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Developing disease insights with genotype-specific regulatory networks

C.E. Credits: CEU P.A.C.E. CE Florida CE
Speaker
  • Instructor, Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School
    Biography

      John Platig is a computational biologist interested in developing quantitative, mechanistically informed models for integrating high throughput biological data. John received a PhD in Physics at the University of Maryland, studying complex networks and biological systems. During his postdoc at the Dana Farber Cancer Institute and Harvard T.H. Chan School of Public Health, he developed methods for analyzing networks of eQTLs and studied the effects of kinase inhibition in Mycobacterium tuberculosis using multi-omic approaches. Starting in 2018, John joined the faculty at the Channing Division of Network Medicine at Brigham and Women's Hospital and Harvard Medical School, where he continues to work on network models for multi-omic data integration. John's current research is focused on models for better understanding how genetic variants influence transcription factor regulatory networks and the subsequent impact on human health and disease.


    Abstract

    The recent explosion in the sample sizes and diversity of omics assays has created exciting new opportunities for biomedical scientists. However, connecting these omics data types in an interpretable way remains a challenge, especially when the data come from different sources and cohorts. One approach to this problem is to build regulatory networks that reflect the support of the data. In this talk, I will discuss a new method for estimating the effects of germline genetic variants on transcription factor (TF) regulatory events. This method, EGRET, integrates an individual's genotype with regulatory data to produce an individual-specific regulatory network. When we applied EGRET to RNA-seq and genotype data collected from 119 individuals in three cell types, we find TF regulatory disruptions that are disease-specific and occur only in the cell type relevant to the disease. Together this suggests that EGRET provides valuable insight into regulatory processes influencing disease.

    Learning Objectives:

    1. Describe the effect size distribution and typical genomic locations of GWAS variants

    2. Explain which data types the EGRET method uses as input


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