05/01/2025
By Irma Silva

The Kennedy College of Sciences, Department of Biological Sciences, invites to you attend a Ph.D. in Applied Biology Dissertation Proposal Defense by Panos Lalagkas titled “Leveraging genetic pleiotropy to uncover disease mechanisms and accelerate drug discovery”.

Candidate: Panagiotis Lalagkas
Degree: Doctoral
Date: Thursday, May 8
Time: 3 – 5 p.m.
Location: Zoom 

Committee Members:

  • Frederic Chain, Associate Professor, University of Massachusetts Lowell
  • Teresa Lee, Assistant Professor, University of Massachusetts Lowell
  • Rachel Melamed, Assistant Professor, University of Massachusetts Lowell
  • Jonine Figueroa, Senior Investigator, Division of Cancer Epidemiology & Genetics, NIH

Title: Leveraging genetic pleiotropy to uncover disease mechanisms and accelerate drug discovery.

Abstract:
Human genetics has significantly advanced our understanding of disease biology and holds great potential to accelerate drug discovery. Genome-wide association studies (GWAS) have identified thousands of disease risk variants, and drugs targeting the implicated genes are more likely to have an effect on disease, either as therapeutic interventions or through unintended side effects. However, two key challenges limit the clinical utility of these findings. First, most GWAS hits fall in non-coding regions, obscuring the identification of disease causal genes. Second, discovering novel disease risk loci requires large sample sizes which is often infeasible, especially for rare diseases. To address these limitations, I propose to create computational methods that harness genetic pleiotropy, the phenomenon where genetic variants or genes influence multiple traits. Pleiotropy is pervasive in the human genome and previous studies have shown that many diseases, even seemingly unrelated, sharing genetic architecture. Building on that, I propose to use knowledge of one disease to make discoveries for another. I will develop computational frameworks to exploit interactions between genetic factors of a disease and its health exposures to discover novel disease risk loci. I will also systematically evaluate whether shared genetics between pairs of diseases can predict novel therapeutic or adverse effects for all FDA-approved drugs. Lastly, I will investigate the genetic overlap between genes mediating therapeutic and adverse effects of drugs and integrate these findings in models to predict whether a drug might have intended or unintended effects on a given disease. Together, this proposal aims to develop pleiotropy-driven computational methods with the goal of unlocking the full potential of genetic data for advancing disease biology insights and drug discovery.