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Therapeutic target prediction for orphan diseases integrating genome-wide and transcriptome-wide association studies
Satoko Namba1,2, Michio Iwata1, Shin-Ichi Nureki3
1Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, Kawazu, Iizuka, Fukuoka, 820-8502, Japan.
Abstract:
Therapeutic target identification is challenging in drug discovery, particularly for rare and orphan diseases. Here, we propose a disease signature, TRESOR, which characterizes the functional mechanisms of each disease through genome-wide association study (GWAS) and transcriptome-wide association study (TWAS) data, and develop machine learning methods for predicting inhibitory and activatory therapeutic targets for various diseases from target perturbation signatures (i.e., gene knockdown and overexpression). TRESOR enables highly accurate identification of target candidate proteins that counteract disease-specific transcriptome patterns, and the Bayesian optimization with omics-based disease similarities achieves the performance enhancement for diseases with few or no known targets. We make comprehensive predictions for 284 diseases with 4345 inhibitory target candidates and 151 diseases with 4040 activatory target candidates, and elaborate the promising targets using several independent cohorts. The methods are expected to be useful for understanding disease-disease relationships and identifying therapeutic targets for rare and orphan diseases.
Insights
We developed TRESOR, a novel disease signature using GWAS and TWAS data, to predict therapeutic targets for rare diseases. This machine learning approach identifies potential drug targets by analyzing gene expression patterns, aiding drug discovery.
Area of Science:
- Computational biology and bioinformatics
- Genomics and transcriptomics
- Drug discovery and development
Background:
- Therapeutic target identification is a critical bottleneck in drug discovery, especially for rare and orphan diseases.
- Existing methods often struggle with diseases lacking extensive genetic or clinical data.
- Understanding disease mechanisms at a molecular level is essential for effective treatment strategies.
Purpose of the Study:
- To introduce TRESOR, a novel disease signature for characterizing functional disease mechanisms.
- To develop machine learning models for predicting inhibitory and activatory therapeutic targets.
- To enhance target identification for rare diseases with limited known targets.
Main Methods:
- Utilized genome-wide association study (GWAS) and transcriptome-wide association study (TWAS) data to define the TRESOR disease signature.
- Developed machine learning algorithms to predict therapeutic targets based on gene perturbation signatures (gene knockdown/overexpression).
- Employed Bayesian optimization with omics-based disease similarities to improve performance for data-poor diseases.
Main Results:
- Successfully identified 4345 inhibitory target candidates across 284 diseases and 4040 activatory target candidates across 151 diseases.
- TRESOR accurately identifies target proteins that counteract disease-specific transcriptome patterns.
- Promising targets were validated using independent cohorts, demonstrating the robustness of the predictions.
Conclusions:
- The TRESOR signature and associated machine learning methods provide a powerful framework for therapeutic target identification.
- This approach significantly aids in discovering targets for rare and orphan diseases.
- The methods can also facilitate a deeper understanding of relationships between different diseases.
