Cell-free DNA (cfDNA) is increasingly recognized as a promising biomarker candidate for disease monitoring. However, its utility in neurodegenerative diseases, like amyotrophic lateral sclerosis (ALS), remains underexplored. Existing biomarker discovery approaches are tailored to a specific disease context or are too expensive to be clinically practical. Here, we address these challenges through a new approach combining advances in molecular and computational technologies. First, we develop statistical tools to select tissue-informative DNA methylation sites relevant to a disease process of interest. We then employ a capture protocol to select these sites and perform targeted methylation sequencing. Multi-modal information about the DNA methylation patterns are then utilized in machine learning algorithms trained to predict disease status and disease progression. We applied our method to two independent cohorts of ALS patients and controls (n=192). Overall, we found that the targeted sites accurately predicted ALS status and replicated between cohorts. Additionally, we identified epigenetic features associated with ALS phenotypes, including disease severity. These findings highlight the potential of cfDNA as a non-invasive biomarker for ALS.

The authors have declared no competing interest.

We would like to thank the contributors and funders of SALSA-SGC (Sporadic ALS Australia- Systems Genomics Consortium) for supporting infrastructure to share clinical data used for the UQ samples. We gratefully acknowledge the funding support provided by the ALS Association, ALS Finding a Cure, the ALS Biomarker Collaboration, the UCSF Weill Award, the Australian National Health and Medical Research Council (NHMRC) and the Motor Neurone Disease Research Institute Australia (MNDRIA). C.C. was partially supported by T32NS048004.

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

All participants provided written informed consent and the study received approval from the Human Research Ethics Committee at the Royal Brisbane and Women's Hospital (HREC/17/QRBW/299) and by the University of California San Francisco Committee on Human Research (IRB 10-05027).

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Fastq files and associated metadata for the samples are freely available at NCBI GEO. Tissue and cell-type WGBS data is freely available on the ENCODE Project and BLUEPRINT Epigenome Project data access portals.