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.

• Klíčová slova
• dystonia, genomics, multi-omics, proteomics, transcriptomics, whole-genome sequencing,
• MeSH
• dospělí MeSH
• dystonické poruchy * genetika   MeSH
• dystonie * genetika   MeSH
• genetická heterogenita MeSH
• genomika * metody   MeSH
• lidé středního věku MeSH
• lidé MeSH
• proteomika * metody   MeSH
• sekvenování celého genomu MeSH
• sekvenování exomu MeSH
• Check Tag
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• mužské pohlaví MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH

Pathogenic variants affecting the BLM gene are responsible for the manifestation of extremely rare cancer‑predisposing Bloom syndrome. The present study reports on a case of an infant with a congenital hypotrophy, short stature and abnormal facial appearance. Initially she was examined using a routine molecular diagnostic algorithm, including the cytogenetic analysis of her karyotype, microarray analysis and methylation‑specific MLPA, however, she remained undiagnosed on a molecular level. Therefore, she and her parents were enrolled in the project of trio‑based exome sequencing (ES) using Human Core Exome kit. She was revealed as a carrier of an extremely rare combination of causative sequence variants altering the BLM gene (NM_000057.4), c.1642C>T and c.2207_2212delinsTAGATTC in the compound heterozygosity, resulting in a diagnosis of Bloom syndrome. Simultaneously, a mosaic loss of heterozygosity of chromosome 11p was detected and then confirmed as a borderline imprinting center 1 hypermethylation on chromosome 11p15. The diagnosis of Bloom syndrome and mosaic copy‑number neutral loss of heterozygosity of chromosome 11p increases a lifetime risk to develop any types of malignancy. This case demonstrates the trio‑based ES as a complex approach for the molecular diagnostics of rare pediatric diseases.

• Klíčová slova
• BLM gene, Bloom syndrome, cancer‑predisposing syndrome, copy‑number neutral loss of heterozygosity, exome sequencing,
• MeSH
• Bloomův syndrom * diagnóza   genetika   patologie   MeSH
• dítě MeSH
• heterozygot MeSH
• kojenec MeSH
• lidé MeSH
• lidský chromozom Y MeSH
• mozaicismus MeSH
• sekvenování exomu MeSH
• Check Tag
• dítě MeSH
• kojenec MeSH
• lidé MeSH
• mužské pohlaví MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• kazuistiky MeSH

BACKGROUND: Integration of multi-omics data can provide a more complex view of the biological system consisting of different interconnected molecular components, the crucial aspect for developing novel personalised therapeutic strategies for complex diseases. Various tools have been developed to integrate multi-omics data. However, an efficient multi-omics framework for regulatory network inference at the genome level that incorporates prior knowledge is still to emerge. RESULTS: We present IntOMICS, an efficient integrative framework based on Bayesian networks. IntOMICS systematically analyses gene expression, DNA methylation, copy number variation and biological prior knowledge to infer regulatory networks. IntOMICS complements the missing biological prior knowledge by so-called empirical biological knowledge, estimated from the available experimental data. Regulatory networks derived from IntOMICS provide deeper insights into the complex flow of genetic information on top of the increasing accuracy trend compared to a published algorithm designed exclusively for gene expression data. The ability to capture relevant crosstalks between multi-omics modalities is verified using known associations in microsatellite stable/instable colon cancer samples. Additionally, IntOMICS performance is compared with two algorithms for multi-omics regulatory network inference that can also incorporate prior knowledge in the inference framework. IntOMICS is also applied to detect potential predictive biomarkers in microsatellite stable stage III colon cancer samples. CONCLUSIONS: We provide IntOMICS, a framework for multi-omics data integration using a novel approach to biological knowledge discovery. IntOMICS is a powerful resource for exploratory systems biology and can provide valuable insights into the complex mechanisms of biological processes that have a vital role in personalised medicine.

• Klíčová slova
• Bayesian networks, Integrative analysis, Knowledge discovery, Multimodal omics, Regulatory networks,
• MeSH
• algoritmy MeSH
• Bayesova věta MeSH
• genové regulační sítě MeSH
• lidé MeSH
• nádory tračníku * MeSH
• systémová biologie metody   MeSH
• variabilita počtu kopií segmentů DNA * MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH