You are here

Joseph Lance V. Casila

Joseph Lance V. Casila

Joseph Lance V. Casila, a junior math, chemistry, and biology major at the University of Guam, analyzed the impact of heterogeneity on associations of SLC16A11 variants with Type 2 Diabetes.

Type 2 Diabetes (T2D) is characterized by persistent hyperglycemia and more than 415 million people suffer from the disease. T2D is influenced by risk variants, which are found through Genome-Wide Association Studies (GWAS). One concern in T2D GWAS is the inclusion of samples that have T2D influenced more by environmental confounders than genetics. This can cause heterogeneity within the dataset and result in decreased signals of potential T2D risk variants. The Broad is the kind of research institute I had never imagined; it’s a place where success is defined by, and dependent on, the level of interdisciplinary collaboration among scientists. I’ve always been in conflict about being equally interested in math, biology, and felt the pressure to just choose one. But at the Broad, combining those interests was encouraged and nurtured. If I were to choose one thing that I’ll be taking away from my experience here, it’s that I now have a better understand of myself and the potential that I am able to reach.In a fine mapping for ~9,000 Latin Americans, variants in SLC16A11 showed strong association with T2D. Following another study in Native Americans, an association was observed only in lean individuals but not obese, suggesting that SLC16A11 variants may interact with covariates like BMI or age. This leads to the hypothesis that heterogeneity is present in analyses for SLC16A11, resulting in decreased statistical associations. To test for heterogeneity, young controls, lean controls, old cases, and obese cases were excluded in four separate meta-analyses for rs2292351 (SLC16A11 variant) in ~8,000 Latin Americans. Samples were categorized using dataset medians as cutoffs (<58=young, ≥58=old, <27.82=lean, ≥27.82=obese). Compared to the baseline meta-analysis without exclusions (OR=1.3), meta-analyses excluding young controls and lean controls decreased risk (OR=1.21, 1.28 respectively) while meta-analyses excluding old cases and obese cases increased risk (OR=1.34, 1.36). This suggests that some old and obese samples causing heterogeneity were excluded, and rsrs2292351 is more associated with leaner and younger individuals. With application to other variants that interact with environmental covariates, this study informs future GWAS the possibility of heterogeneity and its impact on associations.


Project: Analyzing the impact of phenotype heterogeneity on association of SLC16A11 variants with Type 2 Diabetes in Latin Americans

Mentor: Josep Mercader, Metabolism Program