Amyotrophic lateral sclerosis (ALS) is a devastating neurological disease with no cure. Human genetic findings are fundamental because drug targets with genetic support are more likely to succeed in clinical trials.
Here, we have developed a pipeline to prioritize drugs than can be repurposed for treating ALS based on genetics, transcriptomics, and cell-based drug perturbations. Our findings using this method were validated using health claim records from U.S. Medicare beneficiaries.
These computational methodologies represent a step forward in finding effective treatments for ALS and other neurological disorders.
(1) Establishing the gene expression signature in ALS
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A published GWAS study involving 29,612 patients with ALS and 122,656 controls (Van Rheenen et al., 2021)
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S-PrediXcan MASHR prediction models (for detailed info, https://github.com/hakyimlab/MetaXcan/wiki/Tutorial:-GTEx-v8-MASH-models-integration-with-a-Coronary-Artery-Disease-GWAS).
(2) Identifying drug candidates to revert the ALS signature
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SignatureSearch R package (for detailed info, https://www.bioconductor.org/packages/devel/bioc/vignettes/signatureSearch/inst/doc/signatureSearch.html#33_LINCS_Search_Method).
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Library of Integrated Network-Based Cellular Signatures (LINCS) dataset (accessed through SignatureSearch
(3) Mendelian Randomization to test causality between hypertension and ALS (Optional)
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TwoSampleMR package (detailed info here, https://mrcieu.github.io/TwoSampleMR/)
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Hypertension-related GWAS summary stats
(4) Replication using Electronic Health Records (EHR)
Code for each analysis are deposited as individual notebooks in the following folders.
(1) ALS signature using S-PrediXcan
(2) Repurposable drugs using SignatureSearch
(3) Mendelian Randomization using TwoSampleMR