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Priyadarshini Kachroo, PhD

Research Areas: Computational Biology; Multiomics; Networks; Genomics; Metabolomics; Epigenomics; Transcriptomics; Respiratory disease; Asthma; COPD; Prenatal exposures; Aging

Dr. Kachroo is an Instructor in Medicine and Associate Scientist in the Channing Division of Network Medicine (CDNM) at Brigham and Women’s Hospital and Harvard Medical School. She started as a postdoctoral fellow at the CDNM in 2017 with a Ph.D. in Natural Sciences focused on Epigenetics and Bioinformatics of complex Inflammatory diseases from the University of Kiel, Germany. In the past few years, Dr. Kachroo has utilized multiple large-scale, well-characterized population-based cohorts and clinical trial data with comprehensive coverage of the various ‘omes’ (whole genome, metabolome, transcriptome, methylome, quantitative trait loci – QTLs) and led various studies to establish early-life origins of respiratory diseases including asthma and chronic obstructive pulmonary disease (COPD). Her research centers on studying gene-environment interactions and sex-specific differences across life-course through multi-omic integration by applying novel computational and network science approaches. As a recipient of the K99/R00 grant award from the National Heart, Lung and Blood Institute, Dr. Kachroo’s primary focus is on integrating genetic, epigenetic and metabolomic determinants to study lung function in children with asthma. Using state-of-the-art machine learning and statistical methods, she also aims to integrate her findings with large-scale microbiome and proteomic data to examine inflammatory pathways predictive of an early decline in lung function leading to asthma and susceptibility to other chronic lung conditions like COPD. Lung function is a critical aspect of healthy aging. Therefore, by identifying multi-omic biomarkers of disease, her long-term goal is to gain insights into the most complete profile of disease development to enable precision medicine strategies and facilitate improved public health outcomes more generally.

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