Ellen Wijsman
Professor
Medical Genetics
Professor
Biostatistics
Adjunct Professor
Genome Sciences

Faculty Information

Biography
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
Honors: 
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
Contact
Phone: 
(206) 543-8987
Research & Clinical Interests
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.