Jasen Jackson, a molecular biology and bioinformatics major at Loyola University Chicago, investigated the effects of obesity on the genetic architecture of Type 2 Diabetes (T2D) and related traits.
The molecular mechanisms underlying insulin resistance in type 2 diabetes (T2D) may differ in the presence of adiposity and obesity. Understanding how these traits affect the genetic architecture of T2D and related glycemic traits could implicate novel pathways and facilitate the development of targeted therapies. This summer has changed my life. I knew that spending a summer at the Broad would challenge me to improve my scientific abilities and expose me to the bleeding edge of genome-based science. However, I did not anticipate this level of one-on-one mentorship and emphasis on personal development. At the Broad I had access to accomplished and devoted people with a deep sense of generosity and care about nurturing the next generation of scientists. As a result, I am walking away with a level of confidence and self-knowledge that I did not think was possible, and a network of people who care about my personal success. The molecular mechanisms underlying insulin resistance in type 2 diabetes (T2D) may differ in the presence of adiposity and obesity. Understanding how these traits affect the genetic architecture of T2D and related glycemic traits could implicate novel pathways and facilitate the development of targeted therapies. This summer, I performed whole genome sequence (WGS) analyses of T2D and related traits stratified by body mass index (BMI) using data from the NHLBI’s Trans-omic for Precision Medicine (TOPMed) project. The TOPMed project has accrued WGS data from individuals with well-defined phenotypes and clinical outcomes. I developed a robust and efficient pipeline to perform association analyses on these data. Because of the diverse ancestries represented in the dataset, this pipeline incorporates statistical tools that adjust for allele frequency differences between populations. Additionally, the scale of WGS data makes analyses especially cumbersome and computationally expensive. To increase the efficiency of this pipeline, I made use of parallel cloud computing and a technique called scatter-gather parallelism which divides the dataset into smaller chunks and performs many analyses in parallel. I used this pipeline to perform a T2D analysis and then stratified the samples into two subpopulations stratified by a body mass index of 30 kg/m2. These analyses revealed that a number of loci had association values that differed greatly between the stratified groups, suggesting interaction between body mass index and the genetic architecture of T2D in our dataset. In the future, studying the direction of effect of these variants and cross-referencing these results with tissue-specific expression data could lead to biological hypotheses that could be validated experimentally.
Project: Accounting for obesity in whole genome sequencing analyses of T2D
Mentor: Alisa Manning, Metabolism Program