Regression Models for Data Integration in Genetics and Genomics

* * 0 * 0 * 1 * 255 * 1460 * SLRI * 12 * 3 * 1712 * 14.0 * * * * *** * 正常* 0 * * * * * 假*假*假* * en - us *是* X-NONE * * * * * * * * * * * * * * * * * * * * * * * * * ** * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * ** * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * **** /* 样式定义* / *表。MsoNormalTable* {mso-style-name:"Table Normal"；* mso-tstyle-rowband-size: 0;* mso-tstyle-colband-size: 0;* mso-style-noshow:是的;* mso-style-priority: 99;* mso-style-parent:“”;* mso-padding-alt:0cm 5.4pt；* mso-para-margin: 0厘米;* mso-para-margin-bottom: .0001pt;* mso-pagination: widow-orphan;*字体大小:12.0分;*字体类型:威尔士;* mso-ascii-font-family:威尔士;* mso-ascii-theme-font: minor-latin;* mso-hansi-font-family:威尔士;* mso-hansi-theme-font: minor-latin;****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性状分析的基础和转化研究中的问题。我建议开发灵活的统计模型和数据分析方法，整合多个数据来源，考虑到分子生物学研究中遗传、分子和环境因素固有的复杂依赖结构，这些因素导致了个体之间的差异。微阵列、全基因组关联平台和下一代测序等技术的出现，使人们能够对个体之间的全基因组分子和遗传变异进行调查，并有可能更好地描述异质性的来源。大规模组学数据的方法论发展通常是通过一次关注一个或两个数据维度来进行的。主流范式通常进行单变异或单基因关联的测试，而不是关注因素的组合。通过开发、评估和应用具有调查多因素能力的回归方法，我们期望对潜在生物学和人与人之间差异的来源获得新的见解。我建议解决三个具体的统计困难。这些障碍包括：当一套完整的测量方法不能适用于所有人时，当感兴趣的特征在群体中个体之间的分布不均匀时，以及当潜在解释因素的数量非常大时，就会出现障碍。作为研究训练的一部分，生物统计学和统计学的研究生和博士后将在解决我所提出的问题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，我们还计划开发并提供对许多领域的研究人员有用的可访问软件

* * 0* 0* 1* 255* 1460* SLRI* 12* 3* 1712* 14.0* * * * *** * Normal* 0* * * * * false* false* false* * EN-US* JA* X-NONE* * * * * * * * * * * * * * * * * * * * * * * * * ** * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * **** /* Style Definitions */*table.MsoNormalTable* {mso-style-name:"Table Normal";* mso-tstyle-rowband-size:0;* mso-tstyle-colband-size:0;* mso-style-noshow:yes;* mso-style-priority:99;* mso-style-parent:"";* mso-padding-alt:0cm 5.4pt 0cm 5.4pt;* mso-para-margin:0cm;* mso-para-margin-bottom:.0001pt;* mso-pagination:widow-orphan;* font-size:12.0pt;* font-family:Cambria;* mso-ascii-font-family:Cambria;* mso-ascii-theme-font:minor-latin;* mso-hansi-font-family:Cambria;* mso-hansi-theme-font:minor-latin;}****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.***