Integrating Multi-Omics In Population Health Research with Dr. Panyard and Dr. Kechris.

Every large-scale population health study presents complex data challenges for researchers to overcome. From cohort management to data acquisition and interpretation, discovering actionable insights can be elusive for even the most experienced researcher.
Taking an already challenging model and introducing additional datasets does not come without risk, but with the correct preparation and support, large-scale population-based integrative-omics can lead to crucial new discoveries that deepen the understanding of human biology and disease-and it is more approachable than ever before.
In this double-feature webinar, we will introduce you to two leading experts in neuroscience and respiratory disease research, leveraging integrative-omics technologies to uncover new insights. We will discuss their recent research in Alzheimer’s disease and chronic obstructive pulmonary disease (COPD) and outline their recommendations for building successful integrative-omics models, leveraging metabolomics technology to accelerate research objectives.
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AGENDA:
Daniel Panyard, Ph.D.
Strategies for Leveraging Untargeted Metabolomics in Population Cohort Studies.
As it has become easier to generate metabolomics data in research studies, the challenge has shifted to how to make the most of these data. In this session, we’ll walk through several examples of how to use untargeted metabolomics data-both alone and in parallel with other omics-to uncover disease-relevant insights in cohort studies.
Micaiah Ward, Ph.D.
Routine Solutions for Untargeted and Targeted Metabolomics - Analyses, Bioinformatics, Interpretive Insights.
Achieving fast, accurate, and reproducible metabolomic data can be a challenging task that requires investing in people, technology, and novel methods to derive functionally insights. In this short session we will demonstrate how Metabolon’s metabolomics-as-a-service solution can be leveraged to improve metabolite-level insights, today.
Katerina Kechris, Ph.D.
Studying Complex Diseases Using Integrative -Omics and Network Approaches.
With collaborators studying COPD, we have analyzed a variety of omics data sets to gain insight into the development and progression of COPD. In this talk, Dr. Kechris will present her work for integrating omics profiles to identify molecular networks associated with COPD and for identifying COPD subtypes. Her methods are based on high dimensional data approaches including canonical correlation analysis and deep learning.
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BIOS
Katerina Kechris, Ph.D.
Professor, Associate Director of Data Science (CIDA)
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Dr. Katerina Kechris is a professor of biostatistics and informatics in the Colorado School of Public Health at the University of Colorado Anschutz Medical Campus in the metro Denver area. She received her undergraduate degree in Applied Mathematics from the University of California, Los Angeles, and her Ph.D. in Statistics from the University of California Berkeley. She has been in her current position since she completed a post-doctoral fellowship at the University of California San Francisco in Computational Biology.
Dr. Kechris’ research is focused on the development and application of statistical and computational methods for analyzing omics data sets through stages of the data life cycle including processing, storage, analysis, modeling, and visualization. Specific areas include analyzing transcription factor binding and miRNA expression data to study regulation, examining the genetic and epigenetic factors controlling gene expression, exploring the metabolome, and integrating multi-omics data. She collaborates with investigators studying COPD, substance use disorders using animal models, and early life determinants of diabetes and obesity in children.
Daniel Panyard, Ph.D.
Postdoctoral Research Fellow (Stanford); Research Associate (VA)
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Dr. Daniel Panyard is a postdoctoral research fellow in the Stanford Department of Genetics and a research associate with the Department of Veterans Affairs. His research is in molecular epidemiology and precision health, where he specializes in characterizing large-scale genomic, proteomic, and metabolomic data in diverse cohorts and integrating them with clinical data to uncover the biological systems underlying conditions like Alzheimer’s disease. He primarily works with population cohort data, like those from the Million Veteran Program (MVP) and the Alzheimer’s Disease Research Centers (ADRCs). His recent work includes an integrative proteomic and metabolomic analysis of Alzheimer’s (Alzheimer’s & Dementia), a study of microsampling-based multiomics for monitoring longitudinal, personal molecular changes (Nature Biomedical Engineering), and a comprehensive review of the metabolomics of aging (Science Advances).