Mechanisms underlying complex trait human disease

描述（由申请人提供）：现在有大量新的基因组规模数据，这些数据将人类群体内的遗传变异与表型，特别是与常见疾病相关联。微阵列技术已经确定了100多个基因座，其中特定变异的存在与许多常见疾病的风险改变有关;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; 1000个人类基因组的测序提供了一个几乎完整的常见人群变异清单。此外，这些数据只是不断增加的洪水中的第一个，因为更快，更便宜的测序技术即将上线。这些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。提取预期的好处并不简单，需要获得有关遗传变异与疾病之间联系机制的详细知识。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。我们将研究那些与此目标特别相关的复杂性状疾病的基本原理。我们介绍了一个三阶段的机制框架，基因组变异的影响基因产物的功能，这些改变的功能对途径，过程和子系统的影响，最后复杂的性状疾病表型的后果。我们将开发计算方法来解决有关框架的三个主要方面的关键问题（1）复杂性状疾病的基因组变异所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机制，如表达，非同义变化和剪接，在这些变异中发挥什么作用？(2)通过现有技术发现的对疾病表型有强烈影响的基因集有多完整，以及如何从基因组和网络数据中估算缺失的基因？(3)参与疾病机制的基因活性与疾病表型之间的耦合分布是什么？这些结果将加深对复杂疾病的这些方面的理解，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.