Global

Microbiology Experts

Jessica Lasky-su


Genetic Epidemiology
Metabolomic
Japan

Biography

Dr. Lasky-Su is an Associate Professor in Medicine at Brigham and Women’s Hospital and Harvard Medical School. She earned her doctoral degree in Genetic Epidemiology from Harvard School of Public Health in 2005. Dr. Lasky-Su has spent the last 18 years focusing on the identification of genetic, genomic, and metabolomic determinants for complex diseases, with a particular focus on asthma. The accumulation of these efforts has resulted in over 100 peer-reviewed original research manuscripts. Dr. Lasky-Su’s more recent work has focused on analytic and network approaches to integrate metabolomics and other omics data types with the end goal of making strides towards precision medicine. She is currently the principal investigator and co-investigator on several R01 grants that focus on the integration of metabolomics and other omics data types for several diseases. Through these efforts she has developed a metabolomics research program at the Channing Division of Network Medicine that is highly successful and synergistic in nature, as it has drawn together a diverse group of investigators with broad research interests. This work of this group has resulted in the identification of important metabolic pathways and genetic determinants for asthma, macular degeneration, pre-eclampsia, food allergy, and several other complex diseases. Dr. Lasky-Su has made great strides to educate the broader medical research community in metabolomics, which is reflected by development of the 2017 symposium “Metabolomics: Fast growing technology in Precision Medicine,” the Harvard Catalyst nanocourse entitled “Network Medicine: Using Metabolomics Data in Network Medicine,” and the 2017 Metabolomics Society workshop entitled “Network medicine approaches for the analysis of metabolomic data.” Dr. Lasky-Su also serves on the COMETs and the TOPMed metabolomics steering committees with the goal of promoting metabolomics and integrative omics in large population-based cohorts.

Research Interest

Genetic Epidemiology

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