The complex genetic basis of common human diseases and traits necessitates the development of new statistical methods to address new questions and new types of data. Hence, the main focus of the research laboratory of Daniel J. Schaid, Ph.D., is the development and evaluation of statistical methods for the analysis of genetic data.
Much of this effort is motivated by ongoing collaborations in projects that focus on either family-based genetic epidemiology studies (such as the study of the aggregation of diseases in families or analysis of quantitative traits) or genetic association studies with common diseases (such as case-control studies).
General issues addressed by Dr. Schaid's research are study design, data analysis and computational methods. As a result of this research, new software is developed and distributed to the research community.
- Quantitative methods for genetic epidemiology. The overall objectives are to facilitate analyses of complex genetic data by developing innovative statistical methods and software that can be used by biomedical researchers.
The efforts of Dr. Schaid and his colleagues focus on the development of methods to scan sets of genes for their associations with disease or response to treatments; statistical models for the roles of individual genetic variants, genes or genetic pathways on human traits; methods for the association of rare genetic variants with human traits; and ways to combine known information on genes with genetic data for a variety of studies.
- Collaborative genetic analyses. Collaborations are key to research progress, and Dr. Schaid's collaborations cover many different diseases and types of genetic data. In general, he seeks to understand the genetic causes of disease or how genes control the way that different people react differently to drugs used to treat their diseases.
New statistical methods and software developed by Dr. Schaid and his team are used to screen the genome for single genetic variants, variants grouped into genes or even genes grouped into larger biological networks. Dr. Schaid expects that different views of the genome will provide insights into causes of disease and ways to best treat disease.
- Prostate cancer. The major aims of this research program are to localize genetic susceptibility loci in familial prostate cancer by modern DNA sequencing data, and analyze and characterize genes in the candidate regions to identify prostate cancer susceptibility genes. This program is conducted at Mayo Clinic, as well as through an international multi-institution grant that pools data from prostate cancer families to identify novel genes related to familial prostate cancer.
- Pharmacogenetics of phase II drug-metabolizing enzymes (Pharmacogenetic Research Network). The primary focus of this pharmacogenetics research is to extend statistical methods and software that will be used to analyze genetic data associated with either disease or response to treatment of disease. The two major disease foci are breast cancer and major depression.
- Alzheimer's disease. Dr. Schaid's collaborators at Mayo Clinic and Columbia University have access to a large collection of families with Alzheimer's disease and an enormous amount of genetic data. A significant challenge is determining how best to "hunt" for the genes related to Alzheimer's disease, recognizing that there are multiple genes involved in addition to environmental risk factors and natural aging processes.
Significance to patient care
Although Dr. Schaid's research has not yet directly impacted clinical practice and the care of patients, finding genes that increase risk of disease or impact how patients respond to therapies would have a dramatic impact on clinical practice in the near future.
- Recipient, Curtis L. Carlson Family Professor of Genomics Research, Mayo Clinic, 2009-present
- Member, Board of Scientific Counselors, National Cancer Institute, 2006-2011
- Chair, Genomics, Computational Biology and Technology Study Section, National Institutes of Health, 2007-2011
- Fellow, American Statistical Association, 2009
- Leadership Award, International Genetic Epidemiology Society, 2008
- President, International Genetic Epidemiology Society, 2006
- Editor-in-Chief, Genetic Epidemiology, 2000-2005