Featured Researcher: Mykyta Artomov, PhD

Bioinformatics combines the natural sciences and information technology. This rapidly developing field has become an essential piece of genomic medicine, which is currently experiencing unprecedented scientific progress in understanding inherited susceptibilities for many diseases – identification of disease risk genes, pathways and affected cell types.

Mykyta Artomov, PhD, is a principal investigator in the Steve and Cindy Rasmussen Institute for Genomic Medicine (IGM), where he works on developing the concept of systems genetics – the integration of multiple data sources (e.g., DNA sequencing, gene expression data) in statistical frameworks that aid interpretability of experimental outcomes and novel biological hypothesis generation. His primary research focus is on developing methods for genome and exome sequencing data analysis and building statistical models for health outcomes using clinical and genetic data.

The applications of this biomedical technology are potentially vast, with global benefits for scientists, healthcare providers and their patients.

What is your favorite part of your job?

I like coming to work and seeing the progress we are making and how people are generating ideas. My job is to guide the research team and ensure smooth integration between clinical and computational efforts within our lab, but at the same time, we function as one of the integral parts of the Institute for Genomic Medicine, a unique research and clinical infrastructure supporting patient care across many domains. The feeling of the work being accomplished and a meaningful contribution to the advancement of the field is something that motivates me to be here every day.

How does your research serve our patients and our community?

There are a few key questions that we are aiming to answer: why and when do people get sick? In part, the answer is in our DNA. Just recently, we released PGS Browser (https://pgs.nchigm.org) – a large atlas of genetic susceptibilities across nearly 1,000 traits, based on analysis of nearly 500,000 individuals and, among other things, time-of-onset personalized predictive models for nearly 20 common diseases to identify individuals at high risk.

We would also like to pinpoint where the previously undiscovered risk genes or mutations exist. For this, we compare the genetics of healthy individuals (control group) and patients diagnosed with a disease. Creating a shared group of controls that can be utilized by many studies at once is a major resource-saving solution because we can then direct research funding to sequencing the DNA of disease carriers rather than creating separate healthy control cohorts for every study.