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Slieker RC, Münch M, Donnelly LA, Bouland GA, Dragan I, Kuznetsov D, Elders PJM, Rutter GA, Ibberson M, Pearson ER, 't Hart LM, van de Wiel MA, Beulens JWJ. An omics-based machine learning approach to predict diabetes progression: a RHAPSODY study. Diabetologia 2024; 67:885-894. [PMID: 38374450 PMCID: PMC10954972 DOI: 10.1007/s00125-024-06105-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Accepted: 01/05/2024] [Indexed: 02/21/2024]
Abstract
AIMS/HYPOTHESIS People with type 2 diabetes are heterogeneous in their disease trajectory, with some progressing more quickly to insulin initiation than others. Although classical biomarkers such as age, HbA1c and diabetes duration are associated with glycaemic progression, it is unclear how well such variables predict insulin initiation or requirement and whether newly identified markers have added predictive value. METHODS In two prospective cohort studies as part of IMI-RHAPSODY, we investigated whether clinical variables and three types of molecular markers (metabolites, lipids, proteins) can predict time to insulin requirement using different machine learning approaches (lasso, ridge, GRridge, random forest). Clinical variables included age, sex, HbA1c, HDL-cholesterol and C-peptide. Models were run with unpenalised clinical variables (i.e. always included in the model without weights) or penalised clinical variables, or without clinical variables. Model development was performed in one cohort and the model was applied in a second cohort. Model performance was evaluated using Harrel's C statistic. RESULTS Of the 585 individuals from the Hoorn Diabetes Care System (DCS) cohort, 69 required insulin during follow-up (1.0-11.4 years); of the 571 individuals in the Genetics of Diabetes Audit and Research in Tayside Scotland (GoDARTS) cohort, 175 required insulin during follow-up (0.3-11.8 years). Overall, the clinical variables and proteins were selected in the different models most often, followed by the metabolites. The most frequently selected clinical variables were HbA1c (18 of the 36 models, 50%), age (15 models, 41.2%) and C-peptide (15 models, 41.2%). Base models (age, sex, BMI, HbA1c) including only clinical variables performed moderately in both the DCS discovery cohort (C statistic 0.71 [95% CI 0.64, 0.79]) and the GoDARTS replication cohort (C 0.71 [95% CI 0.69, 0.75]). A more extensive model including HDL-cholesterol and C-peptide performed better in both cohorts (DCS, C 0.74 [95% CI 0.67, 0.81]; GoDARTS, C 0.73 [95% CI 0.69, 0.77]). Two proteins, lactadherin and proto-oncogene tyrosine-protein kinase receptor, were most consistently selected and slightly improved model performance. CONCLUSIONS/INTERPRETATION Using machine learning approaches, we show that insulin requirement risk can be modestly well predicted by predominantly clinical variables. Inclusion of molecular markers improves the prognostic performance beyond that of clinical variables by up to 5%. Such prognostic models could be useful for identifying people with diabetes at high risk of progressing quickly to treatment intensification. DATA AVAILABILITY Summary statistics of lipidomic, proteomic and metabolomic data are available from a Shiny dashboard at https://rhapdata-app.vital-it.ch .
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Affiliation(s)
- Roderick C Slieker
- Department of Epidemiology and Data Science, Amsterdam UMC, Vrije Universiteit, Amsterdam, the Netherlands
- Amsterdam Public Health, Amsterdam, the Netherlands
- Amsterdam Cardiovascular Sciences, Amsterdam, the Netherlands
- Department of Cell and Chemical Biology, Leiden University Medical Center, Leiden, the Netherlands
| | - Magnus Münch
- Department of Epidemiology and Data Science, Amsterdam UMC, Vrije Universiteit, Amsterdam, the Netherlands
| | - Louise A Donnelly
- Population Health & Genomics, School of Medicine, University of Dundee, Dundee, UK
| | - Gerard A Bouland
- Department of Cell and Chemical Biology, Leiden University Medical Center, Leiden, the Netherlands
- Delft Bioinformatics Lab, Delft University of Technology, Delft, the Netherlands
| | - Iulian Dragan
- Vital-IT Group, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Dmitry Kuznetsov
- Vital-IT Group, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Petra J M Elders
- Amsterdam Public Health, Amsterdam, the Netherlands
- Amsterdam Cardiovascular Sciences, Amsterdam, the Netherlands
- Department of General Practice, Amsterdam UMC, Vrije Universiteit, Amsterdam, the Netherlands
| | - Guy A Rutter
- CRCHUM, Faculty of Medicine, Université de Montréal, Montréal, QC, Canada
- Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, UK
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Republic of Singapore
| | - Mark Ibberson
- Vital-IT Group, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Ewan R Pearson
- Population Health & Genomics, School of Medicine, University of Dundee, Dundee, UK
| | - Leen M 't Hart
- Department of Epidemiology and Data Science, Amsterdam UMC, Vrije Universiteit, Amsterdam, the Netherlands
- Department of Cell and Chemical Biology, Leiden University Medical Center, Leiden, the Netherlands
- Department of Biomedical Data Sciences, Section of Molecular Epidemiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Mark A van de Wiel
- Department of Epidemiology and Data Science, Amsterdam UMC, Vrije Universiteit, Amsterdam, the Netherlands
- Amsterdam Public Health, Amsterdam, the Netherlands
| | - Joline W J Beulens
- Department of Epidemiology and Data Science, Amsterdam UMC, Vrije Universiteit, Amsterdam, the Netherlands.
- Amsterdam Public Health, Amsterdam, the Netherlands.
- Amsterdam Cardiovascular Sciences, Amsterdam, the Netherlands.
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands.
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Zhang M, Bouland GA, Holstege H, Reinders MJT. Identifying Aging and Alzheimer Disease-Associated Somatic Variations in Excitatory Neurons From the Human Frontal Cortex. Neurol Genet 2023; 9:e200066. [PMID: 37123987 PMCID: PMC10136684 DOI: 10.1212/nxg.0000000000200066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Accepted: 02/03/2023] [Indexed: 05/02/2023]
Abstract
Background and Objectives With age, somatic mutations accumulated in human brain cells can lead to various neurologic disorders and brain tumors. Because the incidence rate of Alzheimer disease (AD) increases exponentially with age, investigating the association between AD and the accumulation of somatic mutation can help understand the etiology of AD. Methods We designed a somatic mutation detection workflow by contrasting genotypes derived from whole-genome sequencing (WGS) data with genotypes derived from scRNA-seq data and applied this workflow to 76 participants from the Religious Order Study and the Rush Memory and Aging Project (ROSMAP) cohort. We focused only on excitatory neurons, the dominant cell type in the scRNA-seq data. Results We identified 196 sites that harbored at least 1 individual with an excitatory neuron-specific somatic mutation (ENSM), and these 196 sites were mapped to 127 genes. The single base substitution (SBS) pattern of the putative ENSMs was best explained by signature SBS5 from the Catalogue of Somatic Mutations in Cancer (COSMIC) mutational signatures, a clock-like pattern correlating with the age of the individual. The count of ENSMs per individual also showed an increasing trend with age. Among the mutated sites, we found 2 sites tend to have more mutations in older individuals (16:6899517 [RBFOX1], p = 0.04; 4:21788463 [KCNIP4], p < 0.05). In addition, 2 sites were found to have a higher odds ratio to detect a somatic mutation in AD samples (6:73374221 [KCNQ5], p = 0.01 and 13:36667102 [DCLK1], p = 0.02). Thirty-two genes that harbor somatic mutations unique to AD and the KCNQ5 and DCLK1 genes were used for gene ontology (GO)-term enrichment analysis. We found the AD-specific ENSMs enriched in the GO-term "vocalization behavior" and "intraspecies interaction between organisms." Of interest we observed both age-specific and AD-specific ENSMs enriched in the K+ channel-associated genes. Discussion Our results show that combining scRNA-seq and WGS data can successfully detect putative somatic mutations. The putative somatic mutations detected from ROSMAP data set have provided new insights into the association of AD and aging with brain somatic mutagenesis.
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Affiliation(s)
- Meng Zhang
- Delft Bioinformatics Lab (M.Z., G.A.B., H.H., M.J.T.R.), Delft University of Technology; Department of Human Genetics (M.Z., H.H.), Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC; and Department of Human Genetics (G.A.B., M.J.T.R.), Leiden University Medical Center, the Netherlands
| | - Gerard A Bouland
- Delft Bioinformatics Lab (M.Z., G.A.B., H.H., M.J.T.R.), Delft University of Technology; Department of Human Genetics (M.Z., H.H.), Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC; and Department of Human Genetics (G.A.B., M.J.T.R.), Leiden University Medical Center, the Netherlands
| | - Henne Holstege
- Delft Bioinformatics Lab (M.Z., G.A.B., H.H., M.J.T.R.), Delft University of Technology; Department of Human Genetics (M.Z., H.H.), Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC; and Department of Human Genetics (G.A.B., M.J.T.R.), Leiden University Medical Center, the Netherlands
| | - Marcel J T Reinders
- Delft Bioinformatics Lab (M.Z., G.A.B., H.H., M.J.T.R.), Delft University of Technology; Department of Human Genetics (M.Z., H.H.), Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC; and Department of Human Genetics (G.A.B., M.J.T.R.), Leiden University Medical Center, the Netherlands
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Bouland GA, Marinus KI, van Kesteren RE, Smit AB, Mahfouz A, Reinders MJT. Single-cell RNA sequencing data reveals rewiring of transcriptional relationships in Alzheimer's Disease associated with risk variants. medRxiv 2023:2023.05.15.23289992. [PMID: 37292975 PMCID: PMC10246028 DOI: 10.1101/2023.05.15.23289992] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Understanding how genetic risk variants contribute to Alzheimer's Disease etiology remains a challenge. Single-cell RNA sequencing (scRNAseq) allows for the investigation of cell type specific effects of genomic risk loci on gene expression. Using seven scRNAseq datasets totalling >1.3 million cells, we investigated differential correlation of genes between healthy individuals and individuals diagnosed with Alzheimer's Disease. Using the number of differential correlations of a gene to estimate its involvement and potential impact, we present a prioritization scheme for identifying probable causal genes near genomic risk loci. Besides prioritizing genes, our approach pin-points specific cell types and provides insight into the rewiring of gene-gene relationships associated with Alzheimer's.
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Affiliation(s)
- Gerard A Bouland
- Delft Bioinformatics Lab, Delft University of Technology, Delft, The Netherlands
- Department of Human Genetics, Leiden University Medical Center, Leiden 2333ZC, The Netherlands
| | - Kevin I Marinus
- Department of Molecular and Cellular Neurobiology, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Ronald E van Kesteren
- Department of Molecular and Cellular Neurobiology, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - August B Smit
- Department of Molecular and Cellular Neurobiology, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Ahmed Mahfouz
- Delft Bioinformatics Lab, Delft University of Technology, Delft, The Netherlands
- Department of Human Genetics, Leiden University Medical Center, Leiden 2333ZC, The Netherlands
- Leiden Computational Biology Center, Leiden University Medical Center, Leiden 2333ZC, The Netherlands
| | - Marcel J T Reinders
- Delft Bioinformatics Lab, Delft University of Technology, Delft, The Netherlands
- Department of Human Genetics, Leiden University Medical Center, Leiden 2333ZC, The Netherlands
- Leiden Computational Biology Center, Leiden University Medical Center, Leiden 2333ZC, The Netherlands
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Slieker RC, Donnelly LA, Akalestou E, Lopez-Noriega L, Melhem R, Güneş A, Abou Azar F, Efanov A, Georgiadou E, Muniangi-Muhitu H, Sheikh M, Giordano GN, Åkerlund M, Ahlqvist E, Ali A, Banasik K, Brunak S, Barovic M, Bouland GA, Burdet F, Canouil M, Dragan I, Elders PJM, Fernandez C, Festa A, Fitipaldi H, Froguel P, Gudmundsdottir V, Gudnason V, Gerl MJ, van der Heijden AA, Jennings LL, Hansen MK, Kim M, Leclerc I, Klose C, Kuznetsov D, Mansour Aly D, Mehl F, Marek D, Melander O, Niknejad A, Ottosson F, Pavo I, Duffin K, Syed SK, Shaw JL, Cabrera O, Pullen TJ, Simons K, Solimena M, Suvitaival T, Wretlind A, Rossing P, Lyssenko V, Legido Quigley C, Groop L, Thorens B, Franks PW, Lim GE, Estall J, Ibberson M, Beulens JWJ, 't Hart LM, Pearson ER, Rutter GA. Identification of biomarkers for glycaemic deterioration in type 2 diabetes. Nat Commun 2023; 14:2533. [PMID: 37137910 PMCID: PMC10156700 DOI: 10.1038/s41467-023-38148-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Accepted: 04/18/2023] [Indexed: 05/05/2023] Open
Abstract
We identify biomarkers for disease progression in three type 2 diabetes cohorts encompassing 2,973 individuals across three molecular classes, metabolites, lipids and proteins. Homocitrulline, isoleucine and 2-aminoadipic acid, eight triacylglycerol species, and lowered sphingomyelin 42:2;2 levels are predictive of faster progression towards insulin requirement. Of ~1,300 proteins examined in two cohorts, levels of GDF15/MIC-1, IL-18Ra, CRELD1, NogoR, FAS, and ENPP7 are associated with faster progression, whilst SMAC/DIABLO, SPOCK1 and HEMK2 predict lower progression rates. In an external replication, proteins and lipids are associated with diabetes incidence and prevalence. NogoR/RTN4R injection improved glucose tolerance in high fat-fed male mice but impaired it in male db/db mice. High NogoR levels led to islet cell apoptosis, and IL-18R antagonised inflammatory IL-18 signalling towards nuclear factor kappa-B in vitro. This comprehensive, multi-disciplinary approach thus identifies biomarkers with potential prognostic utility, provides evidence for possible disease mechanisms, and identifies potential therapeutic avenues to slow diabetes progression.
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Affiliation(s)
- Roderick C Slieker
- Department of Epidemiology and Data Science, Amsterdam Public Health Institute, Amsterdam Cardiovascular Sciences, Amsterdam UMC, location VUMC, Amsterdam, the Netherlands
- Department of Cell and Chemical Biology, Leiden University Medical Center, Leiden, the Netherlands
| | - Louise A Donnelly
- Population Health & Genomics, School of Medicine, University of Dundee, Dundee, UK
| | - Elina Akalestou
- Section of Cell Biology and Functional Genomics, Division of Diabetes, Endocrinology and Metabolism, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Livia Lopez-Noriega
- Section of Cell Biology and Functional Genomics, Division of Diabetes, Endocrinology and Metabolism, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Rana Melhem
- CHUM Research Centre and University of Montreal, Montreal, QC, Canada
| | - Ayşim Güneş
- IRCM and University of Montreal, Montreal, QC, Canada
| | | | - Alexander Efanov
- Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, US
| | - Eleni Georgiadou
- Section of Cell Biology and Functional Genomics, Division of Diabetes, Endocrinology and Metabolism, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Hermine Muniangi-Muhitu
- Section of Cell Biology and Functional Genomics, Division of Diabetes, Endocrinology and Metabolism, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Mahsa Sheikh
- Section of Cell Biology and Functional Genomics, Division of Diabetes, Endocrinology and Metabolism, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | | | - Mikael Åkerlund
- Department of Clinical Sciences, Lund University, Malmö, Sweden
| | - Emma Ahlqvist
- Department of Clinical Sciences, Lund University, Malmö, Sweden
| | - Ashfaq Ali
- Steno Diabetes Center Copenhagen, Gentofte, Denmark
| | - Karina Banasik
- Novo Nordisk Foundation Center for Protein Research, Copenhagen, Denmark
| | - Søren Brunak
- Novo Nordisk Foundation Center for Protein Research, Copenhagen, Denmark
| | - Marko Barovic
- Paul Langerhans Institute Dresden (PLID) of the Helmholtz Center Munich at the University Hospital Carl Gustav Carus and Medical Faculty, Dresden, Germany
| | - Gerard A Bouland
- Department of Cell and Chemical Biology, Leiden University Medical Center, Leiden, the Netherlands
| | - Frédéric Burdet
- Vital-IT Group, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Mickaël Canouil
- INSERM U1283, CNRS UMR 8199, European Genomic Institute for Diabetes (EGID), Institut Pasteur de Lille, University of Lille, Lille University Hospital, Lille, F-59000, France
| | - Iulian Dragan
- Vital-IT Group, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Petra J M Elders
- Department of General Practice and Elderly Care Medicine, Amsterdam Public Health Research Institute, Amsterdam UMC-location VUmc, Amsterdam, the Netherlands
| | | | - Andreas Festa
- Eli Lilly Regional Operations GmbH, Vienna, Austria
- 1st Medical Department, LK Stockerau, Niederösterreich, Austria
| | - Hugo Fitipaldi
- Department of Clinical Sciences, Lund University, Malmö, Sweden
| | - Phillippe Froguel
- INSERM U1283, CNRS UMR 8199, European Genomic Institute for Diabetes (EGID), Institut Pasteur de Lille, University of Lille, Lille University Hospital, Lille, F-59000, France
- Division of Systems Biology, Department of Diabetes, Endocrinology and Metabolism, Imperial College London, London, UK
| | - Valborg Gudmundsdottir
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
- Icelandic Heart Association, Kopavogur, Iceland
| | - Vilmundur Gudnason
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
- Icelandic Heart Association, Kopavogur, Iceland
| | | | - Amber A van der Heijden
- Department of General Practice and Elderly Care Medicine, Amsterdam Public Health Research Institute, Amsterdam UMC-location VUmc, Amsterdam, the Netherlands
| | - Lori L Jennings
- Novartis Institutes for Biomedical Research, Cambridge, MA, 02139, USA
| | - Michael K Hansen
- Cardiovascular and Metabolic Disease Research, Janssen Research & Development, Spring House, PA, USA
| | - Min Kim
- Steno Diabetes Center Copenhagen, Gentofte, Denmark
- Institute of Pharmaceutical Science, Faculty of Life Sciences and Medicines, King's College London, London, UK
| | - Isabelle Leclerc
- Section of Cell Biology and Functional Genomics, Division of Diabetes, Endocrinology and Metabolism, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
- CHUM Research Centre and University of Montreal, Montreal, QC, Canada
| | | | - Dmitry Kuznetsov
- Vital-IT Group, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | | | - Florence Mehl
- Vital-IT Group, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Diana Marek
- Vital-IT Group, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Olle Melander
- Department of Clinical Sciences, Lund University, Malmö, Sweden
| | - Anne Niknejad
- Vital-IT Group, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Filip Ottosson
- Department of Clinical Sciences, Lund University, Malmö, Sweden
- Section for Clinical Mass Spectrometry, Danish Center for Neonatal Screening, Department of Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark
| | - Imre Pavo
- Eli Lilly Regional Operations GmbH, Vienna, Austria
| | - Kevin Duffin
- Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, US
| | - Samreen K Syed
- Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, US
| | - Janice L Shaw
- Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, US
| | - Over Cabrera
- Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, US
| | - Timothy J Pullen
- Section of Cell Biology and Functional Genomics, Division of Diabetes, Endocrinology and Metabolism, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
- Department of Diabetes, Guy's Campus King's College London, London, UK
| | | | - Michele Solimena
- Paul Langerhans Institute Dresden (PLID) of the Helmholtz Center Munich at the University Hospital Carl Gustav Carus and Medical Faculty, Dresden, Germany
- Molecular Diabetology, University Hospital and Medical Faculty Carl Gustav Carus, TU Dresden, Dresden, Germany
| | | | | | - Peter Rossing
- Steno Diabetes Center Copenhagen, Gentofte, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Valeriya Lyssenko
- Department of Clinical Science, Center for Diabetes Research, University of Bergen, Bergen, Norway
- Genomics, Diabetes and Endocrinology Unit, Department of Clinical Sciences Malmö, Lund University Diabetes Centre, Skåne University Hospital, Malmö, Sweden
| | - Cristina Legido Quigley
- Steno Diabetes Center Copenhagen, Gentofte, Denmark
- Institute of Pharmaceutical Science, Faculty of Life Sciences and Medicines, King's College London, London, UK
| | - Leif Groop
- Department of Clinical Sciences, Lund University, Malmö, Sweden
- Finnish Institute of Molecular Medicine, Helsinki University, Helsinki, Finland
| | - Bernard Thorens
- Center for Integrative Genomics, University of Lausanne, CH-1015, Lausanne, Switzerland
| | - Paul W Franks
- Department of Clinical Sciences, Lund University, Malmö, Sweden
- Department of Nutrition, Harvard School of Public Health, Boston, MA, USA
| | - Gareth E Lim
- CHUM Research Centre and University of Montreal, Montreal, QC, Canada
| | | | - Mark Ibberson
- Vital-IT Group, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Joline W J Beulens
- Department of Epidemiology and Data Science, Amsterdam Public Health Institute, Amsterdam Cardiovascular Sciences, Amsterdam UMC, location VUMC, Amsterdam, the Netherlands
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Leen M 't Hart
- Department of Epidemiology and Data Science, Amsterdam Public Health Institute, Amsterdam Cardiovascular Sciences, Amsterdam UMC, location VUMC, Amsterdam, the Netherlands.
- Department of Cell and Chemical Biology, Leiden University Medical Center, Leiden, the Netherlands.
- Department of Biomedical Data Sciences, Section Molecular Epidemiology, Leiden University Medical Center, Leiden, the Netherlands.
| | - Ewan R Pearson
- Population Health & Genomics, School of Medicine, University of Dundee, Dundee, UK.
| | - Guy A Rutter
- Section of Cell Biology and Functional Genomics, Division of Diabetes, Endocrinology and Metabolism, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK.
- CHUM Research Centre and University of Montreal, Montreal, QC, Canada.
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore.
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Bouland GA, Mahfouz A, Reinders MJT. Consequences and opportunities arising due to sparser single-cell RNA-seq datasets. Genome Biol 2023; 24:86. [PMID: 37085823 PMCID: PMC10120229 DOI: 10.1186/s13059-023-02933-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Accepted: 04/10/2023] [Indexed: 04/23/2023] Open
Abstract
With the number of cells measured in single-cell RNA sequencing (scRNA-seq) datasets increasing exponentially and concurrent increased sparsity due to more zero counts being measured for many genes, we demonstrate here that downstream analyses on binary-based gene expression give similar results as count-based analyses. Moreover, a binary representation scales up to ~ 50-fold more cells that can be analyzed using the same computational resources. We also highlight the possibilities provided by binarized scRNA-seq data. Development of specialized tools for bit-aware implementations of downstream analytical tasks will enable a more fine-grained resolution of biological heterogeneity.
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Affiliation(s)
- Gerard A Bouland
- Delft Bioinformatics Lab, Delft University of Technology, Delft, The Netherlands
- Department of Human Genetics, Leiden University Medical Center, Leiden, 2333ZC, The Netherlands
| | - Ahmed Mahfouz
- Delft Bioinformatics Lab, Delft University of Technology, Delft, The Netherlands.
- Department of Human Genetics, Leiden University Medical Center, Leiden, 2333ZC, The Netherlands.
- Leiden Computational Biology Center, Leiden University Medical Center, Leiden, 2333ZC, The Netherlands.
| | - Marcel J T Reinders
- Delft Bioinformatics Lab, Delft University of Technology, Delft, The Netherlands.
- Department of Human Genetics, Leiden University Medical Center, Leiden, 2333ZC, The Netherlands.
- Leiden Computational Biology Center, Leiden University Medical Center, Leiden, 2333ZC, The Netherlands.
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Bouland GA, Beulens JWJ, Nap J, van der Slik AR, Zaldumbide A, 't Hart LM, Slieker RC. Diabetes risk loci-associated pathways are shared across metabolic tissues. BMC Genomics 2022; 23:368. [PMID: 35568807 PMCID: PMC9107144 DOI: 10.1186/s12864-022-08587-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Accepted: 03/23/2022] [Indexed: 11/28/2022] Open
Abstract
Aims/hypothesis Numerous genome-wide association studies have been performed to understand the influence of genetic variation on type 2 diabetes etiology. Many identified risk variants are located in non-coding and intergenic regions, which complicates understanding of how genes and their downstream pathways are influenced. An integrative data approach will help to understand the mechanism and consequences of identified risk variants. Methods In the current study we use our previously developed method CONQUER to overlap 403 type 2 diabetes risk variants with regulatory, expression and protein data to identify tissue-shared disease-relevant mechanisms. Results One SNP rs474513 was found to be an expression-, protein- and metabolite QTL. Rs474513 influenced LPA mRNA and protein levels in the pancreas and plasma, respectively. On the pathway level, in investigated tissues most SNPs linked to metabolism. However, in eleven of the twelve tissues investigated nine SNPs were linked to differential expression of the ribosome pathway. Furthermore, seven SNPs were linked to altered expression of genes linked to the immune system. Among them, rs601945 was found to influence multiple HLA genes, including HLA-DQA2, in all twelve tissues investigated. Conclusion Our results show that in addition to the classical metabolism pathways, other pathways may be important to type 2 diabetes that show a potential overlap with type 1 diabetes. Supplementary Information The online version contains supplementary material available at 10.1186/s12864-022-08587-5.
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Affiliation(s)
- Gerard A Bouland
- Department of Cell and Chemical Biology, Leiden University Medical Center, Einthovenweg 20, 2333ZC, Leiden, the Netherlands
| | - Joline W J Beulens
- Department of Epidemiology and Data Science, Amsterdam UMC, Location VUMC, Amsterdam Public Health Institute, Amsterdam, the Netherlands.,Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Joey Nap
- Department of Cell and Chemical Biology, Leiden University Medical Center, Einthovenweg 20, 2333ZC, Leiden, the Netherlands
| | - Arno R van der Slik
- Department of Immunohematology and Blood Transfusion, Leiden University Medical Center, Leiden, The Netherlands
| | - Arnaud Zaldumbide
- Department of Cell and Chemical Biology, Leiden University Medical Center, Einthovenweg 20, 2333ZC, Leiden, the Netherlands
| | - Leen M 't Hart
- Department of Cell and Chemical Biology, Leiden University Medical Center, Einthovenweg 20, 2333ZC, Leiden, the Netherlands.,Department of Epidemiology and Data Science, Amsterdam UMC, Location VUMC, Amsterdam Public Health Institute, Amsterdam, the Netherlands.,Molecular Epidemiology Section, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands
| | - Roderick C Slieker
- Department of Cell and Chemical Biology, Leiden University Medical Center, Einthovenweg 20, 2333ZC, Leiden, the Netherlands. .,Department of Epidemiology and Data Science, Amsterdam UMC, Location VUMC, Amsterdam Public Health Institute, Amsterdam, the Netherlands.
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Bouland GA, Mahfouz A, Reinders MJT. Differential analysis of binarized single-cell RNA sequencing data captures biological variation. NAR Genom Bioinform 2021; 3:lqab118. [PMID: 34988441 PMCID: PMC8693570 DOI: 10.1093/nargab/lqab118] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 11/04/2021] [Accepted: 12/03/2021] [Indexed: 12/11/2022] Open
Abstract
Single-cell RNA sequencing data is characterized by a large number of zero counts, yet there is growing evidence that these zeros reflect biological variation rather than technical artifacts. We propose to use binarized expression profiles to identify the effects of biological variation in single-cell RNA sequencing data. Using 16 publicly available and simulated datasets, we show that a binarized representation of single-cell expression data accurately represents biological variation and reveals the relative abundance of transcripts more robustly than counts.
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Affiliation(s)
- Gerard A Bouland
- Delft Bioinformatics Lab, Delft University of Technology, Delft 2628 XE, The Netherlands
| | - Ahmed Mahfouz
- Delft Bioinformatics Lab, Delft University of Technology, Delft 2628 XE, The Netherlands
| | - Marcel J T Reinders
- Delft Bioinformatics Lab, Delft University of Technology, Delft 2628 XE, The Netherlands
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Slieker RC, Donnelly LA, Fitipaldi H, Bouland GA, Giordano GN, Åkerlund M, Gerl MJ, Ahlqvist E, Ali A, Dragan I, Elders P, Festa A, Hansen MK, van der Heijden AA, Mansour Aly D, Kim M, Kuznetsov D, Mehl F, Klose C, Simons K, Pavo I, Pullen TJ, Suvitaival T, Wretlind A, Rossing P, Lyssenko V, Legido Quigley C, Groop L, Thorens B, Franks PW, Ibberson M, Rutter GA, Beulens JWJ, 't Hart LM, Pearson ER. Distinct Molecular Signatures of Clinical Clusters in People With Type 2 Diabetes: An IMI-RHAPSODY Study. Diabetes 2021; 70:2683-2693. [PMID: 34376475 PMCID: PMC8564413 DOI: 10.2337/db20-1281] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Accepted: 08/01/2021] [Indexed: 11/23/2022]
Abstract
Type 2 diabetes is a multifactorial disease with multiple underlying aetiologies. To address this heterogeneity, investigators of a previous study clustered people with diabetes according to five diabetes subtypes. The aim of the current study is to investigate the etiology of these clusters by comparing their molecular signatures. In three independent cohorts, in total 15,940 individuals were clustered based on five clinical characteristics. In a subset, genetic (N = 12,828), metabolomic (N = 2,945), lipidomic (N = 2,593), and proteomic (N = 1,170) data were obtained in plasma. For each data type, each cluster was compared with the other four clusters as the reference. The insulin-resistant cluster showed the most distinct molecular signature, with higher branched-chain amino acid, diacylglycerol, and triacylglycerol levels and aberrant protein levels in plasma were enriched for proteins in the intracellular PI3K/Akt pathway. The obese cluster showed higher levels of cytokines. The mild diabetes cluster with high HDL showed the most beneficial molecular profile with effects opposite of those seen in the insulin-resistant cluster. This study shows that clustering people with type 2 diabetes can identify underlying molecular mechanisms related to pancreatic islets, liver, and adipose tissue metabolism. This provides novel biological insights into the diverse aetiological processes that would not be evident when type 2 diabetes is viewed as a homogeneous disease.
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Affiliation(s)
- Roderick C Slieker
- Department of Cell and Chemical Biology, Leiden University Medical Center, Leiden, the Netherlands
- Department of Epidemiology and Data Science, Amsterdam Public Health Institute, Amsterdam UMC, location VUmc, Amsterdam, the Netherlands
| | - Louise A Donnelly
- Population Health & Genomics, School of Medicine, University of Dundee, Dundee, U.K
| | - Hugo Fitipaldi
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Sciences, Clinical Research Centre, Lund University, SUS, Malmö, Sweden
| | - Gerard A Bouland
- Department of Cell and Chemical Biology, Leiden University Medical Center, Leiden, the Netherlands
| | - Giuseppe N Giordano
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Sciences, Clinical Research Centre, Lund University, SUS, Malmö, Sweden
| | - Mikael Åkerlund
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Sciences, Clinical Research Centre, Lund University, SUS, Malmö, Sweden
| | | | - Emma Ahlqvist
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Sciences, Clinical Research Centre, Lund University, SUS, Malmö, Sweden
| | - Ashfaq Ali
- Steno Diabetes Center Copenhagen, Gentofte, Denmark
| | - Iulian Dragan
- Vital-IT Group, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Petra Elders
- Department of General Practice and Elderly Care Medicine, Amsterdam Public Health Research Institute, Amsterdam UMC, location VUmc, Amsterdam, the Netherlands
| | - Andreas Festa
- Eli Lilly Regional Operations GmbH, Vienna, Austria
- 1st Medical Department, LK Stockerau, Niederösterreich, Austria
| | - Michael K Hansen
- Cardiovascular and Metabolic Disease Research, Janssen Research & Development, Spring House, PA
| | - Amber A van der Heijden
- Department of General Practice and Elderly Care Medicine, Amsterdam Public Health Research Institute, Amsterdam UMC, location VUmc, Amsterdam, the Netherlands
| | - Dina Mansour Aly
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Sciences, Clinical Research Centre, Lund University, SUS, Malmö, Sweden
| | - Min Kim
- Steno Diabetes Center Copenhagen, Gentofte, Denmark
- Institute of Pharmaceutical Science, Faculty of Life Sciences and Medicines, King's College London, London, U.K
| | - Dmitry Kuznetsov
- Vital-IT Group, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Florence Mehl
- Vital-IT Group, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | | | | | - Imre Pavo
- Eli Lilly Regional Operations GmbH, Vienna, Austria
| | - Timothy J Pullen
- Department of Diabetes, Guy's Campus, King's College London, London, U.K
- Section of Cell Biology and Functional Genomics, Division of Diabetes, Endocrinology and Metabolism, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, U.K
| | | | | | - Peter Rossing
- Steno Diabetes Center Copenhagen, Gentofte, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Valeriya Lyssenko
- Department of Clinical Science, Center for Diabetes Research, University of Bergen, Bergen, Norway
- Genomics, Diabetes and Endocrinology Unit, Department of Clinical Sciences Malmö, Lund University Diabetes Centre, Skåne University Hospital, Malmö, Sweden
| | - Cristina Legido Quigley
- Steno Diabetes Center Copenhagen, Gentofte, Denmark
- Cardiovascular and Metabolic Disease Research, Janssen Research & Development, Spring House, PA
| | - Leif Groop
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Sciences, Clinical Research Centre, Lund University, SUS, Malmö, Sweden
- Finnish Institute of Molecular Medicine, Helsinki University, Helsinki, Finland
| | - Bernard Thorens
- Center for Integrative Genomics, University of Lausanne, Lausanne, Switzerland
| | - Paul W Franks
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Sciences, Clinical Research Centre, Lund University, SUS, Malmö, Sweden
- Department of Nutrition, Harvard School of Public Health, Boston, MA
| | - Mark Ibberson
- Vital-IT Group, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Guy A Rutter
- Department of Diabetes, Guy's Campus, King's College London, London, U.K
- Lee Kong Chian School of Medicine, Nan Yang Technological University, Singapore
| | - Joline W J Beulens
- Department of Epidemiology and Data Science, Amsterdam Public Health Institute, Amsterdam UMC, location VUmc, Amsterdam, the Netherlands
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Leen M 't Hart
- Department of Cell and Chemical Biology, Leiden University Medical Center, Leiden, the Netherlands
- Department of Epidemiology and Data Science, Amsterdam Public Health Institute, Amsterdam UMC, location VUmc, Amsterdam, the Netherlands
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands
| | - Ewan R Pearson
- Population Health & Genomics, School of Medicine, University of Dundee, Dundee, U.K.
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Slieker RC, Donnelly LA, Fitipaldi H, Bouland GA, Giordano GN, Åkerlund M, Gerl MJ, Ahlqvist E, Ali A, Dragan I, Festa A, Hansen MK, Mansour Aly D, Kim M, Kuznetsov D, Mehl F, Klose C, Simons K, Pavo I, Pullen TJ, Suvitaival T, Wretlind A, Rossing P, Lyssenko V, Legido-Quigley C, Groop L, Thorens B, Franks PW, Ibberson M, Rutter GA, Beulens JWJ, 't Hart LM, Pearson ER. Replication and cross-validation of type 2 diabetes subtypes based on clinical variables: an IMI-RHAPSODY study. Diabetologia 2021; 64:1982-1989. [PMID: 34110439 PMCID: PMC8382625 DOI: 10.1007/s00125-021-05490-8] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Accepted: 03/12/2021] [Indexed: 11/26/2022]
Abstract
AIMS/HYPOTHESIS Five clusters based on clinical characteristics have been suggested as diabetes subtypes: one autoimmune and four subtypes of type 2 diabetes. In the current study we replicate and cross-validate these type 2 diabetes clusters in three large cohorts using variables readily measured in the clinic. METHODS In three independent cohorts, in total 15,940 individuals were clustered based on age, BMI, HbA1c, random or fasting C-peptide, and HDL-cholesterol. Clusters were cross-validated against the original clusters based on HOMA measures. In addition, between cohorts, clusters were cross-validated by re-assigning people based on each cohort's cluster centres. Finally, we compared the time to insulin requirement for each cluster. RESULTS Five distinct type 2 diabetes clusters were identified and mapped back to the original four All New Diabetics in Scania (ANDIS) clusters. Using C-peptide and HDL-cholesterol instead of HOMA2-B and HOMA2-IR, three of the clusters mapped with high sensitivity (80.6-90.7%) to the previously identified severe insulin-deficient diabetes (SIDD), severe insulin-resistant diabetes (SIRD) and mild obesity-related diabetes (MOD) clusters. The previously described ANDIS mild age-related diabetes (MARD) cluster could be mapped to the two milder groups in our study: one characterised by high HDL-cholesterol (mild diabetes with high HDL-cholesterol [MDH] cluster), and the other not having any extreme characteristic (mild diabetes [MD]). When these two milder groups were combined, they mapped well to the previously labelled MARD cluster (sensitivity 79.1%). In the cross-validation between cohorts, particularly the SIDD and MDH clusters cross-validated well, with sensitivities ranging from 73.3% to 97.1%. SIRD and MD showed a lower sensitivity, ranging from 36.1% to 92.3%, where individuals shifted from SIRD to MD and vice versa. People belonging to the SIDD cluster showed the fastest progression towards insulin requirement, while the MDH cluster showed the slowest progression. CONCLUSIONS/INTERPRETATION Clusters based on C-peptide instead of HOMA2 measures resemble those based on HOMA2 measures, especially for SIDD, SIRD and MOD. By adding HDL-cholesterol, the MARD cluster based upon HOMA2 measures resulted in the current clustering into two clusters, with one cluster having high HDL levels. Cross-validation between cohorts showed generally a good resemblance between cohorts. Together, our results show that the clustering based on clinical variables readily measured in the clinic (age, HbA1c, HDL-cholesterol, BMI and C-peptide) results in informative clusters that are representative of the original ANDIS clusters and stable across cohorts. Adding HDL-cholesterol to the clustering resulted in the identification of a cluster with very slow glycaemic deterioration.
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Affiliation(s)
- Roderick C Slieker
- Department of Epidemiology and Data Science, Amsterdam Public Health Institute, Amsterdam UMC, Location VUMC, Amsterdam, the Netherlands
- Department of Cell and Chemical Biology, Leiden University Medical Center, Leiden, the Netherlands
| | - Louise A Donnelly
- Division of Population Health & Genomics, School of Medicine, University of Dundee, Dundee, UK
| | - Hugo Fitipaldi
- Genetic and Molecular Epidemiology Unit, Department of Clinical Sciences, CRC, Lund University Diabetes Centre, Lund University, Malmö, Sweden
| | - Gerard A Bouland
- Department of Cell and Chemical Biology, Leiden University Medical Center, Leiden, the Netherlands
| | - Giuseppe N Giordano
- Genetic and Molecular Epidemiology Unit, Department of Clinical Sciences, CRC, Lund University Diabetes Centre, Lund University, Malmö, Sweden
| | - Mikael Åkerlund
- Genetic and Molecular Epidemiology Unit, Department of Clinical Sciences, CRC, Lund University Diabetes Centre, Lund University, Malmö, Sweden
| | | | - Emma Ahlqvist
- Genetic and Molecular Epidemiology Unit, Department of Clinical Sciences, CRC, Lund University Diabetes Centre, Lund University, Malmö, Sweden
| | - Ashfaq Ali
- Steno Diabetes Center Copenhagen, Gentofte, Denmark
| | - Iulian Dragan
- Vital-IT Group, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Andreas Festa
- Eli Lilly Regional Operations GmbH, Vienna, Austria
- 1st Medical Department, LK Stockerau, Niederösterreich, Austria
| | - Michael K Hansen
- Cardiovascular and Metabolic Disease Research, Janssen Research & Development, Spring House, PA, USA
| | - Dina Mansour Aly
- Genetic and Molecular Epidemiology Unit, Department of Clinical Sciences, CRC, Lund University Diabetes Centre, Lund University, Malmö, Sweden
| | - Min Kim
- Steno Diabetes Center Copenhagen, Gentofte, Denmark
- Institute of Pharmaceutical Science, Faculty of Life Sciences and Medicines, King's College London, London, UK
| | - Dmitry Kuznetsov
- Vital-IT Group, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Florence Mehl
- Vital-IT Group, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | | | | | - Imre Pavo
- Eli Lilly Regional Operations GmbH, Vienna, Austria
| | - Timothy J Pullen
- Department of Diabetes, Guy's Campus King's College London, London, UK
- Section of Cell Biology and Functional Genomics, Division of Diabetes, Endocrinology and Metabolism, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | | | | | | | - Valeriya Lyssenko
- Department of Clinical Science, Center for Diabetes Research, University of Bergen, Bergen, Norway
- Genomics, Diabetes and Endocrinology Unit, Department of Clinical Sciences Malmö, Lund University Diabetes Centre, Skåne University Hospital, Malmö, Sweden
| | - Cristina Legido-Quigley
- Steno Diabetes Center Copenhagen, Gentofte, Denmark
- Institute of Pharmaceutical Science, Faculty of Life Sciences and Medicines, King's College London, London, UK
| | - Leif Groop
- Genetic and Molecular Epidemiology Unit, Department of Clinical Sciences, CRC, Lund University Diabetes Centre, Lund University, Malmö, Sweden
- Finnish Institute of Molecular Medicine, Helsinki University, Helsinki, Finland
| | - Bernard Thorens
- Center for Integrative Genomics, University of Lausanne, Lausanne, Switzerland
| | - Paul W Franks
- Genetic and Molecular Epidemiology Unit, Department of Clinical Sciences, CRC, Lund University Diabetes Centre, Lund University, Malmö, Sweden
- Department of Nutrition, Harvard School of Public Health, Boston, MA, USA
| | - Mark Ibberson
- Vital-IT Group, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Guy A Rutter
- Section of Cell Biology and Functional Genomics, Division of Diabetes, Endocrinology and Metabolism, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Republic of Singapore
| | - Joline W J Beulens
- Department of Epidemiology and Data Science, Amsterdam Public Health Institute, Amsterdam UMC, Location VUMC, Amsterdam, the Netherlands
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Leen M 't Hart
- Department of Epidemiology and Data Science, Amsterdam Public Health Institute, Amsterdam UMC, Location VUMC, Amsterdam, the Netherlands.
- Department of Cell and Chemical Biology, Leiden University Medical Center, Leiden, the Netherlands.
- Department of Biomedical Data Sciences, Section of Molecular Epidemiology, Leiden University Medical Center, Leiden, the Netherlands.
| | - Ewan R Pearson
- Division of Population Health & Genomics, School of Medicine, University of Dundee, Dundee, UK.
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Bouland GA, Beulens JWJ, Nap J, van der Slik AR, Zaldumbide A, 't Hart LM, Slieker RC. CONQUER: an interactive toolbox to understand functional consequences of GWAS hits. NAR Genom Bioinform 2021; 2:lqaa085. [PMID: 33575630 PMCID: PMC7671384 DOI: 10.1093/nargab/lqaa085] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2020] [Revised: 09/01/2020] [Accepted: 09/28/2020] [Indexed: 12/11/2022] Open
Abstract
Numerous large genome-wide association studies have been performed to understand the influence of genetics on traits. Many identified risk loci are in non-coding and intergenic regions, which complicates understanding how genes and their downstream pathways are influenced. An integrative data approach is required to understand the mechanism and consequences of identified risk loci. Here, we developed the R-package CONQUER. Data for SNPs of interest are acquired from static- and dynamic repositories (build GRCh38/hg38), including GTExPortal, Epigenomics Project, 4D genome database and genome browsers. All visualizations are fully interactive so that the user can immediately access the underlying data. CONQUER is a user-friendly tool to perform an integrative approach on multiple SNPs where risk loci are not seen as individual risk factors but rather as a network of risk factors.
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Affiliation(s)
- Gerard A Bouland
- Department of Cell and Chemical Biology, Leiden University Medical Center, 2333 ZA, Leiden, The Netherlands
| | - Joline W J Beulens
- Department of Epidemiology and Biostatistics, Amsterdam Public Health Institute, Amsterdam UMC, location VUmc, 1081 HV, Amsterdam, The Netherlands
| | - Joey Nap
- Department of Cell and Chemical Biology, Leiden University Medical Center, 2333 ZA, Leiden, The Netherlands
| | - Arno R van der Slik
- Department of Immunohematology and Blood Transfusion, Leiden University Medical Center, 2333 ZA, Leiden, The Netherlands
| | - Arnaud Zaldumbide
- Department of Cell and Chemical Biology, Leiden University Medical Center, 2333 ZA, Leiden, The Netherlands
| | - Leen M 't Hart
- Department of Cell and Chemical Biology, Leiden University Medical Center, 2333 ZA, Leiden, The Netherlands
| | - Roderick C Slieker
- Department of Cell and Chemical Biology, Leiden University Medical Center, 2333 ZA, Leiden, The Netherlands
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Canouil M, Bouland GA, Bonnefond A, Froguel P, ’t Hart LM, Slieker RC. NACHO: an R package for quality control of NanoString nCounter data. Bioinformatics 2019; 36:970-971. [PMID: 31504159 PMCID: PMC9883715 DOI: 10.1093/bioinformatics/btz647] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2019] [Revised: 07/24/2019] [Accepted: 08/14/2019] [Indexed: 02/02/2023] Open
Abstract
SUMMARY The NanoStringTM nCounter® is a platform for the targeted quantification of expression data in biofluids and tissues. While software by the manufacturer is available in addition to third parties packages, they do not provide a complete quality control (QC) pipeline. Here, we present NACHO ('NAnostring quality Control dasHbOard'), a comprehensive QC R-package. The package consists of three subsequent steps: summarize, visualize and normalize. The summarize function collects all the relevant data and stores it in a tidy format, the visualize function initiates a dashboard with plots of the relevant QC outcomes. It contains QC metrics that are measured by default by the manufacturer, but also calculates other insightful measures, including the scaling factors that are needed in the normalization step. In this normalization step, different normalization methods can be chosen to optimally preprocess data. Together, NACHO is a comprehensive method that optimizes insight and preprocessing of nCounter® data. AVAILABILITY AND IMPLEMENTATION NACHO is available as an R-package on CRAN and the development version on GitHub https://github.com/mcanouil/NACHO. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
| | | | - Amélie Bonnefond
- Université de Lille, CNRS, Institut Pasteur de Lille, UMR 8199 - EGID, F-59000 Lille, France,Department of Medicine, Section of Genomics of Common Disease, Imperial College London, London SW7 2AZ, UK
| | - Philippe Froguel
- Université de Lille, CNRS, Institut Pasteur de Lille, UMR 8199 - EGID, F-59000 Lille, France,Department of Medicine, Section of Genomics of Common Disease, Imperial College London, London SW7 2AZ, UK
| | - Leen M ’t Hart
- Department of Cell and Chemical Biology, Leiden University Medical Center, 2333 ZC Leiden, The Netherlands,Department of Epidemiology and Biostatistics, Amsterdam Public Health Institute, Amsterdam UMC, VU University Medical Center, Amsterdam 1081 HV, The Netherlands,Molecular Epidemiology Section, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden 2333 ZC, The Netherlands
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