Statistical Methods for High Throughput Genomic Data

高通量基因组数据的统计方法

• 批准号: 217520-2013
• 负责人: Canty, Angelo
• 金额: $ 1.09万
• 依托单位: McMaster University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2016
• 资助国家: 加拿大
• 起止时间: 2016-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=594004
• 关键词: Statistical; Methods; Throughput; Genomic; Data

This research program will develop and validate new statistical methods for the analysis of high throughput genomic data. Advances in technology over the past decade have made it possible to examine multiple genes or genetic variants simultaneously. These new technologies have facilitated many important discoveries in the genetics of human diseases. There has been some disappointment, however, that more of the genetic heritability has not been explained. In this work my research group and I will examine two possible reasons for that; the presence of interactions among genes or between genes and the environment, and the inherent lack of power in single-marker analyses. We shall propose methods to assist in finding gene-gene and gene-environment interactions as well as methods to use aggregated information from many related markers to improve the power of these analyses. Technological advances have not stopped, however, and recent advances make it possible to directly sequence RNA fragments and so give much better measures of gene expression. This new technology produces quite different data from the array based data of the last decade and new methods are required to properly analyze the data. The few methods currently proposed are good starting points but preliminary work with an undergraduate student suggest that they are all somewhat flawed as the assumptions underlying them do not hold for real data. My research team will develop new methods, with fewer restrictive assumptions, which can be used to reliably analyze this type of data. We will also examine ways of incorporating the new data with existing data collected using gene expression microarrays to leverage maximum power from the large amount of data available. This research will address some of the major statistical issues which arise in the analysis of high-throughput genetic data and thus facilitate further further discoveries on the etiology of human disease in Canada. Training a new generation of statisticians to understand the data from genetic experiments and their analysis will also provide Canadian scientists with a rich pool of talent on which to draw when they conduct these powerful but expensive experiments.

该研究计划将开发和验证用于高通量基因组数据分析的新统计方法。过去十年的技术进步使同时检查多个基因或遗传变异成为可能。这些新技术促进了人类疾病遗传学方面的许多重要发现。然而，令人失望的是，更多的遗传性还没有得到解释。在这项工作中，我和我的研究小组将研究两个可能的原因：基因之间或基因与环境之间存在相互作用，以及单标记分析固有的缺乏力量。我们将提出方法来帮助发现基因-基因和基因-环境的相互作用，以及使用来自许多相关标记的聚合信息来提高这些分析的能力的方法。然而，技术的进步并没有停止，最近的进步使直接测序RNA片段成为可能，从而更好地衡量基因表达。这项新技术产生的数据与过去十年基于阵列的数据完全不同，需要新的方法来正确分析数据。目前提出的几种方法是很好的起点，但与本科生的初步工作表明，他们都有点缺陷，因为他们的假设不适用于真实的数据。我的研究团队将开发新的方法，减少限制性假设，可用于可靠地分析这类数据。我们还将研究如何将新数据与使用基因表达微阵列收集的现有数据相结合，以利用大量可用数据的最大功率。这项研究将解决高通量遗传数据分析中出现的一些主要统计问题，从而促进加拿大人类疾病病因学的进一步发现。培训新一代统计学家了解遗传实验数据及其分析也将为加拿大科学家提供丰富的人才库，以便他们在进行这些强大但昂贵的实验时加以利用。