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。 这项正在进行的研究计划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，其主要主题是更好地利用来自多个来源的信息。 我之前的NSERC发现资助的最新进展（例如开发和实施分层错误发现率方法来解释全基因组关联研究的结果，或研究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改善结果排名的方法。 从结果的排名列表中，选择最有趣的结果用于后续研究的进一步调查。 因此，获得更好的排名不仅意味着更好的能力，以发现真正的信号，但也更有效地利用资源的后续研究。 我建议通过有效理解测试之间的相关性以及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的数量，因此很容易开发出一种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，我们的目标是以两种方式解决这一问题。 首先，我建议开发特定于数据类型的方法来定义摘要，这些摘要可以在数量少得多的测量中捕获数据中的内在信号。 其次，我计划开发将这些汇总信号在不同类型的数据中组合起来的方法，以提高预测模型的准确性。在可能的情况下，将对拟议的方法进行编程，并作为公开提供的软件包发布。 因此，通过该提案开发的工作将有利于正在生成大量遗传和基因组数据集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.