Estimating the causal tissues for complex traits and diseases
Open accessPublished version of a preprint
Abstract
How to interpret the biological causes underlying the predisposing markers identified through genome-wide association studies (GWAS) remains an open question. One direct and powerful way to assess the genetic causality behind GWAS is through analysis of expression quantitative trait loci (eQTLs). Here we describe a new approach to estimate the tissues behind the genetic causality of a variety of GWAS traits, using the cis-eQTLs in 44 tissues from the Genotype-Tissue Expression (GTEx) Consortium. We have adapted the regulatory trait concordance (RTC) score to measure the probability of eQTLs being active in multiple tissues and to calculate the probability that a GWAS-associated variant and an eQTL tag the same functional effect. By normalizing the GWAS-eQTL probabilities by the tissue-sharing estimates for eQTLs, we generate relative tissue-causality profiles for GWAS traits. Our approach not only implicates the gene likely mediating individual GWAS signals, but also highlights tissues where the genetic causality for an individual trait is likely manifested.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In parallel, efforts to connect trait-associated regulatory variants to the genes and processes they regulate in disease-relevant cell types have been scaled.
“In parallel, there has been a scaling of efforts to connect trait-associated regulatory variants to the genes and processes that they regulate in cell types relevant to the disease of interest110,111.”
From A brief history of human disease genetics (2020) · cites this paper as [111]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
A global reference for human genetic variation
The 1000 Genomes Project Consortium, authors, C., Auton, A., Abecasis, G. R., Steering committee, Altshuler, D. M., Durbin, R. M., Abecasis, G. R., Bentley, D. R., Chakravarti, A., et al. (2015) · Nature · doi:10.1038/nature15393 · Cited by 18,247 · see citing works
Self-citationThe Human Genome Browser at UCSC
Kent, W. J., Sugnet, C. W., Furey, T. S., Roskin, K. M., Pringle, T. H., Zahler, A. M., & Haussler, A. D. (2002) · Genome Research · doi:10.1101/gr.229102 · Cited by 10,400 · see citing works
<i>The Brain's Default Network</i>: <i>Anatomy, Function, and Relevance to Disease</i>
Buckner, R. L., Andrews‐Hanna, J. R., & Schacter, D. L. (2008) · Annals of the New York Academy of Sciences · doi:10.1196/annals.1440.011 · Cited by 9,761 · see citing works
Statistical significance for genomewide studies
Storey, J. D., & Tibshirani, R. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1530509100 · Cited by 9,692 · see citing works
Insulin signalling and the regulation of glucose and lipid metabolism
Saltiel, A. R., & Kahn, C. R. (2001) · Nature · doi:10.1038/414799a · Cited by 5,326 · see citing works
The Genotype-Tissue Expression (GTEx) pilot analysis: Multitissue gene regulation in humans
The GTEx Consortium, Ardlie, K. G., Deluca, D. S., Segrè, A. V., Sullivan, T. J., Young, T. R., Gelfand, E. T., Trowbridge, C. A., Maller, J. B., Tukiainen, T., et al. (2015) · Science · doi:10.1126/science.1262110 · Cited by 5,062 · see citing works
Self-citationGiambartolomei, C., Vukcevic, D., Schadt, E. E., Franke, L., Hingorani, A. D., Wallace, C., & Plagnol, V. (2014) · PLoS Genetics · doi:10.1371/journal.pgen.1004383 · Cited by 4,261 · see citing works
Genetic effects on gene expression across human tissues
GTEx Consortium (2017) · Nature · doi:10.1038/nature24277 · Cited by 4,036 · see citing works
Self-citationBiological, clinical and population relevance of 95 loci for blood lipids
Teslovich, T. M., Musunuru, K., Smith, A. V., Edmondson, A. C., Stylianou, I. M., Koseki, M., Pirruccello, J. P., Ripatti, S., Chasman, D. I., Willer, C. J., et al. (2010) · Nature · doi:10.1038/nature09270 · Cited by 3,487 · see citing works
Willer (2013) · doi:10.1038/ng.2797 · Cited by 2,983 · see citing works
Genome-wide association studies for complex traits: consensus, uncertainty and challenges
McCarthy, M. I., Abecasis, G. R., Cardon, L. R., Goldstein, D. B., Little, J., Ioannidis, J. P. A., & Hirschhorn, J. N. (2008) · Nature Reviews Genetics · doi:10.1038/nrg2344 · Cited by 2,891 · see citing works
The NHGRI GWAS Catalog, a curated resource of SNP-trait associations
Welter, D., MacArthur, J., Morales, J., Burdett, T., Hall, P., Junkins, H., Klemm, A., Flicek, P., Manolio, T., Hindorff, L., et al. (2014) · Nucleic Acids Research · doi:10.1093/nar/gkt1229 · Cited by 2,707 · see citing works
Pvclust: an R package for assessing the uncertainty in hierarchical clustering
Suzuki, R., & Shimodaira, H. (2006) · Bioinformatics · doi:10.1093/bioinformatics/btl117 · Cited by 2,456 · see citing works
Genomewide Association Analysis of Coronary Artery Disease
Samani, N. J., Erdmann, J., Hall, A. S., Hengstenberg, C., Mangino, M., Mayer, B., Dixon, R. J., Meitinger, T., Braund, P., Wichmann, H.-E., et al. (2007) · New England Journal of Medicine · doi:10.1056/nejmoa072366 · Cited by 2,044 · see citing works
The neuropathology of schizophrenia
Harrison, P. J. (1999) · Brain · doi:10.1093/brain/122.4.593 · Cited by 1,677 · see citing works
Regulation of uric acid metabolism and excretion
Maiuolo, J., Oppedisano, F., Gratteri, S., Muscoli, C., & Mollace, V. (2015) · International Journal of Cardiology · doi:10.1016/j.ijcard.2015.08.109 · Cited by 1,405 · see citing works
Trait-associated SNPs are more likely to be eQTLs: annotation to enhance discovery from GWAS
Nicolae, D. L., Gamazon, E., Zhang, W., Duan, S., Dolan, M. E., & Cox, N. J. (2010) · PLoS Genetics · doi:10.1371/journal.pgen.1000888 · Cited by 1,292 · see citing works
Chapter 11: Genome-wide association studies
Bush, W. S., & Moore, J. H. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002822 · Cited by 1,171 · see citing works
From noncoding variant to phenotype via SORT1 at the 1p13 cholesterol locus
Musunuru, K., Strong, A., Frank-Kamenetsky, M., Lee, N. E., Ahfeldt, T., Sachs, K. V., Li, X., Li, H., Kuperwasser, N., Ruda, V. M., et al. (2010) · Nature · doi:10.1038/nature09266 · Cited by 1,140 · see citing works
Myocardial Infarction Genetics Consortium (2009) · Nature Genetics · doi:10.1038/ng.327 · Cited by 1,088 · see citing works
Han, S.-K., Gottsch, M. L., Lee, K. J., Popa, S. M., Smith, J. T., Jakawich, S. K., Clifton, D. K., Steiner, R. A., & Herbison, A. E. (2005) · The Journal of Neuroscience · doi:10.1523/jneurosci.3328-05.2005 · Cited by 1,036 · see citing works
Nguyen, P., Leray, V., Diez, M., Serisier, S., Bloc’h, J. L., Siliart, B. S., & Dumon, H. (2008) · Journal of Animal Physiology and Animal Nutrition · doi:10.1111/j.1439-0396.2007.00752.x · Cited by 972 · see citing works
The Fine-Scale Structure of Recombination Rate Variation in the Human Genome
McVean, G., Myers, S. R., Hunt, S. E., Deloukas, P., Bentley, D., & Donnelly, P. (2004) · Science · doi:10.1126/science.1092500 · Cited by 968 · see citing works
Common Regulatory Variation Impacts Gene Expression in a Cell Type–Dependent Manner
Dimas, A. S., Deutsch, S., Stranger, B. E., Montgomery, S. B., Borel, C., Attar, H., Ingle, C., Beazley, C., Arcelus, M. G., Sekowska, M., et al. (2009) · Science · doi:10.1126/science.1174148 · Cited by 746 · see citing works
Self-citationMapping cis- and trans-regulatory effects across multiple tissues in twins
The Multiple Tissue Human Expression Resource (MuTHER) Consortium, Grundberg, E., Small, K. S., Hedman, Å. K., Nica, A. C., Buil, A., Keildson, S., Bell, J. T., Yang, T.-P., Meduri, E., et al. (2012) · Nature Genetics · doi:10.1038/ng.2394 · Cited by 729 · see citing works
Self-citationFast and efficient QTL mapper for thousands of molecular phenotypes
Ongen, H., Buil, A., Brown, A. A., Dermitzakis, E. T., & Delaneau, O. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv722 · Cited by 571 · see citing works
Self-citationNica, A. C., Montgomery, S. B., Dimas, A. S., Stranger, B. E., Beazley, C., Barroso, I., & Dermitzakis, E. T. (2010) · PLoS Genetics · doi:10.1371/journal.pgen.1000895 · Cited by 423 · see citing works
Self-citationA complete tool set for molecular QTL discovery and analysis
Delaneau, O., Ongen, H., Brown, A. A., Fort, A., Panousis, N. I., & Dermitzakis, E. T. (2017) · Nature Communications · doi:10.1038/ncomms15452 · Cited by 362 · see citing works
Self-citationHAPGEN2: simulation of multiple disease SNPs
Su, Z., Marchini, J., & Donnelly, P. (2011) · Bioinform. · doi:10.1093/bioinformatics/btr341 · Cited by 352 · see citing works
A Statistical Framework for Joint eQTL Analysis in Multiple Tissues
Flutre, T., Wen, X., Pritchard, J. K., & Stephens, M. (2013) · PLoS Genetics · doi:10.1371/journal.pgen.1003486 · Cited by 252 · see citing works
Tissue-Specific Effects of Genetic and Epigenetic Variation on Gene Regulation and Splicing
Gutiérrez‐Arcelus, M., Ongen, H., Lappalainen, T., Montgomery, S. B., Buil, A., Yurovsky, A., Bryois, J., Padioleau, I., Romano, L., Planchon, A., et al. (2015) · PLoS Genetics · doi:10.1371/journal.pgen.1004958 · Cited by 193 · see citing works
Self-citationFrom expression QTLs to personalized transcriptomics
Montgomery, S. B., & Dermitzakis, E. T. (2011) · Nature Reviews Genetics · doi:10.1038/nrg2969 · Cited by 189 · see citing works
Self-citationTensor decomposition for multiple-tissue gene expression experiments
Hore, V., Viñuela, A., Buil, A., Knight, J. C., McCarthy, M. I., Small, K. S., & Marchini, J. (2016) · Nature Genetics · doi:10.1038/ng.3624 · Cited by 178 · see citing works
Brown, C. D., Mangravite, L. M., & Engelhardt, B. E. (2013) · PLoS Genetics · doi:10.1371/journal.pgen.1003649 · Cited by 173 · see citing works
Using gene expression to investigate the genetic basis of complex disorders
Nica, A. C., & Dermitzakis, E. T. (2008) · Human Molecular Genetics · doi:10.1093/hmg/ddn285 · Cited by 106 · see citing works
Self-citationFrom gene expression to disease risk
Dermitzakis, E. T. (2008) · Nature Genetics · doi:10.1038/ng0508-492 · Cited by 78 · see citing works
Self-citationCorrelation among body height, intelligence, and brain gray matter volume in healthy children
Taki, Y., Hashizume, H., Sassa, Y., Takeuchi, H., Asano, M., Asano, K., Kotozaki, Y., Nouchi, R., Wu, K., Fukuda, H., et al. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.08.092 · Cited by 77 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
The GTEx Consortium atlas of genetic regulatory effects across human tissues
The GTEx Consortium, Aguet, F., Anand, S., Ardlie, K. G., Gabriel, S., Getz, G. A., Graubert, A., Hadley, K., Handsaker, R. E., Huang, K. H., et al. (2020) · Science · doi:10.1126/science.aaz1776 · Cited by 5,547 · see citing works
Self-citationBuniello, A., MacArthur, J. A. L., Cerezo, M., Harris, L. W., Hayhurst, J., Malangone, C., McMahon, A., Morales, J., Mountjoy, E., Sollis, E., et al. (2019) · Nucleic Acids Research · doi:10.1093/nar/gky1120 · Cited by 4,000 · see citing works
Finucane, H. K., Reshef, Y. A., Anttila, V., Slowikowski, K., Gusev, A., Byrnes, A., Gazal, S., Loh, P.-R., Lareau, C., Shoresh, N., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0081-4 · Cited by 1,097 · see citing works
A brief history of human disease genetics
Claussnitzer, M., Cho, J. H., Collins, R., Cox, N. J., Dermitzakis, E. T., Hurles, M. E., Kathiresan, S., Kenny, E. E., Lindgren, C. M., MacArthur, D. G., et al. (2020) · Nature · doi:10.1038/s41586-019-1879-7 · Cited by 697 · see citing works
Self-citationGTEx Consortium, Gamazon, E. R., Segrè, A. V., van de Bunt, M., Wen, X., Xi, H. S., Hormozdiari, F., Ongen, H., Konkashbaev, A., Derks, E. M., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0154-4 · Cited by 412 · see citing works
Self-citationPorcu, E., Rüeger, S., Lepik, K., eQTLGen Consortium, Agbessi, M., Ahsan, H., Alves, I., Andiappan, A., Arindrarto, W., Awadalla, P., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-10936-0 · Cited by 322 · see citing works
Genetic variant effects on gene expression in human pancreatic islets and their implications for T2D
Viñuela, A., Varshney, A., van de Bunt, M., Prasad, R. B., Asplund, O., Bennett, A., Boehnke, M., Brown, A. A., Erdos, M. R., Fadista, J., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-18581-8 · Cited by 152 · see citing works
Self-citationInferring relevant cell types for complex traits by using single-cell gene expression
Calderon, D., Bhaskar, A., Knowles, D. A., Golan, D., Raj, T., Fu, A. Q., & Pritchard, J. K. (2017) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2017.09.009 · Cited by 127 · see citing works
Genetic control of the human brain proteome
Robins, C., Wingo, A. P., Fan, W., Duong, D. M., Meigs, J., Gerasimov, E. S., Dammer, E. B., Cutler, D. J., Jager, P. L. D., Bennett, D. A., et al. (2019) · doi:10.1101/816652 · Cited by 104 · see citing works
Approaches and advances in the genetic causes of autoimmune disease and their implications
Inshaw, J. R. J., Cutler, A. J., Burren, O. S., Stefana, M. I., & Todd, J. A. (2018) · Nature Immunology · doi:10.1038/s41590-018-0129-8 · Cited by 70 · see citing works
- Authors
- GTEx Consortium, Ongen, H., Brown, A. A., Delaneau, O., Panousis, N. I., Nica, A. C., & Dermitzakis, E. T.
- Affiliations
- University of Geneva (CH)
- Corresponding author
- Ongen, Halit
- Dermitzakis, Emmanouil T
- Venue
- Nature Genetics
- Published
- 2017-10-23
- Volume
- 49
- Issue
- 12
- Pages
- 1676-1683
- ISSN
- 1061-4036 (ISSN-L)
- 1546-1718
- OpenAlex
- W2951751247
- Cited by
- 198
- References
- 37
- Citations by year
- 2026: 9 · 2025: 14 · 2024: 16 · 2023: 15 · 2022: 27 · 2021: 33 · 2020: 23 · 2019: 34 · 2018: 22 · 2017: 4
- FWCI
- 15.25
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- cc-by-nc-nd
- www.springer.com/tdm (tdm)
- Locations
- Nature Genetics (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Discovery Research Portal (University of Dundee) (publishedVersion)
- Archive ouverte UNIGE (University of Geneva) (publishedVersion, other-oa)
- IRIS (submittedVersion)
- SERVAL (Université de Lausanne) (submittedVersion)
- Archive ouverte UNIGE (University of Geneva) (submittedVersion, cc-by)
- Funders
- National Science Foundation
- European Commission (258236)
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (149984, 31003A, 170096)
- Swiss Institute of Bioinformatics
- National Institutes of Health
- National Institute of Mental Health (R01MH101814)
- FP7 Ideas: European Research Council (260927)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- expression quantitative trait loci, cis-eQTL, complex traits, genetic causality, GTEx
- MeSH terms
- Genotype, Humans, Organ Specificity, Phenotype, Genetic Predisposition to Disease, Polymorphism, Single Nucleotide, Gene Expression Profiling, Quantitative Trait Loci, Genome-Wide Association Study, Genetic Association Studies
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
