Regression Models for Data Integration in Genetics and Genomics

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* * 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.***