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Resolution of Parkinson-associated Loci and Creation of Predictive Models Using Multi-omic Data Analyses

Study Rationale: Genetic studies have allowed us to understand better the genetic risk of suffering from Parkinson’s disease. However, for most of the findings we do not know the biological meaning or consequence that leads or contributes to the onset of the diseases. Statistics and Informatics tools combining genetic information with other biological layers (proteins and RNA) will help identify the biological consequence of the identified genetic regions and provide potential drug targets along with better diagnostic tools.

Hypothesis: We hypothesize that by combining several layers of different biological data we will be able to better understand regions of the genome that have already been involved in Parkinson’s disease but for which the biological consequences are still unknown.

Study Design: We will combine genetic data with protein measurements in the first aim, and RNA (specifically those that have a circular form) with genetic data in the second one. By performing different association analyses, we will link genes with proteins and circular RNAs that will help resolve the biological meaning of genetic changes that we can modulate using drugs. Additionally, we will use this data along with state-of-the-art bioinformatic tools to develop diagnostic tools to aide in the early diagnostic of Parkinson’s disease.

Impact on Diagnosis/Treatment of Parkinson’s disease: This proposal will provide biological information to better understand the biology of Parkinson’s disease. We will propose novel candidates that we can modulate using known or novel drugs to treat Parkinson’s Disease. Our diagnostic tools, if successful, will provide an earlier and better diagnostic for the community which can benefit from earlier treatment and better life quality.

Next Steps for Development: We will validate drug targets and diagnostic tools. 


  • Laura Ibanez, PhD

    Saint Lous, MO United States

  • Carlos Cruchaga, PhD

    Saint Louis, MO United States

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