Dr. Schaid's Statistical Genetics and Genetic Epidemiology Lab focuses on providing the framework and methodological tools needed to support a wide range of research efforts aimed at improving understanding of the genetic basis and expression of disease.
Our lab works with world-class experts in a variety of diseases and areas of human genetics, ranging from molecular biology and cutting-edge genomic technologies to translation of genetic information into clinical care. Our team collaborates with investigators at Mayo Clinic and other institutions to address some of the most important biological and clinical questions related to human genetics.
Methodology and software development for quantitative analyses of genetic epidemiology
By developing innovative statistical methods and software that can be used widely by biomedical researchers, our lab facilitates analyses of complex genetic mechanisms and their associations with disease.
Our current projects include:
- New methods to integrate data from proteomics, metabolomics and other types of omics to evaluate their associations with clinical endpoints, such as time to disease progression.
- New methods to improve disease prediction from polygenic risk scores across individuals of diverse genetic ancestry and mixed ancestry.
- Methods to aid fine-mapping of likely genetic causal variants, building models that include genomic annotation.
- Causal mediation analysis methods for high-dimensional data.
These types of analyses can help scientists understand biological processes and guide targeted laboratory functional studies.
Our lab generally aims to provide user-friendly software that implements our methods. We strive to make this software widely available to biomedical researchers, including related well-documented procedures and examples of their use. Review our software.
Breast cancer prediction methods
Using the many genetic markers discovered through genome-wide association studies, we're collaborating with investigators to develop models to estimate the chance that someone will develop breast cancer in their remaining lifetime.
Polygenic risk models for diverse ancestries
Dr. Schaid is a co-principal investigator of a grant from the National Institutes of Health as part of the Polygenic Risk Methods in Diverse Populations (PRIMED) Consortium. Our team is developing methods to improve the way that polygenic risk scores can be used to predict disease in diverse communities. Our priority is on the prediction of coronary heart disease.