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Liu C, Mentzelopoulou A, Hatzianestis IH, Tzagkarakis E, Skaltsogiannis V, Ma X, Michalopoulou VA, Romero-Campero FJ, Romero-Losada AB, Sarris PF, Marhavy P, Bölter B, Kanterakis A, Gutierrez-Beltran E, Moschou PN. A proxitome-RNA-capture approach reveals that processing bodies repress coregulated hub genes. Plant Cell 2024; 36:559-584. [PMID: 37971938 PMCID: PMC10896293 DOI: 10.1093/plcell/koad288] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 09/18/2023] [Accepted: 10/18/2023] [Indexed: 11/19/2023]
Abstract
Cellular condensates are usually ribonucleoprotein assemblies with liquid- or solid-like properties. Because these subcellular structures lack a delineating membrane, determining their compositions is difficult. Here we describe a proximity-biotinylation approach for capturing the RNAs of the condensates known as processing bodies (PBs) in Arabidopsis (Arabidopsis thaliana). By combining this approach with RNA detection, in silico, and high-resolution imaging approaches, we studied PBs under normal conditions and heat stress. PBs showed a much more dynamic RNA composition than the total transcriptome. RNAs involved in cell wall development and regeneration, plant hormonal signaling, secondary metabolism/defense, and RNA metabolism were enriched in PBs. RNA-binding proteins and the liquidity of PBs modulated RNA recruitment, while RNAs were frequently recruited together with their encoded proteins. In PBs, RNAs follow distinct fates: in small liquid-like PBs, RNAs get degraded while in more solid-like larger ones, they are stored. PB properties can be regulated by the actin-polymerizing SCAR (suppressor of the cyclic AMP)-WAVE (WASP family verprolin homologous) complex. SCAR/WAVE modulates the shuttling of RNAs between PBs and the translational machinery, thereby adjusting ethylene signaling. In summary, we provide an approach to identify RNAs in condensates that allowed us to reveal a mechanism for regulating RNA fate.
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Affiliation(s)
- Chen Liu
- Department of Biology, University of Crete, Heraklion 70013, Greece
- Department of Molecular Sciences, Uppsala BioCenter, Swedish University of Agricultural Sciences and Linnean Center for Plant Biology, Uppsala 75007, Sweden
| | - Andriani Mentzelopoulou
- Department of Biology, University of Crete, Heraklion 70013, Greece
- Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology-Hellas, Heraklion 70013, Greece
| | - Ioannis H Hatzianestis
- Department of Biology, University of Crete, Heraklion 70013, Greece
- Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology-Hellas, Heraklion 70013, Greece
| | | | - Vasileios Skaltsogiannis
- Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology-Hellas, Heraklion 70013, Greece
| | - Xuemin Ma
- Umeå Plant Science Centre (UPSC), Department of Forest Genetics and Plant Physiology, Swedish University of Agricultural Sciences (SLU), Umeå, Sweden
| | - Vassiliki A Michalopoulou
- Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology-Hellas, Heraklion 70013, Greece
| | - Francisco J Romero-Campero
- Department of Computer Science and Artificial Intelligence, Universidad de Sevilla, Avenida Reina Mercedes s/n, Seville 41012, Spain
- Instituto de Bioquímica Vegetal y Fotosíntesis, Universidad de Sevilla and Consejo Superior de Investigaciones Científicas (CSIC), Seville, Spain
| | - Ana B Romero-Losada
- Department of Computer Science and Artificial Intelligence, Universidad de Sevilla, Avenida Reina Mercedes s/n, Seville 41012, Spain
- Instituto de Bioquímica Vegetal y Fotosíntesis, Universidad de Sevilla and Consejo Superior de Investigaciones Científicas (CSIC), Seville, Spain
| | - Panagiotis F Sarris
- Department of Biology, University of Crete, Heraklion 70013, Greece
- Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology-Hellas, Heraklion 70013, Greece
- Biosciences, University of Exeter, Exeter, UK
| | - Peter Marhavy
- Umeå Plant Science Centre (UPSC), Department of Forest Genetics and Plant Physiology, Swedish University of Agricultural Sciences (SLU), Umeå, Sweden
| | - Bettina Bölter
- Ludwig Maximilians University Munich, Plant Biochemistry, Großhadernerstr. 2-4, Planegg-Martinsried 82152, Germany
| | - Alexandros Kanterakis
- Institute of Computer Science, Foundation for Research and Technology-Hellas, Heraklion, Greece
| | - Emilio Gutierrez-Beltran
- Instituto de Bioquímica Vegetal y Fotosíntesis, Universidad de Sevilla and Consejo Superior de Investigaciones Científicas (CSIC), Seville, Spain
- Departamento de Bioquímica Vegetal y Biología Molecular, Facultad de Biología, Universidad de Sevilla, Sevilla, Spain
| | - Panagiotis N Moschou
- Department of Biology, University of Crete, Heraklion 70013, Greece
- Department of Molecular Sciences, Uppsala BioCenter, Swedish University of Agricultural Sciences and Linnean Center for Plant Biology, Uppsala 75007, Sweden
- Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology-Hellas, Heraklion 70013, Greece
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2
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Potamias G, Gkoublia P, Kanterakis A. The two-stage molecular scenery of SARS-CoV-2 infection with implications to disease severity: An in-silico quest. Front Immunol 2023; 14:1251067. [PMID: 38077337 PMCID: PMC10699200 DOI: 10.3389/fimmu.2023.1251067] [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] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 10/30/2023] [Indexed: 12/18/2023] Open
Abstract
Introduction The two-stage molecular profile of the progression of SARS-CoV-2 (SCOV2) infection is explored in terms of five key biological/clinical questions: (a) does SCOV2 exhibits a two-stage infection profile? (b) SARS-CoV-1 (SCOV1) vs. SCOV2: do they differ? (c) does and how SCOV2 differs from Influenza/INFL infection? (d) does low viral-load and (e) does COVID-19 early host response relate to the two-stage SCOV2 infection profile? We provide positive answers to the above questions by analyzing the time-series gene-expression profiles of preserved cell-lines infected with SCOV1/2 or, the gene-expression profiles of infected individuals with different viral-loads levels and different host-response phenotypes. Methods Our analytical methodology follows an in-silico quest organized around an elaborate multi-step analysis pipeline including: (a) utilization of fifteen gene-expression datasets from NCBI's gene expression omnibus/GEO repository; (b) thorough designation of SCOV1/2 and INFL progression stages and COVID-19 phenotypes; (c) identification of differentially expressed genes (DEGs) and enriched biological processes and pathways that contrast and differentiate between different infection stages and phenotypes; (d) employment of a graph-based clustering process for the induction of coherent groups of networked genes as the representative core molecular fingerprints that characterize the different SCOV2 progression stages and the different COVID-19 phenotypes. In addition, relying on a sensibly selected set of induced fingerprint genes and following a Machine Learning approach, we devised and assessed the performance of different classifier models for the differentiation of acute respiratory illness/ARI caused by SCOV2 or other infections (diagnostic classifiers), as well as for the prediction of COVID-19 disease severity (prognostic classifiers), with quite encouraging results. Results The central finding of our experiments demonstrates the down-regulation of type-I interferon genes (IFN-1), interferon induced genes (ISGs) and fundamental innate immune and defense biological processes and molecular pathways during the early SCOV2 infection stages, with the inverse to hold during the later ones. It is highlighted that upregulation of these genes and pathways early after infection may prove beneficial in preventing subsequent uncontrolled hyperinflammatory and potentially lethal events. Discussion The basic aim of our study was to utilize in an intuitive, efficient and productive way the most relevant and state-of-the-art bioinformatics methods to reveal the core molecular mechanisms which govern the progression of SCOV2 infection and the different COVID-19 phenotypes.
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Affiliation(s)
- George Potamias
- Computational Biomedicine Laboratory (CBML), Institute of Computer Science, Foundation for Research and Technology-Hellas (FORTH), Heraklion, Greece
| | - Polymnia Gkoublia
- Computational Biomedicine Laboratory (CBML), Institute of Computer Science, Foundation for Research and Technology-Hellas (FORTH), Heraklion, Greece
- Graduate Bioinformatics Program, School of Medicine, University of Crete, Heraklion, Greece
| | - Alexandros Kanterakis
- Computational Biomedicine Laboratory (CBML), Institute of Computer Science, Foundation for Research and Technology-Hellas (FORTH), Heraklion, Greece
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Mathioudakis L, Dimovasili C, Bourbouli M, Latsoudis H, Kokosali E, Gouna G, Vogiatzi E, Basta M, Kapetanaki S, Panagiotakis S, Kanterakis A, Boumpas D, Lionis C, Plaitakis A, Simos P, Vgontzas A, Kafetzopoulos D, Zaganas I. Study of Alzheimer's disease- and frontotemporal dementia-associated genes in the Cretan Aging Cohort. Neurobiol Aging 2023; 123:111-128. [PMID: 36117051 DOI: 10.1016/j.neurobiolaging.2022.07.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Revised: 07/03/2022] [Accepted: 07/04/2022] [Indexed: 02/02/2023]
Abstract
Using exome sequencing, we analyzed 196 participants of the Cretan Aging Cohort (CAC; 95 with Alzheimer's disease [AD], 20 with mild cognitive impairment [MCI], and 81 cognitively normal controls). The APOE ε4 allele was more common in AD patients (23.2%) than in controls (7.4%; p < 0.01) and the PSEN2 p.Arg29His and p.Cys391Arg variants were found in 3 AD and 1 MCI patient, respectively. Also, we found the frontotemporal dementia (FTD)-associated TARDBP gene p.Ile383Val variant in 2 elderly patients diagnosed with AD and in 2 patients, non CAC members, with the amyotrophic lateral sclerosis/FTD phenotype. Furthermore, the p.Ser498Ala variant in the positively selected GLUD2 gene was less frequent in AD patients (2.11%) than in controls (16%; p < 0.01), suggesting a possible protective effect. While the same trend was found in another local replication cohort (n = 406) and in section of the ADNI cohort (n = 808), this finding did not reach statistical significance and therefore it should be considered preliminary. Our results attest to the value of genetic testing to study aged adults with AD phenotype.
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Affiliation(s)
- Lambros Mathioudakis
- University of Crete, Medical School, Neurology/Neurogenetics Laboratory, Heraklion, Crete, Greece
| | - Christina Dimovasili
- University of Crete, Medical School, Neurology/Neurogenetics Laboratory, Heraklion, Crete, Greece
| | - Mara Bourbouli
- University of Crete, Medical School, Neurology/Neurogenetics Laboratory, Heraklion, Crete, Greece
| | - Helen Latsoudis
- Minotech Genomics Facility, Institute of Molecular Biology and Biotechnology (IMBB-FORTH), Heraklion, Crete, Greece
| | - Evgenia Kokosali
- University of Crete, Medical School, Neurology/Neurogenetics Laboratory, Heraklion, Crete, Greece
| | - Garyfallia Gouna
- University of Crete, Medical School, Neurology/Neurogenetics Laboratory, Heraklion, Crete, Greece
| | - Emmanouella Vogiatzi
- University of Crete, Medical School, Neurology/Neurogenetics Laboratory, Heraklion, Crete, Greece
| | - Maria Basta
- University of Crete, Medical School, Psychiatry Department, Heraklion, Crete, Greece
| | - Stefania Kapetanaki
- University of Crete, Medical School, Neurology/Neurogenetics Laboratory, Heraklion, Crete, Greece
| | - Simeon Panagiotakis
- University of Crete, Medical School, Internal Medicine Department, Heraklion, Crete, Greece
| | - Alexandros Kanterakis
- Computational BioMedicine Laboratory, Institute of Computer Science, Foundation for Research and Technology - Hellas (ICS-FORTH), Heraklion, Crete, Greece
| | - Dimitrios Boumpas
- University of Crete, Medical School, Internal Medicine Department, Heraklion, Crete, Greece
| | - Christos Lionis
- University of Crete, Medical School, Clinic of Social and Family Medicine, Heraklion, Crete, Greece
| | - Andreas Plaitakis
- University of Crete, Medical School, Neurology/Neurogenetics Laboratory, Heraklion, Crete, Greece
| | - Panagiotis Simos
- University of Crete, Medical School, Psychiatry Department, Heraklion, Crete, Greece
| | - Alexandros Vgontzas
- University of Crete, Medical School, Psychiatry Department, Heraklion, Crete, Greece
| | - Dimitrios Kafetzopoulos
- Minotech Genomics Facility, Institute of Molecular Biology and Biotechnology (IMBB-FORTH), Heraklion, Crete, Greece
| | - Ioannis Zaganas
- University of Crete, Medical School, Neurology/Neurogenetics Laboratory, Heraklion, Crete, Greece.
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4
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Latsoudis H, Stylianakis E, Mavroudi I, Kanterakis A, Pavlidis P, Georgopoulou A, Batsali A, Gontika I, Fragiadaki I, Zamanakou M, Germenis AE, Papadaki HA. Significance of regional population HLA immunogenetic datasets in the efficacy of umbilical cord blood banks and marrow donor registries: a study of Cretan HLA genetic diversity. Cytotherapy 2021; 24:183-192. [PMID: 34465516 DOI: 10.1016/j.jcyt.2021.07.010] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Revised: 07/05/2021] [Accepted: 07/15/2021] [Indexed: 11/28/2022]
Abstract
BACKGROUND AIMS The high genetic diversity of HLA across populations significantly confines the effectiveness of a donor or umbilical cord blood search for allogeneic hematopoietic stem cell transplantation (HSCT). This study aims to probe the HLA immunogenetic profile of the population of Crete, a Greek region with specific geographic and historical characteristics, and to investigate potential patterns in HLA distribution following comparison with the Deutsche Knochenmarkspenderdatei (DKMS) donor registry. It also aims to highlight the importance of regional public cord blood banks (PCBBs) in fulfilling HSCT needs, especially in countries with significant genetic diversity. METHODS A cohort of 1835 samples representative of the Cretan population was typed for HLA class I (HLA-A, HLA-B, HLA-C) and class II (HLA-DRB1, HLA-DQB1, HLA-DPB1) loci by high-resolution second field next-generation sequencing. Data were compared with the respective HLA profiles of 12 DKMS populations (n = 20 032). Advanced statistical and bioinformatics methods were employed to assess specific intra- and inter-population genetic indexes associated with the regional and geographic distribution of HLA alleles and haplotypes. RESULTS A considerable HLA allelic and haplotypic diversity was identified among the Cretan samples and between the latter and the pooled DKMS cohort. Even though the HLA allele and haplotype frequency distribution was similar to regions of close geographic proximity to Crete, a clinal distribution pattern from the northern to southern regions was identified. Significant differences were also observed between Crete and the Greek population of DKMS. CONCLUSIONS This study provides an in-depth characterization of the HLA immunogenetic profile in Crete and reveals the importance of demographic history in HLA heterogeneity and donor selection. The novel HLA allele and haplotype frequency comparative data between the Cretan and other European populations signify the importance of regional PCBBs in prioritizing HLA diversity to efficiently promote the HSCT program at the national level and beyond.
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Affiliation(s)
- Helen Latsoudis
- Institute of Computer Sciences, Foundation for Research and Technology Hellas, Heraklion, Greece
| | - Emmanouil Stylianakis
- Institute of Computer Sciences, Foundation for Research and Technology Hellas, Heraklion, Greece
| | - Irene Mavroudi
- Public Cord Blood Bank of Crete, Department of Hematology, University Hospital of Heraklion, Heraklion, Greece; Haemopoiesis Research Laboratory, School of Medicine, University of Crete, Heraklion, Greece
| | - Alexandros Kanterakis
- Institute of Computer Sciences, Foundation for Research and Technology Hellas, Heraklion, Greece
| | - Pavlos Pavlidis
- Institute of Computer Sciences, Foundation for Research and Technology Hellas, Heraklion, Greece
| | - Anthie Georgopoulou
- Public Cord Blood Bank of Crete, Department of Hematology, University Hospital of Heraklion, Heraklion, Greece; Haemopoiesis Research Laboratory, School of Medicine, University of Crete, Heraklion, Greece
| | - Aristea Batsali
- Public Cord Blood Bank of Crete, Department of Hematology, University Hospital of Heraklion, Heraklion, Greece; Haemopoiesis Research Laboratory, School of Medicine, University of Crete, Heraklion, Greece
| | - Ioanna Gontika
- Public Cord Blood Bank of Crete, Department of Hematology, University Hospital of Heraklion, Heraklion, Greece; Haemopoiesis Research Laboratory, School of Medicine, University of Crete, Heraklion, Greece
| | - Irene Fragiadaki
- Public Cord Blood Bank of Crete, Department of Hematology, University Hospital of Heraklion, Heraklion, Greece; Haemopoiesis Research Laboratory, School of Medicine, University of Crete, Heraklion, Greece
| | | | | | - Helen A Papadaki
- Public Cord Blood Bank of Crete, Department of Hematology, University Hospital of Heraklion, Heraklion, Greece; Haemopoiesis Research Laboratory, School of Medicine, University of Crete, Heraklion, Greece.
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5
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Fragiadaki I, Latsoudis H, Zamanakou M, Kanterakis A, Papadaki HA. Two novel HLA-A alleles, HLA-A*03:399 and -A*24:17:01:02, detected in inhabitants from the island of Crete. HLA 2020; 97:353-356. [PMID: 33241918 DOI: 10.1111/tan.14156] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2020] [Revised: 11/20/2020] [Accepted: 11/23/2020] [Indexed: 11/30/2022]
Abstract
Characterization of two novel HLA-A alleles in two Greek individuals of Cretan origin.
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Affiliation(s)
- Irene Fragiadaki
- Public Cord Blood Bank of Crete, Department of Haematology, University General Hospital of Heraklion, Heraklion, Greece.,Haemopoiesis Research Laboratory, School of Medicine, University of Crete, Heraklion, Greece
| | - Helen Latsoudis
- Information Systems Laboratory, Institute of Computer Sciences, Foundation for Research and Technology Hellas, Heraklion, Greece
| | | | - Alexandros Kanterakis
- Computational Biomedicine Laboratory, Institute of Computer Sciences, Foundation for Research and Technology Hellas, Heraklion, Greece
| | - Helen A Papadaki
- Public Cord Blood Bank of Crete, Department of Haematology, University General Hospital of Heraklion, Heraklion, Greece.,Haemopoiesis Research Laboratory, School of Medicine, University of Crete, Heraklion, Greece
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6
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Mavroudi I, Latsoudis H, Zamanakou M, Kanterakis A, Papadaki HA. Two novel HLA-DRB1 alleles detected in inhabitants from the island of Crete. HLA 2020; 97:163-166. [PMID: 33124731 DOI: 10.1111/tan.14126] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2020] [Revised: 10/23/2020] [Accepted: 10/26/2020] [Indexed: 11/29/2022]
Abstract
Characterization of the novel HLA-DRB1*04:311 and HLA-DRB1*11:277 alleles in two Greek individuals of Cretan origin.
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Affiliation(s)
- Irene Mavroudi
- Public Cord Blood Bank of Crete, Department of Haematology, University General Hospital of Heraklion, Crete, Greece.,Haemopoiesis Research Laboratory, School of Medicine, University of Crete, Crete, Greece
| | - Helen Latsoudis
- Information Systems Laboratory, Institute of Computer Sciences, Foundation for Research and Technology Hellas, Heraklion, Greece
| | | | - Alexandros Kanterakis
- Computational Biomedicine Laboratory, Institute of Computer Sciences, Foundation for Research and Technology Hellas, Heraklion, Greece
| | - Helen A Papadaki
- Public Cord Blood Bank of Crete, Department of Haematology, University General Hospital of Heraklion, Crete, Greece.,Haemopoiesis Research Laboratory, School of Medicine, University of Crete, Crete, Greece
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7
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Kounelis F, Kanterakis A, Kanavos A, Pandi MT, Kordou Z, Manusama O, Vonitsanos G, Katsila T, Tsermpini EE, Lauschke VM, Koromina M, van der Spek PJ, Patrinos GP. Documentation of clinically relevant genomic biomarker allele frequencies in the next-generation FINDbase worldwide database. Hum Mutat 2020; 41:1112-1122. [PMID: 32248568 DOI: 10.1002/humu.24018] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2020] [Revised: 02/25/2020] [Accepted: 03/22/2020] [Indexed: 01/24/2023]
Abstract
FINDbase (http://www.findbase.org) is a comprehensive data resource recording the prevalence of clinically relevant genomic variants in various populations worldwide, such as pathogenic variants underlying genetic disorders as well as pharmacogenomic biomarkers that can guide drug treatment. Here, we report significant new developments and technological advancements in the database architecture, leading to a completely revamped database structure, querying interface, accompanied with substantial extensions of data content and curation. In particular, the FINDbase upgrade further improves the user experience by introducing responsive features that support a wide variety of mobile and stationary devices, while enhancing computational runtime due to the use of a modern Javascript framework such as ReactJS. Data collection is significantly enriched, with the data records being divided in a Public and Private version, the latter being accessed on the basis of data contribution, according to the microattribution approach, while the front end was redesigned to support the new functionalities and querying tools. The abovementioned updates further enhance the impact of FINDbase, improve the overall user experience, facilitate further data sharing by microattribution, and strengthen the role of FINDbase as a key resource for personalized medicine applications and personalized public health.
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Affiliation(s)
- Fotios Kounelis
- Department of Computer Engineering and Informatics, Faculty of Engineering, University of Patras, Patras, Greece
| | - Alexandros Kanterakis
- Biomedical Informatics Laboratory, Foundation of Research and Technology Hellas, Heraklion, Greece.,Department of Pharmacy, School of Health Sciences, University of Patras, Patras, Greece
| | - Andreas Kanavos
- Department of Computer Engineering and Informatics, Faculty of Engineering, University of Patras, Patras, Greece.,Department of Pharmacy, School of Health Sciences, University of Patras, Patras, Greece
| | - Maria-Theodora Pandi
- Department of Pharmacy, School of Health Sciences, University of Patras, Patras, Greece.,Bioinformatics Unit, Department of Pathology, Faculty of Medicine and Health Sciences, Medical Center, Erasmus University, Rotterdam, The Netherlands
| | - Zoe Kordou
- Department of Pharmacy, School of Health Sciences, University of Patras, Patras, Greece
| | - Olivia Manusama
- Department of Immunology, Faculty of Medicine and Health Sciences, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Gerasimos Vonitsanos
- Department of Computer Engineering and Informatics, Faculty of Engineering, University of Patras, Patras, Greece.,Department of Pharmacy, School of Health Sciences, University of Patras, Patras, Greece
| | - Theodora Katsila
- Department of Pharmacy, School of Health Sciences, University of Patras, Patras, Greece
| | | | - Volker M Lauschke
- Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden
| | - Maria Koromina
- Department of Pharmacy, School of Health Sciences, University of Patras, Patras, Greece
| | - Peter J van der Spek
- Bioinformatics Unit, Department of Pathology, Faculty of Medicine and Health Sciences, Medical Center, Erasmus University, Rotterdam, The Netherlands
| | - George P Patrinos
- Department of Pharmacy, School of Health Sciences, University of Patras, Patras, Greece.,Bioinformatics Unit, Department of Pathology, Faculty of Medicine and Health Sciences, Medical Center, Erasmus University, Rotterdam, The Netherlands.,Zayed Center of Health Sciences, United Arab Emirates University, Al-Ain, United Arab Emirates.,Department of Pathology, College of Medicine and Health Sciences, United Arab Emirates University, Al-Ain, United Arab Emirates
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Kyriakis D, Kanterakis A, Manousaki T, Tsakogiannis A, Tsagris M, Tsamardinos I, Papaharisis L, Chatziplis D, Potamias G, Tsigenopoulos CS. Scanning of Genetic Variants and Genetic Mapping of Phenotypic Traits in Gilthead Sea Bream Through ddRAD Sequencing. Front Genet 2019; 10:675. [PMID: 31447879 PMCID: PMC6691846 DOI: 10.3389/fgene.2019.00675] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [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: 10/19/2018] [Accepted: 06/27/2019] [Indexed: 12/31/2022] Open
Abstract
Gilthead sea bream (Sparus aurata) is a teleost of considerable economic importance in Southern European aquaculture. The aquaculture industry shows a growing interest in the application of genetic methods that can locate phenotype–genotype associations with high economic impact. Through selective breeding, the aquaculture industry can exploit this information to maximize the financial yield. Here, we present a Genome Wide Association Study (GWAS) of 112 samples belonging to seven different sea bream families collected from a Greek commercial aquaculture company. Through double digest Random Amplified DNA (ddRAD) Sequencing, we generated a per-sample genetic profile consisting of 2,258 high-quality Single Nucleotide Polymorphisms (SNPs). These profiles were tested for association with four phenotypes of major financial importance: Fat, Weight, Tag Weight, and the Length to Width ratio. We applied two methods of association analysis. The first is the typical single-SNP to phenotype test, and the second is a feature selection (FS) method through two novel algorithms that are employed for the first time in aquaculture genomics and produce groups with multiple SNPs associated to a phenotype. In total, we identified 9 single SNPs and 6 groups of SNPs associated with weight-related phenotypes (Weight and Tag Weight), 2 groups associated with Fat, and 16 groups associated with the Length to Width ratio. Six identified loci (Chr4:23265532, Chr6:12617755, Chr:8:11613979, Chr13:1098152, Chr15:3260819, and Chr22:14483563) were present in genes associated with growth in other teleosts or even mammals, such as semaphorin-3A and neurotrophin-3. These loci are strong candidates for future studies that will help us unveil the genetic mechanisms underlying growth and improve the sea bream aquaculture productivity by providing genomic anchors for selection programs.
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Affiliation(s)
- Dimitrios Kyriakis
- School of Medicine, University of Crete, Heraklion, Greece.,Foundation for Research and Technology-Hellas (FORTH), Heraklion, Greece.,Institute of Marine Biology, Biotechnology and Aquaculture (IMBBC), Hellenic Center for Marine Research (HCMR) Crete, Greece
| | | | - Tereza Manousaki
- Institute of Marine Biology, Biotechnology and Aquaculture (IMBBC), Hellenic Center for Marine Research (HCMR) Crete, Greece
| | - Alexandros Tsakogiannis
- Institute of Marine Biology, Biotechnology and Aquaculture (IMBBC), Hellenic Center for Marine Research (HCMR) Crete, Greece
| | - Michalis Tsagris
- Deparment of Economics, University of Crete, Gallos Campus, Rethymnon, Greece
| | - Ioannis Tsamardinos
- Department of Computer Science, University of Crete, Voutes Campus, Heraklion, Greece
| | | | - Dimitris Chatziplis
- Department of Agriculture Technology, Alexander Technological Education Institute of Thessaloniki, Thessaloniki, Greece
| | - George Potamias
- Foundation for Research and Technology-Hellas (FORTH), Heraklion, Greece
| | - Costas S Tsigenopoulos
- Institute of Marine Biology, Biotechnology and Aquaculture (IMBBC), Hellenic Center for Marine Research (HCMR) Crete, Greece
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9
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Lakiotaki K, Kanterakis A, Kartsaki E, Katsila T, Patrinos GP, Potamias G. Exploring public genomics data for population pharmacogenomics. PLoS One 2017; 12:e0182138. [PMID: 28771511 PMCID: PMC5542428 DOI: 10.1371/journal.pone.0182138] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [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] [Subscribe] [Scholar Register] [Received: 04/12/2017] [Accepted: 07/12/2017] [Indexed: 12/28/2022] Open
Abstract
Racial and ethnic differences in drug responses are now well studied and documented. Pharmacogenomics research seeks to unravel the genetic underpinnings of inter-individual variability with the aim of tailored-made theranostics and therapeutics. Taking into account the differential expression of pharmacogenes coding for key metabolic enzymes and transporters that affect drug pharmacokinetics and pharmacodynamics, we advise that data interpretation and analysis need to occur in light of geographical ancestry, if implications for drug development and global health are to be considered. Herein, we exploit ePGA, a web-based electronic Pharmacogenomics Assistant and publicly available genetic data from the 1000 Genomes Project to explore genotype to phenotype associations among the 1000 Genomes Project populations.
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Affiliation(s)
- Kleanthi Lakiotaki
- Institute of Computer Science, Foundation for Research and Technology, Heraklion, Crete, Greece
| | - Alexandros Kanterakis
- Institute of Computer Science, Foundation for Research and Technology, Heraklion, Crete, Greece
| | - Evgenia Kartsaki
- Institute of Computer Science, Foundation for Research and Technology, Heraklion, Crete, Greece
| | - Theodora Katsila
- Department of Pharmacy, School of Health Sciences, University of Patras, Rio, Patras, Greece
| | - George P. Patrinos
- Department of Pharmacy, School of Health Sciences, University of Patras, Rio, Patras, Greece
- Department of Pathology, College of Medicine and Health Sciences, United Arab Emirates University, Al-Ain, UAE
| | - George Potamias
- Institute of Computer Science, Foundation for Research and Technology, Heraklion, Crete, Greece
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10
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Koumakis L, Kanterakis A, Kartsaki E, Chatzimina M, Zervakis M, Tsiknakis M, Vassou D, Kafetzopoulos D, Marias K, Moustakis V, Potamias G. MinePath: Mining for Phenotype Differential Sub-paths in Molecular Pathways. PLoS Comput Biol 2016; 12:e1005187. [PMID: 27832067 PMCID: PMC5104320 DOI: 10.1371/journal.pcbi.1005187] [Citation(s) in RCA: 18] [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] [Subscribe] [Scholar Register] [Received: 02/29/2016] [Accepted: 10/10/2016] [Indexed: 01/04/2023] Open
Abstract
Pathway analysis methodologies couple traditional gene expression analysis with knowledge encoded in established molecular pathway networks, offering a promising approach towards the biological interpretation of phenotype differentiating genes. Early pathway analysis methodologies, named as gene set analysis (GSA), view pathways just as plain lists of genes without taking into account either the underlying pathway network topology or the involved gene regulatory relations. These approaches, even if they achieve computational efficiency and simplicity, consider pathways that involve the same genes as equivalent in terms of their gene enrichment characteristics. Most recent pathway analysis approaches take into account the underlying gene regulatory relations by examining their consistency with gene expression profiles and computing a score for each profile. Even with this approach, assessing and scoring single-relations limits the ability to reveal key gene regulation mechanisms hidden in longer pathway sub-paths. We introduce MinePath, a pathway analysis methodology that addresses and overcomes the aforementioned problems. MinePath facilitates the decomposition of pathways into their constituent sub-paths. Decomposition leads to the transformation of single-relations to complex regulation sub-paths. Regulation sub-paths are then matched with gene expression sample profiles in order to evaluate their functional status and to assess phenotype differential power. Assessment of differential power supports the identification of the most discriminant profiles. In addition, MinePath assess the significance of the pathways as a whole, ranking them by their p-values. Comparison results with state-of-the-art pathway analysis systems are indicative for the soundness and reliability of the MinePath approach. In contrast with many pathway analysis tools, MinePath is a web-based system (www.minepath.org) offering dynamic and rich pathway visualization functionality, with the unique characteristic to color regulatory relations between genes and reveal their phenotype inclination. This unique characteristic makes MinePath a valuable tool for in silico molecular biology experimentation as it serves the biomedical researchers' exploratory needs to reveal and interpret the regulatory mechanisms that underlie and putatively govern the expression of target phenotypes.
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Affiliation(s)
- Lefteris Koumakis
- Computational BioMedicine Laboratory (CBML), Institute of Computers Science (ICS), Foundation for Research and Technology-Hellas (FORTH), Heraklion, Crete, Greece
| | - Alexandros Kanterakis
- Computational BioMedicine Laboratory (CBML), Institute of Computers Science (ICS), Foundation for Research and Technology-Hellas (FORTH), Heraklion, Crete, Greece
| | - Evgenia Kartsaki
- Computational BioMedicine Laboratory (CBML), Institute of Computers Science (ICS), Foundation for Research and Technology-Hellas (FORTH), Heraklion, Crete, Greece
| | - Maria Chatzimina
- Computational BioMedicine Laboratory (CBML), Institute of Computers Science (ICS), Foundation for Research and Technology-Hellas (FORTH), Heraklion, Crete, Greece
| | - Michalis Zervakis
- School of Electrical and Computer Engineering, Technical University of Crete, Greece
| | - Manolis Tsiknakis
- Computational BioMedicine Laboratory (CBML), Institute of Computers Science (ICS), Foundation for Research and Technology-Hellas (FORTH), Heraklion, Crete, Greece
- Department of Informatics Engineering, Technological Educational Institute of Crete, Greece
| | - Despoina Vassou
- Institute of Molecular Biology & Biotechnology, FORTH, Heraklion, Crete, Greece
| | | | - Kostas Marias
- Computational BioMedicine Laboratory (CBML), Institute of Computers Science (ICS), Foundation for Research and Technology-Hellas (FORTH), Heraklion, Crete, Greece
| | - Vassilis Moustakis
- School of Production Engineering & Management, Technical University of Crete, Greece
| | - George Potamias
- Computational BioMedicine Laboratory (CBML), Institute of Computers Science (ICS), Foundation for Research and Technology-Hellas (FORTH), Heraklion, Crete, Greece
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11
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Kanterakis A, Kuiper J, Potamias G, Swertz MA. PyPedia: using the wiki paradigm as crowd sourcing environment for bioinformatics protocols. Source Code Biol Med 2015; 10:14. [PMID: 26587054 PMCID: PMC4652372 DOI: 10.1186/s13029-015-0042-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/23/2015] [Accepted: 10/20/2015] [Indexed: 11/10/2022]
Abstract
Background Today researchers can choose from many bioinformatics protocols for all types of life sciences research, computational environments and coding languages. Although the majority of these are open source, few of them possess all virtues to maximize reuse and promote reproducible science. Wikipedia has proven a great tool to disseminate information and enhance collaboration between users with varying expertise and background to author qualitative content via crowdsourcing. However, it remains an open question whether the wiki paradigm can be applied to bioinformatics protocols. Results We piloted PyPedia, a wiki where each article is both implementation and documentation of a bioinformatics computational protocol in the python language. Hyperlinks within the wiki can be used to compose complex workflows and induce reuse. A RESTful API enables code execution outside the wiki. Initial content of PyPedia contains articles for population statistics, bioinformatics format conversions and genotype imputation. Use of the easy to learn wiki syntax effectively lowers the barriers to bring expert programmers and less computer savvy researchers on the same page. Conclusions PyPedia demonstrates how wiki can provide a collaborative development, sharing and even execution environment for biologists and bioinformaticians that complement existing resources, useful for local and multi-center research teams. Availability PyPedia is available online at: http://www.pypedia.com. The source code and installation instructions are available at: https://github.com/kantale/PyPedia_server. The PyPedia python library is available at: https://github.com/kantale/pypedia. PyPedia is open-source, available under the BSD 2-Clause License. Electronic supplementary material The online version of this article (doi:10.1186/s13029-015-0042-6) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Alexandros Kanterakis
- University of Groningen, University Medical Center Groningen, Genomics Coordination Center, Postbus 30 001, Groningen, 9700 RB The Netherlands ; Institute of Computer Science, Foundation for Research and Technology Hellas (FORTH), Nikolaou Plastira 100, Heraklion, 71110 Greece
| | - Joël Kuiper
- University of Groningen, University Medical Center Groningen, Genomics Coordination Center, Postbus 30 001, Groningen, 9700 RB The Netherlands
| | - George Potamias
- Institute of Computer Science, Foundation for Research and Technology Hellas (FORTH), Nikolaou Plastira 100, Heraklion, 71110 Greece
| | - Morris A Swertz
- University of Groningen, University Medical Center Groningen, Genomics Coordination Center, Postbus 30 001, Groningen, 9700 RB The Netherlands
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12
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Kanterakis A, Deelen P, van Dijk F, Byelas H, Dijkstra M, Swertz MA. Molgenis-impute: imputation pipeline in a box. BMC Res Notes 2015; 8:359. [PMID: 26286716 PMCID: PMC4541731 DOI: 10.1186/s13104-015-1309-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [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: 07/07/2014] [Accepted: 07/30/2015] [Indexed: 12/12/2022] Open
Abstract
Background Genotype imputation is an important procedure in current genomic analysis such as genome-wide association studies, meta-analyses and fine mapping. Although high quality tools are available that perform the steps of this process, considerable effort and expertise is required to set up and run a best practice imputation pipeline, particularly for larger genotype datasets, where imputation has to scale out in parallel on computer clusters. Results Here we present MOLGENIS-impute, an ‘imputation in a box’ solution that seamlessly and transparently automates the set up and running of all the steps of the imputation process. These steps include genome build liftover (liftovering), genotype phasing with SHAPEIT2, quality control, sample and chromosomal chunking/merging, and imputation with IMPUTE2. MOLGENIS-impute builds on MOLGENIS-compute, a simple pipeline management platform for submission and monitoring of bioinformatics tasks in High Performance Computing (HPC) environments like local/cloud servers, clusters and grids. All the required tools, data and scripts are downloaded and installed in a single step. Researchers with diverse backgrounds and expertise have tested MOLGENIS-impute on different locations and imputed over 30,000 samples so far using the 1,000 Genomes Project and new Genome of the Netherlands data as the imputation reference. The tests have been performed on PBS/SGE clusters, cloud VMs and in a grid HPC environment. Conclusions MOLGENIS-impute gives priority to the ease of setting up, configuring and running an imputation. It has minimal dependencies and wraps the pipeline in a simple command line interface, without sacrificing flexibility to adapt or limiting the options of underlying imputation tools. It does not require knowledge of a workflow system or programming, and is targeted at researchers who just want to apply best practices in imputation via simple commands. It is built on the MOLGENIS compute workflow framework to enable customization with additional computational steps or it can be included in other bioinformatics pipelines. It is available as open source from: https://github.com/molgenis/molgenis-imputation. Electronic supplementary material The online version of this article (doi:10.1186/s13104-015-1309-3) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Alexandros Kanterakis
- Department of Genetics, Genomics Coordination Center, University Medical Center Groningen and University of Groningen, Genetics, UMCG, PO Box 30 001, 9700 RB, Groningen, The Netherlands.
| | - Patrick Deelen
- Department of Genetics, Genomics Coordination Center, University Medical Center Groningen and University of Groningen, Genetics, UMCG, PO Box 30 001, 9700 RB, Groningen, The Netherlands.
| | - Freerk van Dijk
- Department of Genetics, Genomics Coordination Center, University Medical Center Groningen and University of Groningen, Genetics, UMCG, PO Box 30 001, 9700 RB, Groningen, The Netherlands.
| | - Heorhiy Byelas
- Department of Genetics, Genomics Coordination Center, University Medical Center Groningen and University of Groningen, Genetics, UMCG, PO Box 30 001, 9700 RB, Groningen, The Netherlands.
| | - Martijn Dijkstra
- Department of Genetics, Genomics Coordination Center, University Medical Center Groningen and University of Groningen, Genetics, UMCG, PO Box 30 001, 9700 RB, Groningen, The Netherlands.
| | - Morris A Swertz
- Department of Genetics, Genomics Coordination Center, University Medical Center Groningen and University of Groningen, Genetics, UMCG, PO Box 30 001, 9700 RB, Groningen, The Netherlands.
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13
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van Leeuwen EM, Kanterakis A, Deelen P, Kattenberg MV, Slagboom PE, de Bakker PIW, Wijmenga C, Swertz MA, Boomsma DI, van Duijn CM, Karssen LC, Hottenga JJ. Population-specific genotype imputations using minimac or IMPUTE2. Nat Protoc 2015; 10:1285-96. [PMID: 26226460 DOI: 10.1038/nprot.2015.077] [Citation(s) in RCA: 66] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
In order to meaningfully analyze common and rare genetic variants, results from genome-wide association studies (GWASs) of multiple cohorts need to be combined in a meta-analysis in order to obtain enough power. This requires all cohorts to have the same single-nucleotide polymorphisms (SNPs) in their GWASs. To this end, genotypes that have not been measured in a given cohort can be imputed on the basis of a set of reference haplotypes. This protocol provides guidelines for performing imputations with two widely used tools: minimac and IMPUTE2. These guidelines were developed and used by the Genome of the Netherlands (GoNL) consortium, which has created a population-specific reference panel for genetic imputations and used this reference to impute various Dutch biobanks. We also describe several factors that might influence the final imputation quality. This protocol, which has been used by the largest Dutch biobanks, should take approximately several days, depending on the sample size of the biobank and the computer resources available.
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Affiliation(s)
| | - Alexandros Kanterakis
- 1] University of Groningen, University Medical Center Groningen, Genomics Coordination Center, Groningen, the Netherlands. [2] University of Groningen, University Medical Center Groningen, Department of Genetics, Groningen, the Netherlands
| | - Patrick Deelen
- 1] University of Groningen, University Medical Center Groningen, Genomics Coordination Center, Groningen, the Netherlands. [2] University of Groningen, University Medical Center Groningen, Department of Genetics, Groningen, the Netherlands
| | | | | | - P Eline Slagboom
- 1] Department of Molecular Epidemiology, Leiden University Medical Center, Leiden, the Netherlands. [2] Netherlands Consortium for Healthy Ageing, Leiden University Medical Center, Leiden, the Netherlands
| | - Paul I W de Bakker
- 1] Department of Medical Genetics, University Medical Center Utrecht, Utrecht, the Netherlands. [2] Department of Epidemiology, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Cisca Wijmenga
- 1] University of Groningen, University Medical Center Groningen, Genomics Coordination Center, Groningen, the Netherlands. [2] University of Groningen, University Medical Center Groningen, Department of Genetics, Groningen, the Netherlands
| | - Morris A Swertz
- 1] University of Groningen, University Medical Center Groningen, Genomics Coordination Center, Groningen, the Netherlands. [2] University of Groningen, University Medical Center Groningen, Department of Genetics, Groningen, the Netherlands
| | - Dorret I Boomsma
- Department of Biological Psychology, VU University, Amsterdam, the Netherlands
| | | | - Lennart C Karssen
- 1] Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands. [2] PolyOmica, Groningen, the Netherlands
| | - Jouke Jan Hottenga
- Department of Biological Psychology, VU University, Amsterdam, the Netherlands
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14
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van Leeuwen EM, Karssen LC, Deelen J, Isaacs A, Medina-Gomez C, Mbarek H, Kanterakis A, Trompet S, Postmus I, Verweij N, van Enckevort DJ, Huffman JE, White CC, Feitosa MF, Bartz TM, Manichaikul A, Joshi PK, Peloso GM, Deelen P, van Dijk F, Willemsen G, de Geus EJ, Milaneschi Y, Penninx BWJH, Francioli LC, Menelaou A, Pulit SL, Rivadeneira F, Hofman A, Oostra BA, Franco OH, Mateo Leach I, Beekman M, de Craen AJM, Uh HW, Trochet H, Hocking LJ, Porteous DJ, Sattar N, Packard CJ, Buckley BM, Brody JA, Bis JC, Rotter JI, Mychaleckyj JC, Campbell H, Duan Q, Lange LA, Wilson JF, Hayward C, Polasek O, Vitart V, Rudan I, Wright AF, Rich SS, Psaty BM, Borecki IB, Kearney PM, Stott DJ, Adrienne Cupples L, Jukema JW, van der Harst P, Sijbrands EJ, Hottenga JJ, Uitterlinden AG, Swertz MA, van Ommen GJB, de Bakker PIW, Eline Slagboom P, Boomsma DI, Wijmenga C, van Duijn CM. Genome of The Netherlands population-specific imputations identify an ABCA6 variant associated with cholesterol levels. Nat Commun 2015; 6:6065. [PMID: 25751400 PMCID: PMC4366498 DOI: 10.1038/ncomms7065] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.7] [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: 07/09/2014] [Accepted: 12/09/2014] [Indexed: 01/13/2023] Open
Abstract
Variants associated with blood lipid levels may be population-specific. To identify
low-frequency variants associated with this phenotype, population-specific reference
panels may be used. Here we impute nine large Dutch biobanks (~35,000
samples) with the population-specific reference panel created by the Genome of the
Netherlands Project and perform association testing with blood lipid levels. We
report the discovery of five novel associations at four loci (P value
<6.61 × 10−4), including a rare missense
variant in ABCA6
(rs77542162, p.Cys1359Arg, frequency 0.034), which is predicted to be deleterious.
The frequency of this ABCA6
variant is 3.65-fold increased in the Dutch and its effect
(βLDL-C=0.135,
βTC=0.140) is estimated to be very similar to those
observed for single variants in well-known lipid genes, such as LDLR. Frequencies of rare variants fluctuate over populations, hampering
gene discovery. Here the authors use a population-specific reference panel, the Genome
of the Netherlands, to discover four novel loci involved in lipid metabolism, including
an exonic variant in ABCA6.
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Affiliation(s)
| | - Lennart C Karssen
- Department of Epidemiology, Erasmus Medical Center, Rotterdam 3000 CA, The Netherlands
| | - Joris Deelen
- Department of Molecular Epidemiology, Leiden University Medical Center, Leiden 2300 RC, The Netherlands
| | - Aaron Isaacs
- Department of Epidemiology, Erasmus Medical Center, Rotterdam 3000 CA, The Netherlands
| | - Carolina Medina-Gomez
- Department of Epidemiology and Internal Medicine, Erasmus Medical Center, Rotterdam 3000 CA, The Netherlands
| | - Hamdi Mbarek
- Department of Biological Psychology, VU University Amsterdam and EMGO+ Institute for Health and Care Research, Amsterdam 1081BT, The Netherlands
| | - Alexandros Kanterakis
- Department of Genetics, Genomics Coordination Center, University of Groningen, University Medical Center Groningen, Groningen 9700 RB, The Netherlands
| | - Stella Trompet
- Department of Cardiology, Leiden University Medical Center, Leiden 2300 RC, The Netherlands
| | - Iris Postmus
- Department of Gerontology and Geriatrics, Leiden University Medical Center, Leiden 2300 RC, The Netherlands
| | - Niek Verweij
- Department of Cardiology, University Medical Center Groningen, University of Groningen, Groningen 9700 RB, The Netherlands
| | | | - Jennifer E Huffman
- MRC Human Genetics Unit, MRC IGMM, University of Edinburgh, Edinburgh EH4 2XU, UK
| | - Charles C White
- Department of Biostatistics, Boston U School of Public Health, Boston, Massachusetts 02118, USA
| | - Mary F Feitosa
- Department of Genetics, Washington University School of Medicine, St Louis, Missouri 63108, USA
| | - Traci M Bartz
- Department of Biostatistics and Medicine, University of Washington, Seattle, Washington 98101, USA
| | - Ani Manichaikul
- Department of Public Health Sciences, Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia 22908, USA
| | - Peter K Joshi
- Centre for Population Health Sciences, University of Edinburgh, Edinburgh, Scotland EH8 9AG, UK
| | - Gina M Peloso
- Center for Human Genetic Research, Massachusetts General Hospital, Boston, Massachusetts 02176, USA
| | - Patrick Deelen
- Department of Genetics, Genomics Coordination Center, University of Groningen, University Medical Center Groningen, Groningen 9700 RB, The Netherlands
| | - Freerk van Dijk
- Department of Genetics, Genomics Coordination Center, University of Groningen, University Medical Center Groningen, Groningen 9700 RB, The Netherlands
| | - Gonneke Willemsen
- Department of Biological Psychology, VU University Amsterdam and EMGO+ Institute for Health and Care Research, Amsterdam 1081BT, The Netherlands
| | - Eco J de Geus
- Department of Biological Psychology, VU University Amsterdam and EMGO+ Institute for Health and Care Research, Amsterdam 1081BT, The Netherlands
| | - Yuri Milaneschi
- Department of Psychiatry, VU University Medical Center Amsterdam/GGZinGeest, EMGO+ Institute for Health and Care Research, Neuroscience Campus Amsterdam, Amsterdam 1081HL, The Netherlands
| | - Brenda W J H Penninx
- Department of Psychiatry, VU University Medical Center Amsterdam/GGZinGeest, EMGO+ Institute for Health and Care Research, Neuroscience Campus Amsterdam, Amsterdam 1081HL, The Netherlands
| | - Laurent C Francioli
- Department of Medical Genetics, Center for Molecular Medicine, University Medical Center Utrecht, Utrecht 3584 CG, The Netherlands
| | - Androniki Menelaou
- Department of Medical Genetics, Center for Molecular Medicine, University Medical Center Utrecht, Utrecht 3584 CG, The Netherlands
| | - Sara L Pulit
- Department of Medical Genetics, Center for Molecular Medicine, University Medical Center Utrecht, Utrecht 3584 CG, The Netherlands
| | - Fernando Rivadeneira
- Department of Epidemiology and Internal Medicine, Erasmus Medical Center, Rotterdam 3000 CA, The Netherlands
| | - Albert Hofman
- Department of Epidemiology, Erasmus Medical Center, Rotterdam 3000 CA, The Netherlands
| | - Ben A Oostra
- Department of Clinical Genetics, Erasmus Medical Center, Rotterdam 3000 CA, The Netherlands
| | - Oscar H Franco
- Department of Epidemiology, Erasmus Medical Center, Rotterdam 3000 CA, The Netherlands
| | - Irene Mateo Leach
- Department of Cardiology, University Medical Center Groningen, University of Groningen, Groningen 9700 RB, The Netherlands
| | - Marian Beekman
- Department of Molecular Epidemiology, Leiden University Medical Center, Leiden 2300 RC, The Netherlands
| | - Anton J M de Craen
- Department of Gerontology and Geriatrics, Leiden University Medical Center, Leiden 2300 RC, The Netherlands
| | - Hae-Won Uh
- Department of Genetical Statistics, Leiden University Medical Center, Leiden 2300 RC, The Netherlands
| | - Holly Trochet
- MRC Human Genetics Unit, MRC IGMM, University of Edinburgh, Edinburgh EH4 2XU, UK
| | - Lynne J Hocking
- Division of Applied Health Sciences, University of Aberdeen, Aberdeen AB25 2ZD, UK
| | - David J Porteous
- Centre for Genomic and Experimental Medicine, MRC IGMM, University of Edinburgh, Edinburgh EH4 2XU, UK
| | - Naveed Sattar
- BHF Glasgow Cardiovascular Research Centre, Faculty of Medicine, University of Glasgow, Glasgow G12 8QQ, UK
| | - Chris J Packard
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow G12 8QQ, UK
| | - Brendan M Buckley
- Department of Pharmacology and Therapeutics, University College Cork, Cork, Ireland
| | - Jennifer A Brody
- Department of Medicine, University of Washington, Seattle, Washington 98101, USA
| | - Joshua C Bis
- Department of Medicine, University of Washington, Seattle, Washington 98101, USA
| | - Jerome I Rotter
- Institute for Translational Genomics and Population Sciences, Los Angeles BioMedical Research Institute at Harbor-UCLA Medical Center, Torrance, California 90502, USA
| | - Josyf C Mychaleckyj
- Department of Public Health Sciences, Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia 22908, USA
| | - Harry Campbell
- Centre for Population Health Sciences, University of Edinburgh, Edinburgh, Scotland EH8 9AG, UK
| | - Qing Duan
- Department of Genetics, University of North Carolina, Chapel Hill, North Carolina NC 27599, USA
| | - Leslie A Lange
- Department of Genetics, University of North Carolina, Chapel Hill, North Carolina NC 27599, USA
| | - James F Wilson
- Centre for Population Health Sciences, University of Edinburgh, Edinburgh, Scotland EH8 9AG, UK
| | - Caroline Hayward
- MRC Human Genetics Unit, MRC IGMM, University of Edinburgh, Edinburgh EH4 2XU, UK
| | - Ozren Polasek
- Department of Public Health, Faculty of Medicine, University of Split, Split 21000, Croatia
| | - Veronique Vitart
- MRC Human Genetics Unit, MRC IGMM, University of Edinburgh, Edinburgh EH4 2XU, UK
| | - Igor Rudan
- Centre for Population Health Sciences, University of Edinburgh, Edinburgh, Scotland EH8 9AG, UK
| | - Alan F Wright
- MRC Human Genetics Unit, MRC IGMM, University of Edinburgh, Edinburgh EH4 2XU, UK
| | - Stephen S Rich
- Department of Public Health Sciences, Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia 22908, USA
| | - Bruce M Psaty
- Department of Medicine and Epidemiology, University of Washington, Seattle, Washington 98101, USA
| | - Ingrid B Borecki
- Department of Genetics and Biostatistics, Washington University School of Medicine, St Louis, Missouri 63108, USA
| | - Patricia M Kearney
- Department of Pharmacology and Therapeutics, University College Cork, Cork, Ireland
| | - David J Stott
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow G12 8QQ, UK
| | - L Adrienne Cupples
- 1] Department of Biostatistics, Boston U School of Public Health, Boston, Massachusetts 02118, USA [2] Framingham Heart Study, Framingham, Massachusetts 01702, USA
| | | | - J Wouter Jukema
- Department of Cardiology, Leiden University Medical Center, Leiden 2300 RC, The Netherlands
| | - Pim van der Harst
- Department of Cardiology, University Medical Center Groningen, University of Groningen, Groningen 9700 RB, The Netherlands
| | - Eric J Sijbrands
- Department of Internal Medicine, Erasmus Medical Center, Rotterdam 3000 CA, The Netherlands
| | - Jouke-Jan Hottenga
- Department of Biological Psychology, VU University Amsterdam and EMGO+ Institute for Health and Care Research, Amsterdam 1081BT, The Netherlands
| | - Andre G Uitterlinden
- Department of Epidemiology and Internal Medicine, Erasmus Medical Center, Rotterdam 3000 CA, The Netherlands
| | - Morris A Swertz
- Department of Genetics, Genomics Coordination Center, University of Groningen, University Medical Center Groningen, Groningen 9700 RB, The Netherlands
| | - Gert-Jan B van Ommen
- Department of Human Genetics, Leiden University Medical Center, Leiden P.O. Box 9600, 2300 RC, The Netherlands
| | - Paul I W de Bakker
- 1] Department of Medical Genetics, Center for Molecular Medicine, University Medical Center Utrecht, Utrecht 3584 CG, The Netherlands [2] Department of Epidemiology, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht 3584 CG, The Netherlands
| | - P Eline Slagboom
- Department of Molecular Epidemiology, Leiden University Medical Center, Leiden 2300 RC, The Netherlands
| | - Dorret I Boomsma
- Department of Biological Psychology, VU University Amsterdam, Amsterdam 1081BT, The Netherlands
| | - Cisca Wijmenga
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen 9700 RB, The Netherlands
| | - Cornelia M van Duijn
- Department of Epidemiology, Erasmus Medical Center, Rotterdam 3000 CA, The Netherlands
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Deelen P, Menelaou A, van Leeuwen EM, Kanterakis A, van Dijk F, Medina-Gomez C, Francioli LC, Hottenga JJ, Karssen LC, Estrada K, Kreiner-Møller E, Rivadeneira F, van Setten J, Gutierrez-Achury J, Westra HJ, Franke L, van Enckevort D, Dijkstra M, Byelas H, van Duijn CM, de Bakker PIW, Wijmenga C, Swertz MA. Improved imputation quality of low-frequency and rare variants in European samples using the 'Genome of The Netherlands'. Eur J Hum Genet 2014; 22:1321-6. [PMID: 24896149 PMCID: PMC4200431 DOI: 10.1038/ejhg.2014.19] [Citation(s) in RCA: 74] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2013] [Revised: 11/01/2013] [Accepted: 01/16/2014] [Indexed: 12/23/2022] Open
Abstract
Although genome-wide association studies (GWAS) have identified many common variants associated with complex traits, low-frequency and rare variants have not been interrogated in a comprehensive manner. Imputation from dense reference panels, such as the 1000 Genomes Project (1000G), enables testing of ungenotyped variants for association. Here we present the results of imputation using a large, new population-specific panel: the Genome of The Netherlands (GoNL). We benchmarked the performance of the 1000G and GoNL reference sets by comparing imputation genotypes with 'true' genotypes typed on ImmunoChip in three European populations (Dutch, British, and Italian). GoNL showed significant improvement in the imputation quality for rare variants (MAF 0.05-0.5%) compared with 1000G. In Dutch samples, the mean observed Pearson correlation, r(2), increased from 0.61 to 0.71. We also saw improved imputation accuracy for other European populations (in the British samples, r(2) improved from 0.58 to 0.65, and in the Italians from 0.43 to 0.47). A combined reference set comprising 1000G and GoNL improved the imputation of rare variants even further. The Italian samples benefitted the most from this combined reference (the mean r(2) increased from 0.47 to 0.50). We conclude that the creation of a large population-specific reference is advantageous for imputing rare variants and that a combined reference panel across multiple populations yields the best imputation results.
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Affiliation(s)
- Patrick Deelen
- 1] University of Groningen, University Medical Center Groningen, Department of Genetics, Groningen, The Netherlands [2] University of Groningen, University Medical Center Groningen, Genomics Coordination Center, Groningen, The Netherlands
| | - Androniki Menelaou
- Department of Medical Genetics, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Elisabeth M van Leeuwen
- Department of Epidemiology, Genetic Epidemiology Unit, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Alexandros Kanterakis
- 1] University of Groningen, University Medical Center Groningen, Department of Genetics, Groningen, The Netherlands [2] University of Groningen, University Medical Center Groningen, Genomics Coordination Center, Groningen, The Netherlands
| | - Freerk van Dijk
- 1] University of Groningen, University Medical Center Groningen, Department of Genetics, Groningen, The Netherlands [2] University of Groningen, University Medical Center Groningen, Genomics Coordination Center, Groningen, The Netherlands
| | - Carolina Medina-Gomez
- 1] Department of Internal Medicine, Erasmus Medical Center, Rotterdam, The Netherlands [2] Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands [3] Netherlands Genomics Initiative (NGI)-sponsored Netherlands Consortium for Healthy Aging (NCHA), Rotterdam, The Netherlands
| | - Laurent C Francioli
- Department of Medical Genetics, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Jouke Jan Hottenga
- Department of Biological Psychology, VU University Amsterdam, Amsterdam, The Netherlands
| | - Lennart C Karssen
- Department of Epidemiology, Genetic Epidemiology Unit, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Karol Estrada
- 1] Department of Internal Medicine, Erasmus Medical Center, Rotterdam, The Netherlands [2] Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands [3] Department of Medicine, Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA [4] Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Eskil Kreiner-Møller
- 1] Department of Internal Medicine, Erasmus Medical Center, Rotterdam, The Netherlands [2] Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands [3] COPSAC; Copenhagen Prospective Studies on Asthma in Childhood; Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Fernando Rivadeneira
- 1] Department of Internal Medicine, Erasmus Medical Center, Rotterdam, The Netherlands [2] Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands [3] Netherlands Genomics Initiative (NGI)-sponsored Netherlands Consortium for Healthy Aging (NCHA), Rotterdam, The Netherlands
| | - Jessica van Setten
- Department of Medical Genetics, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Javier Gutierrez-Achury
- University of Groningen, University Medical Center Groningen, Department of Genetics, Groningen, The Netherlands
| | - Harm-Jan Westra
- University of Groningen, University Medical Center Groningen, Department of Genetics, Groningen, The Netherlands
| | - Lude Franke
- University of Groningen, University Medical Center Groningen, Department of Genetics, Groningen, The Netherlands
| | - David van Enckevort
- 1] University of Groningen, University Medical Center Groningen, Genomics Coordination Center, Groningen, The Netherlands [2] NBIC BioAssist, Netherlands Bioinformatics Center, Nijmegen, The Netherlands
| | - Martijn Dijkstra
- 1] University of Groningen, University Medical Center Groningen, Department of Genetics, Groningen, The Netherlands [2] University of Groningen, University Medical Center Groningen, Genomics Coordination Center, Groningen, The Netherlands
| | - Heorhiy Byelas
- 1] University of Groningen, University Medical Center Groningen, Department of Genetics, Groningen, The Netherlands [2] University of Groningen, University Medical Center Groningen, Genomics Coordination Center, Groningen, The Netherlands
| | - Cornelia M van Duijn
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | | | - Paul I W de Bakker
- 1] Department of Medical Genetics, University Medical Center Utrecht, Utrecht, The Netherlands [2] Department of Epidemiology, University Medical Center Utrecht, Utrecht, The Netherlands [3] Division of Genetics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA [4] Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Cisca Wijmenga
- University of Groningen, University Medical Center Groningen, Department of Genetics, Groningen, The Netherlands
| | - Morris A Swertz
- 1] University of Groningen, University Medical Center Groningen, Department of Genetics, Groningen, The Netherlands [2] University of Groningen, University Medical Center Groningen, Genomics Coordination Center, Groningen, The Netherlands
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16
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Almeida R, Ricaño-Ponce I, Kumar V, Deelen P, Szperl A, Trynka G, Gutierrez-Achury J, Kanterakis A, Westra HJ, Franke L, Swertz MA, Platteel M, Bilbao JR, Barisani D, Greco L, Mearin L, Wolters VM, Mulder C, Mazzilli MC, Sood A, Cukrowska B, Núñez C, Pratesi R, Withoff S, Wijmenga C. Fine mapping of the celiac disease-associated LPP locus reveals a potential functional variant. Hum Mol Genet 2014; 23:2481-9. [PMID: 24334606 PMCID: PMC3976328 DOI: 10.1093/hmg/ddt619] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.7] [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] [Subscribe] [Scholar Register] [Received: 07/02/2013] [Revised: 11/24/2013] [Accepted: 12/06/2013] [Indexed: 12/15/2022] Open
Abstract
Using the Immunochip for genotyping, we identified 39 non-human leukocyte antigen (non-HLA) loci associated to celiac disease (CeD), an immune-mediated disease with a worldwide frequency of ∼1%. The most significant non-HLA signal mapped to the intronic region of 70 kb in the LPP gene. Our aim was to fine map and identify possible functional variants in the LPP locus. We performed a meta-analysis in a cohort of 25 169 individuals from six different populations previously genotyped using Immunochip. Imputation using data from the Genome of the Netherlands and 1000 Genomes projects, followed by meta-analysis, confirmed the strong association signal on the LPP locus (rs2030519, P = 1.79 × 10(-49)), without any novel associations. The conditional analysis on this top SNP-indicated association to a single common haplotype. By performing haplotype analyses in each population separately, as well as in a combined group of the four populations that reach the significant threshold after correction (P < 0.008), we narrowed down the CeD-associated region from 70 to 2.8 kb (P = 1.35 × 10(-44)). By intersecting regulatory data from the ENCODE project, we found a functional SNP, rs4686484 (P = 3.12 × 10(-49)), that maps to several B-cell enhancer elements and a highly conserved region. This SNP was also predicted to change the binding motif of the transcription factors IRF4, IRF11, Nkx2.7 and Nkx2.9, suggesting its role in transcriptional regulation. We later found significantly low levels of LPP mRNA in CeD biopsies compared with controls, thus our results suggest that rs4686484 is the functional variant in this locus, while LPP expression is decreased in CeD.
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Affiliation(s)
- Rodrigo Almeida
- Department of Genetics, University of Groningen, University Medical Center Groningen, PO Box 30001, Groningen 9700 RB, The Netherlands
- Graduate Program in Health Sciences, University of Brasilia School of Health Sciences, Brasilia, Brazil
| | - Isis Ricaño-Ponce
- Department of Genetics, University of Groningen, University Medical Center Groningen, PO Box 30001, Groningen 9700 RB, The Netherlands
| | - Vinod Kumar
- Department of Genetics, University of Groningen, University Medical Center Groningen, PO Box 30001, Groningen 9700 RB, The Netherlands
| | - Patrick Deelen
- Department of Genetics, University of Groningen, University Medical Center Groningen, PO Box 30001, Groningen 9700 RB, The Netherlands
| | - Agata Szperl
- Department of Genetics, University of Groningen, University Medical Center Groningen, PO Box 30001, Groningen 9700 RB, The Netherlands
| | - Gosia Trynka
- Department of Genetics, University of Groningen, University Medical Center Groningen, PO Box 30001, Groningen 9700 RB, The Netherlands
| | - Javier Gutierrez-Achury
- Department of Genetics, University of Groningen, University Medical Center Groningen, PO Box 30001, Groningen 9700 RB, The Netherlands
| | - Alexandros Kanterakis
- Department of Genetics, University of Groningen, University Medical Center Groningen, PO Box 30001, Groningen 9700 RB, The Netherlands
| | - Harm-Jan Westra
- Department of Genetics, University of Groningen, University Medical Center Groningen, PO Box 30001, Groningen 9700 RB, The Netherlands
| | - Lude Franke
- Department of Genetics, University of Groningen, University Medical Center Groningen, PO Box 30001, Groningen 9700 RB, The Netherlands
| | - Morris A. Swertz
- Department of Genetics, University of Groningen, University Medical Center Groningen, PO Box 30001, Groningen 9700 RB, The Netherlands
| | - Mathieu Platteel
- Department of Genetics, University of Groningen, University Medical Center Groningen, PO Box 30001, Groningen 9700 RB, The Netherlands
| | - Jose Ramon Bilbao
- Immunogenetics Research Laboratory, Hospital Universitario de Cruces, Barakaldo, Bizkaia 48903, Spain
| | - Donatella Barisani
- Department of Experimental Medicine, Faculty of Medicine, University of Milano-Bicocca, Monza, Italy
| | - Luigi Greco
- European Laboratory for Food Induced Disease, University of Naples Federico II, Naples, Italy
| | - Luisa Mearin
- Department of Pediatric Gastroenterology, Leiden University Medical Centre, Leiden, The Netherlands
| | - Victorien M. Wolters
- Department of Pediatric Gastroenterology, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - Chris Mulder
- Department of Gastroenterology, VU Medical Center, Amsterdam, The Netherlands
| | | | - Ajit Sood
- Dayanand Medical College and Hospital, Ludhiana, Punjab, India
| | - Bozena Cukrowska
- Department of Pathology, Children's Memorial Health Institute, Warsaw, Poland
| | - Concepción Núñez
- Depatment of Immunology, H. Clínico S. Carlos, Instituto de Investigación Sanitaria San Carlos (IdISSC), Madrid, Spain
| | - Riccardo Pratesi
- Graduate Program in Health Sciences, University of Brasilia School of Health Sciences, Brasilia, Brazil
| | - Sebo Withoff
- Department of Genetics, University of Groningen, University Medical Center Groningen, PO Box 30001, Groningen 9700 RB, The Netherlands
| | - Cisca Wijmenga
- Department of Genetics, University of Groningen, University Medical Center Groningen, PO Box 30001, Groningen 9700 RB, The Netherlands
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17
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Boomsma DI, Wijmenga C, Slagboom EP, Swertz MA, Karssen LC, Abdellaoui A, Ye K, Guryev V, Vermaat M, van Dijk F, Francioli LC, Hottenga JJ, Laros JFJ, Li Q, Li Y, Cao H, Chen R, Du Y, Li N, Cao S, van Setten J, Menelaou A, Pulit SL, Hehir-Kwa JY, Beekman M, Elbers CC, Byelas H, de Craen AJM, Deelen P, Dijkstra M, den Dunnen JT, de Knijff P, Houwing-Duistermaat J, Koval V, Estrada K, Hofman A, Kanterakis A, Enckevort DV, Mai H, Kattenberg M, van Leeuwen EM, Neerincx PBT, Oostra B, Rivadeneira F, Suchiman EHD, Uitterlinden AG, Willemsen G, Wolffenbuttel BH, Wang J, de Bakker PIW, van Ommen GJ, van Duijn CM. The Genome of the Netherlands: design, and project goals. Eur J Hum Genet 2014; 22:221-7. [PMID: 23714750 PMCID: PMC3895638 DOI: 10.1038/ejhg.2013.118] [Citation(s) in RCA: 178] [Impact Index Per Article: 17.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2012] [Revised: 02/28/2013] [Accepted: 03/24/2013] [Indexed: 11/09/2022] Open
Abstract
Within the Netherlands a national network of biobanks has been established (Biobanking and Biomolecular Research Infrastructure-Netherlands (BBMRI-NL)) as a national node of the European BBMRI. One of the aims of BBMRI-NL is to enrich biobanks with different types of molecular and phenotype data. Here, we describe the Genome of the Netherlands (GoNL), one of the projects within BBMRI-NL. GoNL is a whole-genome-sequencing project in a representative sample consisting of 250 trio-families from all provinces in the Netherlands, which aims to characterize DNA sequence variation in the Dutch population. The parent-offspring trios include adult individuals ranging in age from 19 to 87 years (mean=53 years; SD=16 years) from birth cohorts 1910-1994. Sequencing was done on blood-derived DNA from uncultured cells and accomplished coverage was 14-15x. The family-based design represents a unique resource to assess the frequency of regional variants, accurately reconstruct haplotypes by family-based phasing, characterize short indels and complex structural variants, and establish the rate of de novo mutational events. GoNL will also serve as a reference panel for imputation in the available genome-wide association studies in Dutch and other cohorts to refine association signals and uncover population-specific variants. GoNL will create a catalog of human genetic variation in this sample that is uniquely characterized with respect to micro-geographic location and a wide range of phenotypes. The resource will be made available to the research and medical community to guide the interpretation of sequencing projects. The present paper summarizes the global characteristics of the project.
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Affiliation(s)
- Dorret I Boomsma
- Department of Biological Psychology, VU University Amsterdam, Netherlands Twin Register, Amsterdam, The Netherlands
| | - Cisca Wijmenga
- University of Groningen, University Medical Center Groningen, Department of Genetics, Groningen, The Netherlands
| | - Eline P Slagboom
- Molecular Epidemiology Section, Leiden University Medical Center, Netherlands Consortium for Healthy Ageing, Leiden, The Netherlands
| | - Morris A Swertz
- University of Groningen, University Medical Center Groningen, Department of Genetics, Groningen, The Netherlands
| | - Lennart C Karssen
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Abdel Abdellaoui
- Department of Biological Psychology, VU University Amsterdam, Netherlands Twin Register, Amsterdam, The Netherlands
| | - Kai Ye
- Molecular Epidemiology Section, Leiden University Medical Center, Netherlands Consortium for Healthy Ageing, Leiden, The Netherlands
| | - Victor Guryev
- European Research Institute for the Biology of Ageing, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
- Hubrecht Institute, Royal Netherlands Academy of Arts and Sciences, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Martijn Vermaat
- Netherlands Bioinformatics Centre, Nijmegen, The Netherlands
- Department of Human Genetics, Center for Human and Clinical Genetics, Leiden University Medical Center, Leiden, The Netherlands
- Leiden Genome Technology Center, Leiden University Medical Center, Leiden, The Netherlands
| | - Freerk van Dijk
- University of Groningen, University Medical Center Groningen, Department of Genetics, Groningen, The Netherlands
| | - Laurent C Francioli
- Department of Medical Genetics, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Jouke Jan Hottenga
- Department of Biological Psychology, VU University Amsterdam, Netherlands Twin Register, Amsterdam, The Netherlands
| | - Jeroen F J Laros
- Netherlands Bioinformatics Centre, Nijmegen, The Netherlands
- Department of Human Genetics, Center for Human and Clinical Genetics, Leiden University Medical Center, Leiden, The Netherlands
- Leiden Genome Technology Center, Leiden University Medical Center, Leiden, The Netherlands
| | | | | | | | | | | | - Ning Li
- BGI-Europe, Copenhagen, Denmark
| | | | - Jessica van Setten
- Department of Medical Genetics, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Androniki Menelaou
- Department of Medical Genetics, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Sara L Pulit
- Department of Medical Genetics, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Jayne Y Hehir-Kwa
- Department of Human Genetics, Radboud University Medical Centre, Nijmegen, The Netherlands
| | - Marian Beekman
- Department of Gerontology and Geriatrics, Leiden University Medical Centre, Leiden, The Netherlands
| | - Clara C Elbers
- Department of Medical Genetics, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Heorhiy Byelas
- University of Groningen, University Medical Center Groningen, Department of Genetics, Groningen, The Netherlands
| | - Anton J M de Craen
- Department of Gerontology and Geriatrics, Leiden University Medical Centre, Leiden, The Netherlands
| | - Patrick Deelen
- University of Groningen, University Medical Center Groningen, Department of Genetics, Groningen, The Netherlands
| | - Martijn Dijkstra
- University of Groningen, University Medical Center Groningen, Department of Genetics, Groningen, The Netherlands
| | - Johan T den Dunnen
- Department of Human Genetics, Center for Human and Clinical Genetics, Leiden University Medical Center, Leiden, The Netherlands
- Leiden Genome Technology Center, Leiden University Medical Center, Leiden, The Netherlands
| | - Peter de Knijff
- Department of Human Genetics, Center for Human and Clinical Genetics, Leiden University Medical Center, Leiden, The Netherlands
- Leiden Genome Technology Center, Leiden University Medical Center, Leiden, The Netherlands
| | - Jeanine Houwing-Duistermaat
- Department of Medical Statistics and Bioinformatics, Leiden University Medical Centre, Leiden, The Netherlands
| | - Vyacheslav Koval
- Erasmus Medical Centre, Genetic Laboratory Internal Medicine, Rotterdam, The Netherlands
| | - Karol Estrada
- Erasmus Medical Centre, Genetic Laboratory Internal Medicine, Rotterdam, The Netherlands
| | - Albert Hofman
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Alexandros Kanterakis
- University of Groningen, University Medical Center Groningen, Department of Genetics, Groningen, The Netherlands
| | | | - Hailiang Mai
- Netherlands Bioinformatics Centre, Nijmegen, The Netherlands
| | - Mathijs Kattenberg
- Department of Biological Psychology, VU University Amsterdam, Netherlands Twin Register, Amsterdam, The Netherlands
| | | | - Pieter B T Neerincx
- University of Groningen, University Medical Center Groningen, Department of Genetics, Groningen, The Netherlands
| | - Ben Oostra
- Department of Clinical Genetics, Erasmus University Medical School, Rotterdam, The Netherlands
| | - Fernanodo Rivadeneira
- Erasmus Medical Centre, Genetic Laboratory Internal Medicine, Rotterdam, The Netherlands
| | - Eka H D Suchiman
- Molecular Epidemiology Section, Leiden University Medical Center, Netherlands Consortium for Healthy Ageing, Leiden, The Netherlands
| | - Andre G Uitterlinden
- Erasmus Medical Centre, Genetic Laboratory Internal Medicine, Rotterdam, The Netherlands
| | - Gonneke Willemsen
- Department of Biological Psychology, VU University Amsterdam, Netherlands Twin Register, Amsterdam, The Netherlands
| | - Bruce H Wolffenbuttel
- LifeLines Cohort Study & Department of Endocrinology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Jun Wang
- BGI-Shenzhen, Shenzhen, China
- Department of Biology, University of Copenhagen, Copenhagen, Denmark
- The Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark
| | - Paul I W de Bakker
- Department of Medical Genetics, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Gert-Jan van Ommen
- Department of Human Genetics, Leiden University Medical Centre, Leiden, The Netherlands
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18
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Szperl AM, Ricaño-Ponce I, Li JK, Deelen P, Kanterakis A, Plagnol V, van Dijk F, Westra HJ, Trynka G, Mulder CJ, Swertz M, Wijmenga C, Zheng HCH. Exome sequencing in a family segregating for celiac disease. Clin Genet 2011; 80:138-47. [PMID: 21627641 DOI: 10.1111/j.1399-0004.2011.01714.x] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Celiac disease is a multifactorial disorder caused by an unknown number of genetic factors interacting with an environmental factor. Hence, most patients are singletons and large families segregating with celiac disease are rare. We report on a three-generation family with six patients in which the inheritance pattern is consistent with an autosomal dominant model. To date, 27 loci explain up to 40% of the heritable disease risk. We hypothesized that part of the missing heritability is because of low frequency or rare variants. Such causal variants could be more prominent in multigeneration families where private mutations might co-segregate with the disease. They can be identified by linkage analysis combined with whole exome sequencing. We found three linkage regions on 4q32.3-4q33, 8q24.13-8q24.21 and 10q23.1-10q23.32 that segregate with celiac disease in this family. We performed exome sequencing on two affected individuals to investigate the positional candidate regions and the remaining exome for causal nonsense variants. We identified 12 nonsense mutations with a low frequency (minor allele frequency <10%) present in both individuals, but none mapped to the linkage regions. Two variants in the CSAG1 and KRT37 genes were present in all six affected individuals. Two nonsense variants in the MADD and GBGT1 genes were also present in 5 of 6 and 4 of 6 individuals, respectively; future studies should determine if any of these nonsense variants is causally related to celiac disease.
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Affiliation(s)
- A M Szperl
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
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19
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Swertz MA, Dijkstra M, Adamusiak T, van der Velde JK, Kanterakis A, Roos ET, Lops J, Thorisson GA, Arends D, Byelas G, Muilu J, Brookes AJ, de Brock EO, Jansen RC, Parkinson H. The MOLGENIS toolkit: rapid prototyping of biosoftware at the push of a button. BMC Bioinformatics 2010; 11 Suppl 12:S12. [PMID: 21210979 PMCID: PMC3040526 DOI: 10.1186/1471-2105-11-s12-s12] [Citation(s) in RCA: 76] [Impact Index Per Article: 5.4] [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] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND There is a huge demand on bioinformaticians to provide their biologists with user friendly and scalable software infrastructures to capture, exchange, and exploit the unprecedented amounts of new *omics data. We here present MOLGENIS, a generic, open source, software toolkit to quickly produce the bespoke MOLecular GENetics Information Systems needed. METHODS The MOLGENIS toolkit provides bioinformaticians with a simple language to model biological data structures and user interfaces. At the push of a button, MOLGENIS' generator suite automatically translates these models into a feature-rich, ready-to-use web application including database, user interfaces, exchange formats, and scriptable interfaces. Each generator is a template of SQL, JAVA, R, or HTML code that would require much effort to write by hand. This 'model-driven' method ensures reuse of best practices and improves quality because the modeling language and generators are shared between all MOLGENIS applications, so that errors are found quickly and improvements are shared easily by a re-generation. A plug-in mechanism ensures that both the generator suite and generated product can be customized just as much as hand-written software. RESULTS In recent years we have successfully evaluated the MOLGENIS toolkit for the rapid prototyping of many types of biomedical applications, including next-generation sequencing, GWAS, QTL, proteomics and biobanking. Writing 500 lines of model XML typically replaces 15,000 lines of hand-written programming code, which allows for quick adaptation if the information system is not yet to the biologist's satisfaction. Each application generated with MOLGENIS comes with an optimized database back-end, user interfaces for biologists to manage and exploit their data, programming interfaces for bioinformaticians to script analysis tools in R, Java, SOAP, REST/JSON and RDF, a tab-delimited file format to ease upload and exchange of data, and detailed technical documentation. Existing databases can be quickly enhanced with MOLGENIS generated interfaces using the 'ExtractModel' procedure. CONCLUSIONS The MOLGENIS toolkit provides bioinformaticians with a simple model to quickly generate flexible web platforms for all possible genomic, molecular and phenotypic experiments with a richness of interfaces not provided by other tools. All the software and manuals are available free as LGPLv3 open source at http://www.molgenis.org.
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Affiliation(s)
- Morris A Swertz
- Genomics Coordination Center, Groningen Bioinformatics Center, University of Groningen & Department of Genetics, University Medical Center Groningen, P.O. Box 30001, 9700 RB Groningen, The Netherlands.
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20
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Tsiknakis M, Sfakianakis S, Zacharioudakis G, Umakis L, Kanterakis A, Potamias G, Kafetzopoulos D. A semantically aware platform for the authoring and secure enactment of bioinformatics workflows. Annu Int Conf IEEE Eng Med Biol Soc 2010; 2009:5625-8. [PMID: 19964401 DOI: 10.1109/iembs.2009.5333787] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Recent advances in the field of bioinformatics present a number of challenges in the secure and efficient management and analysis of biological data resources. Workflow technologies aim to assist scientists and domain experts in the design of complex, long running, data and computing intensive experiments that involve many data processing and analysis tasks with the objective of generating new knowledge or formulate new hypothesis. In this paper we present a bioinformatics workflow authoring and execution environment that intends to greatly facilitate the whole lifecycle of such experiments. Emphasis is given on the security and ethical requirements of these scenarios and the corresponding technological response. In addition we present our semantic framework used for supporting specific user-requirements related to the reasoning and inference capabilities of the environment.
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Affiliation(s)
- M Tsiknakis
- Institute of Computer Science, Foundation for Research and Technology-Hellas, Heraklion, Crete, Greece.
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21
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Kanterakis A, Potamias G, Zacharioudakis G, Koumakis L, Sfakianakis S, Tsiknakis M. Scientific discovery workflows in bioinformatics: a scenario for the coupling of molecular regulatory pathways and gene-expression profiles. Stud Health Technol Inform 2010; 160:1304-1308. [PMID: 20841895] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/29/2023]
Abstract
Scientific workflow technologies and tools have become an important weapon in the arsenal of the bioinformaticians and computational biologists. To support this view we present a typical exploratory data analysis scenario involving the combination of information from Gene Regulatory Networks and gene expression data. We further describe the implementation of this scenario using the Workflow Environment implemented in the context of a large EU funded project. In this process desirable features that similar environments should offer are identified and analyzed. The ICT platform presented is evaluated using the chosen scenario as a benchmark. Finally we conclude with an outlook to future work.
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Affiliation(s)
- Alexandros Kanterakis
- Foundation for Research and Technology-Hellas, Institute of Computer Science, Heraklion, Crete, Greece
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