对癌症样本中单个外显子组的完整测序发现了各种基因中的许多体细胞突变；对1000个人类基因组的测序提供了常见群体变异的几乎完整的清单。此外，随着更快、更便宜的测序技术上线，这些数据只是不断增加的洪流中的第一个。这一结果有望在人类常见疾病的治疗和诊断方面取得重大进展。提取预期的益处并不简单，而且需要获得将基因变异与疾病联系起来的机制的详细知识。该项目侧重于这一挑战的一个方面--使用新的基因组数据来确定新的治疗机会。我们将研究与这一目标特别相关的复杂性状疾病背后的那些原理。我们介绍了一个三阶段机制框架，将基因组变异与受影响的基因产物的功能联系起来，这些改变的功能对途径、过程和子系统的影响，最后是复杂性状疾病表型的后果。我们将开发计算方法来解决框架的三个主要方面的关键问题：(1)复杂性状疾病背后的基因组变异带来的蛋白质功能变化有多大？不同类型的基因组和蛋白质水平的机制，如表达、非同义变化和剪接，在这些变异中起到什么作用？(2)对现有技术发现的疾病表型有很强影响的基因集有多完整，如何从基因组和网络数据中归因于缺失的基因？(3)参与疾病机制的基因的活性与疾病表型之间的耦合分布是什么？这一结果将加深对复杂疾病这些方面的理解，并为从GWAS和其他基因组研究中确定潜在的新药靶点提供基础。

DESCRIPTION (provided by applicant): There is now an explosion of new genome scale data relating genetic variation within the human population to phenotype, and particularly to common disease. Microarray technology has identified 100s of loci where the presence of particular variants is associated with altered risk of many common diseases; complete sequencing of individual exomes in cancer samples has discovered many somatic mutations in a variety of genes; and sequencing of 1000 human genomes has provided an almost complete inventory of common population variants. Further, these data are only the first in an ever-increasing flood, as even faster and cheaper sequencing technologies come on line. The results hold promise for major advances in treatment and diagnosis of common human diseases. Extracting the expected benefits is not straightforward, and will necessitate acquiring detailed knowledge of the mechanisms linking genetic variation to disease. This project focuses on one aspect of this challenge - using the new genomic data to identify new therapeutic opportunities. We will investigate those principles underlying complex trait disease that are particularly relevant to tha goal. We introduce a three stage mechanistic framework, relating genomic variation to the function of impacted gene products, the impact of these altered functions on pathways, processes and subsystems; and finally the consequences for complex trait disease phenotypes. We will develop computational methods to address key questions concerning three major aspects of the framework (1) How large are the changes in protein function brought about by the genomic variants underlying complex trait disease? What role do different classes of genomic and protein level mechanism, such as expression, non-synonymous changes and splicing, play in these variants? (2) How complete is the set genes with strong influence on the disease phenotypes discovered by current technologies, and how can missing genes be imputed from the genomic and network data? (3) What is the distribution of coupling between the activity of genes involved in disease mechanism and disease phenotypes? The results will deepen understanding of these aspects of complex disease, and provide a basis for identifying potential new drug targets from GWAS and other genomic studies.