An overview of papers, tools and databases using or citing eQTLGen Consortium data.
Last update: 21 April 2020.

If you use the data on this website, please cite our paper:
Võsa, U., Claringbould, A., (…), Franke, L.; 2018; Unraveling the polygenic architecture of complex traits using blood eQTL meta-analysis

Papers using eQTLGen data (before publication)
  1. Karlsson Linnér, R., et al. (2019). Genome-wide association analyses of risk tolerance and risky behaviors in over 1 million individuals identify hundreds of loci and shared genetic influences. Nature Genetics, 51(2), 245–257.
  2. Lepik, K., et al. (2017). C-reactive protein upregulates the whole blood expression of CD59 - an integrative analysis. PLOS Computational Biology, 13(9), e1005766.
  3. Männik, K., et al. (2019). Leveraging biobank-scale rare and common variant analyses to identify ASPHD1 as the main driver of reproductive traits in the 16p11.2 locus. BioRxiv, 716415.
  4. Porcu, E., et al. (2019). Mendelian randomization integrating GWAS and eQTL data reveals genetic determinants of complex and clinical traits. Nature Communications, 10(1), 3300.
  5. Thompson, D., et al. (2019). Genetic predisposition to mosaic Y chromosome loss in blood is associated with genomic instability in other tissues and susceptibility to non-haematological cancers. BioRxiv, 514026.
  6. Timmers, P. R., et al. (2019). Genomics of 1 million parent lifespans implicates novel pathways and common diseases and distinguishes survival chances. ELife, 8.
  7. Qi, T., et al. (2018). Identifying gene targets for brain-related traits using transcriptomic and methylomic data from blood. Nature Communications, 9(1), 2282.
  8. Xue, A., et al. (2018). Genome-wide association analyses identify 143 risk variants and putative regulatory mechanisms for type 2 diabetes. Nature Communications, 9(1), 2941.
  9. Wray, N. R., et al. (2018). Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression. Nature Genetics, 50(5), 668–681.
Papers using eQTLGen data (after publication)
  1. Akosile, W., et al. (2019). NLRP3 is associated with coronary artery disease in Vietnam veterans. Gene, 144163.
  2. Allsup, D., et al. (2019). Multicentre Genome Wide Association Study Identifies Risk Alleles for Progressive Chronic Lymphocytic Leukaemia. Blood, 134(Supplement_1), 1740–1740.
  3. Andrews, S. J., & Goate, A. (2019). Mendelian randomization indicates that TNF is not causally associated with Alzheimer’s disease. Neurobiology of Aging.
  4. Arbeeva, L., et al. (2020). Genome-wide meta-analysis identified novel variant associated with hallux valgus in Caucasians. Journal of Foot and Ankle Research, 13(1), 11.
  5. Arvanitis, M., et al. (2020). Genome-wide association and multi-omic analyses reveal ACTN2 as a gene linked to heart failure. Nature Communications, 11(1), 1122.
  6. Cai, M., et al. (2020). IGREX for quantifying the impact of genetically regulated expression on phenotypes. NAR Genomics and Bioinformatics, 2(1).
  7. Cai, X.-Y., et al. (2019). GWAS Follow-up Study Discovers a Novel Genetic Signal on 10q21.2 for Atopic Dermatitis in Chinese Han Population. Frontiers in Genetics, 10, 174.
  8. Carmeli, C., et al (2020). Gene regulation contributes to explain the impact of early life socioeconomic disadvantage on adult inflammatory levels in two European cohort studies. MedRxiv, 2020.04.03.20050872.
  9. Censin, J. C., et al. (2020). Colocalization highlights genes in hypothalamic–pituitary–gonadal axis as potentially mediating polycystic ovary syndrome risk. BioRxiv, 2020.01.10.901116.
  10. Chauquet, S., et al. (2020). Investigating the potential effect of antihypertensive medication on psychiatric disorders: a mendelian randomisation study. MedRxiv, 2020.03.19.20039412.
  11. Fernandez-Jimenez, N., & Bilbao, J. R. (2019). Mendelian Randomization analysis of celiac GWAS reveals a blood expression signature with diagnostic potential in absence of gluten consumption. Human Molecular Genetics.
  12. Ferreira, R. C., et al. (2019). Chronic Immune Activation in Systemic Lupus Erythematosus and the Autoimmune PTPN22 Trp620 Risk Allele Drive the Expansion of FOXP3+ Regulatory T Cells and PD-1 Expression. Frontiers in Immunology, 10.
  13. Fine, R. S., et al. (2019). Benchmarker: An Unbiased, Association-Data-Driven Strategy to Evaluate Gene Prioritization Algorithms. The American Journal of Human Genetics, 104(6), 1025–1039.
  14. Foley, C. N., et al. (2019). A fast and efficient colocalization algorithm for identifying shared genetic risk factors across multiple traits. BioRxiv, 592238.
  15. Folkersen, L., et al. (2020). Genomic evaluation of circulating proteins for drug target characterisation and precision medicine. BioRxiv, 2020.04.03.023804.
  16. Gibson, J., et al. (2019). A meta-analysis of genome-wide association studies of epigenetic age acceleration. PLOS Genetics, 15(11), e1008104.
  17. Grenn, F. P., et al. (2020). The Parkinson’s Disease GWAS Locus Browser. BioRxiv Genetics, 2020.04.01.020404.
  18. Han, Y., et al. (2020). Genome-wide analysis highlights contribution of immune system pathways to the genetic architecture of asthma. Nature Communications, 11(1), 1776.
  19. Hillary, R. F., et al. (2019). Genome and epigenome wide studies of neurological protein biomarkers in the Lothian Birth Cohort 1936. Nature Communications, 10(1).
  20. Hillary, R. F., et al (2020). Integrative omics approach to identify the molecular architecture of inflammatory protein levels in healthy older adults. BioRxiv, 2020.02.17.952135.
  21. Hobbs, B. D., et al. (2019). Overlap of Genetic Risk Between Interstitial Lung Abnormalities and Idiopathic Pulmonary Fibrosis. American Journal of Respiratory and Critical Care Medicine.
  22. Hyvärinen, K., et al. (2020). Meta-Analysis of Genome-Wide Association and Gene Expression Studies Implicates Donor T Cell Function and Cytokine Pathways in Acute GvHD. Frontiers in Immunology, 11.
  23. Inshaw, J. R. J., et al. (2019). Genetic Variants Predisposing Most Strongly to Type 1 Diabetes Diagnosed Under Age 7 Years Lie Near Candidate Genes That Function in the Immune System and in Pancreatic β-Cells. Diabetes Care, dc190803.
  24. Iwaki, H., et al. (2019). Genetic risk of Parkinson disease and progression: Neurology Genetics, 5(4), e348.
  25. Iwaki, H., et al. (2019). Genomewide association study of Parkinson’s disease clinical biomarkers in 12 longitudinal patients’ cohorts. Movement Disorders, 34(12), 1839–1850.
  26. Jabbari, E., et al. (2020). Common variation at the LRRK2 locus is associated with survival in the primary tauopathy progressive supranuclear palsy. BioRxiv, 2020.02.04.932335.
  27. Jacobs, B. M., et al. (2020). Summary-data-based mendelian randomisation reveals druggable targets for multiple sclerosis. BioRxiv, 2020.01.20.907451.
  28. Jaeger, M., et al. (2019). A genome-wide functional genomics approach identifies susceptibility pathways to fungal bloodstream infection in humans. The Journal of Infectious Diseases.
  29. Jamieson, E., et al. (2019). Smoking, DNA methylation and lung function: a Mendelian randomization analysis to investigate causal relationships. bioRxiv.
  30. Kamat, M. A., et al. (2019). PhenoScanner V2: an expanded tool for searching human genotype–phenotype associations. Bioinformatics, 35(22), 4851–4853.
  31. Karaesmen, E., et al. (2019). Multiple functional variants in the IL1RL1 region are pretransplant markers for risk of GVHD and infection deaths. Blood Advances, 3(16), 2512–2524.
  32. Ke, J., et al. (2019). Evaluation of polymorphisms in microRNA‐binding sites and pancreatic cancer risk in Chinese population. Journal of Cellular and Molecular Medicine, jcmm.14906.
  33. Kolberg, L., et al. (2020). Co-expression analysis reveals interpretable gene modules controlled by trans-acting genetic variants. BioRxiv, 2020.04.22.055335.
  34. Krebs, K., et al. (2020). Genome-wide study identifies association between HLA-B*55:01 and penicillin allergy. BioRxiv, 2020.02.27.967497.
  35. Le, K. T. T., et al. (2019). Functional Annotation of Genetic Loci Associated With Sepsis Prioritizes Immune and Endothelial Cell Pathways. Frontiers in Immunology, 10, 1949.
  36. Le Guen, et al. (2020). Enhancer locus in ch14q23.1 modulates brain asymmetric temporal regions involved in language processing.
  37. Leyden, G. M., et al. (2020). A factorial Mendelian randomization study to systematically prioritize genetic targets for the treatment of cardiovascular disease. MedRxiv, 2020.02.16.20023010.
  38. Liang, Y., et al. (2020). Scalable unified framework of total and allele-specific counts for cis-QTL, fine-mapping, and prediction. BioRxiv, 2020.04.22.050666.
  39. Liu, G., et al. (2020). rs4147929 variant minor allele increases ABCA7 gene expression and ABCA7 shows increased gene expression in Alzheimer’s disease patients compared with controls. Acta Neuropathologica, 1–4.
  40. Madan, N., et al. (2019). Functionalization of CD36 Cardiovascular Disease and Expression Associated Variants by Interdisciplinary High Throughput Analysis. PLOS Genetics, 15(7), e1008287.
  41. Marderstein, A. R., et al. (2019). Age, Sex, and Genetics Influence the Abundance of Infiltrating Immune Cells in Human Tissues. BioRxiv, 614305.
  42. Milet, J., et al. (2019). First genome-wide association study of non-severe malaria in two birth cohorts in Benin. Human Genetics.
  43. Nath, A. P., et al. (2019). Multivariate Genome-wide Association Analysis of a Cytokine Network Reveals Variants with Widespread Immune, Haematological, and Cardiometabolic Pleiotropy. The American Journal of Human Genetics, 105(6), 1076–1090.
  44. Nalls, M. A., et al. (2019). Identification of novel risk loci, causal insights, and heritable risk for Parkinson’s disease: a meta-analysis of genome-wide association studies. The Lancet Neurology, 18(12), 1091–1102.
  45. Nudel, R., et al. (2020). A large population-based investigation into the genetics of susceptibility to gastrointestinal infections and the link between gastrointestinal infections and mental illness. Human Genetics, 1–12.
  46. O’Mara, T. A., et al. (2019). Analysis of Promoter-Associated Chromatin Interactions Reveals Biologically Relevant Candidate Target Genes at Endometrial Cancer Risk Loci. Cancers, 11(10), 1440.
  47. Patrick, M. T., et al. (2019). Integrative Approach to Reveal Cell Type Specificity and Gene Candidates for Psoriatic Arthritis Outside the MHC. Frontiers in Genetics, 10, 304.
  48. Porcu, E., et al. (2020). The role of gene expression on human sexual dimorphism: too early to call. BioRxiv, 2020.04.15.042986.
  49. Raffield, L. M., et al. (2020). Allelic Heterogeneity at the CRP Locus Identified by Whole-Genome Sequencing in Multi-ancestry Cohorts. American Journal of Human Genetics, 106(1), 112–120.
  50. Revez, J. A., et al. (2020). Genome-wide association study identifies 143 loci associated with 25 hydroxyvitamin D concentration. Nature Communications, 11(1), 1–12.
  51. Richardson, T. G., et al. (2019). A transcriptome-wide Mendelian randomization study to uncover tissue-dependent regulatory mechanisms across the human phenome. BioRxiv, 563379.
  52. Saferali, A., et al. (2019). Analysis of genetically driven alternative splicing identifies FBXO38 as a novel COPD susceptibility gene. PLOS Genetics, 15(7), e1008229.
  53. Shadrin, A. A., et al. (2019). A genome-wide genetic pleiotropy approach identified shared loci between multiple system atrophy and inflammatory bowel disease. BioRxiv, 751354.
  54. Shah, S., et al. (2020). Genome-wide association and Mendelian randomisation analysis provide insights into the pathogenesis of heart failure. Nature Communications, 1–12.
  55. Shi, C., et al. (2020). An active chromatin interactome elucidates the biological mechanisms underlying genetic risk factors of dermatological conditions in disease relevant cell lines. BioRxiv, 2020.03.05.973271.
  56. Stevelink, R., et al. (2019). Assessing the genetic association between vitamin B6 metabolism and genetic generalized epilepsy. Molecular Genetics and Metabolism Reports, 21, 100518.
  57. Storm, C. S. et al. (2020). Using Mendelian randomization to understand and develop treatments for neurodegenerative disease. Brain Communications.
  58. Tan, J. Y., & Marques, A. C. (2020). The activity of human enhancers is modulated by the splicing of their associated lncRNAs. BioRxiv, 2020.04.17.045971.
  59. Tang, H., et al. (2019). Validation of genetic associations with acute GVHD and nonrelapse mortality in DISCOVeRY-BMT. Blood Advances, 3(15), 2337–2341.
  60. Taub, M. A., et al. (2019). Novel genetic determinants of telomere length from a multi-ethnic analysis of 75,000 whole genome sequences in TOPMed. BioRxiv, 749010.
  61. Venkateswaran, S., et al. (2019). Neutrophil GM-CSF signaling in inflammatory bowel disease patients is influenced by non-coding genetic variants. Scientific Reports, 9(1), 9168.
  62. de Vries, D. H., et al. (2020). Integrating GWAS with bulk and single-cell RNA-sequencing reveals a role for LY86 in the anti-Candida host response. PLOS Pathogens, 16(4), e1008408.
  63. Vuckovic, et al. (2020). The Polygenic and Monogenic Basis of Blood Traits and Diseases. MedRxiv, 2020.02.02.20020065.
  64. Wang, J., et al. (2019). Genome-wide association analyses identify variants in IRF4 associated with Acute Myeloid Leukemia and Myelodysplastic Syndrome susceptibility. BioRxiv, 773952.
  65. Wang, X., et al., (2019). Integrating genome-wide association study and expression quantitative trait loci data identifies NEGR1 as a causal risk gene of major depression disorder. Journal of Affective Disorders.
  66. Wheeler, H. E., et al. (2019). Imputed gene associations identify replicable trans-acting genes enriched in transcription pathways and complex traits. Genetic Epidemiology, 1-13.
  67. Wu, Y., et al. (2019). Genome-wide association study of gastrointestinal disorders reinforces the link between the digestive tract and the nervous system. BioRxiv, 811737.
  68. Yang, F., et al. (2019). CCmed: cross-condition mediation analysis for identifying robust trans-eQTLs and assessing their effects on human traits. BioRxiv, 803106.
  69. Yang, Y., et al. (2019). Genetic and Expression Analysis of COPI Genes and Alzheimer’s Disease Susceptibility. Frontiers in Genetics, 10.
  70. Yu, H., et al. (2020). Integration analysis of methylation quantitative trait loci and GWAS identify three schizophrenia risk variants. Neuropsychopharmacology.
  71. Zeng, B., et al. (2019). Comprehensive Multiple eQTL Detection and Its Application to GWAS Interpretation. Genetics.
  72. Zheng, Z., et al. (2019). QTLbase: an integrative resource for quantitative trait loci across multiple human molecular phenotypes. Nucleic Acids Research.
  73. Zhou, X., et al. (2019). Non-coding variability at the APOE locus contributes to the Alzheimer’s risk. Nature Communications, 10(1), 3310.
  74. Zhu, X., et al. (2020). Modeling regulatory network topology improves genome-wide analyses of complex human traits. BioRxiv, 2020.03.13.990010.
Papers citing eQTLGen
  1. Cai, M., et al. (2019). Quantifying the impact of genetically regulated expression on complex traits and diseases. BioRxiv, 546580.
  2. Cheng, F.-F., et al., (2019). Towards the identification of causal genes for age-related macular degeneration. BioRxiv.
  3. Hawe, J. S., et al. (2019). Inferring interaction networks from multi-omics data. Frontiers in Genetics, 10(JUN).
  4. Kalayci, S., et al. (2019). ImmuneRegulation: a web-based tool for identifying human immune regulatory elements. Nucleic Acids Research, 47(W1), W142–W150.
  5. Kerimov, N., et al. (2020). eQTL Catalogue: a compendium of uniformly processed human gene expression and splicing QTLs. BioRxiv, 2020.01.29.924266.
  6. Lappalainen, T., et al. (2019). Genomic Analysis in the Age of Human Genome Sequencing. Cell, 177(1), 70–84.
  7. Liu, X., et al. (2019). Trans Effects on Gene Expression Can Drive Omnigenic Inheritance. Cell, 177(4), 1022-1034.e6.
  8. Lu, L., et al. (2019). Robust Hi-C chromatin loop maps in human neurogenesis and brain tissues at high-resolution. BioRxiv, 744540.
  9. Neumeyer, S., et al (2019). Strengthening Causal Inference for Complex Disease Using Molecular Quantitative Trait Loci. Trends in Molecular Medicine, 1–10.
  10. Rotival, M. (2019). Characterising the genetic basis of immune response variation to identify causal mechanisms underlying disease susceptibility. HLA, 94(3), 275–284.
  11. Sidorenko, J., et al. (2019). The effect of X-linked dosage compensation on complex trait variation. Nature Communications, 10(1), 3009.
  12. Storm, C. S. et al. (2020). Using Mendelian randomization to understand and develop treatments for neurodegenerative disease. Brain Communications.
  13. Wainberg, M., et al. (2019). Opportunities and challenges for transcriptome-wide association studies. Nature Genetics 2019 51:4, 51(4), 592.
  14. van der Wijst, M. G., et al. (2020). The single-cell eQTLGen consortium. ELife, 9.
  15. Yao, D. W., et al. (2019). Quantifying genetic effects on disease mediated by assayed gene expression levels. BioRxiv, 730549.
  16. Ye, Y., et al. (2020). A Multi-Omics Perspective of Quantitative Trait Loci in Precision Medicine. Trends in Genetics.
  17. Yeung, K.-F., et al. (2019). CoMM: A Collaborative Mixed Model That Integrates GWAS and eQTL Data Sets to Investigate the Genetic Architecture of Complex Traits. Bioinformatics and Biology Insights, 13, 117793221988143.
  18. Zheng, J., et al (2019, November 21). Use of Mendelian Randomization to Examine Causal Inference in Osteoporosis. Frontiers in Endocrinology. Frontiers Media S.A.
  19. Zwir, I., et al. (2019). Uncovering the complex genetics of human personality: response from authors on the PGMRA Model. Molecular Psychiatry, 1.
Computational methods
Online computational methods that use eQTLGen data and came available after submitting our initial manuscript: