Sofer Lab
applying statistical and computational methods to use
genetics and omics to study health and disease.
Research
Prediction using genetics and omics
We create and use prediction models to estimate the likelihood of individuals developing cardiovascular traits through genetic predisposition, lifestyle, and omics measures. In doing so, this allows to potentially identify novel avenues in improving an individuals’ cardiovascular health.
Using polygenic risk scores to study health
We measure the genetics of traits and diseases using polygenic risk scores in order to categorize risk groups. This allows healthcare resources to used more efficiently and improve studies on cumulative effects of individual’s genetic predisposition.
Statistical methodology for studying diverse and admixed populations
We develop and study methods to identify determinants and correlates of health and disease outcomes. While we primarily use genetics and omics, which are high-dimensional, in relation to phenotypes, we also establish methods in low-dimensional settings. Our goal is to rely on methods that are statistically robust and have good properties (power, type 1 error, confidence interval coverage) so that conclusions made while using them are reliable.
Leading the way
Computational and statistical methods using a wealth of molecular (and other) data to responsibly and powerfully study health in diverse populations.
Team
Our Team On The Forefront Of Research
Our Latest Updates
News And Updates About Our Research And People
Here's a figure!
We sometimes develop packages. This figure visualizes an object in our MathParquet R package, developed by Ziqing Wang and Mike Cassidy.