Rochester, Minnesota




The complex genetic basis of common human diseases and traits necessitates developing new statistical methods to address new questions and new types of data. Hence, the main focus of the Statistical Genetics and Genetic Epidemiology Laboratory directed by Daniel J. Schaid, Ph.D., is developing and evaluating statistical methods to analyze genetic data.

Much of Dr. Schaid's work is motivated by collaborating on projects that focus on the genetics of complex and common diseases and the genetics of immune response. These diseases include breast and prostate cancers and cardiovascular disease. Dr. Schaid's research also addresses study design, data analysis and computational methods, and the use of genetic information to predict disease development or response to treatments. Through Dr. Schaid's work, new software is created and distributed to the research community.

Focus areas

  • Quantitative methods for genetic epidemiology. The overall objective of this research is to facilitate analyses of complex genetic data by developing innovative statistical methods and software for biomedical researchers. One main research focus is developing methods to integrate different types of complex data, a type of mediation analysis such as linking genomics with intermediate traits, and linking these intermediate traits with disease outcomes. Another focus is developing methods to use single-nucleotide polymorphisms from genome-wide association studies to predict disease through polygenic risk scores.
  • Collaborative genetic analyses. Collaboration is key to research progress. Dr. Schaid collaborates on many different diseases and types of genetic data. In general, Dr. Schaid seeks to understand the genetic causes of disease and how genes control the different ways people react to medicine used to treat their disease. New statistical methods and software developed by Dr. Schaid and his team are used to screen the genome for single genetic variants, for variants grouped into genes, or even for genes grouped into larger biological networks. Dr. Schaid expects that different views of the genome will provide insights into causes of disease and best ways to treat disease.
  • Prostate cancer. A major aim of this research is to localize genetic susceptibility loci that increase the risk of prostate cancer. Another major aim is to analyze and characterize genes in the candidate regions to identify prostate cancer susceptibility genes. This research is conducted through international collaborations that compile a large number of study participants to seek the many different genetic variants that influence the risk of prostate cancer.
  • Breast cancer. This research aims to evaluate how genes influence response to breast cancer treatment and how multiple genetic markers influence the likelihood of developing breast cancer.
  • Cardiovascular disease. Dr. Schaid and his colleagues study the use of genetic risk scores for heart disease in diverse populations. Dr. Schaid is a co-investigator on the national Electronic Medical Records and Genomics (eMERGE) study. The eMERGE study investigates genetics to help doctors treat and prevent certain common conditions. Dr. Schaid also is a co-principal investigator on the Polygenic Risk Methods in Diverse Populations (PRIMED) Consortium. His work in PRIMED develops and evaluates methods to improve the use of polygenic risk scores to predict disease and health outcomes in diverse ancestry populations. Both studies have funding from the National Institutes of Health (NIH).

Significance to patient care

Finding genes that increase the risk of disease or affect response to treatment would have a dramatic impact on clinical practice. Using genetic information to predict common diseases helps guide physicians and patients on how best to screen for disease for early diagnosis or prevention.

Professional highlights

  • Chair, Department of Quantitative Health Sciences, Mayo Clinic, 2020-present.
  • Fellow, American Association for the Advancement of Science, 2021.
  • Chair, Genomics, Computational Biology and Technology Study Section, National Institutes of Health, 2007-2011.
  • Member, Board of Scientific Counselors, National Cancer Institute, 2006-2011.
  • Fellow, American Statistical Association, 2009.
  • Leadership Award, International Genetic Epidemiology Society, 2008.
  • Curtis L. Carlson Family Professor of Genomics Research, Mayo Clinic, 2007.
  • President, International Genetic Epidemiology Society, 2006.
  • Editor in chief, Genetic Epidemiology, 2000-2005.


Primary Appointment

  1. Consultant, Division of Computational Biology, Department of Quantitative Health Sciences
  2. Enterprise Chair, Department of Quantitative Health Sciences

Academic Rank

  1. Professor of Biostatistics


  1. Mayo Clinic Scholar - Statistical Genetics Department of Biometry and Genetics, Louisiana State University Medical Center
  2. PhD - Biostatistics University of Pittsburgh, Pittsburgh
  3. MS - Human Genetics University of Pittsburgh, Pittsburgh
  4. BS - Biology St Vincent College

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