This is unpublished


Genetic Epidemiology and Analytic Methods
Professor Emeritus, Medical Genetics


Education & Training

B.S. in Biology, Michigan State University (1975)

Ph.D. in Population Genetics with Statistics minor, University of Wisconsin, Madison (1981)

Postdoctoral Fellow in Human Population Genetics, Stanford University (1981-1984)


Phi Beta Kappa(1974)

NSF predoctoral Fellowship (1975-1978)

NIH predoctoral Fellowship (1978-1981)

Metropolitan Life Foundation Award for Medical Research (1995)

Award for best paper of 1998 in Genetic Epidemiology (1999)

School of Public Health and Community Medicine, Faculty Community Service Award (2002)

President-elect, President, Past-President, International Genetic Epidemiology Society (2004-2006)

International Genetic Epidemiology Society Leadership Award (2007)

L. Adrienne Cupples Award for Excellence in Teaching, Research, and Service in Biostatistics, Boston University (2013)


Research Interests 

Dr. Wijsman's research is directed towards the development and application of quantitative methods for analysis of human genetic data. This includes techniques of pedigree-based analysis,  identifying regions of identity-by-descent,  identification of disease-marker association, and investigation of population structure. Disorders under investigation currently include Alzheimer's disease, dyslexia, and autism. Dr. Wijsman’s research also has a component that is directed towards improvement of analytical tools. Large pedigrees with dense markers are very challenging to analyze. Monte Carlo Markov chain (MCMC) methods provide options for situations where other methods are impractical. This framework allowed us to develop practical approaches that can handle data analysis of very large and complex pedigrees. More recently MCMC has provided a framework for practical imputation of dense genotype data in large pedigrees, with a particular focus on rare variants. This provides an efficient mechanism for obtaining information from dense genotype data (e.g., from sequencing) without the need for direct genotyping of all subjects. Our current extensions also enable identification of the haplotype carrying risk allele(s) in pedigrees of interest, and we continue development, both for improved computational speed and functionality.