Rishika De is a PhD candidate in computational genetics in the Department of Genetics with experience in obesity-related interdisciplinary research combining genetics, computer science and statistics, and a deep-set interest in the business of healthcare.
The global obesity epidemic no longer affects only industrialized nations, it is making its mark in developing nations as well, especially among children. Obesity is a well-known risk factor for various diseases such as heart disease, type 2 diabetes, and certain types of cancer. Although our lifestyle and food choices are important in helping us control obesity there is also a strong genetic component. A common measure used to assess obesity is an individual’s Body Mass Index (BMI). Researchers have shown that obesity tends to cluster within families, and identical twins show greater similarity in their BMI levels than fraternal twins.
Technical advancements in the field have made genome-wide association studies (GWAS) possible, which allow researchers to query a million or more genetic variants called Single Nucleotide Polymorphisms (SNPs), which are mutations at single points in the DNA within the genome. Such studies enable researchers to capture most of the variation observed in the human genome. So far, researchers have focused on studying genetic variants one at a time, to test their association with a certain disease. Unfortunately, these studies have identified only a few genetic variants that have been able to explain a moderate or large increase in disease risk. This highlights the importance of considering the genomic and environmental context of each SNP. Our search is motivated by the phenomenon of ‘epistasis’ – multiple interacting SNPs which have smaller individual effects on a phenotype and may display a larger effect via their interaction.
One of my projects focuses on studying interactions between genes that are highly associated with BMI, using innovative bioinformatics methods in addition to traditional biostatistics. Thus far, my work has identified 6 novel interactions between SNPs that were highly associated with BMI in a population of 18,686 individuals.
However, the culmination of our work lies in predictive, personalized, and precision medicine – where we can ultimately use genetic information to predict an individual’s chance of developing obesity. With the advent of new technologies, personal genetic testing kits have become inexpensive and widely available to consumers. The Graduate Alumni Research Award enabled me to submit a DNA sample for personal genetic testing. This process furthered my understanding of the effects of personalized medicine on a consumer or patient. I was able to experience first-hand the decision making process involved in agreeing to undergo genetic testing. Specifically, I was able to identify important issues for an individual to consider – such as concerns regarding privacy, data ownership, genetic discrimination, and how to interpret and utilize the data provided in the test results. Ultimately, I was not only able to appreciate the utility and consequences of our research, but also recognize that there is still much to be done in interpreting the complex relationship between an individual’s genotype, environment and phenotype.