Regression Models for Data Integration in Genetics and Genomics

该研究计划的总体目标是开发统计工具，以解决人类生物学和复杂特征分析中的基础研究和翻译研究中的问题。我建议开发灵活的统计模型和数据分析方法，整合多种数据来源，同时考虑到导致个体差异的遗传、分子和环境因素的分子生物学研究中固有的复杂依赖结构。微阵列、全基因组关联平台和下一代测序等技术的出现，使人们能够询问全基因组范围的分子和个体间的遗传变异，并有可能更好地描绘异质性的来源。大规模组学数据方法论的发展通常是务实地进行的，每次只关注一个或两个数据维度。主导范式通常进行单一变量或单基因关联的测试，而不是关注因素的组合。通过开发、评估和应用能够调查多种因素的回归方法，我们期望对潜在的生物学和人与人之间差异的来源有新的见解。 我建议解决三个特殊的统计困难。这些障碍包括当不是每个人都有一套完整的测量方法时，当感兴趣的特征在人群中的个人中分布不均匀时，以及当潜在解释因素的数量非常大时，出现的障碍。作为研究培训的一部分，生物统计学和统计学的研究生和博士后研究员将在为我提出的问题制定解决方案方面发挥不可或缺的作用。除了将研究结果传播给其他统计研究人员外，我们还计划开发和提供对许多领域的研究人员有用的易用软件。

The general objective of the research program is to develop statistical tools to address problems in basic and translational research in human biology and complex trait analysis. I propose to develop flexible statistical models and methods of data analysis that integrate multiple sources of data, taking into account the complex dependence structures inherent in molecular biologic studies of the genetic, molecular and environmental factors responsible for differences among individuals. The advent of technologies such as microarrays, genome-wide association platforms, and next generation sequencing allows interrogation of genome-wide molecular and genetic variation among individuals, and the potential to better delineate sources of heterogeneity. The development of methodology for large-scale ‘omics data has generally proceeded pragmatically by focusing on one or two data dimensions at a time. The dominant paradigms have typically performed tests of single-variant or single-gene associations rather than focusing on combinations of factors. By developing, evaluating and applying regression methods that have the capacity to investigate multiple factors, we expect to gain new insight into underlying biology and the sources of differences among people. There are three particular statistical difficulties that I propose to address. These include obstacles that arise when a complete set of measurements is not available on everyone, when the trait of interest is not evenly distributed across individuals in the population, and when the number of potential explanatory factors is very large. As part of their research training, graduate students and post-doctoral fellows in biostatistics and statistics will play an integral role in developing solutions to the problems I have posed. In addition to disseminating the results of the research to other statistical researchers, we plan to develop and provide accessible software useful to investigators in many fields.