T. Conrad Gilliam, Ph.D.

APPOINTMENTS

  • Dean for Research and Graduate Education, Biological Sciences Division
  • Marjorie I. and Bernard A. Mitchell Professor, Department of Human Genetics
  • Committee on Genetics, Committee on Molecular Medicine/MPMM

EDUCATION

Ph.D., University of Missouri, 1981

CONTACT INFORMATION

The University of Chicago
CLSC 507B 
920 East 58th Street
Chicago, Illinois 60637

cgilliam@bsd.uchicago.edu

Phone:  (773) 834-0525

Website (Dept. of Human Genetics)

RESEARCH SUMMARY

My research focuses on the identification and characterization of heritable mutations that affect the nervous system. Research projects vary from genetic mapping of rare (Mendelian) disease mutations and characterization of their downstream consequences to the study of common heritable disorders using mouse models as well as genomic and bioinformatic approaches.

Study of rare genetic disorders has focused broadly on neurological disorders including the spinal muscular atrophies (motorneuron disease), different forms of epilepsy, and Wilson disease, a defect in copper transport that primarily affects the liver, but leads to neuropsychiatric symptoms when toxic levels of copper accumulate in the brain. 

With new opportunities in genome science and bioinformatics, research has shifted toward the study of common heritable disorders including autism, bipolar disorder, anxiety disorders, schizophrenia, and Alzheimer’s disease. We use mouse models and disease-related ‘endophenotypes’ to inform the search for human disease genes. In the study of human anxiety disorders, we target an adaptive fear-related behavior - fear conditioning – that can be studied in humans and other model organisms.  We collaborate in these studies with Dr. Abraham Palmer (Human Genetics).  Similarly, in our study of schizophrenia we focus on bio-behavioral markers like working memory and executive functioning skills.  Our group and others have shown that heritable genetic variation in genes that alter dopamine metabolism at the synaptic nerve terminals affect performance on the specific cognitive tasks that are compromised in schizophrenia patients. In the study of autism, we have used gender and language-based endophenotypes as biomarkers to reduce the genetic complexity of the target phenotype. By focusing on genetically tractable disease sub-phenotypes, we hope to gain insight into the genetic architecture of complex human diseases. 

Another approach we are pursuing is to shift the focus of gene mapping from individual gene mutations to the prediction of multi-gene patterns of inheritance. Genetic susceptibility to complex disorders arises from the fateful combination of heritable mutations distributed among multiple genes. Identification of these multigenic patterns of inheritance has proven elusive because the vast number of gene combinations that could lead to disease so dramatically exceeds the number of experimental observations possible in human studies. Assuming our genomes consist of ~ 25,000 genes, the number of possible combinations of 2, 3, and 10 genes increases exponentially from 109 to 1012 to 1030 respectively, in comparison to 103 to 104 observable ‘meiotic events’ in the largest human genetic studies. Thus, the ‘curse of dimensionality’ has thwarted progress in the study of common heritable disorders.

We are collaborating with experts in data-mining and network topology, systems biology, large-scale computing, and statistical genetics to develop new approaches to map the multi-gene determinants of common neuropsychiatric disorders. Our current approach uses technology developed by Rzhetsky and coworkers (Medicine and Human Genetics, beginning mid-2007) to first, extract molecular interaction networks from the electronic literature, and second, combine interaction data and genetic linkage data in a common probabilistic framework that allows us to survey disease-related inheritance across groups of interacting genes. By reducing possible gene-gene combinations to those documented in the literature or whole genome databases, we avoid the penalties of multiple-testing. We are collaborating to evaluate and benchmark the new approach, to develop models for the detection of gene-gene interactions, and ultimately, to use the new approach to identify multigenic patterns of inheritance that predict an individual’s susceptibility to major neuropsychiatric disorders.

Research Papers in PubMed