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

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，即癌症或疾病易感性等复杂表型不是由所有个体中同一基因的单一DNA序列变异引起的。相反，导致这些表型的遗传或体细胞变异会影响细胞信号传导、调节和代谢途径中的多个基因。新的基因组测序技术目前正在大量样本中提供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。迫切需要计算技术来识别与表型相关的途径或基因组。该项目将开发强大的算法和统计技术，以应对在已知和新的基因-基因相互作用背景下分析DNA序列变异的四个挑战。(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，以确定基因组尺度相互作用网络的子网络，该网络丰富了多个样本的遗传变异。(2)推导稳健的统计检验，克服网络分析中的多重假设检验问题。包含数万到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，并对错误发现率有严格的限制。(3)在没有相互作用网络的情况下进行基因群的从头鉴定。提出的工作将检查组合方法来提取改变的基因子集，而不需要事先了解它们的相互作用。这些方法将利用越来越多的可用的测序样本。(4)实现对来自两个应用的生物学数据进行评估的算法：(a)癌症基因组测序研究中鉴定的体细胞突变，(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，本科生将参与实施所提出的算法。最后，这个项目的研究将被纳入多个本科和研究生课程的教学实例。

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.