Enhanced MEAGA (Minimum distance-based Enrichment Analysis for Genetic Association) – UROP Spring Symposium 2021

Enhanced MEAGA (Minimum distance-based Enrichment Analysis for Genetic Association)

Justin Li

Justin Li

Pronouns: he/him/his

Research Mentor(s): Lam Tsoi, Assistant Professor
Research Mentor School/College/Department: Dermatology / Computational Medicine and Bioinformatics, Michigan Medicine
Presentation Date: Thursday, April 22, 2021
Session: Session 6 (4pm-4:50pm)
Breakout Room: Room 17
Presenter: 7

Event Link


The goal of the MEAGA (Minimum distance-based Enrichment Analysis for Genetic Association) is to help researchers understand complex traits derived from the correlation between genes. More specifically, this project aims at creating a network of genes with edge weights being their correlation with each other. This will allow researchers to test hypotheses between genetic pathways/functions and the correlation between specific genes. There currently exists a genetic network consisting of genes, but their connections/edge weights are binary (weight of 1 or 0). The goal right now is to convert the existing binary network into a continuous network, so the edges can have continuous values indicating the correlation between genes. The project focuses on graph algorithms such as Prim’s, Dijkstra’s, and Kou’s algorithm to create a genetic network that can create Steiner trees of indicated genes. Thus far, a gene to gene correlation file has been created with different correlation cutoffs of 0.3, 0.5, 0.7, and 0.9. This research can be valuable in identifying associations between genes and certain traits or diseases. This research may help the medical field when identifying the causes of conditions.

Authors: Lam Tsoi
Research Method: Computer Programming

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