Finding important associations in genetic and genomic data

即将到来的遗传学和基因组学研究时代可以被描述为数据集成时代。通过结合各种各样的数据，可能以不同的方式，在不同的时间或组织，或从几个来源测量，希望能更好地了解影响疾病或特征的相关因素，以及它们是如何联系在一起的。这个正在进行的研究项目的长期目标是开发适用于分析各种类型的高通量遗传和基因组数据的统计方法，其首要主题是更好地利用来自多个来源的信息。我之前的NSERC发现基金的最新进展（如开发和实施一种分层的错误发现率方法来解释全基因组关联研究的结果，或调查罕见遗传变异的途径方法），导致了未来五年的主要目标。**两个主要目标是：**(1)在进行大量显著性检验时，提出了通过纳入外部信息来提高结果排名的方法。从结果的排序列表中，选择最有趣的结果在后续研究中进行进一步调查。因此，提高排名不仅意味着更好地发现真实信号的能力，也意味着更有效地利用资源进行后续研究。我建议通过有效理解测试之间的相关性和使用外部基因组注释信息来改进方法。**(2)由于基因组学中可以测量的数量远远大于个体数量，因此很容易开发出针对单个数据集进行优化的预测模型，但在新数据上表现不佳。在第二个目标中，我们的目标是通过两种方式解决这个问题。首先，我建议开发特定于数据类型的方法，用于定义以更少数量的度量捕获数据中的固有信号的摘要。其次，我计划开发跨不同类型数据组合这些汇总信号的方法，以提高预测模型的准确性。**只要有可能，建议的方法将被编程并作为公开可用的软件包发布。因此，通过本提案开发的工作主体将有利于产生大量遗传和基因组数据集的合作者，以及正在寻找有效分析方法的合作者。

The upcoming era of research in genetics and genomics can be characterized as a data integration era. By combining various kinds of data, measured possibly in different ways, at different times or tissues, or from several sources, the hope is to better understand what are the relevant factors influencing disease or traits, and how they are linked together. This ongoing program of research has the long-term objective of developing statistical methods applicable to analysis of high-throughput genetic and genomic data of various types, with the over-arching theme of better using information from multiple sources. Recent progress from my previous NSERC Discovery Grant (such as developing and implementing a stratified false discovery rate approach for interpreting the results of genome-wide association studies, or investigating pathway methods for rare genetic variants), has led to the main objectives proposed here over the next five years. **The two main objectives are: **(1) When performing a large number of tests of significance, methods are proposed that will improve the ranking of the results by incorporating external information. From a ranked list of results, the most interesting ones are selected for further investigation in subsequent studies. Therefore obtaining improved rankings implies not only a better power to find true signals, but also a more effective use of resources for the follow-up research. I propose methodological improvements through effective understanding of the correlations between the tests, and using external genomic annotation information.**(2) Since the number of quantities that can be measured in genomics is so much larger than the number of individuals, it is easy to develop a predictive model that is optimized for one single data set, but performs poorly on new data. In this second objective, the goal is to address this concern in two ways. Firstly, I propose to work on development of data-type-specific methods for defining summaries that capture the intrinsic signals in the data in a much smaller number of measures. Then secondly, I plan to develop ways of combining these summary signals across different types of data, to give better accuracy of the prediction models.**Whenever possible, the proposed methods will be programmed and released as publicly available software packages. Hence, the body of work developed through this proposal will be beneficial to collaborators who are generating massive genetic and genomic data sets, and who are searching for effective methods of analysis.