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，即所有个体中同一基因中的单个DNA序列变体等复杂的表型不会引起癌症或疾病易感性。 相反，负责这些表型的遗传或体细胞变体会影响细胞信号，调节和代谢途径的多个基因。 现在，新的基因组测序技术正在提供大量样本中这些序列变体的测量，而其他技术正在测量基因之间的全基因组相互作用网络。迫切需要计算技术来识别与表型相关的途径或基因组。该项目将在已知和新型基因相互作用的背景下，在分析DNA序列变体的分析中，针对四个挑战开发可靠的算法和统计技术。 （1）结合基因相互作用的先验知识。 该项目开发了一个扩散模型，以确定基因组规模相互作用网络的子网，该网络富含多个样品的遗传变异。 （2）在网络分析中得出可靠的统计检验，以克服多个假设检验问题。 包含数十万至数十万个节点和边缘的生物相互作用网络具有大量的子网，这些子网可能会富含变体。 这项拟议的工作将设计技术，以评估多个候选子网，并在错误的发现率上进行严格的界限。 （3）对没有相互作用网络的基因组进行从头识别。拟议的工作将检查组合方法，以提取改变基因的子集，而没有事先了解其相互作用。 这些方法将利用越来越多的测序样品可用。 （4）实施来自两个应用的生物学数据评估算法：（a）在癌症基因组测序研究中鉴定出的体细胞突变，以及（b）人类关联研究中罕见的遗传变异。 这些应用将与两个生物医学研究小组合作进行。 该提案中开发的算法将被实施并发布为开源软件，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，该项目的研究将在多个本科和研究生课程中用作教学示例。