Objective: In 2021, the Clinical Genome Resource (ClinGen) amyotrophic lateral sclerosis (ALS) spectrum disorders Gene Curation Expert Panel (GCEP) was established to evaluate the strength of evidence for genes previously reported to be associated with ALS. Through this endeavor, we will provide standardized guidance to laboratories on which genes should be included in clinical genetic testing panels for ALS. In this manuscript, we aimed to assess the heterogeneity in the current global landscape of clinical genetic testing for ALS. Methods: We reviewed the National Institutes of Health (NIH) Genetic Testing Registry (GTR) and members of the ALS GCEP to source frequently used testing panels and compare the genes included on the tests. Results: 14 clinical panels specific to ALS from 14 laboratories covered 4 to 54 genes. All panels report on ANG, SOD1, TARDBP, and VAPB; 50% included or offered the option of including C9orf72 hexanucleotide repeat expansion (HRE) analysis. Of the 91 genes included in at least one of the panels, 40 (44.0%) were included on only a single panel. We could not find a direct link to ALS in the literature for 14 (15.4%) included genes. Conclusions: The variability across the surveyed clinical genetic panels is concerning due to the possibility of reduced diagnostic yields in clinical practice and risk of a missed diagnoses for patients. Our results highlight the necessity for consensus regarding the appropriateness of gene inclusions in clinical genetic ALS tests to improve its application for patients living with ALS and their families.

Background: Amyotrophic lateral sclerosis (ALS) is a fatal heterogeneous neurodegenerative disease that typically leads to death from respiratory failure within two to five years. Despite the identification of several genetic risk factors, the biological processes involved in ALS pathogenesis remain poorly understood. The motor cortex is an ideal region to study dysregulated pathological processes in ALS as it is affected from the earliest stages of the disease. In this study, we investigated motor-cortex gene expression of cases and controls to gain new insight into the molecular footprint of ALS. Methods: We performed a large case-control differential expression analysis of two independent post-mortem motor cortex bulk RNA-sequencing (RNAseq) datasets from the King's College London BrainBank (N = 171) and TargetALS (N = 132). Differentially expressed genes from both datasets were subjected to gene and pathway enrichment analysis. Genes common to both datasets were also reviewed for their involvement with known mechanisms of ALS pathogenesis to identify potential candidate genes. Finally, we performed a correlation analysis of genes implicated in pathways enriched in both datasets with clinical outcomes such as the age of onset and survival. Results: Differential expression analysis identified 2,290 and 402 differentially expressed genes in KCL BrainBank and TargetALS cases, respectively. Enrichment analysis revealed significant synapse-related processes in the KCL BrainBank dataset, while the TargetALS dataset carried an immune system-related signature. There were 44 differentially expressed genes which were common to both datasets, which represented previously recognised mechanisms of ALS pathogenesis, such as lipid metabolism, mitochondrial energy homeostasis and neurovascular unit dysfunction. Differentially expressed genes in both datasets were significantly enriched for the neuropeptide signalling pathway. By looking at the relationship between the expression of neuropeptides and their receptors with clinical measures, we found that in both datasets NPBWR1, TAC3 and SSTR1 correlated with age of onset, and GNRH1, TACR1 with survival. We provide access to gene-level expression results to the broader research community through a publicly available web application (https://alsgeexplorer.er.kcl.ac.uk). Conclusion: This study identified motor-cortex specific pathways altered in ALS patients, potential molecular targets for therapeutic disease intervention and a set of neuropeptides and receptors for investigation as potential biomarkers.

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Summary The current widespread adoption of next-generation sequencing (NGS) in all branches of basic research and clinical genetics fields means that users with highly variable informatics skills, computing facilities and application purposes need to process, analyse, and interpret NGS data. In this landscape, versatility, scalability, and user-friendliness are key characteristics for an NGS analysis software. We developed DNAscan2, a highly flexible, end-to-end pipeline for the analysis of NGS data, which (i) can be used for the detection of multiple variant types, including SNVs, small indels, transposable elements, short tandem repeats and other large structural variants; (ii) covers all standard steps of NGS analysis, from quality control of raw data and genome alignment to variant calling, annotation and generation of reports for the interpretation and prioritisation of results; (iii) is highly adaptable as it can be deployed and run via either a graphic user interface for non-bioinformaticians and a command line tool for personal computer usage; (iv) is scalable as it can be executed in parallel as a Snakemake workflow, and; (v) is computationally efficient by minimising RAM and CPU time requirements. Availability and Implementation DNAscan2 is implemented in Python3 and is available at https://github.com/KHP-Informatics/DNAscanv2. Supplementary information Supplementary data are available at Bioinformatics online.