Amyotrophic lateral sclerosis (ALS) is an incurable neurodegenerative disease defined by the selective toxicity to upper and lower motor neurons (MN) within the motor cortex and spinal cord respectively (Cooper-Knock, Jenkins, & Shaw, 2013). Onset is typically between 50 and 70 years of age, and death usually results within 2 to 5 years due to respiratory failure. Currently we do not have a good understanding of the molecular basis of ALS. What understanding we do have rests largely on the study of a small number of high penetrance genetic variants which cause monogenic ALS. This has led to a successfully targeted therapeutic intervention for ALS caused by mutations within SOD1 (Miller et al., 2022). The majority (90%) of ALS, while pathologically and clinically overlapping with the rarer monogenic forms, is an archetypal complex disease underpinned by gene-environment interactions (Cooper-Knock, Harvey, et al., 2021); common parlance is to call this sporadic ALS. Understanding the genetic architecture that predisposes to sporadic ALS is crucial to enabling the personalised intervention for the majority of ALS patients that has been so successful for monogenic ALS. Less than 10% of sporadic ALS patients are associated with a known monogenic risk variant, in part because of a focus on mutations within exons which alter amino acid sequence within translated proteins.

In this Chapter we argue that a relative lack of understanding of the contribution of the non-coding genome is one reason for limited progress in the discovery of the genetic architecture underlying sporadic ALS. The non-coding genome consists of all sequence that does not explicitly code for protein amino acid sequence. While much of the non-coding genome has structural importance for the genome itself, the focus of this Chapter is upon genetic sequence within so-called regulatory elements i.e. the promoters, enhancers, repressors and so-forth that are the binding motifs for transcription factors and other proteins which regulate expression of coding sequence. The other major source of non-coding genetic variants which modulate risk of ALS is structural variants, including intronic repeat-expansions within C9ORF72 (DeJesus-Hernandez et al., 2011, Renton et al., 2011) and ATXN2 (Elden et al., 2010, Ghahremani Nezhad et al., 2021). Repeat-expansions will not be a focus of this Chapter.

The non-coding genome accounts for the largest component of human genetic material and contains the majority of inherited risk for human diseases (Zhang & Lupski, 2015). The challenges to study of the non-coding genome are many but, with gradually reducing cost of whole genome sequencing (WGS), the largest challenge is now biological interpretability. Project MinE (https://www.projectmine.com/) has led the effort to perform WGS for ALS patients and matched controls leading to the largest disease-specific WGS effort for any disease. To date Project MinE includes ∼7000 sporadic ALS patients and ∼3000 controls.

Biological interpretability of genetic variants within the non-coding genome is largely a function of cell-specific function. This is because regulation of gene expression is one of the key determinants of cellular identity (Almeida et al., 2021). In this Chapter we highlight genetic drivers of ALS that have been identified within the regulatory non-coding genome. The majority of these discoveries rest upon an understanding of genetic regulation within a motor neuron (MN), the primary cell affected in ALS. However, we know that MN death is not determined by MN dysfunction alone and that there is likely to be a role for genetic variants which function within other neuronal types, particularly interneurons (Allodi, Montañana-Rosell, Selvan, Löw, & Kiehn, 2021); and within glial cells such as astrocytes (Yamanaka & Komine, 2018), microglia (Clarke & Patani, 2020) and oligodendrocytes (Ferraiuolo et al., 2016). In this Chapter we discuss how emerging technologies such as single-cell epigenetic profiling and spatial transcriptomics are leading to progress in this area. We have summarised this research challenge as we see it in Fig. 1.