My primary goal is to contribute to the identification of the genetic basis of complex diseases by developing and utilizing computational, statistical, and bioinformatics approaches. I developed methods, pipelines, and resources that aid in the identification of candidate genes for complex traits by primarily using pedigree information. Next generation sequencing technologies have made dense genetic datasets common. Although most genetic analysis software has been developed for population-based studies, recognition of the importance of rare variants has again made smaller studies on pedigrees common where multiple copies of such rare variants may be found. I developed pedigree-based analysis pipeline software (PBAP; faculty.washington.edu/wijsman/progdists/pbap/pbap_v1.00.tar.gz) that allows users to analyze dense genetic data in extended families. PBAP performs numerous file manipulations and preparations, checks relationships within pedigrees, critically selects a subset of markers from a dense panel, and accesses other programs to sample inheritance vectors (IVs) that are useful for downstream analysis. In addition, I developed quality control (QC) procedures that ensure concordance of data among cohorts of the first phase of the Population Architecture using Genomics and Epidemiology (PAGE) Consortium (pagestudy.org). I also lead the construction and maintenance of the combined linkage-physical map of the human genome, i.e., Rutgers Maps (compgen.rutgers.edu/rutgers_maps.shtml).
Topics of interest include: (1) developing computational methods, pipelines, and resources for complex diseases, (2) constructing combined linkage-physical maps for eukaryotic organisms that do not currently have current generation maps, and (3) collaborating on omics-projects to translate findings into predictive or therapeutic clinical care for precision medicine.