ProfessorMedical Genetics ProfessorBiostatistics Adjunct ProfessorGenome Sciences Related Links: Dr. Wijsman's faculty pageFaculty Information Biography Education & Training: B.S. in BiologyMichigan State University1975 Ph.D. in Population Genetics with Statistics minor University of WisconsinMadison1981 Postdoctoral Fellow in Human Population GeneticsStanford University1981-1984 Honors: Phi Beta Kappa1974 NSF predoctoral Fellowship 1975-1978 NIH predoctoral Fellowship 1978-1981 Metropolitan Life Foundation Award for Medical Research1995 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 Email: email@example.comPhone: (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.