Pain Oliver, Jones Ashley, Al Khleifat Ahmad, Agarwal Devika, Hramyka Dzmitry, Karoui Hajer, Kubica Jędrzej, Llewellyn David J, Ranson Janice M, Yao Zhi, Iacoangeli Alfredo, Al-Chalabi Ammar
Maurice Wohl Clinical Neuroscience Institute, Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.
Wellcome Centre for Human Genetics, Nuffield Department of Medicine, Old Road Campus, University of Oxford, Oxford, United Kingdom.
medRxiv. 2023 Jan 24:2023.01.18.23284589. doi: 10.1101/2023.01.18.23284589.
Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease. This study integrates 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.
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.
SNP-based fine-mapping, TWAS and PWAS identified 117 genes associated with ALS, with TWAS and PWAS providing novel mechanistic insights. Drug repurposing analyses identified five drugs significantly enriched for interactions with ALS associated genes, with directional analyses highlighting α-glucosidase inhibitors may exacerbate ALS pathology. Additionally, drug class enrichment analysis showed calcium channel blockers may reduce ALS risk. Across the two observed expression target samples, ALS polytranscriptomic scores significantly predicted ALS risk ( = 4%; -value = 2.1×10).
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.
肌萎缩侧索硬化症（ALS）是一种致命的神经退行性疾病。本研究将最新的ALS全基因组关联研究（GWAS）汇总统计数据与功能基因组注释相结合，旨在深入了解ALS风险位点的机制，推断药物再利用机会，并加强对ALS风险和临床特征的预测。
使用GWAS汇总统计方法，包括基于SuSiE SNP的精细定位以及转录组和蛋白质组全关联研究（TWAS/PWAS）分析，确定与ALS相关的基因。通过多种方法，将基因关联与DrugTargetor药物-基因相互作用数据库整合，以确定可重新用于治疗ALS的药物。此外，将TWAS中的ALS基因关联与两个外部ALS病例对照数据集中观察到的血液表达相结合，计算多转录组评分，并评估其在预测ALS风险和临床特征（包括发病部位、发病年龄和生存期）方面的效用。
基于SNP的精细定位、TWAS和PWAS确定了117个与ALS相关的基因，TWAS和PWAS提供了新的机制见解。药物再利用分析确定了五种与ALS相关基因相互作用显著富集的药物，定向分析突出显示α-葡萄糖苷酶抑制剂可能会加重ALS病理。此外，药物类别富集分析表明钙通道阻滞剂可能会降低ALS风险。在两个观察到的表达目标样本中，ALS多转录组评分显著预测了ALS风险（ = 4%；-值 = 2.1×10）。
对ALS GWAS汇总统计数据进行功能信息分析，为ALS病因学提供了新的机制见解，突出了几个治疗研究途径，并能够对ALS风险进行具有统计学意义的预测。
