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CRISPR-based functional evaluation of schizophrenia risk variants.
Prashanth Rajarajan1, Erin Flaherty1, Schahram Akbarian2
1Graduate School of Biomedical Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States of America; Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States of America; Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States of America.
Schizophrenia Research
|July 7, 2019
Summary
Researchers are using CRISPR genome engineering and patient-derived stem cells to understand how genetic variants contribute to schizophrenia risk. This approach aims to identify specific gene functions and cellular effects linked to the disease.
Area of Science:
- Neuroscience
- Genetics
- Stem Cell Biology
Background:
- Genetic and genomic studies increasingly identify variants linked to neuropsychiatric disease risk.
- Understanding the functional impact of these genetic risk factors is crucial for advancing disease insights.
Purpose of the Study:
- To survey recent findings on rare and common variants associated with schizophrenia risk.
- To explore novel methods for validating these genetic associations and understanding their functional consequences.
Main Methods:
- Reviewing current research on genetic variants implicated in schizophrenia.
- Discussing validation efforts using post-mortem brain tissue.
- Highlighting the integration of CRISPR-based genome engineering with patient-specific human induced pluripotent stem cell (hiPSC)-based models.
Main Results:
- Identification of putative causal schizophrenia loci through combined CRISPR and hiPSC approaches.
- Demonstration of cell-type-specific effects of schizophrenia-associated variants.
- Exploration of strategies to link patient genotype data to diagnostic and treatment response predictions.
Conclusions:
- CRISPR-genome engineering combined with hiPSC models offers a powerful strategy to elucidate the functional effects of schizophrenia-associated genetic variants.
- This integrated approach holds promise for improving disease diagnosis and predicting treatment outcomes.
- Further advances in hiPSC model fidelity are necessary for comprehensive understanding.
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