Publications by authors named "Morris Swertz"

140 Publications

Large-scale cis- and trans-eQTL analyses identify thousands of genetic loci and polygenic scores that regulate blood gene expression.

Nat Genet 2021 Sep 2;53(9):1300-1310. Epub 2021 Sep 2.

Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland.

Trait-associated genetic variants affect complex phenotypes primarily via regulatory mechanisms on the transcriptome. To investigate the genetics of gene expression, we performed cis- and trans-expression quantitative trait locus (eQTL) analyses using blood-derived expression from 31,684 individuals through the eQTLGen Consortium. We detected cis-eQTL for 88% of genes, and these were replicable in numerous tissues. Distal trans-eQTL (detected for 37% of 10,317 trait-associated variants tested) showed lower replication rates, partially due to low replication power and confounding by cell type composition. However, replication analyses in single-cell RNA-seq data prioritized intracellular trans-eQTL. Trans-eQTL exerted their effects via several mechanisms, primarily through regulation by transcription factors. Expression of 13% of the genes correlated with polygenic scores for 1,263 phenotypes, pinpointing potential drivers for those traits. In summary, this work represents a large eQTL resource, and its results serve as a starting point for in-depth interpretation of complex phenotypes.
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http://dx.doi.org/10.1038/s41588-021-00913-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8432599PMC
September 2021

The ELIXIR Human Copy Number Variations Community: building bioinformatics infrastructure for research.

F1000Res 2020 13;9. Epub 2020 Oct 13.

Centre for Skin Sciences, University of Bradford, Bradford, UK.

Copy number variations (CNVs) are major causative contributors both in the genesis of genetic diseases and human neoplasias. While "High-Throughput" sequencing technologies are increasingly becoming the primary choice for genomic screening analysis, their ability to efficiently detect CNVs is still heterogeneous and remains to be developed. The aim of this white paper is to provide a guiding framework for the future contributions of ELIXIR's recently established with implications beyond human disease diagnostics and population genomics. This white paper is the direct result of a strategy meeting that took place in September 2018 in Hinxton (UK) and involved representatives of 11 ELIXIR Nodes. The meeting led to the definition of priority objectives and tasks, to address a wide range of CNV-related challenges ranging from detection and interpretation to sharing and training. Here, we provide suggestions on how to align these tasks within the ELIXIR Platforms strategy, and on how to frame the activities of this new ELIXIR Community in the international context.
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http://dx.doi.org/10.12688/f1000research.24887.1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8311797PMC
August 2021

Strategies in Rapid Genetic Diagnostics of Critically Ill Children: Experiences From a Dutch University Hospital.

Front Pediatr 2021 31;9:600556. Epub 2021 May 31.

Department of Genetics, University of Groningen, University Medical Centre Groningen, Groningen, Netherlands.

Genetic disorders are a substantial cause of infant morbidity and mortality and are frequently suspected in neonatal intensive care units. Non-specific clinical presentation or limitations to physical examination can result in a plethora of genetic testing techniques, without clear strategies on test ordering. Here, we review our 2-years experiences of rapid genetic testing of NICU patients in order to provide such recommendations. We retrospectively included all patients admitted to the NICU who received clinical genetic consultation and genetic testing in our University hospital. We documented reasons for referral for genetic consultation, presenting phenotypes, differential diagnoses, genetic testing requested and their outcomes, as well as the consequences of each (rapid) genetic diagnostic approach. We calculated diagnostic yield and turnaround times (TATs). Of 171 included infants that received genetic consultation 140 underwent genetic testing. As a result of testing as first tier, 13/14 patients received a genetic diagnosis from QF-PCR; 14/115 from SNP-array; 12/89 from NGS testing, of whom 4/46 were diagnosed with a small gene panel and 8/43 with a large OMIM-morbid based gene panel. Subsequent secondary or tertiary analysis and/or additional testing resulted in five more diagnoses. TATs ranged from 1 day (QF-PCR) to a median of 14 for NGS and SNP-array testing, with increasing TAT in particular when many consecutive tests were performed. Incidental findings were detected in 5/140 tested patients (3.6%). We recommend implementing a broad NGS gene panel in combination with CNV calling as the first tier of genetic testing for NICU patients given the often unspecific phenotypes of ill infants and the high yield of this large panel.
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http://dx.doi.org/10.3389/fped.2021.600556DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8200558PMC
May 2021

Solve-RD: systematic pan-European data sharing and collaborative analysis to solve rare diseases.

Eur J Hum Genet 2021 Sep 1;29(9):1325-1331. Epub 2021 Jun 1.

Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany.

For the first time in Europe hundreds of rare disease (RD) experts team up to actively share and jointly analyse existing patient's data. Solve-RD is a Horizon 2020-supported EU flagship project bringing together >300 clinicians, scientists, and patient representatives of 51 sites from 15 countries. Solve-RD is built upon a core group of four European Reference Networks (ERNs; ERN-ITHACA, ERN-RND, ERN-Euro NMD, ERN-GENTURIS) which annually see more than 270,000 RD patients with respective pathologies. The main ambition is to solve unsolved rare diseases for which a molecular cause is not yet known. This is achieved through an innovative clinical research environment that introduces novel ways to organise expertise and data. Two major approaches are being pursued (i) massive data re-analysis of >19,000 unsolved rare disease patients and (ii) novel combined -omics approaches. The minimum requirement to be eligible for the analysis activities is an inconclusive exome that can be shared with controlled access. The first preliminary data re-analysis has already diagnosed 255 cases form 8393 exomes/genome datasets. This unprecedented degree of collaboration focused on sharing of data and expertise shall identify many new disease genes and enable diagnosis of many so far undiagnosed patients from all over Europe.
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http://dx.doi.org/10.1038/s41431-021-00859-0DOI Listing
September 2021

Feasibility of predicting allele specific expression from DNA sequencing using machine learning.

Sci Rep 2021 May 19;11(1):10606. Epub 2021 May 19.

Genomics Coordination Center, University of Groningen and University Medical Center Groningen, Antonius Deusinglaan 1, 9713 AV, Groningen, The Netherlands.

Allele specific expression (ASE) concerns divergent expression quantity of alternative alleles and is measured by RNA sequencing. Multiple studies show that ASE plays a role in hereditary diseases by modulating penetrance or phenotype severity. However, genome diagnostics is based on DNA sequencing and therefore neglects gene expression regulation such as ASE. To take advantage of ASE in absence of RNA sequencing, it must be predicted using only DNA variation. We have constructed ASE models from BIOS (n = 3432) and GTEx (n = 369) that predict ASE using DNA features. These models are highly reproducible and comprise many different feature types, highlighting the complex regulation that underlies ASE. We applied the BIOS-trained model to population variants in three genes in which ASE plays a clinically relevant role: BRCA2, RET and NF1. This resulted in predicted ASE effects for 27 variants, of which 10 were known pathogenic variants. We demonstrated that ASE can be predicted from DNA features using machine learning. Future efforts may improve sensitivity and translate these models into a new type of genome diagnostic tool that prioritizes candidate pathogenic variants or regulators thereof for follow-up validation by RNA sequencing. All used code and machine learning models are available at GitHub and Zenodo.
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http://dx.doi.org/10.1038/s41598-021-89904-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8134421PMC
May 2021

The EU Child Cohort Network's core data: establishing a set of findable, accessible, interoperable and re-usable (FAIR) variables.

Eur J Epidemiol 2021 May 21;36(5):565-580. Epub 2021 Apr 21.

Department of Social Medicine, Faculty of Medicine, University of Crete, Heraklion, Crete, Greece.

The Horizon2020 LifeCycle Project is a cross-cohort collaboration which brings together data from multiple birth cohorts from across Europe and Australia to facilitate studies on the influence of early-life exposures on later health outcomes. A major product of this collaboration has been the establishment of a FAIR (findable, accessible, interoperable and reusable) data resource known as the EU Child Cohort Network. Here we focus on the EU Child Cohort Network's core variables. These are a set of basic variables, derivable by the majority of participating cohorts and frequently used as covariates or exposures in lifecourse research. First, we describe the process by which the list of core variables was established. Second, we explain the protocol according to which these variables were harmonised in order to make them interoperable. Third, we describe the catalogue developed to ensure that the network's data are findable and reusable. Finally, we describe the core data, including the proportion of variables harmonised by each cohort and the number of children for whom harmonised core data are available. EU Child Cohort Network data will be analysed using a federated analysis platform, removing the need to physically transfer data and thus making the data more accessible to researchers. The network will add value to participating cohorts by increasing statistical power and exposure heterogeneity, as well as facilitating cross-cohort comparisons, cross-validation and replication. Our aim is to motivate other cohorts to join the network and encourage the use of the EU Child Cohort Network by the wider research community.
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http://dx.doi.org/10.1007/s10654-021-00733-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8159791PMC
May 2021

Lifelines COVID-19 cohort: investigating COVID-19 infection and its health and societal impacts in a Dutch population-based cohort.

BMJ Open 2021 03 17;11(3):e044474. Epub 2021 Mar 17.

Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.

Purpose: The Lifelines COVID-19 cohort was set up to assess the psychological and societal impacts of the COVID-19 pandemic and investigate potential risk factors for COVID-19 within the Lifelines prospective population cohort.

Participants: Participants were recruited from the 140 000 eligible participants of Lifelines and the Lifelines NEXT birth cohort, who are all residents of the three northern provinces of the Netherlands. Participants filled out detailed questionnaires about their physical and mental health and experiences on a weekly basis starting in late March 2020, and the cohort consists of everyone who filled in at least one questionnaire in the first 8 weeks of the project. FINDINGS TO DATE: >71 000 unique participants responded to the questionnaires at least once during the first 8 weeks, with >22 000 participants responding to seven questionnaires. Compiled questionnaire results are continuously updated and shared with the public through the Corona Barometer website. Early results included a clear signal that younger people living alone were experiencing greater levels of loneliness due to lockdown, and subsequent results showed the easing of anxiety as lockdown was eased in June 2020.

Future Plans: Questionnaires were sent on a (bi)weekly basis starting in March 2020 and on a monthly basis starting July 2020, with plans for new questionnaire rounds to continue through 2020 and early 2021. Questionnaire frequency can be increased again for subsequent waves of infections. Cohort data will be used to address how the COVID-19 pandemic developed in the northern provinces of the Netherlands, which environmental and genetic risk factors predict disease susceptibility and severity and the psychological and societal impacts of the crisis. Cohort data are linked to the extensive health, lifestyle and sociodemographic data held for these participants by Lifelines, a 30-year project that started in 2006, and to data about participants held in national databases.
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http://dx.doi.org/10.1136/bmjopen-2020-044474DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7977080PMC
March 2021

Implementing Individually Tailored Prescription of Physical Activity in Routine Clinical Care: Protocol of the Physicians Implement Exercise = Medicine (PIE=M) Development and Implementation Project.

JMIR Res Protoc 2020 Nov 2;9(11):e19397. Epub 2020 Nov 2.

Department of Rehabilitation Medicine, University Medical Center Groningen, University of Groningen, Groningen, Netherlands.

Background: The prescription of physical activity (PA) in clinical care has been advocated worldwide. This "exercise is medicine" (E=M) concept can be used to prevent, manage, and cure various lifestyle-related chronic diseases. Due to several challenges, E=M is not yet routinely implemented in clinical care.

Objective: This paper describes the rationale and design of the Physicians Implement Exercise = Medicine (PIE=M) study, which aims to facilitate the implementation of E=M in hospital care.

Methods: PIE=M consists of 3 interrelated work packages. First, levels and determinants of PA in different patient and healthy populations will be investigated using existing cohort data. The current implementation status, facilitators, and barriers of E=M will also be investigated using a mixed-methods approach among clinicians of participating departments from 2 diverse university medical centers (both located in a city, but one serving an urban population and one serving a more rural population). Implementation strategies will be connected to these barriers and facilitators using a systematic implementation mapping approach. Second, a generic E=M tool will be developed that will provide tailored PA prescription and referral. Requirements for this tool will be investigated among clinicians and department managers. The tool will be developed using an iterative design process in which all stakeholders reflect on the design of the E=M tool. Third, we will pilot-implement the set of implementation strategies, including the E=M tool, to test its feasibility in routine care of clinicians in these 2 university medical centers. An extensive learning process evaluation will be performed among clinicians, department managers, lifestyle coaches, and patients using a mixed-methods design based on the RE-AIM framework.

Results: This project was approved and funded by the Dutch grant provider ZonMW in April 2018. The project started in September 2018 and continues until December 2020 (depending on the course of the COVID-19 crisis). All data from the first work package have been collected and analyzed and are expected to be published in 2021. Results of the second work package are described. The manuscript is expected to be published in 2021. The third work package is currently being conducted in clinical practice in 4 departments of 2 university medical hospitals among clinicians, lifestyle coaches, hospital managers, and patients. Results are expected to be published in 2021.

Conclusions: The PIE=M project addresses the potential of providing patients with PA advice to prevent and manage chronic disease, improve recovery, and enable healthy ageing by developing E=M implementation strategies, including an E=M tool, in routine clinical care. The PIE=M project will result in a blueprint of implementation strategies, including an E=M screening and referral tool, which aims to improve E=M referral by clinicians to improve patients' health, while minimizing the burden on clinicians.
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http://dx.doi.org/10.2196/19397DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7669441PMC
November 2020

Metabolic Age Based on the BBMRI-NL H-NMR Metabolomics Repository as Biomarker of Age-related Disease.

Circ Genom Precis Med 2020 10 14;13(5):541-547. Epub 2020 Aug 14.

Department of Internal Medicine, Maastricht University Medical Center, the Netherlands (C.D.A.S., C.J.H.v.d.K., M.M.J.v.G.).

Background: The blood metabolome incorporates cues from the environment and the host's genetic background, potentially offering a holistic view of an individual's health status.

Methods: We have compiled a vast resource of proton nuclear magnetic resonance metabolomics and phenotypic data encompassing over 25 000 samples derived from 26 community and hospital-based cohorts.

Results: Using this resource, we constructed a metabolomics-based age predictor (metaboAge) to calculate an individual's biological age. Exploration in independent cohorts demonstrates that being judged older by one's metabolome, as compared with one's chronological age, confers an increased risk on future cardiovascular disease, mortality, and functionality in older individuals. A web-based tool for calculating metaboAge (metaboage.researchlumc.nl) allows easy incorporation in other epidemiological studies. Access to data can be requested at bbmri.nl/samples-images-data.

Conclusions: In summary, we present a vast resource of metabolomics data and illustrate its merit by constructing a metabolomics-based score for biological age that captures aspects of current and future cardiometabolic health.
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http://dx.doi.org/10.1161/CIRCGEN.119.002610DOI Listing
October 2020

CAPICE: a computational method for Consequence-Agnostic Pathogenicity Interpretation of Clinical Exome variations.

Genome Med 2020 08 24;12(1):75. Epub 2020 Aug 24.

Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.

Exome sequencing is now mainstream in clinical practice. However, identification of pathogenic Mendelian variants remains time-consuming, in part, because the limited accuracy of current computational prediction methods requires manual classification by experts. Here we introduce CAPICE, a new machine-learning-based method for prioritizing pathogenic variants, including SNVs and short InDels. CAPICE outperforms the best general (CADD, GAVIN) and consequence-type-specific (REVEL, ClinPred) computational prediction methods, for both rare and ultra-rare variants. CAPICE is easily added to diagnostic pipelines as pre-computed score file or command-line software, or using online MOLGENIS web service with API. Download CAPICE for free and open-source (LGPLv3) at https://github.com/molgenis/capice .
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http://dx.doi.org/10.1186/s13073-020-00775-wDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7446154PMC
August 2020

The LifeCycle Project-EU Child Cohort Network: a federated analysis infrastructure and harmonized data of more than 250,000 children and parents.

Eur J Epidemiol 2020 Jul 23;35(7):709-724. Epub 2020 Jul 23.

ISGlobal, Barcelona, Spain.

Early life is an important window of opportunity to improve health across the full lifecycle. An accumulating body of evidence suggests that exposure to adverse stressors during early life leads to developmental adaptations, which subsequently affect disease risk in later life. Also, geographical, socio-economic, and ethnic differences are related to health inequalities from early life onwards. To address these important public health challenges, many European pregnancy and childhood cohorts have been established over the last 30 years. The enormous wealth of data of these cohorts has led to important new biological insights and important impact for health from early life onwards. The impact of these cohorts and their data could be further increased by combining data from different cohorts. Combining data will lead to the possibility of identifying smaller effect estimates, and the opportunity to better identify risk groups and risk factors leading to disease across the lifecycle across countries. Also, it enables research on better causal understanding and modelling of life course health trajectories. The EU Child Cohort Network, established by the Horizon2020-funded LifeCycle Project, brings together nineteen pregnancy and childhood cohorts, together including more than 250,000 children and their parents. A large set of variables has been harmonised and standardized across these cohorts. The harmonized data are kept within each institution and can be accessed by external researchers through a shared federated data analysis platform using the R-based platform DataSHIELD, which takes relevant national and international data regulations into account. The EU Child Cohort Network has an open character. All protocols for data harmonization and setting up the data analysis platform are available online. The EU Child Cohort Network creates great opportunities for researchers to use data from different cohorts, during and beyond the LifeCycle Project duration. It also provides a novel model for collaborative research in large research infrastructures with individual-level data. The LifeCycle Project will translate results from research using the EU Child Cohort Network into recommendations for targeted prevention strategies to improve health trajectories for current and future generations by optimizing their earliest phases of life.
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http://dx.doi.org/10.1007/s10654-020-00662-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7387322PMC
July 2020

Lack of Association Between Genetic Variants at and Genes Involved in SARS-CoV-2 Infection and Human Quantitative Phenotypes.

Front Genet 2020 8;11:613. Epub 2020 Jun 8.

Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, Netherlands.

Coronavirus disease 2019 (COVID-19) shows a wide variation in expression and severity of symptoms, from very mild or no symptoms, to flu-like symptoms, and in more severe cases, to pneumonia, acute respiratory distress syndrome, and even death. Large differences in outcome have also been observed between males and females. The causes for this variability are likely to be multifactorial, and to include genetics. The SARS-CoV-2 virus responsible for the infection depends on two human genes: the human receptor angiotensin converting enzyme 2 () for cell invasion, and the serine protease for S protein priming. Genetic variation in these two genes may thus modulate an individual's genetic predisposition to infection and virus clearance. While genetic data on COVID-19 patients is being gathered, we carried out a phenome-wide association scan (PheWAS) to investigate the role of these genes in other human phenotypes in the general population. We examined 178 quantitative phenotypes including cytokines and cardio-metabolic biomarkers, as well as usage of 58 medications in 36,339 volunteers from the Lifelines population cohort, in relation to 1,273 genetic variants located in or near and . While none reached our threshold for significance, we observed several interesting suggestive associations. For example, single nucleotide polymorphisms (SNPs) near the genes were associated with thrombocytes count ( = 1.8 × 10). SNPs within the gene were associated with (1) the use of angiotensin II receptor blockers (ARBs) combination therapies ( = 5.7 × 10), an association that is significantly stronger in females ( = 0.01), and (2) with the use of non-steroid anti-inflammatory and antirheumatic products ( = 5.5 × 10). While these associations need to be confirmed in larger sample sizes, they suggest that these variants could play a role in diseases such as thrombocytopenia, hypertension, and chronic inflammation that are often observed in the more severe COVID-19 cases. Further investigation of these genetic variants in the context of COVID-19 is thus promising for better understanding of disease variability. Full results are available at https://covid19research.nl.
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http://dx.doi.org/10.3389/fgene.2020.00613DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7295011PMC
June 2020

Deconvolution of bulk blood eQTL effects into immune cell subpopulations.

BMC Bioinformatics 2020 Jun 12;21(1):243. Epub 2020 Jun 12.

Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.

Background: Expression quantitative trait loci (eQTL) studies are used to interpret the function of disease-associated genetic risk factors. To date, most eQTL analyses have been conducted in bulk tissues, such as whole blood and tissue biopsies, which are likely to mask the cell type-context of the eQTL regulatory effects. Although this context can be investigated by generating transcriptional profiles from purified cell subpopulations, current methods to do this are labor-intensive and expensive. We introduce a new method, Decon2, as a framework for estimating cell proportions using expression profiles from bulk blood samples (Decon-cell) followed by deconvolution of cell type eQTLs (Decon-eQTL).

Results: The estimated cell proportions from Decon-cell agree with experimental measurements across cohorts (R ≥ 0.77). Using Decon-cell, we could predict the proportions of 34 circulating cell types for 3194 samples from a population-based cohort. Next, we identified 16,362 whole-blood eQTLs and deconvoluted cell type interaction (CTi) eQTLs using the predicted cell proportions from Decon-cell. CTi eQTLs show excellent allelic directional concordance with eQTL (≥ 96-100%) and chromatin mark QTL (≥87-92%) studies that used either purified cell subpopulations or single-cell RNA-seq, outperforming the conventional interaction effect.

Conclusions: Decon2 provides a method to detect cell type interaction effects from bulk blood eQTLs that is useful for pinpointing the most relevant cell type for a given complex disease. Decon2 is available as an R package and Java application (https://github.com/molgenis/systemsgenetics/tree/master/Decon2) and as a web tool (www.molgenis.org/deconvolution).
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http://dx.doi.org/10.1186/s12859-020-03576-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7291428PMC
June 2020

BBMRI-ERIC's contributions to research and knowledge exchange on COVID-19.

Eur J Hum Genet 2020 06 22;28(6):728-731. Epub 2020 May 22.

BBMRI-ERIC, Neue Stiftingtalstraße 2/B/6, 8010, Graz, Austria.

During the COVID-19 pandemic, the European biobanking infrastructure is in a unique position to preserve valuable biological material complemented with detailed data for future research purposes. Biobanks can be either integrated into healthcare, where preservation of the biological material is a fork in clinical routine diagnostics and medical treatment processes or they can also host prospective cohorts or material related to clinical trials. The paper discussed objectives of BBMRI-ERIC, the European research infrastructure established to facilitate access to quality-defined biological materials and data for research purposes, with respect to the COVID-19 crisis: (a) to collect information on available European as well as non-European COVID-19-relevant biobanking resources in BBMRI-ERIC Directory and to facilitate access to these via BBMRI-ERIC Negotiator platform; (b) to help harmonizing guidelines on how data and biological material is to be collected to maximize utility for future research, including large-scale data processing in artificial intelligence, by participating in activities such as COVID-19 Host Genetics Initiative; (c) to minimize risks for all involved parties dealing with (potentially) infectious material by developing recommendations and guidelines; (d) to provide a European-wide platform of exchange in relation to ethical, legal, and societal issues (ELSI) specific to the collection of biological material and data during the COVID-19 pandemic.
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http://dx.doi.org/10.1038/s41431-020-0634-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7242892PMC
June 2020

Gene-educational attainment interactions in a multi-ancestry genome-wide meta-analysis identify novel blood pressure loci.

Mol Psychiatry 2021 Jun 5;26(6):2111-2125. Epub 2020 May 5.

Health Disparities Research Section, Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health, Baltimore, MD, 21224, USA.

Educational attainment is widely used as a surrogate for socioeconomic status (SES). Low SES is a risk factor for hypertension and high blood pressure (BP). To identify novel BP loci, we performed multi-ancestry meta-analyses accounting for gene-educational attainment interactions using two variables, "Some College" (yes/no) and "Graduated College" (yes/no). Interactions were evaluated using both a 1 degree of freedom (DF) interaction term and a 2DF joint test of genetic and interaction effects. Analyses were performed for systolic BP, diastolic BP, mean arterial pressure, and pulse pressure. We pursued genome-wide interrogation in Stage 1 studies (N = 117 438) and follow-up on promising variants in Stage 2 studies (N = 293 787) in five ancestry groups. Through combined meta-analyses of Stages 1 and 2, we identified 84 known and 18 novel BP loci at genome-wide significance level (P < 5 × 10). Two novel loci were identified based on the 1DF test of interaction with educational attainment, while the remaining 16 loci were identified through the 2DF joint test of genetic and interaction effects. Ten novel loci were identified in individuals of African ancestry. Several novel loci show strong biological plausibility since they involve physiologic systems implicated in BP regulation. They include genes involved in the central nervous system-adrenal signaling axis (ZDHHC17, CADPS, PIK3C2G), vascular structure and function (GNB3, CDON), and renal function (HAS2 and HAS2-AS1, SLIT3). Collectively, these findings suggest a role of educational attainment or SES in further dissection of the genetic architecture of BP.
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http://dx.doi.org/10.1038/s41380-020-0719-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7641978PMC
June 2021

Habitual dietary intake of IBD patients differs from population controls: a case-control study.

Eur J Nutr 2021 Feb 24;60(1):345-356. Epub 2020 Apr 24.

Department of Gastroenterology and Hepatology, University Medical Centre Groningen and University of Groningen, Hanzeplein 1, P.O. box 30.001, 9700RB, Groningen, The Netherlands.

Background: Since evidence-based dietary guidelines are lacking for IBD patients, they tend to follow "unguided" dietary habits; potentially leading to nutritional deficiencies and detrimental effects on disease course. Therefore, we compared dietary intake of IBD patients with controls.

Methods: Dietary intake of macronutrients and 25 food groups of 493 patients (207 UC, 286 CD), and 1291 controls was obtained via a food frequency questionnaire.

Results: 38.6% of patients in remission had protein intakes below the recommended 0.8 g/kg and 86.7% with active disease below the recommended 1.2 g/kg. Multinomial logistic regression, corrected for age, gender and BMI, showed that (compared to controls) UC patients consumed more meat and spreads, but less alcohol, breads, coffee and dairy; CD patients consumed more non-alcoholic drinks, potatoes, savoury snacks and sugar and sweets but less alcohol, dairy, nuts, pasta and prepared meals. Patients with active disease consumed more meat, soup and sugar and sweets but less alcohol, coffee, dairy, prepared meals and rice; patients in remission consumed more potatoes and spreads but less alcohol, breads, dairy, nuts, pasta and prepared meals.

Conclusions: Patients avoiding potentially favourable foods and gourmandizing potentially unfavourable foods are of concern. Special attention is needed for protein intake in the treatment of these patients.
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http://dx.doi.org/10.1007/s00394-020-02250-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7867519PMC
February 2021

RNA-Sequencing Highlights Inflammation and Impaired Integrity of the Vascular Wall in Brain Arteriovenous Malformations.

Stroke 2020 01 4;51(1):268-274. Epub 2019 Dec 4.

Department of Neurology, Donders Institute of Brain Cognition & Behaviour, Center for Neuroscience, Radboud University Medical Center, Nijmegen, the Netherlands (C.J.M.K.).

Background and Purpose- Interventional treatment of unruptured brain arteriovenous malformations (BAVMs) has become increasingly controversial. Because medical therapy is still lacking, we aimed to obtain insight into the disease mechanisms implicated in BAVMs and to identify potential targets for medical treatment to prevent rupture of a BAVM. Methods- We used next-generation RNA sequencing to identify differential expression on a transcriptome-wide level comparing tissue samples of 12 BAVMs to 16 intracranial control arteries. We identified differentially expressed genes by negative binominal generalized log-linear regression (false discovery rate corrected <0.05). We selected 10 genes for validation using droplet digital polymerase chain reaction. We performed functional pathway analysis accounting for potential gene-length bias, to establish enhancement of biological pathways involved in BAVMs. We further assessed which Gene Ontology terms were enriched. Results- We found 736 upregulated genes in BAVMs including genes implicated in the cytoskeletal machinery and cell-migration and genes encoding for inflammatory cytokines and secretory products of neutrophils and macrophages. Furthermore, we found 498 genes downregulated including genes implicated in extracellular matrix composition, the binary angiopoietin-TIE system, and TGF (transforming growth factor)-β signaling. We confirmed the differential expression of top 10 ranked genes. Functional pathway analysis showed enrichment of the protein digestion and absorption pathway (false discovery rate-adjusted =1.70×10). We identified 47 enriched Gene Ontology terms (false discovery rate-adjusted <0.05) implicated in cytoskeleton network, cell-migration, endoplasmic reticulum, transmembrane transport, and extracellular matrix composition. Conclusions- Our genome-wide RNA-sequencing study points to involvement of inflammatory mediators, loss of cerebrovascular quiescence, and impaired integrity of the vascular wall in the pathophysiology of BAVMs. Our study may lend support to potential receptivity of BAVMs to medical therapeutics, including those promoting vessel maturation, and anti-inflammatory and immune-modifying drugs.
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http://dx.doi.org/10.1161/STROKEAHA.119.025657DOI Listing
January 2020

Multi-ancestry sleep-by-SNP interaction analysis in 126,926 individuals reveals lipid loci stratified by sleep duration.

Nat Commun 2019 11 12;10(1):5121. Epub 2019 Nov 12.

Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, Netherlands.

Both short and long sleep are associated with an adverse lipid profile, likely through different biological pathways. To elucidate the biology of sleep-associated adverse lipid profile, we conduct multi-ancestry genome-wide sleep-SNP interaction analyses on three lipid traits (HDL-c, LDL-c and triglycerides). In the total study sample (discovery + replication) of 126,926 individuals from 5 different ancestry groups, when considering either long or short total sleep time interactions in joint analyses, we identify 49 previously unreported lipid loci, and 10 additional previously unreported lipid loci in a restricted sample of European-ancestry cohorts. In addition, we identify new gene-sleep interactions for known lipid loci such as LPL and PCSK9. The previously unreported lipid loci have a modest explained variance in lipid levels: most notable, gene-short-sleep interactions explain 4.25% of the variance in triglyceride level. Collectively, these findings contribute to our understanding of the biological mechanisms involved in sleep-associated adverse lipid profiles.
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http://dx.doi.org/10.1038/s41467-019-12958-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6851116PMC
November 2019

Associations of autozygosity with a broad range of human phenotypes.

Nat Commun 2019 10 31;10(1):4957. Epub 2019 Oct 31.

Department of Neurology, Brain Centre Rudolf Magnus, University Medical Centre Utrecht, Utrecht University, Utrecht, 3584 CX, The Netherlands.

In many species, the offspring of related parents suffer reduced reproductive success, a phenomenon known as inbreeding depression. In humans, the importance of this effect has remained unclear, partly because reproduction between close relatives is both rare and frequently associated with confounding social factors. Here, using genomic inbreeding coefficients (F) for >1.4 million individuals, we show that F is significantly associated (p < 0.0005) with apparently deleterious changes in 32 out of 100 traits analysed. These changes are associated with runs of homozygosity (ROH), but not with common variant homozygosity, suggesting that genetic variants associated with inbreeding depression are predominantly rare. The effect on fertility is striking: F equivalent to the offspring of first cousins is associated with a 55% decrease [95% CI 44-66%] in the odds of having children. Finally, the effects of F are confirmed within full-sibling pairs, where the variation in F is independent of all environmental confounding.
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http://dx.doi.org/10.1038/s41467-019-12283-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6823371PMC
October 2019

Leveraging European infrastructures to access 1 million human genomes by 2022.

Nat Rev Genet 2019 11 27;20(11):693-701. Epub 2019 Aug 27.

Global Alliance for Genomics and Health, Toronto, Ontario, Canada.

Human genomics is undergoing a step change from being a predominantly research-driven activity to one driven through health care as many countries in Europe now have nascent precision medicine programmes. To maximize the value of the genomic data generated, these data will need to be shared between institutions and across countries. In recognition of this challenge, 21 European countries recently signed a declaration to transnationally share data on at least 1 million human genomes by 2022. In this Roadmap, we identify the challenges of data sharing across borders and demonstrate that European research infrastructures are well-positioned to support the rapid implementation of widespread genomic data access.
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http://dx.doi.org/10.1038/s41576-019-0156-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7115898PMC
November 2019

Gene Mosaicism Screening Using Single-Molecule Molecular Inversion Probes in Routine Diagnostics for Systemic Autoinflammatory Diseases.

J Mol Diagn 2019 11 20;21(6):943-950. Epub 2019 Aug 20.

Department of Genetics, University Medical Center Utrecht, Utrecht, the Netherlands.

Diagnosis of systemic autoinflammatory diseases (SAIDs) is often difficult to achieve and can delay the start of proper treatments and result in irreversible organ damage. In several patients with dominantly inherited SAID, postzygotic mutations have been detected as the disease-causing gene defects. Mutations with allele frequencies <5% have been detected, even in patients with severe phenotypes. Next-generation sequencing techniques are currently used to detect mutations in SAID-associated genes. However, even if the genomic region is highly covered, this approach is usually not able to distinguish low-grade postzygotic variants from background noise. We, therefore, developed a sensitive deep sequencing assay for mosaicism detection in SAID-associated genes using single-molecule molecular inversion probes. Our results show the accurate detection of postzygotic variants with allele frequencies as low as 1%. The probability of calling mutations with allele frequencies ≥3% exceeds 99.9%. To date, we have detected three patients with mosaicism, two carrying likely pathogenic NLRP3 variants and one carrying a likely pathogenic TNFRSF1A variant with an allele frequency of 1.3%, confirming the relevance of the technology. The assay shown herein is a flexible, robust, fast, cost-effective, and highly reliable method for mosaicism detection; therefore, it is well suited for routine diagnostics.
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http://dx.doi.org/10.1016/j.jmoldx.2019.06.009DOI Listing
November 2019

Dutch genome diagnostic laboratories accelerated and improved variant interpretation and increased accuracy by sharing data.

Hum Mutat 2019 12 3;40(12):2230-2238. Epub 2019 Sep 3.

Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.

Each year diagnostic laboratories in the Netherlands profile thousands of individuals for heritable disease using next-generation sequencing (NGS). This requires pathogenicity classification of millions of DNA variants on the standard 5-tier scale. To reduce time spent on data interpretation and increase data quality and reliability, the nine Dutch labs decided to publicly share their classifications. Variant classifications of nearly 100,000 unique variants were catalogued and compared in a centralized MOLGENIS database. Variants classified by more than one center were labeled as "consensus" when classifications agreed, and shared internationally with LOVD and ClinVar. When classifications opposed (LB/B vs. LP/P), they were labeled "conflicting", while other nonconsensus observations were labeled "no consensus". We assessed our classifications using the InterVar software to compare to ACMG 2015 guidelines, showing 99.7% overall consistency with only 0.3% discrepancies. Differences in classifications between Dutch labs or between Dutch labs and ACMG were mainly present in genes with low penetrance or for late onset disorders and highlight limitations of the current 5-tier classification system. The data sharing boosted the quality of DNA diagnostics in Dutch labs, an initiative we hope will be followed internationally. Recently, a positive match with a case from outside our consortium resulted in a more definite disease diagnosis.
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http://dx.doi.org/10.1002/humu.23896DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6900155PMC
December 2019

Improving the diagnostic yield of exome- sequencing by predicting gene-phenotype associations using large-scale gene expression analysis.

Nat Commun 2019 06 28;10(1):2837. Epub 2019 Jun 28.

University of Groningen, University Medical Center Groningen, Department of Genetics, 9700 VB, Groningen, The Netherlands.

The diagnostic yield of exome and genome sequencing remains low (8-70%), due to incomplete knowledge on the genes that cause disease. To improve this, we use RNA-seq data from 31,499 samples to predict which genes cause specific disease phenotypes, and develop GeneNetwork Assisted Diagnostic Optimization (GADO). We show that this unbiased method, which does not rely upon specific knowledge on individual genes, is effective in both identifying previously unknown disease gene associations, and flagging genes that have previously been incorrectly implicated in disease. GADO can be run on www.genenetwork.nl by supplying HPO-terms and a list of genes that contain candidate variants. Finally, applying GADO to a cohort of 61 patients for whom exome-sequencing analysis had not resulted in a genetic diagnosis, yields likely causative genes for ten cases.
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http://dx.doi.org/10.1038/s41467-019-10649-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6599066PMC
June 2019

Gut microbiota composition and functional changes in inflammatory bowel disease and irritable bowel syndrome.

Sci Transl Med 2018 12;10(472)

University of Groningen and University Medical Center Groningen, Department of Gastroenterology and Hepatology, Groningen, Netherlands.

Changes in the gut microbiota have been associated with two of the most common gastrointestinal diseases, inflammatory bowel disease (IBD) and irritable bowel syndrome (IBS). Here, we performed a case-control analysis using shotgun metagenomic sequencing of stool samples from 1792 individuals with IBD and IBS compared with control individuals in the general population. Despite substantial overlap between the gut microbiome of patients with IBD and IBS compared with control individuals, we were able to use gut microbiota composition differences to distinguish patients with IBD from those with IBS. By combining species-level profiles and strain-level profiles with bacterial growth rates, metabolic functions, antibiotic resistance, and virulence factor analyses, we identified key bacterial species that may be involved in two common gastrointestinal diseases.
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http://dx.doi.org/10.1126/scitranslmed.aap8914DOI Listing
December 2018

NIPTeR: an R package for fast and accurate trisomy prediction in non-invasive prenatal testing.

BMC Bioinformatics 2018 Dec 17;19(1):531. Epub 2018 Dec 17.

Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.

Background: Various algorithms have been developed to predict fetal trisomies using cell-free DNA in non-invasive prenatal testing (NIPT). As basis for prediction, a control group of non-trisomy samples is needed. Prediction accuracy is dependent on the characteristics of this group and can be improved by reducing variability between samples and by ensuring the control group is representative for the sample analyzed.

Results: NIPTeR is an open-source R Package that enables fast NIPT analysis and simple but flexible workflow creation, including variation reduction, trisomy prediction algorithms and quality control. This broad range of functions allows users to account for variability in NIPT data, calculate control group statistics and predict the presence of trisomies.

Conclusion: NIPTeR supports laboratories processing next-generation sequencing data for NIPT in assessing data quality and determining whether a fetal trisomy is present. NIPTeR is available under the GNU LGPL v3 license and can be freely downloaded from https://github.com/molgenis/NIPTeR or CRAN.
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http://dx.doi.org/10.1186/s12859-018-2557-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6296037PMC
December 2018
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