Artomov Lab

Integration of multiple types of biomedical data provides exciting opportunities for assessing individual disease risks and predicting health outcomes. We are aiming to leverage systems genetics approaches to better understand mechanisms underlying time and severity of the disease onset.

Disease risks can be roughly categorized as either inherited or acquired through environmental exposures. To assess inherited risks, we analyze genetic data and we consider longitudinal clinical data to account for non-inherited factors. By combining multiple data types and using advanced statistical methods, we hope to gain valuable insights into disease mechanisms and the timing of disease onset.

We combine large-scale clinical and genetic data from major biobanks as well as local data at Nationwide Children's with statistical analyses techniques.

Meet Our Team

Enabling Genetic Data Sharing

We aim to make genetic data and large-scale resources available to the broad community through developing creative computational solutions preserving data privacy but enabling data analysis.

Integration of Clinical and Genetic Data for Disease Risk Prediction

We use large-scale genetic data from major biobanks as well as local data to assess individual disease risk prediction.

Complex Trait Genetics

We analyze large-scale sequencing and genotyping data across multiple populations to broaden the diversity in genetic research.

Computational Genetics

We develop tools for identification of novel disease genes and mechanisms.