Dystonia is a rare disease trait for which large-scale genomic investigations are still underrepresented. Genetic heterogeneity among patients with unexplained dystonia warrants interrogation of entire genome sequences, but this has not yet been systematically evaluated. To significantly enhance our understanding of the genetic contribution to dystonia, we (re)analysed 2874 whole-exome sequencing (WES), 564 whole-genome sequencing (WGS), as well as 80 fibroblast-derived proteomics datasets, representing the output of high-throughput analyses in 1990 patients and 973 unaffected relatives from 1877 families. Recruitment and precision-phenotyping procedures were driven by long-term collaborations of international experts with access to overlooked populations. By exploring WES data, we found that continuous scaling of sample sizes resulted in steady gains in the number of associated disease genes without plateauing. On average, every second diagnosis involved a gene not previously implicated in our cohort. Second-line WGS focused on a subcohort of undiagnosed individuals with high likelihood of having monogenic forms of dystonia, comprising large proportions of patients with early onset (81.3%), generalized symptom distribution (50.8%) and/or coexisting features (68.9%). We undertook extensive searches for variants in nuclear and mitochondrial genomes to uncover 38 (ultra)rare diagnostic-grade findings in 37 of 305 index patients (12.1%), many of which had remained undetected due to methodological inferiority of WES or pipeline limitations. WGS-identified elusive variations included alterations in exons poorly covered by WES, RNA-gene variants, mitochondrial-DNA mutations, small copy-number variants, complex rearranged genome structure and short tandem repeats. For improved variant interpretation in WGS-inconclusive cases, we employed systematic integration of quantitative proteomics. This aided in verifying diagnoses related to technically challenging variants and in upgrading a variant of uncertain significance (3 of 70 WGS-inconclusive index patients, 4.3%). Further, unsupervised proteomic outlier analysis supplemented with transcriptome sequencing revealed pathological gene underexpression induced by transcript disruptions in three more index patients with underlying (deep) intronic variants (3/70, 4.3%), highlighting the potential for targeted antisense-oligonucleotide therapy development. Finally, trio-WGS prioritized a de novo missense change in the candidate PRMT1, encoding a histone methyltransferase. Data-sharing strategies supported the discovery of three distinct PRMT1 de novo variants in four phenotypically similar patients, associated with loss-of-function effects in in vitro assays. This work underscores the importance of continually expanding sequencing cohorts to characterize the extensive spectrum of gene aberrations in dystonia. We show that a pool of unresolved cases is amenable to WGS and complementary multi-omic studies, directing advanced aetiopathological concepts and future diagnostic-practice workflows for dystonia.

• Keywords
• dystonia, genomics, multi-omics, proteomics, transcriptomics, whole-genome sequencing,
• MeSH
• Adult MeSH
• Dystonic Disorders * genetics   MeSH
• Dystonia * genetics   MeSH
• Genetic Heterogeneity MeSH
• Genomics * methods   MeSH
• Middle Aged MeSH
• Humans MeSH
• Proteomics * methods   MeSH
• Whole Genome Sequencing MeSH
• Exome Sequencing MeSH
• Check Tag
• Adult MeSH
• Middle Aged MeSH
• Humans MeSH
• Male MeSH
• Female MeSH
• Publication type
• Journal Article MeSH

We present here the co-expressed protein-protein interactions algorithm. In addition to minimizing correlation-causality imbalance and contextualizing protein-protein interactions to the investigated systems, it combines protein-protein interactions and protein co-expression networks to identify differentially correlated functional modules. To test the algorithm, we processed a set of proteomic profiles from different brain regions of controls and subjects affected by idiopathic Parkinson's disease or carrying a GBA1 mutation. Its robustness was supported by the extraction of functional modules, related to translation and mitochondria, whose involvement in Parkinson's disease pathogenesis is well documented. Furthermore, the selection of hubs and bottlenecks from the weightedprotein-protein interactions networks provided molecular clues consistent with the Parkinson pathophysiology. Of note, like quantification, the algorithm revealed less variations when comparing disease groups than when comparing diseased and controls. However, correlation and quantification results showed low overlap, suggesting the complementarity of these measures. An observation that opens the way to a new investigation strategy that takes into account not only protein expression, but also the level of coordination among proteins that cooperate to perform a given function.

• Keywords
• PPI network, Parkinson, co-expression network, proteomics, topology,
• MeSH
• Algorithms * MeSH
• Glucosylceramidase genetics   metabolism   MeSH
• Humans MeSH
• Protein Interaction Mapping * methods   MeSH
• Protein Interaction Maps MeSH
• Brain metabolism   MeSH
• Parkinson Disease * metabolism   genetics   physiopathology   MeSH
• Proteomics methods   MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Names of Substances
• Glucosylceramidase MeSH