Tara C. Matise, Ph.D.

Tara C. Matise, Ph.D.

I head the Laboratory of Computational Genetics, in the Department of Genetics at Rutgers University. Computational Genetics represents my joint interests in human genetics, computer science, statistical genetics, and bioinformatics. The goal of our projects is to contribute to the identification of human disease genes.

Since 2008 I have been running the Coordinating Center (CC) for The PAGE Study (PAGE I 2008-2012; PAGE II 2013-2017). This study investigates population-specific variation in genetic disease susceptibility, focusing on non-European populations. You can read a 2013 press announcement here. For this NIH-funded project the Rutgers coordinating center manages the quality control, integration, and dissemination of the large-scale genotype data generated in PAGE, performs genotype imputation and ancestry deconvolution, organizes group discussions and meetings, facilitates collaborations, and oversees the logistics of the PAGE study. Our work is facilitated by collaborators at the USC/Information Sciences Institute and Stanford University. The CC team members bring expertise in population, statistical, and quantitative human genetics, computer science, genome sequencing and bioinformatics. The CC's combined expertise will help advance PAGE II's research on understanding the global relevance of disease-associated alleles across diverse human populations.

My lab also maintains The Rutgers Maps. An extension of our early work constructing genetic linkage maps using my MultiMap map-building software, the Rutgers Maps incorporate marker physical positions into a novel mapping approach to create combined physical-linkage maps. These maps contain over 28,000 markers and represent very comprehensive linkage maps. The Rutgers Maps also serve as a framework for interpolating the genetic map position of any marker, gene, or physical position in the genome.

Other recent projects in the lab have included: (a) identification and characterization of schizophrenia (SZ) candidate gene regions from existing SZ genetic studies, (b) prioritization of genes in SZ candidate regions for further study, and (c) development of a bioinformatics pipeline that assists in identifying candidate genes for mouse developmental QTL.

PubMed link to list of my publications.


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