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Current Research Projects

The Molecular Genetics and Bioinformatics Lab unites human genetics, biomedical informatics and functional studies to help translate big data into clinically-useful knowledge. Our research encompasses a broad range of projects focused on finding efficient ways to translate biomedical data into clinically useful information for disorders of the brain. Summaries of current research directions and ongoing projects in the lab are provided below.

To help translate genetic findings into clinically-useful information, it is essential to understand how genetic factors contribute to variable expressivity of symptoms across patient populations, or comorbidities in the same individual. Variability in the manifestation of complex disorders may be explained by genetic heterogeneity. Current projects in this research direction include:

  • Deciphering multi-modal risk factors influencing expression of dementia – This project will establish a machine learning pipeline for analysis of big data in Alzheimer’s Disease with a future goal to build a resource useful to the broader research community at KUMC. We are using existing data from Alzheimer’s Disease Centers across the United States to test whether mitochondrial genetic variation can predict family history using brain imaging as an endophenotype and determine if a maternal transmission symptom profile exists.

It is well-established that genetic factors influence risk for disorders affecting the brain. Interestingly, the biological functions for recurrently implicated genes suggest involvement of shared molecular mechanisms. It is still unclear how pleiotropic effects of genetic mechanisms that are implicated in more than one disorder contribute to variable expressivity of core symptoms and expression of comorbidities. This research aims to better characterize pleiotropic effects and further our knowledge of how convergent underlying genetic architecture contributes to seemingly distinct disorders that affect the brain. Current projects in this research direction include:

  • Identifying genetic pathways with pleiotropic effects on distinct brain disorders – This project aims to use whole-genome and RNA-sequencing to help decipher the genetic mechanisms dysregulated in post-mortem brain tissue from individuals with schizophrenia having pleiotropic effects influencing expression of sleep disturbances. The primary goals are to identify potential drug targets useful to treating insomnia-related sleep problems which are among the most common co-occurring conditions in these patients. This project also proposes to build a database containing data generated from this study, and future projects which will be made available to the broad biomedical research community. The project is leveraging publicly available big data resources and the University of Kansas Medical Center Psychiatry and Behavioral Sciences Brain Bank—containing whole brains from ~200 human donors.

One way to expand the scope of the utility of genetics in treatment is to use EHR-derived data. For example, these data are useful to pinpointing biological systems underlying adverse events. Furthermore, EHR-derived data offers unprecedented opportunity to validate previously reported genetic associations in clinical populations and help reveal pleiotropic genetic effects influencing expression of co-occurring conditions. This, in turn, may inform personalized treatment approaches. Current projects in this research direction include:

  • Elucidating the causes and consequences of sleep problems in rare genetic syndromes – This project will further our understanding of the connection between sleep, neurodevelopment, obesity, and genetics. We will leverage EHR-derived datasets to define the relationship of disturbed sleep to symptom severity in children with genetically determined neurodevelopmental syndromes (i.e., Prader-Willi syndrome). By evaluating differences in sleep-related traits between children with distinct genetic causes of Prader-Willi syndrome, results should provide an avenue for identification of novel treatments of sleep disturbances in these patient populations.

As incorporating genetic data into patient care is largely dependent on clinicians’ perspectives of its utility, it is necessary for basic science researchers to help inform clinicians about relevant genetic findings that may be beneficial to optimizing treatment. There are several excellent resources available that allow for developing clinically useful ways to interpret information from genetic studies. Clinicians with the most opportunity to enact personalized approaches to healthcare often have limited time to spend training in the skills required to use these tools. This represents a pressing issue given the current push to begin using evidence related to clinically actionable genetic variation to guide preventative interventions and clinical decision making. Current projects in this research direction include:

  • Efficient translation of genetics research for clinical decision support – This project is developing an automated method for prioritizing clinically actionable results from genetic studies of complex human disease. Furthermore, the validated method is being streamlined in an easy-to-use, highly accessible mobile application that presents complicated genetics results in a format useful to inform clinicians as to if genetic testing will be beneficial toward optimizing treatment for a patient with a complex disease. The goal is to rapidly deliver important information in such a manner that the clinician can be more informed when making key decisions regarding the benefits of ordering genetic testing. You can find our current release of the mobile application in the Google Play app store
Psychiatry and Behavioral Sciences

University of Kansas Medical Center
Psychiatry and Behavioral Sciences
Mailstop 4015
3901 Rainbow Boulevard
Kansas City, KS 66160
Phone: 913-588-6400
Fax: 913-588-6414