Epistatic Interactions Associated with Obesity

Joseph Patacsil

Mentor: Dr. MaryBeth Martin, Departments of Oncology and Biochemistry & Molecular Biology, Georgetown University Medical Center Dr. Markus Hoffmann, Georgetown University Medical Center.

Date/Time: August 25th, 2026 at 3:00 PM.

Abstract: Obesity is a risk factor that has been associated with numerous comorbidities, including metabolic diseases, cardiovascular diseases, and cancer, with women obesity-associated cancers projected to increase by 30% by 2030. While obesity is strongly associated with lifestyle factors, such as overeating and lack of exercise, environmental and genetic factors have been identified as potential risk factors. The patients in this study were around 40% patients who self-identified as African American (AA) and around 60% patients who self-identified as European American (EA).

Here, we develop a custom bioinformatics script, utilizing Nextflow, that takes a VCF as input and uses the NeEDL tool to detect epistatic interactions with BMI. We saw that SNPs (rs4252499 and rs4252372) in the TRPV5 region could potentially be associated with increased Cd and Ca concentrations and increased BMI, in both the AA and EA populations. Additionally, we discovered an epistatic interaction with the intron variant, “rs1414529”, in the ESRRG gene that could be implicated in increased BMI in the AA population. This epistatic interactions with the SNP had a maximum likelihood model (MLM) score of 205 (p-val < 0.05 relative to baseline). This epistatic interaction was only found to be significant for the AA and was not found in the EA population.

The TRPV6 calcium channel ancestral variant, which is more prevalent in African American populations, has been shown to interact with heavy metals and increase obesity risk. This study found that a neighboring gene, TRPV5, could potentially have a more prevalent role in obesity than originally thought. Future studies could investigate the effects of the intron variants we found and determine if there is a quantifiable relationship with increased heavy metal concentrations. The nextflow script created for this analysis could be upscaled to use large scale sequencing data to detect if more SNPs in the ESRRG or related genes is associated with increased BMI.