III: Small: Algorithmic Approaches for Pathway and Gene Group Analysis in Genetic Studies

III：小：遗传研究中通路和基因组分析的算法方法

• 批准号: 1016648
• 负责人: Benjamin Raphael
• 金额: $ 50万
• 依托单位: Brown University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-08-15 至 2015-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1016648&HistoricalAwards=false
• 关键词: III; Small; Algorithmic; Approaches; Pathway

Recent cancer genome sequencing projects and human genome-wide association studies (GWAS) have underscored the principle that complex phenotypes like cancer or disease susceptibility do not result from single DNA sequence variants in the same gene in all individuals. Rather, the inherited or somatic variants responsible for these phenotypes affect multiple genes in cellular signaling, regulatory, and metabolic pathways. New genome sequencing technologies are now providing measurements of these sequence variants in large numbers of samples, while other technologies are measuring whole-genome networks of interactions between genes. There is an urgent need for computational techniques to identify pathways, or groups of genes, that are associated to a phenotype.This project will develop robust algorithmic and statistical techniques for four challenges in the analysis of DNA sequence variants in the context of known and novel gene-gene interactions. (1) Incorporating prior knowledge of gene interactions. This project develops a diffusion model to determine subnetworks of a genome-scale interaction network that are enriched for genetic variants across multiple samples. (2) Deriving robust statistical tests to overcome multiple hypothesis-testing problems in network analysis. Biological interaction networks containing tens to hundreds of thousands of nodes and edges have an enormous number of subnetworks that might be enriched for variants. This proposed work will design techniques to evaluate multiple candidate subnetworks with rigorous bounds on the false discovery rate. (3) Performing de novo identification of gene groups without an interaction network. The proposed work will examine combinatorial approaches to extract subsets of altered genes without prior knowledge of their interactions. These approaches will leverage the increasingly large number of sequenced samples that are becoming available. (4) Implementation of algorithms for evaluation on biological data from two applications: (a) somatic mutations identified in cancer genome sequencing studies, and (b) rare genetic variants in human association studies. These applications will be conducted in collaboration with two biomedical research groups. Algorithms developed in this proposal will be implemented and released as open-source software for use by the biological and medical community. The project will partially support the training of graduate students, and undergraduates will be involved in implementing proposed algorithms. Finally, research from this project will use incorporated as pedagogical examples in multiple undergraduate and graduate courses.

最近的癌症基因组测序项目和人类全基因组关联研究（GWAS）强调了这样一个原则，即复杂的表型（如癌症或疾病易感性）并不是由所有个体中相同基因中的单个DNA序列变异引起的。 相反，负责这些表型的遗传或体细胞变异影响细胞信号传导、调节和代谢途径中的多个基因。 新的基因组测序技术现在提供了大量样本中这些序列变异的测量，而其他技术正在测量基因之间相互作用的全基因组网络。目前迫切需要计算技术来识别与表型相关的途径或基因组。本项目将针对已知和新基因-基因相互作用背景下DNA序列变异分析中的四个挑战开发强大的算法和统计技术。(1)解释基因相互作用的先验知识。 该项目开发了一个扩散模型，以确定基因组规模的相互作用网络的子网络，这些网络在多个样本中富集了遗传变异。 (2)导出稳健的统计测试，以克服网络分析中的多个假设测试问题。 包含数万到数十万个节点和边的生物相互作用网络有大量的子网络，这些子网络可能会因变体而丰富。 这项工作将设计技术来评估多个候选子网的错误发现率的严格界限。 (3)在没有相互作用网络的情况下进行基因组的从头鉴定。拟议的工作将检查组合的方法来提取改变基因的子集，而无需事先了解它们的相互作用。 这些方法将利用越来越多的测序样本。 (4)对来自两种应用的生物数据进行评估的算法的实施：（a）癌症基因组测序研究中识别的体细胞突变，和（B）人类关联研究中的罕见遗传变异。 这些应用将与两个生物医学研究小组合作进行。 本提案中开发的算法将作为开源软件实施和发布，供生物和医学界使用。该项目将部分支持研究生的培训，本科生将参与实施拟议的算法。 最后，本项目的研究将在多个本科生和研究生课程中使用整合作为教学实例。