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 gene mapping; modeling modes of inheritancer; identifying regions of identity-by-descent through linkage disequilibrium, haplotype imputation, and genome-wide association analysis; and investigating the impact of copy number variation and rare variants on neuropsychiatric diseases. Disorders under investigation currently include Alzheimer's disease, dyslexia, autism,cardiovascular disease, and schizophrenia. Computational constraints have lead to a search for alternative methods of analysis. Dr. Wijsman is working on the development and evaluation of Monte Carlo Markov chain (MCMC) methods for use in situations where current methods are computationally impractical. Current studies indicate that methods based on a MCMC-framework provide a mechanism for identifying both the number of underlying contributory loci and their genome locations by providing a computationally tractable approach to multipoint analysis of large pedigrees in the presence of complex modes of inheritance. This MCMC-based framework is also providing a mechanism under which imputation of dense genotype data is possible in large pedigrees, thus providing an efficient mechanism for obtaining dense genotype data (e.g., from sequencing) without the need for direct genotyping of all subjects.