Cell Specific eQTL Analysis without Sorting Cells

  1. Westra, Harm-Jan
  2. Arends, Danny
  3. Esko, Tõnu
  4. Peters, Marjolein J.
  5. Schurmann, Claudia
  6. Schramm, Katharina
  7. Kettunen, Johannes
  8. Yaghootkar, Hanieh
  9. Fairfax, Benjamin P.
  10. Andiappan, Anand Kumar
  11. Li, Yang
  12. Fu, Jingyuan
  13. Karjalainen, Juha
  14. Platteel, Mathieu
  15. Visschedijk, Marijn
  16. Weersma, Rinse K.
  17. Kasela, Silva
  18. Milani, Lili
  19. Tserel, Liina
  20. Peterson, Pärt
  21. Reinmaa, Eva
  22. Hofman, Albert
  23. Uitterlinden, André G.
  24. Rivadeneira, Fernando
  25. Homuth, Georg
  26. Petersmann, Astrid
  27. Lorbeer, Roberto
  28. Prokisch, Holger
  29. Meitinger, Thomas
  30. Herder, Christian ORCID logo
  31. Roden, Michael
  32. Grallert, Harald
  33. Ripatti, Samuli
  34. Perola, Markus
  35. Wood, Andrew R.
  36. Melzer, David
  37. Ferrucci, Luigi
  38. Singleton, Andrew B.
  39. Hernandez, Dena G.
  40. Knight, Julian C.
  41. Melchiotti, Rossella
  42. Lee, Bernett
  43. Poidinger, Michael
  44. Zolezzi, Francesca
  45. Larbi, Anis
  46. Wang, De Yun
  47. van den Berg, Leonard H.
  48. Veldink, Jan H.
  49. Rotzschke, Olaf
  50. Makino, Seiko
  51. Salomaa, Veikko
  52. Strauch, Konstantin
  53. Völker, Uwe
  54. van Meurs, Joyce B. J.
  55. Metspalu, Andres
  56. Wijmenga, Cisca
  57. Jansen, Ritsert C.
  58. Franke, Lude

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1000 Titel
  • Cell Specific eQTL Analysis without Sorting Cells
1000 Autor/in
  1. Westra, Harm-Jan |
  2. Arends, Danny |
  3. Esko, Tõnu |
  4. Peters, Marjolein J. |
  5. Schurmann, Claudia |
  6. Schramm, Katharina |
  7. Kettunen, Johannes |
  8. Yaghootkar, Hanieh |
  9. Fairfax, Benjamin P. |
  10. Andiappan, Anand Kumar |
  11. Li, Yang |
  12. Fu, Jingyuan |
  13. Karjalainen, Juha |
  14. Platteel, Mathieu |
  15. Visschedijk, Marijn |
  16. Weersma, Rinse K. |
  17. Kasela, Silva |
  18. Milani, Lili |
  19. Tserel, Liina |
  20. Peterson, Pärt |
  21. Reinmaa, Eva |
  22. Hofman, Albert |
  23. Uitterlinden, André G. |
  24. Rivadeneira, Fernando |
  25. Homuth, Georg |
  26. Petersmann, Astrid |
  27. Lorbeer, Roberto |
  28. Prokisch, Holger |
  29. Meitinger, Thomas |
  30. Herder, Christian |
  31. Roden, Michael |
  32. Grallert, Harald |
  33. Ripatti, Samuli |
  34. Perola, Markus |
  35. Wood, Andrew R. |
  36. Melzer, David |
  37. Ferrucci, Luigi |
  38. Singleton, Andrew B. |
  39. Hernandez, Dena G. |
  40. Knight, Julian C. |
  41. Melchiotti, Rossella |
  42. Lee, Bernett |
  43. Poidinger, Michael |
  44. Zolezzi, Francesca |
  45. Larbi, Anis |
  46. Wang, De Yun |
  47. van den Berg, Leonard H. |
  48. Veldink, Jan H. |
  49. Rotzschke, Olaf |
  50. Makino, Seiko |
  51. Salomaa, Veikko |
  52. Strauch, Konstantin |
  53. Völker, Uwe |
  54. van Meurs, Joyce B. J. |
  55. Metspalu, Andres |
  56. Wijmenga, Cisca |
  57. Jansen, Ritsert C. |
  58. Franke, Lude |
1000 Erscheinungsjahr 2015
1000 LeibnizOpen
1000 Art der Datei
1000 Publikationstyp
  1. Artikel |
1000 Online veröffentlicht
  • 2015-05-08
1000 Erschienen in
1000 Quellenangabe
  • 11(5):e1005223
1000 FRL-Sammlung
1000 Copyrightjahr
  • 2015
1000 Lizenz
1000 Verlagsversion
  • https://doi.org/10.1371/journal.pgen.1005223 |
  • https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4425538/ |
1000 Ergänzendes Material
  • http://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1005223#sec013 |
1000 Publikationsstatus
1000 Begutachtungsstatus
1000 Sprache der Publikation
1000 Abstract/Summary
  • The functional consequences of trait associated SNPs are often investigated using expression quantitative trait locus (eQTL) mapping. While trait-associated variants may operate in a cell-type specific manner, eQTL datasets for such cell-types may not always be available. We performed a genome-environment interaction (GxE) meta-analysis on data from 5,683 samples to infer the cell type specificity of whole blood cis-eQTLs. We demonstrate that this method is able to predict neutrophil and lymphocyte specific cis-eQTLs and replicate these predictions in independent cell-type specific datasets. Finally, we show that SNPs associated with Crohn’s disease preferentially affect gene expression within neutrophils, including the archetypal NOD2 locus.
1000 Sacherschließung
lokal Meta-analysis
lokal Gene expression
lokal Monocytes
lokal Blood
lokal Genome-wide association studies
lokal Neutrophils
lokal Principal component analysis
lokal Gene mapping
1000 Fachgruppe
  1. Medizin |
1000 Fächerklassifikation (DDC)
1000 Liste der Beteiligten
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1000 (Academic) Editor
1000 Label
1000 Förderer
  1. European Community
  2. BioSHaRE
  3. ENGAGE Consortium
  4. Academy of Finland
  5. Finnish Foundation for Cardiovascular Research
  6. Sigrid Juselius Foundation
  7. Federal Ministry of Education and Research
  8. Deutsche Forschungsgemeinschaft
  9. Ministry of Cultural Affairs of Mecklenburg-West Pomerania
  10. Social Ministry of the Federal State of Mecklenburg-West Pomerania
  11. Siemens Healthcare, Erlangen, Germany
  12. Siemens AG
  13. InterSystems GmbH
  14. Estonian Government
  15. Ministries of Research, Science and Social Affairs
  16. University of Tartu
  17. European Union
  18. Erasmus MC Rotterdam
  19. German Bundesministerium fuer Forschung und Technology
  20. European Commission
  21. the Netherlands Organization of Scientific Research
  22. Research Institute for Diseases in the Elderly
  23. Netherlands Genomics Initiative (NGI)
  24. Netherlands Consortium for Healthy Aging (NCHA)
  25. NWO VIDI grant
  26. Erasmus Medical Center
  27. Erasmus University Rotterdam
  28. Netherlands Organization for Health Research and Development
  29. Research Institute for Diseases in the Elderly (RIDE)
  30. Ministry of Education, Culture and Science
  31. Ministry for Health, Welfare and Sports
  32. Dutch Government
  33. Netherlands Organization for Scientific Research
  34. Dutch Digestive Disease Foundation
  35. Horizon Breakthrough grant from the Netherlands Genomics Initiative
  36. Prinses Beatrix Fonds
  37. Kersten Foundation
  38. ALS Foundation
  39. the Adessium Foundation
  40. BBMRI NL Functional Genomics Project
  41. Netherlands Organization for Scientific Research
  42. Centre for BioSystems Genomics (CBSG)
  43. the Netherlands Consortium of Systems Biology (NCSB)
  44. Wellcome Trust
  45. US National Institute on Aging (NIA)
  46. Intramural Research Program NIA
  47. National Institute of Health (NIH)
  48. Peninsula NIHR Clinical Research Facility
  49. Helmholtz Zentrum München
  50. German Research Center for Environmental Health
  51. State of Bavaria
  52. German Federal Ministry of Health
  53. the Ministry of School, Science and Research of the State of North-Rhine-Westphalia
  54. German Research Foundation
  55. DZHK (Deutsches Zentrum für Herz-Kreislauf-Forschung – German Centre for Cardiovascular Research)
  56. BMBF
  57. the Leibniz Association
  58. NIHR Oxford Biomedical Research Centre
  59. Singapore Chinese functional genomics cohort
1000 Fördernummer
  1. 201413; 245536; 259867; 281824
  2. 261433
  3. HEALTH-F4-2007- 201413
  4. 213506; 129680; 251217; 139635; 129322; 118065
  5. -
  6. -
  7. 01ZZ9603; 01ZZ0103; 01ZZ0403; 03IS2061A; 03ZIK012
  8. DFG GRK840-D2
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  14. SF0180142s08
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  19. 01 AK 803 A-H; 01 IG 07015 G
  20. HEALTH-F2-2008-201865 GEFOS; HEALTH-F2-2008 35627; TREAT-OA 200800
  21. 1750102005011 911-03-012
  22. 014-93-015; RIDE2
  23. -
  24. 050-060-810
  25. 917103521
  26. -
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  30. -
  31. -
  32. BSIK03009
  33. NWO-VICI grant 918.66.620; NWO-VENI grant 916.10.135
  34. MLDS WO11-30
  35. 92519031
  36. -
  37. -
  38. -
  39. -
  40. -
  41. 184021007
  42. -
  43. -
  44. 083270/Z/07/Z; 074318; 088891; 075491/Z/04
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  54. DFG; RA 459/2-1
  55. -
  56. -
  57. -
  58. -
  59. SIgN-06-006; SIgN-08-020; SIgN-10-029
1000 Förderprogramm
  1. Seventh Framework Programme (FP7/2007-2013)
  2. -
  3. Center of Excellence in Complex Disease Genetics; SALVE program "Pubgensense"
  4. -
  5. -
  6. -
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  8. -
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  10. -
  11. -
  12. Center of Knowledge Interchange' program
  13. Caché Campus program
  14. -
  15. -
  16. framework of the Center of Translational Genomics
  17. European Regional Development Fund
  18. -
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  55. -
  56. Systems Biology of Metabotypes; National Genome Research Network NGFNplus Atherogenomics
  57. -
  58. -
  59. -
1000 Dateien
  1. Cell Specific eQTL Analysis without Sorting Cells
    Cell Specific eQTL Analysis without Sorting Cells
1000 Objektart article
1000 Beschrieben durch
1000 @id frl:6408675.rdf
1000 Erstellt am 2018-07-11T12:39:27.081+0200
1000 Erstellt von 218
1000 beschreibt frl:6408675
1000 Bearbeitet von 218
1000 Zuletzt bearbeitet Fri Jan 31 00:13:21 CET 2020
1000 Objekt bearb. Tue Sep 04 10:33:47 CEST 2018
1000 Vgl. frl:6408675
1000 Oai Id
  1. oai:frl.publisso.de:frl:6408675 |
1000 Sichtbarkeit Metadaten public
1000 Sichtbarkeit Daten public
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