Orienting the causal relationship between imprecisely measured traits using GWAS summary data.
Open accessPublished version of a preprint
Abstract
Inference about the causal structure that induces correlations between two traits can be achieved by combining genetic associations with a mediation-based approach, as is done in the causal inference test (CIT). However, we show that measurement error in the phenotypes can lead to the CIT inferring the wrong causal direction, and that increasing sample sizes has the adverse effect of increasing confidence in the wrong answer. This problem is likely to be general to other mediation-based approaches. Here we introduce an extension to Mendelian randomisation, a method that uses genetic associations in an instrumentation framework, that enables inference of the causal direction between traits, with some advantages. First, it can be performed using only summary level data from genome-wide association studies; second, it is less susceptible to bias in the presence of measurement error or unmeasured confounding. We apply the method to infer the causal direction between DNA methylation and gene expression levels. Our results demonstrate that, in general, DNA methylation is more likely to be the causal factor, but this result is highly susceptible to bias induced by systematic differences in measurement error between the platforms, and by horizontal pleiotropy. We emphasise that, where possible, implementing MR and appropriate sensitivity analyses alongside other approaches such as CIT is important to triangulate reliable conclusions about causality.
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?
The study performed MR and identified 17 unique proteins as putatively causal for AD (FDR-corrected P<0.05) with confirmed directionality using the Steiger test.
“We next performed MR47 and identified 17 unique proteins as putatively causal for AD (Fig. 4A, Supp. Table S40&41, FDR-corrected P<0.05) with confirmed directionality using the Steiger test52 (Supp. Table S42).”
From Proteogenomic analysis of human cerebrospinal fluid identifies neurologically relevant regulation and implicates causal proteins for Alzheimer’s disease (2024) · cites this paper as [52]
Directionality in the MR analysis was assessed using MR-Steiger.
“We analyzed directionality using MR-Steiger52.”
From Proteogenomic analysis of human cerebrospinal fluid identifies neurologically relevant regulation and implicates causal proteins for Alzheimer’s disease (2024) · cites this paper as [52]
Estimates from two-stage analyses with binary variables using logistic regression are sensitive to correct specification of the first-stage regression model.
“although in this case estimates are sensitive to correct specification of the first-stage regression model 74”
From Guidelines for performing Mendelian randomization investigations (2020) · cites this paper as [74]
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
Bowden, J., Davey Smith, G., & Burgess, S. (2015) · International Journal of Epidemiology · doi:10.1093/ije/dyv080 · Cited by 9,816 · see citing works
Bowden, J., Davey Smith, G., Haycock, P. C., & Burgess, S. (2016) · Genetic Epidemiology · doi:10.1002/gepi.21965 · Cited by 8,922 · see citing works
DNA methylation patterns and epigenetic memory
Bird, A. (2002) · Genes & Development · doi:10.1101/gad.947102 · Cited by 7,124 · see citing works
Davey Smith, G., & Ebrahim, S. (2003) · International Journal of Epidemiology · doi:10.1093/ije/dyg070 · Cited by 5,882 · see citing works
Mendelian randomization: genetic anchors for causal inference in epidemiological studies
Davey Smith, G., & Hemani, G. (2014) · Human Molecular Genetics · doi:10.1093/hmg/ddu328 · Cited by 4,677 · see citing works
Self-citationTests for comparing elements of a correlation matrix
Steiger, J. H. (1980) · Psychological Bulletin · doi:10.1037/0033-2909.87.2.245 · Cited by 4,621 · cited 1× · see citing works
“This can be achieved by assessing which of the two variables has the biggest absolute correlation with g (S2 Text), formalised by testing for a difference in the correlations ρgx and ρgy using Steiger’s Z-test for correlated correlations within a population [55].”
MethodIntegration of summary data from GWAS and eQTL studies predicts complex trait gene targets
Zhu, Z., Zhang, F., Hu, H., Bakshi, A., Robinson, M. R., Powell, J. E., Montgomery, G. W., Goddard, M. E., Wray, N. R., Visscher, P. M., et al. (2016) · Nature Genetics · doi:10.1038/ng.3538 · Cited by 3,352 · see citing works
DNA methylation arrays as surrogate measures of cell mixture distribution
Houseman, E. A., Accomando, W. P., Koestler, D. C., Christensen, B. C., Marsit, C. J., Nelson, H. H., Wiencke, J. K., & Kelsey, K. T. (2012) · BMC Bioinformatics · doi:10.1186/1471-2105-13-86 · Cited by 3,175 · see citing works
A structural approach to selection bias
Hernán, M. A., Hernández-Díaz, S., & Robins, J. M. (2004) · Epidemiology · doi:10.1097/01.ede.0000135174.63482.43 · Cited by 2,844 · cited 1× · see citing works
“This is because adjusting the outcome by the exposure induces a collider effect between the SNP and outcome [31], and in order to fully abrogate this association one must also adjust for all (hidden or otherwise) confounders.”
BackgroundSterne, J. A. C. (2001) · BMJ · doi:10.1136/bmj.322.7280.226 · Cited by 1,466 · see citing works
Pierce, B. L., & Burgess, S. (2013) · American Journal of Epidemiology · doi:10.1093/aje/kwt084 · Cited by 1,389 · see citing works
<i>FTO</i> Obesity Variant Circuitry and Adipocyte Browning in Humans
Claussnitzer, M., Dankel, S. N., Kim, K.-H., Quon, G., Meuleman, W., Haugen, C., Glunk, V., Sousa, I. S., Beaudry, J. L., Puviindran, V., et al. (2015) · New England Journal of Medicine · doi:10.1056/nejmoa1502214 · Cited by 1,332 · see citing works
Mendelian randomization: prospects, potentials, and limitations
Smith, G. D. (2004) · International Journal of Epidemiology · doi:10.1093/ije/dyh132 · Cited by 1,222 · see citing works
Triangulation in aetiological epidemiology
Lawlor, D. A., Tilling, K., & Davey Smith, G. (2017) · International Journal of Epidemiology · doi:10.1093/ije/dyw314 · Cited by 1,129 · see citing works
Self-citationAn integrative genomics approach to infer causal associations between gene expression and disease
Schadt, E. E., Lamb, J., Yang, X., Zhu, J., Edwards, S., GuhaThakurta, D., Sieberts, S. K., Monks, S., Reitman, M., Zhang, C., et al. (2005) · Nature Genetics · doi:10.1038/ng1589 · Cited by 1,043 · see citing works
Liu, Y., Aryee, M. J., Padyukov, L., Fallin, M. D., Hesselberg, E., Rúnarsson, Á. R., Reinius, L. E., Acevedo, N., Taub, M. A., Ronninger, M., et al. (2013) · Nature Biotechnology · doi:10.1038/nbt.2487 · Cited by 974 · see citing works
Chen, Y.-A., Lemire, M., Choufani, S., Butcher, D., Grafodatskaya, D., Zanke, B., Gallinger, S., Hudson, T. J., & Weksberg, R. (2013) · Epigenetics · doi:10.4161/epi.23470 · Cited by 908 · see citing works
Common variants associated with plasma triglycerides and risk for coronary artery disease
Do, R., Willer, C. J., Schmidt, E. M., Sengupta, S., Gao, C., Peloso, G. M., Gustafsson, S., Kanoni, S., Ganna, A., Chen, J., et al. (2013) · Nature Genetics · doi:10.1038/ng.2795 · Cited by 862 · see citing works
(1994) · American Economic Review · Cited by 852 · see citing works
Estimates of the Economic Return to Schooling from a New Sample of Twins
Krueger, A. B., & Ashenfelter, O. (1992) · National Bureau of Economic Research · doi:10.3386/w4143 · Cited by 601 · see citing works
Relton, C. L., & Davey Smith, G. (2012) · International Journal of Epidemiology · doi:10.1093/ije/dyr233 · Cited by 567 · see citing works
Self-citationBurgess, S., Daniel, R. M., Butterworth, A. S., Thompson, S. G., & the EPIC-InterAct Consortium (2015) · International Journal of Epidemiology · doi:10.1093/ije/dyu176 · Cited by 543 · see citing works
Cole, D. A., & Preacher, K. J. (2013) · Psychological Methods · doi:10.1037/a0033805 · Cited by 469 · see citing works
Data dredging, bias, or confounding: They can all get you into the BMJ and the Friday papers
Smith, G. D., & Ebrahim, S. (2002) · BMJ · doi:10.1136/bmj.325.7378.1437 · Cited by 434 · see citing works
The Health Risk of Obesity—Better Metrics Imperative
Ahima, R. S., & Lazar, M. A. (2013) · Science · doi:10.1126/science.1241244 · Cited by 384 · see citing works
Measurement and Meaning in Biology
Houle, D., Pélabon, C., Wagner, G. P., & Hansen, T. F. (2011) · The Quarterly Review of Biology · doi:10.1086/658408 · Cited by 366 · see citing works
Timpson, N. J., Nordestgaard, B. G., Harbord, R. M., Zacho, J., Frayling, T. M., Tybjærg-Hansen, A., & Davey Smith, G. (2011) · International Journal of Obesity · doi:10.1038/ijo.2010.137 · Cited by 310 · see citing works
Self-citationsystemfit: A Package for Estimating Systems of Simultaneous Equations inR
Henningsen, A., & Hamann, J. D. (2007) · Journal of Statistical Software · doi:10.18637/jss.v023.i04 · Cited by 281 · see citing works
Phillips, A. N., & Smith, G. D. (1991) · Journal of Clinical Epidemiology · doi:10.1016/0895-4356(91)90155-3 · Cited by 275 · see citing works
Self-citationMancuso, N., Shi, H., Goddard, P. C., Kichaev, G., Gusev, A., & Paşaniuc, B. (2017) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2017.01.031 · Cited by 265 · see citing works
Estimates of the Economic Return to Schooling from a New Sample of Twins
Ashenfelter, O., & Krueger, A. (1997) · Edward Elgar Publishing eBooks · doi:10.4337/9781035377541.00026 · Cited by 255 · see citing works
Hong, X., Hao, K., Ladd‐Acosta, C., Hansen, K. D., Tsai, H., Liu, X., Xu, X. P., Thornton, T. A., Caruso, D., Keet, C. A., et al. (2015) · Nature Communications · doi:10.1038/ncomms7304 · Cited by 247 · see citing works
Disentangling molecular relationships with a causal inference test
Millstein, J., Zhang, B., Zhu, J. W., & Schadt, E. E. (2009) · BMC Genetics · doi:10.1186/1471-2156-10-23 · Cited by 230 · see citing works
Invited Commentary: Causal Diagrams and Measurement Bias
Hernán, M. Á., & Cole, S. R. (2009) · American Journal of Epidemiology · doi:10.1093/aje/kwp293 · Cited by 221 · see citing works
Population Variation and Genetic Control of Modular Chromatin Architecture in Humans
Waszak, S. M., Delaneau, O., Gschwind, A. R., Kilpinen, H., Raghav, S. K., Witwicki, R. M., Orioli, A., Wiederkehr, M., Panousis, N. I., Yurovsky, A., et al. (2015) · Cell · doi:10.1016/j.cell.2015.08.001 · Cited by 214 · see citing works
Varbo, A., Benn, M., Smith, G. D., Timpson, N. J., Tybjærg‐Hansen, A., & Nordestgaard, B. G. (2014) · Circulation Research · doi:10.1161/circresaha.116.304846 · Cited by 171 · see citing works
Richmond, R. C., Davey Smith, G., Ness, A. R., den Hoed, M., McMahon, G., & Timpson, N. J. (2014) · PLoS Medicine · doi:10.1371/journal.pmed.1001618 · Cited by 167 · see citing works
Using genetic markers to orient the edges in quantitative trait networks: The NEO software
Aten, J. E., Fuller, T. F., Lusis, A. J., & Horvath, S. (2008) · BMC Systems Biology · doi:10.1186/1752-0509-2-34 · Cited by 147 · see citing works
Challenges and novel approaches for investigating molecular mediation
Richmond, R. C., Hemani, G., Tilling, K. M., Smith, G. D., & Relton, C. L. (2016) · Human Molecular Genetics · doi:10.1093/hmg/ddw197 · Cited by 133 · see citing works
Self-citationHemani, G., Zheng, J., Wade, K. H., Laurin, C., Elsworth, B., Burgess, S., Bowden, J., Langdon, R., Tan, V. Y., Yarmolinsky, J., et al. (2016) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/078972 · Cited by 133 · see citing works
Self-citationOn the Validity of Covariate Adjustment for Estimating Causal EffectsPreprint
Shpitser, I., VanderWeele, T. J., & Robins, J. M. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1203.3515 · Cited by 124 · see citing works
Burgess, S. (2018) · Apollo (University of Cambridge) · doi:10.17863/cam.26250 · Cited by 118 · see citing works
An integrated epigenomic analysis for type 2 diabetes susceptibility loci in monozygotic twins
Yuan, W., Xia, Y., Bell, C. G., Yet, İ., Ferreira, T. C., Ward, K. J., Gao, F., Loomis, A. K., Hyde, C. L., Wu, H., et al. (2014) · Nature Communications · doi:10.1038/ncomms6719 · Cited by 113 · see citing works
Using Multivariable Mendelian Randomization to Disentangle the Causal Effects of Lipid Fractions
Burgess, S., Freitag, D. F., Khan, H., Gorman, D. N., & Thompson, S. G. (2014) · PLoS ONE · doi:10.1371/journal.pone.0108891 · Cited by 95 · see citing works
Bose, M., Wu, C., Pankow, J. S., Demerath, E. W., Bressler, J., Fornage, M., Grove, M. L., Mosley, T. H., Hicks, C., North, K., et al. (2014) · BMC Bioinformatics · doi:10.1186/1471-2105-15-312 · Cited by 86 · see citing works
le Cessie, S., Debeij, J., Rosendaal, F. R., Cannegieter, S. C., & Vandenbroucke, J. P. (2012) · Epidemiology · doi:10.1097/ede.0b013e318254f5de · Cited by 81 · see citing works
Harper, K. N., Peters, B. A., & Gamble, M. V. (2013) · Cancer Epidemiology Biomarkers & Prevention · doi:10.1158/1055-9965.epi-13-0114 · Cited by 79 · see citing works
Misclassification of the mediator matters when estimating indirect effects
Blakely, T. A., McKenzie, S., & Carter, K. N. (2013) · Journal of Epidemiology & Community Health · doi:10.1136/jech-2012-201813 · Cited by 76 · see citing works
systemfit: A Package for Estimating Systems of Simultaneous Equations in R
Henningsen, A., Hamann, J. D., & Forest Informatics Inc (2007) · Cited by 75 · see citing works
Genotype-based treatment of type 2 diabetes with an α2A-adrenergic receptor antagonist
Tang, Y., Axelsson, A. S., Spégel, P., Andersson, L. E., Mulder, H., Groop, L., Renström, E., & Rosengren, A. H. (2014) · Science Translational Medicine · doi:10.1126/scitranslmed.3009934 · Cited by 74 · see citing works
Pierce, B. L., & VanderWeele, T. J. (2012) · International Journal of Epidemiology · doi:10.1093/ije/dys141 · Cited by 65 · see citing works
Lee, S., & Wray, N. R. (2013) · PLoS ONE · doi:10.1371/journal.pone.0071494 · Cited by 39 · see citing works
Probabilistic Computational Causal Discovery for Systems Biology
Lagani, V., Triantafillou, S., Ball, G., Tegner, J. N., & Tsamardinos, I. (2015) · Studies in mechanobiology, tissue engineering and biomaterials · doi:10.1007/978-3-319-21296-8_3 · Cited by 39 · see citing works
Koestler, D. C., Chalise, P., Cicek, M. S., Cunningham, J. M., Armasu, S. M., Larson, M. C., CHIEN, J. R., Block, M. S., Kalli, K. R., Sellers, T. A., et al. (2014) · BMC Medical Genomics · doi:10.1186/1755-8794-7-8 · Cited by 35 · see citing works
Bryant, P. A., Smyth, G. K., Robins-Browne, R. M., et al. (2011) · PLoS ONE · doi:10.1371/journal.pone.0019556 · Cited by 27 · see citing works
Shared genetic control of expression and methylation in peripheral blood
Shakhbazov, K., Powell, J. E., Hemani, G., Henders, A. K., Martin, N. G., Visscher, P. M., Montgomery, G. W., & McRae, A. F. (2016) · BMC Genomics · doi:10.1186/s12864-016-2498-4 · Cited by 11 · see citing works
Self-citationImpact of Noise on Molecular Network Inference
Nagarajan, R., & Scutari, M. (2013) · PLoS ONE · doi:10.1371/journal.pone.0080735 · Cited by 7 · see citing works
Detection of Regulator Genes and eQTLs in Gene Networks
Wang, L., & Michoel, T. (2016) · Springer eBooks · doi:10.1007/978-3-319-43335-6_1 · Cited by 4 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
The MR-Base platform supports systematic causal inference across the human phenome
Hemani, G., Zheng, J., Elsworth, B., Wade, K. H., Haberland, V., Baird, D., Laurin, C., Burgess, S., Bowden, J., Langdon, R., et al. (2018) · eLife · doi:10.7554/elife.34408 · Cited by 7,959 · see citing works
Self-citationSanderson, E., Glymour, M. M., Holmes, M. V., Kang, H., Morrison, J., Munafò, M. R., Palmer, T., Schooling, C. M., Wallace, C., Zhao, Q., et al. (2022) · Nature Reviews Methods Primers · doi:10.1038/s43586-021-00092-5 · Cited by 2,229 · see citing works
Mullins, N., Forstner, A. J., O’Connell, K. S., Coombes, B., Coleman, J. R. I., Qiao, Z., Als, T. D., Bigdeli, T. B., Børte, S., Bryois, J., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00857-4 · Cited by 1,517 · see citing works
Evaluating the potential role of pleiotropy in Mendelian randomization studies
Hemani, G., Bowden, J., & Davey Smith, G. (2018) · Human Molecular Genetics · doi:10.1093/hmg/ddy163 · Cited by 1,496 · see citing works
Self-citationGuidelines for performing Mendelian randomization investigations
Burgess, S., Davey Smith, G., Davies, N. M., Dudbridge, F., Gill, D., Glymour, M. M., Hartwig, F. P., Holmes, M. V., Minelli, C., Relton, C. L., et al. (2020) · Wellcome Open Research · doi:10.12688/wellcomeopenres.15555.2 · Cited by 1,273 · see citing works
Mendelian Randomization: Concepts and Scope
Richmond, R. C., & Davey Smith, G. (2022) · Cold Spring Harbor Perspectives in Medicine · doi:10.1101/cshperspect.a040501 · Cited by 841 · see citing works
Self-citationZheng, J., Haberland, V., Baird, D., Walker, V., Haycock, P., Hurle, M., Gutteridge, A., Erola, P., Liu, Y., Luo, S., et al. (2019) · doi:10.1101/627398 · Cited by 812 · see citing works
Self-citationWootton, R. E., Richmond, R. C., Stuijfzand, B. G., Lawn, R. B., Sallis, H. M., Taylor, G. M. J., Hemani, G., Jones, H. J., Zammit, S., Davey Smith, G., et al. (2020) · Psychological Medicine · doi:10.1017/s0033291719002678 · Cited by 587 · see citing works
Self-citationGenomic and phenotypic insights from an atlas of genetic effects on DNA methylation
Min, J. L., Hemani, G., Hannon, E., Dekkers, K. F., Castillo-Fernandez, J., Luijk, R., Carnero-Montoro, E., Lawson, D. J., Burrows, K., Suderman, M., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00923-x · Cited by 506 · see citing works
Self-citationGWAS identifies 14 loci for device-measured physical activity and sleep duration
Doherty, A., Smith-Byrne, K., Ferreira, T., Holmes, M. V., Holmes, C., Pulit, S. L., & Lindgren, C. M. (2018) · Nature Communications · doi:10.1038/s41467-018-07743-4 · Cited by 408 · see citing works
Potential drug targets for multiple sclerosis identified through Mendelian randomization analysis
Lin, J., Zhou, J., & Xu, Y. (2023) · Brain · doi:10.1093/brain/awad070 · Cited by 271 · see citing works
Type 2 Diabetes as a Determinant of Parkinson's Disease Risk and Progression
Chohan, H., Senkevich, K., Patel, R. K., Bestwick, J. P., Jacobs, B. M., Bandres Ciga, S., Gan‐Or, Z., & Noyce, A. J. (2021) · Movement Disorders · doi:10.1002/mds.28551 · Cited by 228 · see citing works
Richardson, T. G., Harrison, S., Hemani, G., & Davey Smith, G. (2019) · eLife · doi:10.7554/elife.43657 · Cited by 198 · see citing works
Self-citationPerry, B. I., Upthegrove, R., Kappelmann, N., Jones, P. B., Burgess, S., & Khandaker, G. M. (2021) · Brain, Behavior, and Immunity · doi:10.1016/j.bbi.2021.07.009 · Cited by 187 · see citing works
Yu, Z., Coresh, J., Qi, G., Grams, M., Boerwinkle, E., Snieder, H., Teumer, A., Pattaro, C., Köttgen, A., Chatterjee, N., et al. (2020) · Kidney International · doi:10.1016/j.kint.2020.04.044 · Cited by 135 · see citing works
Western, D., Timsina, J., Wang, L., Wang, C., Yang, C., Phillips, B., Wang, Y., Liu, M., Ali, M., Beric, A., et al. (2024) · Nature Genetics · doi:10.1038/s41588-024-01972-8 · Cited by 111 · see citing works
Polimanti, R., Peterson, R. E., Ong, J.-S., MacGregor, S., Edwards, A. C., Clarke, T.-K., Frank, J., Gerring, Z., Gillespie, N. A., Lind, P. A., et al. (2019) · Psychological Medicine · doi:10.1017/s0033291719000667 · Cited by 108 · see citing works
Xu, L., Borges, M. C., Hemani, G., & Lawlor, D. A. (2017) · Diabetologia · doi:10.1007/s00125-017-4396-y · Cited by 104 · see citing works
Self-citationAre Mendelian randomization investigations immune from bias due to reverse causation?
Burgess, S., Swanson, S. A., & Labrecque, J. A. (2021) · European Journal of Epidemiology · doi:10.1007/s10654-021-00726-8 · Cited by 106 · see citing works
Lower Lymphocyte Count is Associated With Increased Risk of Parkinson's Disease
Jensen, M. P., Jacobs, B. M., Dobson, R., Bandres‐Ciga, S., Blauwendraat, C., Schrag, A., Noyce, A. J., & The International Parkinson's Disease Genomics Consortium (IPDGC) (2021) · Annals of Neurology · doi:10.1002/ana.26034 · Cited by 91 · see citing works
Hemani, G., Bowden, J., Haycock, P., Zheng, J., Davis, O., Flach, P., Gaunt, T., & Smith, G. D. (2017) · doi:10.1101/173682 · Cited by 85 · see citing works
Self-citationYang, Q., Sanderson, E., Tilling, K., Borges, M. C., & Lawlor, D. A. (2019) · doi:10.1101/19009605 · Cited by 85 · see citing works
Self-citationVermeulen, J. M., Wootton, R. E., Treur, J. L., Sallis, H. M., Jones, H. J., Zammit, S., van den Brink, W., Goodwin, G. M., de Haan, L., & Munafò, M. R. (2021) · The British Journal of Psychiatry · doi:10.1192/bjp.2019.202 · Cited by 78 · see citing works
Associations of Adiposity, Circulating Protein Biomarkers, and Risk of Major Vascular Diseases
Pang, Y., Kartsonaki, C., Lv, J., Fairhurst-Hunter, Z., Millwood, I. Y., Yu, C., Guo, Y., Chen, Y., Bian, Z., Yang, L., et al. (2021) · JAMA Cardiology · doi:10.1001/jamacardio.2020.6041 · Cited by 75 · see citing works
Korologou-Linden, R., Bhatta, L., Brumpton, B. M., Howe, L. D., Millard, L. A. C., Kolaric, K., Ben-Shlomo, Y., Williams, D. M., Smith, G. D., Anderson, E. L., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-32183-6 · Cited by 71 · 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
Tylee, D. S., Lee, Y. K., Wendt, F. R., Pathak, G. A., Levey, D. F., De Angelis, F., Gelernter, J., & Polimanti, R. (2022) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2022.0914 · Cited by 65 · see citing works
Huang, Y., Liu, Y., Ma, Y., Tu, T., Liu, N., Bai, F., Xiao, Y., Liu, C., Hu, Z., Lin, Q., et al. (2022) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2022.840866 · Cited by 54 · see citing works
Chen, D., Zhang, Y., Huang, T., & Jia, J. (2023) · Psychological Medicine · doi:10.1017/s0033291723000867 · Cited by 52 · see citing works
Zheng, J., Wheeler, E., Pietzner, M., Andlauer, T. F. M., Yau, M. S., Hartley, A. E., Brumpton, B. M., Rasheed, H., Kemp, J. P., Frysz, M., et al. (2023) · Arthritis & Rheumatology · doi:10.1002/art.42538 · Cited by 44 · see citing works
Schooling, C., Zhao, J., Au Yeung, S., & Leung, G. (2020) · eLife · doi:10.7554/elife.58567 · Cited by 39 · see citing works
Daghlas, I., Guo, Y., & Chasman, D. I. (2020) · European Journal of Neurology · doi:10.1111/ene.14111 · Cited by 32 · see citing works
Minică, C. C., Boomsma, D. I., Dolan, C. V., de Geus, E., & Neale, M. C. (2020) · International Journal of Epidemiology · doi:10.1093/ije/dyaa013 · Cited by 23 · see citing works
Gut microbiota and sepsis and sepsis-related death: a Mendelian randomization investigation
Shang, W., Zhang, S., Qian, H., Huang, S., Li, H., Liu, J., & Chen, D. (2024) · Frontiers in Immunology · doi:10.3389/fimmu.2024.1266230 · Cited by 23 · see citing works
Analysis of gut microbiota and depression and anxiety: Mendelian randomization from three datasets
Lai, Y., & Xiong, P. (2025) · General Hospital Psychiatry · doi:10.1016/j.genhosppsych.2025.03.012 · Cited by 20 · see citing works
Zheng, S., Liu, L., Liang, K., Yan, J., Meng, D., Liu, Z., Tian, S., & Shan, Y. (2024) · Communications Biology · doi:10.1038/s42003-024-06680-x · Cited by 15 · see citing works
Brown, B. C., & Knowles, D. A. (2021) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2021.10.006 · Cited by 15 · see citing works
Zhang, Q., Xu, F., Wang, L., Zhang, W.-D., Sun, C.-Q., & Deng, H.-W. (2020) · Aging · doi:10.18632/aging.103983 · Cited by 13 · see citing works
Genetic determinants of plasma protein levels in the Estonian population
Kalnapenkis, A., Jõeloo, M., Lepik, K., Kukuškina, V., Kals, M., Alasoo, K., Estonian Biobank Research Team, Metspalu, A., Milani, L., Nelis, M., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-57966-3 · Cited by 11 · see citing works
Polimanti, R., Peterson, R. E., Ong, J.-S., MacGregor, S., Edwards, A. C., Clarke, T.-K., Frank, J., Gerring, Z., Gillespie, N. A., Lind, P. A., et al. (2018) · doi:10.1101/412098 · Cited by 108 · see citing works
Wang, Z., Wang, X., Zhao, X., Hu, Z., Sun, D., Wu, D., & Xing, Y. (2022) · Frontiers in Genetics · doi:10.3389/fgene.2022.970933 · Cited by 10 · see citing works
Zheng, X., Wang, Y., & Qiu, X. (2024) · European Journal of Pharmacology · doi:10.1016/j.ejphar.2024.176840 · Cited by 10 · see citing works
Wang, T., Wang, A., Chen, Z., & Lin, X. (2026) · Journal of Global Health · doi:10.7189/jogh.16.04259
- Authors
- Hemani, G., Tilling, K., & Davey Smith, G.
- Affiliations
- University of Bristol (GB)
- Corresponding author
- Hemani, Gibran
- Venue
- PLoS Genetics
- Publisher
- Public Library of Science (PLoS)
- Published
- 2017-11-17
- Volume
- 13
- Issue
- 11
- Pages
- e1007081
- ISSN
- 1553-7390 (ISSN-L)
- 1553-7404
- PMC
- PMC5711033
- OpenAlex
- W2770598799
- Cited by
- 2,607
- References
- 58
- Citations by year
- 2026: 308 · 2025: 603 · 2024: 851 · 2023: 392 · 2022: 191 · 2021: 121 · 2020: 69 · 2019: 42 · 2018: 23 · 2017: 7
- FWCI
- 25.33
- Type
- journal article
- Language
- en
- Update notices
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS Genetics (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Explore Bristol Research (submittedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Figshare (submittedVersion, cc-by)
- Funders
- Medical Research Council (GB) (MC_UU_12013/1, MC_UU_12013/9)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Epigenetics and DNA Methylation
- Keywords
- causal direction inference, measurement error, Mendelian randomization, mediation analysis, DNA methylation, gene expression, horizontal pleiotropy, unmeasured confounding, sensitivity analysis
- MeSH terms
- Gene Expression Regulation, Phenotype, Bias, Sample Size, DNA Methylation, Genome-Wide Association Study, Genetic Association Studies, Mendelian Randomization Analysis
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
