UROP Project

Interaction between Genomic Variants and Social Determinants of Health on Pain Frequency in Sickle Cell Disease: A Brazil-Based Study

genomics, social determinants of health, pain, sickle cell disease
Taylor_B - 14. Headshot.jpeg
Research Mentor: Dr. Brittany N. Taylor, she/her
Department, College, Affiliation: N/A, Nursing
Contact Email: bt26i@fsu.edu
Research Assistant Supervisor (if different from mentor):
Research Assistant Supervisor Email:
Faculty Collaborators:
Faculty Collaborators Email:
Looking for Research Assistants: Yes
Number of Research Assistants: 3
Relevant Majors: Nursing, biomedical sciences
Project Location: University of Sao Paulo
Research Assistant Transportation Required: No, the project is remote
Remote or In-person: Fully Remote
Approximate Weekly Hours: 10, During business hours
Roundtable Times and Zoom Link:
  • Day: Tuesday, September 1
    Start Time: 2:00
    End Time: 5:00
    Zoom Link: https://fsu.zoom.us/j/92866103602
  • Day: Wednesday, September 2
    Start Time: 12:00
    End Time: 2:00
    Zoom Link: https://fsu.zoom.us/j/94184668281

Project Description

This project investigates the phenotypic variability in sickle cell disease (SCD) by examining how genomic modifiers interact with social seterminants of health to influence pain frequency. Utilizing the large-scale REDS-III Brazil cohort (n=2,793), we leverage whole genome sequencing data from the NHLBI TOPMed program to identify novel gene-environment interactions. Unlike traditional studies that treat social factors as static variables, this research applies machine learning (Random Forest and XGBoost) to model how socioeconomic inequities, such as low income and education, can amplify the expression of high-risk genetic variants. The goal is to develop a biological basis for phenotypic differences and support precision medicine and health equity for marginalized SCD populations globally

Research Tasks: Conduct comprehensive literature reviews on identified SCD genomic modifiers (e.g., CTNNA2, METTL4, UHRF2) and their interactions with social stressors. Perform data cleaning and preparation for sociodemographic variables (income, education) and clinical outcomes (VOC hospitalization frequency). Assist in the implementation and validation of machine learning models to identify non-linear predictors of high-risk pain phenotypes.
Analyze genomic data using bioinformatics pipelines (e.g., GotCloud or Hail) to adjust for the intense miscegenation of the Brazilian population.
Prepare visualizations (e.g., Manhattan plots and Heat maps) to interpret predictive model features.

Skills that research assistant(s) may need: Required: Proficiency in R or Python for data analysis.
Required: Basic understanding of genetics and biostatistics.
Recommended: Experience with machine learning frameworks (e.g., scikit-learn or Caret).
Recommended: Strong interest in health equity, global health, and health informatics.

Mentoring Philosophy

My mentoring philosophy is rooted in fostering a collaborative and inclusive environment where undergraduate and graduate students transition from learners to empowered investigators. I approach the mentor-mentee relationship as a partnership in professional development, focusing on the mastery of both technical informatics skills and critical social-scientific thinking. I provide structured guidance through regular milestones and feedback loops, ensuring students are proficient in data analysis tools like R and Python while understanding the ethical and biological implications of health equity research. My goal is to equip mentees with the expertise to address global health disparities through precision medicine, encouraging active participation in data interpretation and providing opportunities for co-authorship on peer-reviewed publications

Additional Information

This project is part of a multicenter international collaboration involving the University of São Paulo and the NHLBI, offering students unique exposure to global health data and high-resolution genomic resources.

Link to Publications


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