Research Statement

How can omics measurements be incorporated into epidemiologic science? The advent of next generation sequencing presents an opportunity for scientists to ‘see inside’ the black box of molecular epidemiology. But the nature of omics measurements presents important data science challenges and causal inference considerations. I’m interested in methods for integrating omics measurements with sociodemographic and environmental data in order to interrogate causal relationships in multifactorial diseases.

Many omics measurements are high-dimensional and compositional in nature. Extracting valuable signals from noise in this context is an important consideration. My research incorporates different techniques for dimension reduction and data mining, including clustering algorithms, dimension reduction techniques and supervised machine learning.

Omic measurements can act as biomarkers of past exposures, of disease susceptibility or of ongoing disease processes. Omics biomarkers can help inform public health surveillance and risk assessment at the population level and can be incorporated into the practice of personalized medicine.

Two of the omes that I study - the epigenome and the microbiome - can be modified by environmental and social exposures, and are therefore tantalizing candidates for mediating the causal flow from upstream, modifiable risk factors to downstream, biological outcomes. Understanding if and how these omes act as mediators between environment and disease can inform public health practice and the development of future interventions.

My doctoral training has incorporated courses in biostatistics, epidemiology and bioinformatics.

Funding

Current support

T32 HG008341 “Vanderbilt Genomic Medicine Training Program” (Postdoctoral Fellow). National Institute of Health; National Human Genome Research Institute.

Previous support c

F31 DE029992 “Environmental and Genetic Factors Contributing to the Development of the Early-Life Oral Microbiome and its Influence on Early Childhood Caries” PI: Blostein, Freida. National Institute of Health; National Institute for Dental and Craniofacial Research. $35,128 per year for September 2020-December 2022

T32 5T32HG000040-25 “University of Michigan Training Program in Genomic Science” PI: Boehnke, Michael. National Institute of Health; National Human Genome Research Institute. September 2018-August 2020