Less than half of patients with suspected genetic disease receive a molecular diagnosis. We have therefore integrated next-generation sequencing (NGS), bioinformatics, and clinical data into an effective diagnostic workflow. We used variants in the 2741 established Mendelian disease genes [the disease-associated genome (DAG)] to develop a targeted enrichment DAG panel (7.1 Mb), which achieves a coverage of 20-fold or better for 98% of bases. Furthermore, we established a computational method [Phenotypic Interpretation of eXomes (PhenIX)] that evaluated and ranked variants based on pathogenicity and semantic similarity of patients’ phenotype described by Human Phenotype Ontology (HPO) terms to those of 3991 Mendelian diseases. In computer simulations, ranking genes based on the variant score put the true gene in first place less than 5% of the time; PhenIX placed the correct gene in first place more than 86% of the time. In a retrospective test of PhenIX on 52 patients with previously identified mutations and known diagnoses, the correct gene achieved a mean rank of 2.1. In a prospective study on 40 individuals without a diagnosis, PhenIX analysis enabled a diagnosis in 11 cases (28%, at a mean rank of 2.4). Thus, the NGS of the DAG followed by phenotype-driven bioinformatic analysis allows quick and effective differential diagnostics in medical genetics.

Background:People with multiple sclerosis (MS) are a vulnerable group for severe COVID- 19, particularly those taking immunosuppressive disease-modifying therapies (DMTs). We examined the characteristics of COVID-19 severity in an international sample of people with MS.Methods:Data from 12 data-sources in 28 countries were aggregated (sources could include patients from 1-12 countries). Demographic (age, sex), clinical (MS-phenotype, disability), and DMT (untreated, alemtuzumab, cladribine, dimethyl-fumarate, glatiramer acetate, interferon, natalizumab, ocrelizumab, rituximab, siponimod, other DMTs) covariates were queried, alongside COVID-19 severity outcomes, hospitalisation, ICU admission, requiring artificial ventilation, and death. Characteristics of outcomes were assessed in patients with suspected/confirmed COVID-19 using multilevel mixed-effects logistic regression, adjusted for age, sex, MS-phenotype, and EDSS.Results:657(28.1%) with suspected and 1,683(61.9%) with confirmed COVID-19 were analysed. Among suspected+confirmed and confirmed-only COVID-19, 20.9% and 26.9% were hospitalised, 5.4% and 7.2% were admitted to ICU, 4.1% and 5.4% required artificial ventilation, and 3.2% and 3.9% died. Older age, progressive MS-phenotype, and higher disability were associated with worse COVID-19 outcomes. Compared to dimethyl-fumarate, ocrelizumab and rituximab were associated with hospitalisation (aOR=1.56,95%CI=1.01- 2.41; aOR=2.43,95%CI=1.48-4.02) and ICU admission (aOR=2.30,95%CI=0.98-5.39;aOR=3.93,95%CI=1.56-9.89), though only rituximab was associated with higher risk of artificial ventilation (aOR=4.00,95%CI=1.54-10.39). Compared to pooled other DMTs, ocrelizumab and rituximab were associated with hospitalisation (aOR=1.75,95%CI=1.29- 2.38; aOR=2.76,95%CI=1.87-4.07) and ICU admission (aOR=2.55,95%CI=1.49-4.36;aOR=4.32,95%CI=2.27-8.23) but only rituximab with artificial ventilation (aOR=6.15,95%CI=3.09-12.27). Compared to natalizumab, ocrelizumab and rituximab wereassociated with hospitalisation (aOR=1.86,95%CI=1.13-3.07; aOR=2.88,95%CI=1.68-4.92) and ICU admission (aOR=2.13,95%CI=0.85-5.35; aOR=3.23,95%CI=1.17-8.91), but only rituximab with ventilation (aOR=5.52,95%CI=1.71-17.84). Importantly, associations persisted on restriction to confirmed COVID-19 cases. No associations were observed between DMTs and death. Stratification by age, MS-phenotype, and EDSS found no indications that DMT associations with COVID-19 severity reflected differential DMT allocation by underlying COVID-19 severity.Conclusions:Using the largest cohort of people with MS and COVID-19 available, we demonstrated consistent associations of rituximab with increased risk of hospitalisation, ICU admission, and requiring artificial ventilation, and ocrelizumab with hospitalisation and ICU admission. Despite the study’s cross-sectional design, the internal and external consistency of these results with prior studies suggests rituximab/ocrelizumab use may be a risk factor for more severe COVID-19.

Massively parallel sequencing greatly facilitates the discovery of novel disease genes causing Mendelian and oligogenic disorders. However, many mutations are present in any individual genome, and identifying which ones are disease causing remains a largely open problem. We introduce eXtasy, an approach to prioritize nonsynonymous single-nucleotide variants (nSNVs) that substantially improves prediction of disease-causing variants in exome sequencing data by integrating variant impact prediction, haploinsufficiency prediction and phenotype-specific gene prioritization.

Genomic studies and high-throughput experiments often produce large lists of candidate genes among which only a small fraction are truly relevant to the disease, phenotype or biological process of interest. Gene prioritization tackles this problem by ranking candidate genes by profiling candidates across multiple genomic data sources and integrating this heterogeneous information into a global ranking. We describe an extended version of our gene prioritization method, Endeavour, now available for six species and integrating 75 data sources. The performance (Area Under the Curve) of Endeavour on cross-validation benchmarks using ‘gold standard’ gene sets varies from 88% (for human phenotypes) to 95% (for worm gene function). In addition, we have also validated our approach using a time-stamped benchmark derived from the Human Phenotype Ontology, which provides a setting close to prospective validation. With this benchmark, using 3854 novel gene–phenotype associations, we observe a performance of 82%. Altogether, our results indicate that this extended version of Endeavour efficiently prioritizes candidate genes. The Endeavour web server is freely available at https://endeavour.esat.kuleuven.be/.

Background: People with multiple sclerosis (MS) are a vulnerable group for severe COVID-19, particularly those taking immunosuppressive disease-modifying therapies (DMTs). We examined the characteristics of COVID-19 severity in an international sample of people with MS. Methods: Data from 12 data-sources in 28 countries were aggregated. Demographic and clinical covariates were queried, alongside COVID-19 clinical severity outcomes, hospitalisation, admission to ICU, requiring artificial ventilation, and death. Characteristics of outcomes were assessed in patients with suspected/confirmed COVID-19 using multilevel mixed-effects logistic regression. Results: 657 (28.1%) with suspected and 1,683 (61.9%) with confirmed COVID-19 were analysed. Older age, progressive MS-phenotype, and higher disability were associated with worse COVID-19 outcomes. Compared to dimethyl fumarate, ocrelizumab and rituximab were associated with hospitalisation (aOR=1.56,95%CI=1.01-2.41; aOR=2.43,95%CI=1.48-4.02) and ICU admission (aOR=2.30,95%CI=0.98-5.39; aOR=3.93,95%CI=1.56-9.89), though only rituximab was associated with higher risk of artificial ventilation (aOR=4.00,95%CI=1.54-10.39). Compared to pooled other DMTs, ocrelizumab and rituximab were associated with hospitalisation (aOR=1.75,95%CI=1.29-2.38; aOR=2.76,95%CI=1.87-4.07) and ICU admission (aOR=2.55,95%CI=1.49-4.36; aOR=4.32,95%CI=2.27-8.23) but only rituximab with artificial ventilation (aOR=6.15,95%CI=3.09-12.27). Compared to natalizumab, ocrelizumab and rituximab were associated with hospitalisation (aOR=1.86,95%CI=1.13-3.07; aOR=2.88,95%CI=1.68-4.92) and ICU admission (aOR=2.13,95%CI=0.85-5.35; aOR=3.23,95%CI=1.17-8.91), but only rituximab with ventilation (aOR=5.52,95%CI=1.71-17.84). Importantly, associations persisted on restriction to confirmed COVID-19 cases. No associations were observed between DMTs and death. Conclusions: Using the largest cohort of people with MS and COVID-19 available, we demonstrated consistent associations of rituximab with increased risk of hospitalisation, ICU admission, and requiring artificial ventilation, and ocrelizumab with hospitalisation and ICU admission, suggesting their use may be a risk factor for more severe COVID-19.

BackgroundThe deployment of Genome-wide association studies (GWASs) requires genomic information of a large population to produce reliable results. This raises significant privacy concerns, making people hesitate to contribute their genetic information to such studies.ResultsWe propose two provably secure solutions to address this challenge: (1) a somewhat homomorphic encryption (HE) approach, and (2) a secure multiparty computation (MPC) approach. Unlike previous work, our approach does not rely on adding noise to the input data, nor does it reveal any information about the patients. Our protocols aim to prevent data breaches by calculating the χ2 statistic in a privacy-preserving manner, without revealing any information other than whether the statistic is significant or not. Specifically, our protocols compute the χ2 statistic, but only return a yes/no answer, indicating significance. By not revealing the statistic value itself but only the significance, our approach thwarts attacks exploiting statistic values. We significantly increased the efficiency of our HE protocols by introducing a new masking technique to perform the secure comparison that is necessary for determining significance.ConclusionsWe show that full-scale privacy-preserving GWAS is practical, as long as the statistics can be computed by low degree polynomials. Our implementations demonstrated that both approaches are efficient. The secure multiparty computation technique completes its execution in approximately 2 ms for data contributed by one million subjects.

MotivationComputational gene prioritization can aid in disease gene identification. Here, we propose pBRIT (prioritization using Bayesian Ridge regression and Information Theoretic model), a novel adaptive and scalable prioritization tool, integrating Pubmed abstracts, Gene Ontology, Sequence similarities, Mammalian and Human Phenotype Ontology, Pathway, Interactions, Disease Ontology, Gene Association database and Human Genome Epidemiology database, into the prediction model. We explore and address effects of sparsity and inter-feature dependencies within annotation sources, and the impact of bias towards specific annotations.ResultspBRIT models feature dependencies and sparsity by an Information-Theoretic (data driven) approach and applies intermediate integration based data fusion. Following the hypothesis that genes underlying similar diseases will share functional and phenotype characteristics, it incorporates Bayesian Ridge regression to learn a linear mapping between functional and phenotype annotations. Genes are prioritized on phenotypic concordance to the training genes. We evaluated pBRIT against nine existing methods, and on over 2000 HPO-gene associations retrieved after construction of pBRIT data sources. We achieve maximum AUC scores ranging from 0.92 to 0.96 against benchmark datasets and of 0.80 against the time-stamped HPO entries, indicating good performance with high sensitivity and specificity. Our model shows stable performance with regard to changes in the underlying annotation data, is fast and scalable for implementation in routine pipelines.Availability and implementation http://biomina.be/apps/pbrit/; https://bitbucket.org/medgenua/pbrit.Supplementary information Supplementary data are available at Bioinformatics online.

In 2016, guidelines for diagnostic Next Generation Sequencing (NGS) have been published by EuroGentest in order to assist laboratories in the implementation and accreditation of NGS in a diagnostic setting. These guidelines mainly focused on Whole Exome Sequencing (WES) and targeted (gene panels) sequencing detecting small germline variants (Single Nucleotide Variants (SNVs) and insertions/deletions (indels)). Since then, Whole Genome Sequencing (WGS) has been increasingly introduced in the diagnosis of rare diseases as WGS allows the simultaneous detection of SNVs, Structural Variants (SVs) and other types of variants such as repeat expansions. The use of WGS in diagnostics warrants the re-evaluation and update of previously published guidelines. This work was jointly initiated by EuroGentest and the Horizon2020 project Solve-RD. Statements from the 2016 guidelines have been reviewed in the context of WGS and updated where necessary. The aim of these recommendations is primarily to list the points to consider for clinical (laboratory) geneticists, bioinformaticians, and (non-)geneticists, to provide technical advice, aid clinical decision-making and the reporting of the results.