Using a Trust Game Framework to Evaluate Risk Aversion to Sharing Genomic Data

Bioinformatics research project for the Vanderbilt Summer Science Academy. The project used game theory principles to gauge how how participants weighed risk/reward in situations involving their genomic data versus their wealth.

There is an increasing tendency for patients to have their genomic data sequenced and processed in the clinical domain. While there is an opportunity to personalize an individual's care, such data disclosure also raised various privacy risks.

This project gauged the degree to which an individual will assume such risks in exchange for the potential benefit that may be realized by sharing genetic data for biomedical research purposes. The methodology built on a two-participant trust game framework, a behavioral economics technique. The project's purpose was to find if there would be a significant difference between participants' risk aversion to sharing their genetic data versus the risk aversion to suffering a monetary loss. The findings could provide insight into public policy recommendations on how to obtain, process, and secure personal genetic data.

Poster Presentation

Trust Game Poster Presentation

Features

  • Statistical analyses including chi-square testing, bidirectional sign tests, and two-prop Z-tests

  • Graph and/or chart output for each different type of test

  • Analyses were ran on raw csv data directly imported from MTurk

Skills Learned

  • Using MechanicalTurk to collect data and importing it into Jupyter Notebook as a csv

  • Using Python to analyze the data

  • Using the pandas and scipy to conduct statistical analyses

  • Using matplotlib library to create graphs and charts based on the analyses

  • Explaining the methodology and the significant findings during a 20-minute lecture as well as a poster presentation

Acknowledgments

  • This research project was designed by Vanderbilt University's Zeeshan Samad, MPP, MA with support from Myrna Wooders, PhD; Bradley Malin, PhD; and Yevgeniy Vorobeychik, Phd.

  • My participation in this project was a part of the Vanderbilt Summer Science Academy

  • Special thanks to Professor Bradley Malin for bringing me into his lab and thoroughly explaining fascinating bioinformatics and economics concepts.

  • Steve Nyemba, MS, was instrumental in training me in using Jupyter Notebooks as well as MTurk

  • Thanks to everyone at GetPreCiSe (The Center for Genetic Privacy and Identity in a Community Setting) for their support and welcoming, as well as the incredible weekly talks

  • My participation in the VSSA was supported by NSF REU grant #1757644