Sharlee Climer, PhD

Assistant Professor, UMSL Mathematics and Computer Science

Development of combinatorial methods for the identification of patterns in genetic data

My primary research interest is the development of combinatorial methods for biological applications. A key focus is to identify patterns in genetic data and test associations of these patterns with complex traits, such as Alzheimer’s disease. To this end, we have developed network-based approaches to identify combinations of markers that are inter-correlated. Two key characteristics differentiate these approaches from conventional methods: a multi-faceted correlation measure is used to capture heterogeneity and the network scaffold is expanded to increase information retention. These unique properties have enabled us to identify genetic patterns that have been previously overlooked. For example, we have discovered ‘yin-yang’ sequences on human chromosome 14 that suggest two completely divergent evolutionary paths rapidly progressed in our past, presumably achieving the shared goal of enhancing gephyrin, a gene that is vital for signal transmissions in the human brain.

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