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- Laurie, Steven, et al.
(author)
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The RD-Connect Genome-Phenome Analysis Platform : Accelerating diagnosis, research, and gene discovery for rare diseases
- 2022
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In: Human Mutation. - : John Wiley & Sons. - 1059-7794 .- 1098-1004. ; 43:6, s. 717-733
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Journal article (peer-reviewed)abstract
- Rare disease patients are more likely to receive a rapid molecular diagnosis nowadays thanks to the wide adoption of next-generation sequencing. However, many cases remain undiagnosed even after exome or genome analysis, because the methods used missed the molecular cause in a known gene, or a novel causative gene could not be identified and/or confirmed. To address these challenges, the RD-Connect Genome-Phenome Analysis Platform (GPAP) facilitates the collation, discovery, sharing, and analysis of standardized genome-phenome data within a collaborative environment. Authorized clinicians and researchers submit pseudonymised phenotypic profiles encoded using the Human Phenotype Ontology, and raw genomic data which is processed through a standardized pipeline. After an optional embargo period, the data are shared with other platform users, with the objective that similar cases in the system and queries from peers may help diagnose the case. Additionally, the platform enables bidirectional discovery of similar cases in other databases from the Matchmaker Exchange network. To facilitate genome-phenome analysis and interpretation by clinical researchers, the RD-Connect GPAP provides a powerful user-friendly interface and leverages tens of information sources. As a result, the resource has already helped diagnose hundreds of rare disease patients and discover new disease causing genes.
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| 2. |
- Matalonga, L, et al.
(author)
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Solving patients with rare diseases through programmatic reanalysis of genome-phenome data
- 2021
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In: European journal of human genetics : EJHG. - : Springer Science and Business Media LLC. - 1476-5438 .- 1018-4813. ; 29:9, s. 1337-1347
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Journal article (peer-reviewed)abstract
- Reanalysis of inconclusive exome/genome sequencing data increases the diagnosis yield of patients with rare diseases. However, the cost and efforts required for reanalysis prevent its routine implementation in research and clinical environments. The Solve-RD project aims to reveal the molecular causes underlying undiagnosed rare diseases. One of the goals is to implement innovative approaches to reanalyse the exomes and genomes from thousands of well-studied undiagnosed cases. The raw genomic data is submitted to Solve-RD through the RD-Connect Genome-Phenome Analysis Platform (GPAP) together with standardised phenotypic and pedigree data. We have developed a programmatic workflow to reanalyse genome-phenome data. It uses the RD-Connect GPAP’s Application Programming Interface (API) and relies on the big-data technologies upon which the system is built. We have applied the workflow to prioritise rare known pathogenic variants from 4411 undiagnosed cases. The queries returned an average of 1.45 variants per case, which first were evaluated in bulk by a panel of disease experts and afterwards specifically by the submitter of each case. A total of 120 index cases (21.2% of prioritised cases, 2.7% of all exome/genome-negative samples) have already been solved, with others being under investigation. The implementation of solutions as the one described here provide the technical framework to enable periodic case-level data re-evaluation in clinical settings, as recommended by the American College of Medical Genetics.
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| 3. |
- Esteve-Codina, Anna, et al.
(author)
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Gender specific airway gene expression in COPD sub-phenotypes supports a role of mitochondria and of different types of leukocytes
- 2021
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In: Scientific Reports. - : Springer Nature. - 2045-2322. ; 11:1
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Journal article (peer-reviewed)abstract
- Chronic obstructive pulmonary disease (COPD) is a destructive inflammatory disease and the genes expressed within the lung are crucial to its pathophysiology. We have determined the RNAseq transcriptome of bronchial brush cells from 312 stringently defined ex-smoker patients. Compared to healthy controls there were for males 40 differentially expressed genes (DEGs) and 73 DEGs for females with only 26 genes shared. The gene ontology (GO) term "response to bacterium" was shared, with several different DEGs contributing in males and females. Strongly upregulated genes TCN1 and CYP1B1 were unique to males and females, respectively. For male emphysema (E)-dominant and airway disease (A)-dominant COPD (defined by computed tomography) the term "response to stress" was found for both sub-phenotypes, but this included distinct up-regulated genes for the E-sub-phenotype (neutrophil-related CSF3R, CXCL1, MNDA) and for the A-sub-phenotype (macrophage-related KLF4, F3, CD36). In E-dominant disease, a cluster of mitochondria-encoded (MT) genes forms a signature, able to identify patients with emphysema features in a confirmation cohort. The MT-CO2 gene is upregulated transcriptionally in bronchial epithelial cells with the copy number essentially unchanged. Both MT-CO2 and the neutrophil chemoattractant CXCL1 are induced by reactive oxygen in bronchial epithelial cells. Of the female DEGs unique for E- and A-dominant COPD, 88% were detected in females only. In E-dominant disease we found a pronounced expression of mast cell-associated DEGs TPSB2, TPSAB1 and CPA3. The differential genes discovered in this study point towards involvement of different types of leukocytes in the E- and A-dominant COPD sub-phenotypes in males and females.
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