Welch-weighted Egger regression reduces false positives due to correlated pleiotropy in Mendelian randomization
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Abstract
Modern population-scale biobanks contain simultaneous measurements of many phenotypes, providing unprecedented opportunity to study the relationship between biomarkers and disease. However, inferring causal effects from observational data is notoriously challenging. Mendelian randomization (MR) has recently received increased attention as a class of methods for estimating causal effects using genetic associations. However, standard methods result in pervasive false positives when two traits share a heritable, unobserved common cause. This is the problem of correlated pleiotropy. Here, we introduce a flexible framework for simulating traits with a common genetic confounder that generalizes recently proposed models, as well as a simple approach we call Welch-weighted Egger regression (WWER) for estimating causal effects. We show in comprehensive simulations that our method substantially reduces false positives due to correlated pleiotropy while being fast enough to apply to hundreds of phenotypes. We apply our method first to a subset of the UK Biobank consisting of blood traits and inflammatory disease, and then to a broader set of 411 heritable phenotypes. We detect many effects with strong literature support, as well as numerous behavioral effects that appear to stem from physician advice given to people at high risk for disease. We conclude that WWER is a powerful tool for exploratory data analysis in ever-growing databases of genotypes and phenotypes.
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Steiger filtering attempts to correct for misspecification by removing SNPs that explain more variation in the outcome than the exposure.
“Steiger filtering attempts to correct for this misspecification by removing SNPs that explain more variation in the outcome than the exposure.94”
From Mendelian randomization (2022) · cites this paper as [94]
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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
The UK Biobank resource with deep phenotyping and genomic data
Bycroft, C., Freeman, C., Petkova, D., Band, G., Elliott, L. T., Sharp, K., Motyer, A., Vukcevic, D., Delaneau, O., O’Connell, J., et al. (2018) · Nature · doi:10.1038/s41586-018-0579-z · Cited by 8,876 · see citing works
Verbanck, M., Chen, C.-Y., Neale, B., & Do, R. (2018) · Nature Genetics · doi:10.1038/s41588-018-0099-7 · Cited by 8,591 · see citing works
THE GENERALIZATION OF ‘STUDENT'S’ PROBLEM WHEN SEVERAL DIFFERENT POPULATION VARLANCES ARE INVOLVED
WELCH, B. L. (1947) · Biometrika · doi:10.1093/biomet/34.1-2.28 · Cited by 4,135 · see citing works
Robust inference in summary data Mendelian randomization via the zero modal pleiotropy assumption
Hartwig, F. P., Davey Smith, G., & Bowden, J. (2017) · International Journal of Epidemiology · doi:10.1093/ije/dyx102 · Cited by 3,485 · see citing works
Orienting the causal relationship between imprecisely measured traits using GWAS summary data.
Hemani, G., Tilling, K., & Davey Smith, G. (2017) · PLoS Genetics · doi:10.1371/journal.pgen.1007081 · Cited by 2,607 · see citing works
Elevated C-reactive protein levels in overweight and obese adults
Visser, M. (1999) · JAMA · doi:10.1001/jama.282.22.2131 · Cited by 2,494 · see citing works
Platelets and the immune continuum
Semple, J. W., Italiano, J. E., & Freedman, J. J. (2011) · Nature reviews. Immunology · doi:10.1038/nri2956 · Cited by 1,704 · see citing works
Detection and interpretation of shared genetic influences on 42 human traits
Pickrell, J. K., Berisa, T., Liu, J. Z., Ségurel, L., Tung, J. Y., & Hinds, D. A. (2016) · Nature Genetics · doi:10.1038/ng.3570 · Cited by 1,151 · see citing works
Zhao, Q., Wang, J., Hemani, G., Bowden, J., & Small, D. S. (2020) · The Annals of Statistics · doi:10.1214/19-aos1866 · Cited by 854 · see citing works
Genetics of 35 blood and urine biomarkers in the UK Biobank
Sinnott-Armstrong, N., Tanigawa, Y., Amar, D., Mars, N., Benner, C., Aguirre, M., Venkataraman, G. R., Wainberg, M., Ollila, H. M., Kiiskinen, T., et al. (2021) · Nature Genetics · doi:10.1038/s41588-020-00757-z · Cited by 744 · see citing works
A robust and efficient method for Mendelian randomization with hundreds of genetic variants
Burgess, S., Foley, C. N., Allara, E., Staley, J. R., & Howson, J. M. M. (2020) · Nature Communications · doi:10.1038/s41467-019-14156-4 · Cited by 681 · see citing works
Imtiaz, F., Shafique, K., Mirza, S. S., Ayoob, Z., Vart, P., & Rao, S. (2012) · International Archives of Medicine · doi:10.1186/1755-7682-5-2 · Cited by 650 · see citing works
Morrison, J., Knoblauch, N., Marcus, J. H., Stephens, M., & He, X. (2020) · Nature Genetics · doi:10.1038/s41588-020-0631-4 · Cited by 578 · see citing works
Dissecting the genetics of complex traits using summary association statistics
Paşaniuc, B., & Price, A. L. (2016) · Nature Reviews Genetics · doi:10.1038/nrg.2016.142 · Cited by 444 · see citing works
Sanmuganathan, P. S., Ghahramani, P., Jackson, P. R., Wallis, E. J., & Ramsay, L. E. (2001) · Heart · doi:10.1136/heart.85.3.265 · Cited by 409 · see citing works
Distinguishing genetic correlation from causation across 52 diseases and complex traits
O’Connor, L. J., & Price, A. L. (2018) · Nature Genetics · doi:10.1038/s41588-018-0255-0 · Cited by 394 · see citing works
Inflammation, not Cholesterol, Is a Cause of Chronic Disease
Τσούπρας, Α., Lordan, R., & Zabetakis, I. (2018) · Nutrients · doi:10.3390/nu10050604 · Cited by 321 · 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
Vitamin D deficiency and diabetes
Berridge, M. J. (2017) · Biochemical Journal · doi:10.1042/bcj20170042 · Cited by 271 · see citing works
Qi, G., & Chatterjee, N. (2019) · Nature Communications · doi:10.1038/s41467-019-09432-2 · Cited by 207 · see citing works
Psoriasis alters HDL composition and cholesterol efflux capacity
Holzer, M., Wolf, P., Ćurčić, S., Birner‐Gruenberger, R., Weger, W., Inzinger, M., El‐Gamal, D., Wadsack, C., Heinemann, Á., & Marsche, G. (2012) · Journal of Lipid Research · doi:10.1194/jlr.m027367 · Cited by 173 · 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
The roles of cells and cytokines in the pathogenesis of psoriasis
Coimbra, S., Figueiredo, A., Castro, E. M. B., Rocha‐Pereira, P., & Santos‐Silva, A. (2012) · International Journal of Dermatology · doi:10.1111/j.1365-4632.2011.05154.x · Cited by 149 · see citing works
A rare IL33 loss-of-function mutation reduces blood eosinophil counts and protects from asthma
Smith, D. E., Helgason, H., Sulem, P., Björnsdóttir, U. S., Lim, A. C., Sveinbjörnsson, G., Hasegawa, H., Brown, M. E., Ketchem, R. R., Gavala, M. L., et al. (2017) · PLoS Genetics · doi:10.1371/journal.pgen.1006659 · Cited by 140 · see citing works
The Role of T Lymphocytes in Asthma
Kay, A. B. (2005) · KARGER eBooks · doi:10.1159/000090230 · Cited by 138 · see citing works
Shafiee, M., Tayefi, M., Hassanian, S. M., Ghaneifar, Z., Parizadeh, M. R., Avan, A., Rahmani, F., Khorasanchi, Z., Azarpajouh, M. R., Safarian, H., et al. (2017) · Psychoneuroendocrinology · doi:10.1016/j.psyneuen.2017.06.021 · Cited by 127 · see citing works
Zhu, Y., Zhang, Y., & Choi, H. K. (2010) · Lara D. Veeken · doi:10.1093/rheumatology/keq256 · Cited by 116 · see citing works
Demarche, S. F., Schleich, F., Henket, M. A., Paulus, V. A., Hees, T. J. V., & Louis, R. E. (2016) · BMC Pulmonary Medicine · doi:10.1186/s12890-016-0208-2 · Cited by 100 · see citing works
Beyond Mendelian randomization: how to interpret evidence of shared genetic predictors
Burgess, S., Butterworth, A. S., & Thompson, J. R. (2015) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2015.08.001 · Cited by 92 · 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
Jukema, R., Ahmed, T. A.-H. N., & Tardif, J. (2019) · BMC Medicine · doi:10.1186/s12916-019-1433-3 · Cited by 81 · see citing works
Is blood neutrophil-lymphocyte ratio an independent predictor of knee osteoarthritis severity?
Taşoğlu, Ö., Şenlikçi, H. B., Onat, Ş. Ş., Taşoğlu, İ., & Özgirgin, N. (2016) · Clinical Rheumatology · doi:10.1007/s10067-016-3170-8 · Cited by 75 · see citing works
C-reactive protein levels in the serum of asthmatic patients
Fujita, M., Ueki, S., Ito, W., Chiba, T., Takeda, M., Saito, N., Kayaba, H., & Chihara, J. (2007) · Annals of Allergy Asthma & Immunology · doi:10.1016/s1081-1206(10)60620-5 · Cited by 73 · see citing works
Erythrocyte damage in mild and severe psoriasis
Rocha‐Pereira, P., Santos‐Silva, A., Rebelo, I., FigneiRedo, A., Quintanilha, A. T., & Teixeira, F. J. (2004) · British Journal of Dermatology · doi:10.1111/j.1365-2133.2004.05801.x · Cited by 72 · see citing works
Burgess, S., Bowden, J., Dudbridge, F., & Thompson, S. G. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1606.03729 · Cited by 63 · see citing works
Cappello, M., Randazzo, C., Bravatà, I., Licata, A., Peralta, S., Craxı̀, A., & Almasio, P. L. (2014) · Clinical Medicine Insights Gastroenterology · doi:10.4137/cgast.s13125 · Cited by 55 · see citing works
Mosli, R. H., & Mosli, H. H. (2017) · Diabetes Metabolic Syndrome and Obesity · doi:10.2147/dmso.s149832 · Cited by 53 · see citing works
The Role of Eosinophils in the Pathophysiology of Asthmaa
Calhoun, W. J., Sedgwick, J. B., & Busse, W. W. (1991) · Annals of the New York Academy of Sciences · doi:10.1111/j.1749-6632.1991.tb37961.x · Cited by 50 · see citing works
Narasaraju, T. A., Chen, H., Weng, T., Bhaskaran, M., Jin, N., Chen, J., Chen, Z., Chinoy, M. R., & Liu, L. (2005) · Journal of Cellular Biochemistry · doi:10.1002/jcb.20653 · Cited by 49 · see citing works
Schulte, M. T., Frick, K., Gnandt, E., Jurkovic, S., Burschel, S., Labatzke, R., Aierstock, K., Fiegen, D., Wohlwend, D., Gerhardt, S., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-10429-0 · Cited by 47 · see citing works
Serum lipoproteins in middle-aged men with psoriasis
Vahlquist, C., MICHAELSSON, G., & Vessby, B. (1987) · Acta Dermato Venereologica · doi:10.2340/00015555671215 · Cited by 45 · see citing works
Monocytes accumulate in the airways of children with fatal asthma
Eguíluz‐Gracia, I., Malmström, K., Dheyauldeen, S. A., Lohi, J., Sajantila, A., Aaløkken, R., Sundaram, A. Y. M., Gilfillan, G. D., Mäkelä, M. J., Bækkevold, E. S., et al. (2018) · Clinical & Experimental Allergy · doi:10.1111/cea.13265 · Cited by 44 · see citing works
Neutrophilic asthma features increased airway classical monocytes
Niessen, N. M., Baines, K. J., Simpson, J. L., Scott, H. A., Qin, L., Gibson, P. G., & Fricker, M. (2020) · Clinical & Experimental Allergy · doi:10.1111/cea.13811 · Cited by 39 · see citing works
Relationship of serum cholesterol levels to atopy in the US population
Fessler, M. B., Jaramillo, R., Crockett, P. W., & Zeldin, D. C. (2009) · Allergy · doi:10.1111/j.1398-9995.2009.02287.x · Cited by 31 · see citing works
Sathiyapriya, V., Bobby, Z., Kumar, S. V., Selvaraj, N., Parthibane, V., & Gupta, S. (2005) · Clinica Chimica Acta · doi:10.1016/j.cca.2005.11.001 · Cited by 31 · see citing works
MacDonald, T. M., & Wei, L. (2006) · Basic & Clinical Pharmacology & Toxicology · doi:10.1111/j.1742-7843.2006.pto_371.x · Cited by 30 · see citing works
Short stature is an inflammatory disadvantage among middle-aged Japanese men
Shimizu, Y., Yoshimine, H., Nagayoshi, M., Kadota, K., Takahashi, K., Izumino, K., Inoue, K., & Maeda, T. (2016) · Environmental Health and Preventive Medicine · doi:10.1007/s12199-016-0538-y · Cited by 28 · see citing works
Blood glucose: of emerging importance in COPD exacerbations
Baker, E. H., & Bell, D. (2009) · Thorax · doi:10.1136/thx.2009.118638 · Cited by 27 · see citing works
Immune-Mediated Diseases: Where Do We Stand?
Shurin, M. R., & Smolkin, Y. S. (2007) · Advances in experimental medicine and biology · doi:10.1007/978-0-387-72005-0_1 · Cited by 25 · see citing works
Serum lipids and lipoproteins in patients with psoriasis.
Sarvtin, M. T., Hedayati, M. T., Shokohi, T., & Hajheydari, Z. (2014) · PubMed · Cited by 23 · see citing works
Qi, G., & Chatterjee, N. (2020) · International Journal of Epidemiology · doi:10.1093/ije/dyaa262 · Cited by 21 · see citing works
Red Blood Cell Distribution Width is a Reliable Marker of Inflammation in Plaque Psoriasis.
Doğan, S., & Atakan, N. (2017) · PubMed · Cited by 18 · see citing works
The correlation between asthma disease and neutrophil to lymphocyte ratio.
Güngen, A. C., & Aydemir, Y. (2017) · Cited by 15 · see citing works
(2007) · Advances in experimental medicine and biology · doi:10.1007/978-0-387-72005-0 · Cited by 14 · see citing works
Serum insulin-like growth factor-1, asthma, and lung function among British adults
Han, Y., Yan, Q., Chen, W., Forno, E., & Celedon, J. C. (2020) · Annals of Allergy Asthma & Immunology · doi:10.1016/j.anai.2020.12.005 · Cited by 12 · see citing works
Barendregt, J., de Jong, M., Haans, J. J. L., van Hoek, B., Hardwick, J. C. H., Veenendaal, R. A., Jong, A. E. V. D. M., Srivastava, N., Stuyt, R. J. L., & Maljaars, J. (2016) · International Journal of Colorectal Disease · doi:10.1007/s00384-016-2706-3 · Cited by 9 · see citing works
Assessment of bronchial asthma exacerbation: the utility of platelet indices
Hafez, M. R., Eid, H. A., Elsawy, S. B., Eldin, N. E., & Elmadbouly, A. A. E. (2019) · Egyptian Journal of Bronchology · doi:10.4103/ejb.ejb_69_19 · Cited by 6 · see citing works
Platelets in Asthma: Does Size Matter?
Stoll, P., & Lommatzsch, M. (2014) · Respiration · doi:10.1159/000362798 · Cited by 4 · see citing works
Platelet abnormalities in asthma and allergy
Ellaurie, M., & Wang, G. (2004) · Journal of Allergy and Clinical Immunology · doi:10.1016/j.jaci.2004.01.009 · Cited by 4 · see citing works
Study of Serum Lipids and Lipoproteins in Patients with Psoriasis
Sarvtin, M. T., Hedayati, M. T., Shokohi, T., & Hajheydari, Z. (2013) · Cited by 3 · see citing works
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Hartley, A. E., Power, G. M., Sanderson, E. C. M., & Smith, G. D. (2022) · JBMR Plus · doi:10.1002/jbm4.10675 · Cited by 31 · see citing works
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Tuo, L., Yan, L., Liu, Y., & Yang, X. (2024) · Frontiers in Endocrinology · doi:10.3389/fendo.2024.1315046 · Cited by 5 · see citing works
Lee, T., George, C. D., Chen, J., Asgari, M. M., Nijsten, T. E. C., Pardo, L. M., & Choquet, H. (2023) · JAAD International · doi:10.1016/j.jdin.2023.11.005 · Cited by 4 · see citing works
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RetractedMapping Causal Biology: Mendelian Randomization in the Era of Big Data
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- Authors
- Brown, B. C., & Knowles, D. A.
- Affiliations
- New York Genome Center (US)
- Corresponding author
- Brown, Brielin C.
- Knowles, David A.
- Venue
- The American Journal of Human Genetics
- Publisher
- Elsevier BV
- Published
- 2021-12
- Volume
- 108
- Issue
- 12
- Pages
- 2319-2335
- ISSN
- 0002-9297 (ISSN-L)
- PMC
- PMC8715134
- OpenAlex
- W3217250127
- Cited by
- 15
- References
- 61
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 5 · 2023: 4 · 2022: 3
- FWCI
- 1.69
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- The American Journal of Human Genetics (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nd)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Data Science Institute, Columbia University
- 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 › Genetics › Genetics and Physical Performance
- Keywords
- Mendelian randomization, false positives, causal effect estimation, genetic confounding, blood characteristics, inflammatory diseases, UK Biobank, behavioral effects, physician recommendations
- MeSH terms
- Computer Simulation, False Positive Reactions, Female, Humans, Inflammation, Male, Models, Genetic, Phenotype, Regression Analysis, Polymorphism, Single Nucleotide, Mendelian Randomization Analysis, Genetic Pleiotropy
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