13th Annual IGSS Conference • September 30-October 1, 2022

Integrating Genetics and the Social Sciences 2022

Keynote Speaker: Jennifer Beam Dowd

Professor of Demography and Population Health, Department of Sociology
Deputy Director, Leverhulme Centre for Demographic Science
University of Oxford

We contain multitudes: microbes, genes, and the future of biosocial science

The microbiome has been called our "second genome." It is estimated that there are as many microbial cells as human cells in our bodies, and 100 microbial genes for every one human gene. Perhaps even more than the genome, the plasticity of this "second genome" leaves large scope for social and environmental influences across the life course. This talk will provide an overview of current knowledge of the social determinants of the microbiome and invite discussion of how social and population scientists can take lessons from work in sociogenomics to contribute to this emerging field.

Statistical Genetics Workshop: Applied Microbiome Analyses

Jennifer Fouquier

University of Colorado Anschutz Medical Campus

Learn how to perform a complete microbiome analysis using raw DNA data to create publication-ready figures.

Jennifer Fouquier is pursuing her PhD in Computational Bioscience at the University of Colorado Anschutz Medical Campus in Aurora, Colorado. She is funded as an NIH/NLM T15 Informatics trainee under the guidance of Dr. Catherine Lozupone. She began her career in the wet lab, received her BS in Microbiology from the University of California, San Diego (2007), and transitioned to bioinformatics. She received her MS in Bioinformatics and Medical Informatics from San Diego State University (2015) where she performed research on the fungal microbiome of the built environment. She then became a bioinformatics programmer at The Scripps Research Institute where she developed interactive web-based tools for a citizen science project on natural language processing. Jennifer’s current research involves the development of novel computational methods to identify important features in longitudinal microbiome studies using machine learning methods. Her projects include microbiome studies on autism spectrum disorder and a diet intervention in HIV-infected individuals.