Multi-omics: Analysis of Omics Data for Research Studies
Explore multi-omics data analysis in this three-day boot camp through seminars and hands-on sessions, mastering methods to integrate genetic, gene expression, and exposomic data in observational studies.
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Course Description
This three-day intensive boot camp provides a comprehensive introduction to analyzing multi-omic data, combining seminars and hands-on labs to explore methods for integrating diverse omic datasets in observational studies.
- Analyze multiple omic data types using a range of approaches, balancing simplicity and complexity to address specific research questions in genetic and environmental epidemiology.
- Apply data reduction techniques, such as clustering, and advanced regression methods, including regularized regression, hierarchical models, and partial least squares, to multi-omic datasets.
- Investigate gene-environment interactions using Genome-wide Interaction Scans (GWIS) and perform mediation analyses to explore underlying biological mechanisms.
- Integrate polygenic and polyexposure risk scores and utilize GWAS summary statistics for comprehensive multi-omic data analysis in observational studies.
To contact support for this course, please email [email protected].
Course Prerequisites
The Multi-omics Boot Camp welcomes investigators from all institutions and career stages, with a special emphasis on trainees and early-stage researchers. Participants should have a basic background in statistics and epidemiology, be familiar with R, and have a personal laptop with a free RStudio Cloud account for hands-on lab sessions.
What You Will Learn
By the end of the workshop, participants will be familiar with the following topics:
- Data reduction, including clustering
- Regression analysis, including regularized regression, hierarchical models, and partial least squares
- Interaction analysis and Genome-wide interaction scans (GWIS)
- Mediation analysis
- Polygenic/polyexposure analysis
- Integrative analysis using genome-wide association study (GWAS) summary statistics
Instructors
David V. Conti, PhD, is a Professor of Population and Public Health Sciences at the Keck School of Medicine of USC, holding the Kenneth T. Norris, Jr., Chair in Cancer Prevention. He serves as the Associate Director for Data Science Integration Sciences and is the Acting Division Chief of Biostatistics. Dr. Conti's research focuses on genetic and environmental epidemiology, emphasizing the development of statistical methods to identify and characterize risk factors across diverse
William Gauderman, PhD, is a Professor of Population and Public Health Sciences at the Keck School of Medicine of USC and serves as the Division Chief of Biostatistics. His research focuses on developing statistical methods for genetic epidemiologic analysis and designing studies that assess the impact of environmental exposures on health outcomes. Dr. Gauderman has contributed significantly to understanding gene-environment interactions and the effects of air pollution on respiratory health in children.
Jesse A. Goodrich, PhD, is an Assistant Professor of Population and Public Health Sciences at the Keck School of Medicine of USC. His research integrates environmental exposure data with molecular biology to investigate how pollutants influence the risk of obesity, type 2 diabetes, and cancer, particularly in children and adolescents. Dr. Goodrich is also the Associate Director of Data Science at the Center for Translational Exposomics Research, focusing on translating environmental health research into practical solutions.
Juan Pablo Lewinger, PhD, is an Assistant Professor of Clinical Population and Public Health Sciences at the Keck School of Medicine of USC. His research focuses on developing statistical methods for gene-environment interaction studies, particularly in colorectal cancer risk assessment. Dr. Lewinger co-directs the Los Angeles Biostatistics and Data Science Summer Training Program (LA’s BeST), aiming to diversify the field by mentoring underrepresented students.
Nicholas Mancuso, PhD, is an Associate Professor of Population and Public Health Sciences and Quantitative and Computational Biology at the Keck School of Medicine of USC. His research focuses on developing computational and statistical methods to elucidate the genetic underpinnings of complex diseases, including integrating molecular phenotypes with large-scale genome-wide association studies. Dr. Mancuso's work also examines the genetic architecture of diseases and the influence of natural selection on allele effect-size distributions.
Kimberly Siegmund, PhD, is a Professor of Population and Public Health Sciences and the Associate Chief of Education in Biostatistics at the Keck School of Medicine of USC. As a biostatistician, her research focuses on cancer modeling and the statistical analysis of epigenetic data, particularly DNA methylation, to understand disease mechanisms. Dr. Siegmund also teaches courses on the statistical analysis of high-dimensional data and develops mathematical models to study cancer progression and aging.