Harnessing transcriptomic signals for amyotrophic lateral sclerosis to identify novel drugs and enhance risk prediction
Oliver Pain,
Ashley Jones,
Ahmad Al Khleifat,
Devika Agarwal,
Dzmitry Hramyka,
Hajer Karoui,
Jędrzej Kubica,
David J. Llewellyn,
Janice M. Ranson,
Zhi Yao,
Alfredo Iacoangeli,
Ammar Al-Chalabi
Affiliations
Oliver Pain
Maurice Wohl Clinical Neuroscience Institute, Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom; Corresponding author.
Ashley Jones
Maurice Wohl Clinical Neuroscience Institute, Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
Ahmad Al Khleifat
Maurice Wohl Clinical Neuroscience Institute, Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
Devika Agarwal
Wellcome Centre for Human Genetics, Nuffield Department of Medicine, Old Road Campus, University of Oxford, Oxford, United Kingdom
Dzmitry Hramyka
Core Unit Bioinformatics (CUBI), Berlin Institute of Health, Charité – Universitätsmedizin Berlin, Berlin, Germany; Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
Hajer Karoui
Multiple Sclerosis and Parkinson's Tissue Bank, Department of Brain Sciences, Imperial College London, London, United Kingdom
Jędrzej Kubica
Laboratory of Structural Bioinformatics, Institute of Evolutionary Biology, University of Warsaw, Poland; Laboratory of Theory of Biopolimers, Faculty of Chemistry, University of Warsaw, Poland
David J. Llewellyn
University of Exeter Medical School, Exeter, United Kingdom; Alan Turing Institute, London, United Kingdom
Janice M. Ranson
University of Exeter Medical School, Exeter, United Kingdom
Zhi Yao
LifeArc, Stevenage, United Kingdom
Alfredo Iacoangeli
Maurice Wohl Clinical Neuroscience Institute, Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom; Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom; National Institute for Health Research Biomedical Research Centre and Dementia Unit at South London and Maudsley NHS Foundation Trust and King's College London, London, United Kingdom
Ammar Al-Chalabi
Maurice Wohl Clinical Neuroscience Institute, Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
Introduction: Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease. This study integrates common genetic association results from the latest ALS genome-wide association study (GWAS) summary statistics with functional genomic annotations with the aim of providing mechanistic insights into ALS risk loci, inferring drug repurposing opportunities, and enhancing prediction of ALS risk and clinical characteristics. Methods: Genes associated with ALS were identified using GWAS summary statistic methodology including SuSiE SNP-based fine-mapping, and transcriptome- and proteome-wide association study (TWAS/PWAS) analyses. Using several approaches, gene associations were integrated with the DrugTargetor drug-gene interaction database to identify drugs that could be repurposed for the treatment of ALS. Furthermore, ALS gene associations from TWAS were combined with observed blood expression in two external ALS case-control datasets to calculate polytranscriptomic scores and evaluate their utility for prediction of ALS risk and clinical characteristics, including site of onset, age at onset, and survival. Results: SNP-based fine-mapping, TWAS and PWAS identified 118 genes associated with ALS, with TWAS and PWAS providing novel mechanistic insights. Drug repurposing analyses identified six drugs significantly enriched for interactions with ALS associated genes, though directionality could not be determined. Additionally, drug class enrichment analysis showed gene signatures linked to calcium channel blockers may reduce ALS risk, whereas antiepileptic drugs may increase ALS risk. Across the two observed expression target samples, ALS polytranscriptomic scores significantly predicted ALS risk (R2 = 5.1 %; p-value = 3.2 × 10−27) and clinical characteristics. Conclusions: Functionally-informed analyses of ALS GWAS summary statistics identified novel mechanistic insights into ALS aetiology, highlighted several therapeutic research avenues, and enabled statistically significant prediction of ALS risk.
