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Activity Number:
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70
- Utilizing High-Dimensional and Complex Data in Personalized Medicine
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Type:
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Contributed
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Date/Time:
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Sunday, July 30, 2017 : 4:00 PM to 5:50 PM
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Sponsor:
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Mental Health Statistics Section
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Abstract #324957
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Title:
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An Integrative Genomics Approach to Infer Gene Expression Statistical Causal Network
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Author(s):
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Akram Yazdani* and Azam Yazdani and Panos Roussos
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Companies:
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Icahn School of Medicine at Mount Sinai and The University of Texas School of Public Health, Houston and Icahn School of Medicine at Mount Sinai
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Keywords:
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causal network ;
instrumental variable ;
gene expression ;
DNA-variants
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Abstract:
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Complex diseases encompass a range of conditions, most of which are heritable. Although static genetic biomarkers are key components of heritability, enormous effort in understanding the role of genes has identified few variants highly associated with disease. Since biological systems constructed from hierarchies of organization, the relationships between components can be represented as a complex network. An essential attribute for increasing confidence in potential clinical validity of gene variation with disease will be the elucidation of gene interactions. We detailed a procedure for identifying potential key drivers of the complex traits by integrating DNA-variation and gene-expression data using Bayesian probabilistic model. We systematically assessed if variations in DNA support statistical causative or independent function to the traits. Using the DNA-variation as instrumental variables, we elucidated gene regularity network over 23,208 gene of 415 Caucasians from the CommonMind Consortium. The relationships among genes are obtained for 209 cases with schizophrenia and 206 controls separately. We determined modules and compared the result between cases and controls.
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Authors who are presenting talks have a * after their name.
