Towards a diagnostic test for sporadic ALS utilising deep learning and SNP microarrays

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Abstract A variety of common and rare genetic factors have been implicated in the development of amyotrophic lateral sclerosis (ALS), and the evidence is that a genetic component is present in most affected individuals. However, our current understanding of ALS genetics causally explains only a small proportion of sporadic cases which represent over 90% of all people with ALS. This limits the utility of genetic testing in screening, diagnosis and management to the 15-20% of people with ALS who carry a known pathogenic variant. Capsule Networks (CapsNets) constitute a deep learning method that has demonstrated strong performance in using genotyping data to predict individuals at risk for ALS. However, their use is constrained by a lack of generalised, flexible, and validated implementations across comprehensive datasets that account for the technical, biological, and clinical heterogeneity found in real-world disease scenarios. In this study, we build upon this method to address existing limitations, to develop a new model that is validated across diverse ALS populations, can handle discrepancies between genotyping technologies, and is applicable to individual external samples. Using large-scale datasets from over 47,000 individuals from 13 countries, genotyped with nine different genotyping platforms, our model achieved high precision and sensitivity in distinguishing between individuals with ALS and non-affected controls. Moreover, in simulations of population screening for ALS, its performance was comparable to that of conventional genetic screening for known ALS gene mutations, such as FUS and C9orf72 . Our results demonstrate that this flexible and validated method could support the development of a genetic screening test for identifying individuals at risk and expediting ALS diagnosis. This would be applicable to all individuals, regardless of their family history or presence of known ALS mutations.

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