Use of allele scores as instrumental variables for Mendelian randomization
Open accessAbstract
BACKGROUND: An allele score is a single variable summarizing multiple genetic variants associated with a risk factor. It is calculated as the total number of risk factor-increasing alleles for an individual (unweighted score), or the sum of weights for each allele corresponding to estimated genetic effect sizes (weighted score). An allele score can be used in a Mendelian randomization analysis to estimate the causal effect of the risk factor on an outcome. METHODS: Data were simulated to investigate the use of allele scores in Mendelian randomization where conventional instrumental variable techniques using multiple genetic variants demonstrate 'weak instrument' bias. The robustness of estimates using the allele score to misspecification (for example non-linearity, effect modification) and to violations of the instrumental variable assumptions was assessed. RESULTS: Causal estimates using a correctly specified allele score were unbiased with appropriate coverage levels. The estimates were generally robust to misspecification of the allele score, but not to instrumental variable violations, even if the majority of variants in the allele score were valid instruments. Using a weighted rather than an unweighted allele score increased power, but the increase was small when genetic variants had similar effect sizes. Naive use of the data under analysis to choose which variants to include in an allele score, or for deriving weights, resulted in substantial biases. CONCLUSIONS: Allele scores enable valid causal estimates with large numbers of genetic variants. The stringency of criteria for genetic variants in Mendelian randomization should be maintained for all variants in an allele score.
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When many variants are included in the allele score, bias and coverage probabilities of the instrumental variable estimates are improved compared with estimates from the two stage least squares approach.
“When many variants are included in the score, bias and coverage probabilities of the instrumental variable estimates are improved compared with estimates from the two stage least squares approach.80”
From Strengthening the Reporting of Observational Studies in Epidemiology using Mendelian Randomisation (STROBE-MR): explanation and elaboration. (2021) · cites this paper as [80]
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Cites
R: A Language and Environment for Statistical Computing
R Core Team (2000) · doi:10.32614/r.manuals · Cited by 355,706 · see citing works
Mostly Harmless Econometrics: An Empiricist's Companion
Angrist, J. D., & Pischke, J.-S. (2009) · doi:10.1515/9781400829828 · Cited by 9,467 · see citing works
Testing for Weak Instruments in Linear IV Regression
Stock, J. H., & Yogo, M. (2005) · Identification and Inference for Econometric Models · doi:10.1017/cbo9780511614491.006 · Cited by 7,211 · see citing works
Instrumental Variables Regression with Weak Instruments
Staiger, D., & Stock, J. H. (1997) · Econometrica · doi:10.2307/2171753 · Cited by 7,081 · 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
Estimation and Inference in Econometrics.
Chambers, M. J., Davidson, R., & MacKinnon, J. G. (1994) · The Economic Journal · doi:10.2307/2234656 · Cited by 5,536 · see citing works
Instrumental Variables Regression with Weak Instruments
Staiger, D., & Stock, J. (1994) · doi:10.3386/t0151 · Cited by 5,302 · cited 1× · see citing works
“Conventionally, instruments with an F statistic less than 10 are labelled as “weak” [28], although so-called ‘weak instrument bias’ is a continuous rather than a binary phenomenon.”
BackgroundMendelian randomization: Using genes as instruments for making causal inferences in epidemiology
Lawlor, D. A., Harbord, R. M., Sterne, J. A. C., Timpson, N., & Davey Smith, G. (2008) · Statistics in Medicine · doi:10.1002/sim.3034 · Cited by 4,618 · see citing works
Common SNPs explain a large proportion of the heritability for human height
Yang, J., Benyamin, B., McEvoy, B. P., Gordon, S., Henders, A. K., Nyholt, D. R., Madden, P. A., Heath, A. C., Martin, N. G., Montgomery, G. W., et al. (2010) · Nature Genetics · doi:10.1038/ng.608 · Cited by 4,446 · see citing works
Avoiding bias from weak instruments in Mendelian randomization studies
Burgess, S., Thompson, S. G., & CRP CHD Genetics Collaboration (2011) · International Journal of Epidemiology · doi:10.1093/ije/dyr036 · Cited by 3,952 · see citing works
Self-citationInstrumental Variables and GMM: Estimation and Testing
Baum, C. F., Schaffer, M. E., & Stillman, S. (2003) · The Stata Journal Promoting communications on statistics and Stata · doi:10.1177/1536867x0300300101 · Cited by 2,632 · see citing works
Genetic variants in novel pathways influence blood pressure and cardiovascular disease risk
The International Consortium for Blood Pressure Genome-Wide Association Studies (2011) · Nature · doi:10.1038/nature10405 · Cited by 2,033 · see citing works
Testing for Weak Instruments in Linear IV Regression
Stock, J., & Yogo, M. (2002) · doi:10.3386/t0284 · Cited by 1,842 · see citing works
Pierce, B. L., Ahsan, H., & VanderWeele, T. J. (2010) · International Journal of Epidemiology · doi:10.1093/ije/dyq151 · Cited by 1,837 · see citing works
Enhanced Routines for Instrumental Variables/Generalized Method of Moments Estimation and Testing
Baum, C. F., Schaffer, M. E., & Stillman, S. (2007) · The Stata Journal Promoting communications on statistics and Stata · doi:10.1177/1536867x0800700402 · Cited by 1,290 · see citing works
Using multiple genetic variants as instrumental variables for modifiable risk factors
Palmer, T. M., Lawlor, D. A., Harbord, R. M., Sheehan, N. A., Tobias, J. H., Timpson, N. J., Smith, G. D., & Sterne, J. A. (2012) · Statistical Methods in Medical Research · doi:10.1177/0962280210394459 · Cited by 1,289 · 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
An introduction to instrumental variables for epidemiologists
Greenland, S. (2000) · International Journal of Epidemiology · doi:10.1093/ije/29.4.722 · Cited by 1,156 · see citing works
(2012) · The Lancet · doi:10.1016/s0140-6736(12)60110-x · Cited by 1,146 · see citing works
Epistasis: what it means, what it doesn't mean, and statistical methods to detect it in humans
Cordell, H. J. (2002) · Human Molecular Genetics · doi:10.1093/hmg/11.20.2463 · Cited by 1,067 · see citing works
Mendelian randomization as an instrumental variable approach to causal inference
Didelez, V., & Sheehan, N. (2007) · Statistical Methods in Medical Research · doi:10.1177/0962280206077743 · Cited by 923 · see citing works
C Reactive Protein Coronary Heart Disease Genetics Collaboration (CCGC) (2011) · BMJ · doi:10.1136/bmj.d548 · Cited by 642 · see citing works
Alternative Approximations to the Distributions of Instrumental Variable Estimators
Bekker, P. A. (1994) · Econometrica · doi:10.2307/2951662 · Cited by 471 · see citing works
TEACHER'S CORNER: Structural Equation Modeling With the sem Package in R
Fox, J. (2006) · Structural Equation Modeling A Multidisciplinary Journal · doi:10.1207/s15328007sem1303_7 · Cited by 433 · see citing works
Bias in causal estimates from Mendelian randomization studies with weak instruments
Burgess, S., & Thompson, S. G. (2011) · Statistics in Medicine · doi:10.1002/sim.4197 · Cited by 421 · see citing works
Self-citationEbrahim, S., & Davey Smith, G. (2008) · Human Genetics · doi:10.1007/s00439-007-0448-6 · Cited by 410 · see citing works
Nelson, C. R., & Startz, R. (1990) · The Journal of Business · doi:10.1086/296497 · Cited by 380 · see citing works
Mendelian Randomisation and Causal Inference in Observational Epidemiology
Sheehan, N. A., Didelez, V., Burton, P. R., & Tobin, M. D. (2008) · PLoS Medicine · doi:10.1371/journal.pmed.0050177 · Cited by 373 · see citing works
Nelson, C. R., & Startz, R. (1988) · National Bureau of Economic Research · doi:10.3386/t0069 · Cited by 364 · see citing works
Estimation with weak instruments: Accuracy of higher‐order bias and MSE approximations
Hahn, J., Hausman, J. A., & Kuersteiner, G. M. (2004) · Econometrics Journal · doi:10.1111/j.1368-423x.2004.00131.x · Cited by 342 · see citing works
Alternative Approximations to the Distributions of Instrumental Variable EstimatorsPreprint
Bekker, P. A. (1992) · RePEc: Research Papers in Economics · Cited by 239 · see citing works
Assumptions of IV Methods for Observational Epidemiology.
Didelez, V., Meng, S., & Sheehan, N. A. (2010) · Statistical Science · doi:10.1214/09-sts316 · Cited by 214 · see citing works
Thanassoulis, G., & O’Donnell, C. J. (2009) · JAMA · doi:10.1001/jama.2009.812 · Cited by 209 · see citing works
Plasma HDL cholesterol and risk of myocardial infarction: a mendelian randomisation study
Voight, B. F., Peloso, G. M., Orho-Melander, M., Frikke-Schmidt, R., Barbalic, M., Jensen, M. K., Hindy, G., Hólm, H., Ding, E. L., Johnson, T., et al. (2012) · The Lancet · doi:10.1016/s0140-6736(12)60312-2 · Cited by 148 · see citing works
Lin, X., Song, K. S., Lim, N., Yuan, X., Johnson, T., Abderrahmani, A., Vollenweider, P., Stirnadel-Farrant, H. A., Sundseth, S. S., Lai, E., et al. (2009) · Diabetologia · doi:10.1007/s00125-008-1254-y · Cited by 118 · see citing works
Small sample properties of LIML and jackknife IV estimators: experiments with weak instruments
Blomquist, S., & Dahlberg, M. (1999) · Journal of Applied Econometrics · doi:10.1002/(sici)1099-1255(199901/02)14:1 · Cited by 100 · see citing works
Burgess, S., & Thompson, S. G. (2012) · Statistics in Medicine · doi:10.1002/sim.4498 · Cited by 87 · see citing works
Self-citationPalmer, T., Thompson, J. R., Tobin, M. D., Sheehan, N. A., & Burton, P. R. (2008) · International Journal of Epidemiology · doi:10.1093/ije/dyn080 · Cited by 87 · see citing works
Burgess, S. (2013) · Statistics in Medicine · doi:10.1002/sim.5871 · Cited by 72 · see citing works
Self-citationChapter 4. Instrumental Variables in Action: Sometimes You Get What You Need
Angrist, J. D., & Pischke, J. (2009) · Princeton University Press eBooks · doi:10.1515/9781400829828-009 · Cited by 58 · see citing works
Association of a Fasting Glucose Genetic Risk Score With Subclinical Atherosclerosis
Rasmussen‐Torvik, L. J., Li, M., Kao, W. H., Couper, D. J., Boerwinkle, E., Bielinski, S. J., Folsom, A. R., & Pankow, J. S. (2010) · Diabetes · doi:10.2337/db10-0839 · Cited by 50 · see citing works
Random Allocation in Observational Data
Smith, G. D. (2011) · Epidemiology · doi:10.1097/ede.0b013e31821d0426 · Cited by 48 · see citing works
Missing Data Methods in Mendelian Randomization Studies With Multiple Instruments
Burgess, S., Seaman, S. R., Lawlor, D. A., Casas, J. P., & Thompson, S. G. (2011) · American Journal of Epidemiology · doi:10.1093/aje/kwr235 · Cited by 17 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 1
- [20]Hayashi F Econometrics 2000 Princeton, NJ Princeton University Press
Cited by
Sorted by citations
Bowden, J., Davey Smith, G., & Burgess, S. (2015) · International Journal of Epidemiology · doi:10.1093/ije/dyv080 · Cited by 9,816 · see citing works
Self-citationMendelian 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
Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians
Davies, N. M., Holmes, M. V., & Davey Smith, G. (2018) · BMJ · doi:10.1136/bmj.k601 · Cited by 4,404 · see citing works
Mendelian Randomization as an Approach to Assess Causality Using Observational Data
Sekula, P., Del Greco M, F., Pattaro, C., & Köttgen, A. (2016) · Journal of the American Society of Nephrology · doi:10.1681/asn.2016010098 · Cited by 2,351 · see citing works
Sanderson, 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
Burgess, S., Bowden, J., Fall, T., Ingelsson, E., & Thompson, S. G. (2017) · Epidemiology · doi:10.1097/ede.0000000000000559 · Cited by 2,017 · see citing works
Self-citationEPIC- InterAct Consortium, Burgess, S., Scott, R. A., Timpson, N. J., Davey Smith, G., & Thompson, S. G. (2015) · European Journal of Epidemiology · doi:10.1007/s10654-015-0011-z · Cited by 1,803 · see citing works
Self-citationSkrivankova, V. W., Richmond, R. C., Woolf, B. A. R., Davies, N. M., Swanson, S. A., VanderWeele, T. J., Timpson, N. J., Higgins, J. P. T., Dimou, N., Langenberg, C., et al. (2021) · BMJ · doi:10.1136/bmj.n2233 · Cited by 1,648 · see citing works
Bias due to participant overlap in two‐sample Mendelian randomization
Burgess, S., Davies, N. M., & Thompson, S. G. (2016) · Genetic Epidemiology · doi:10.1002/gepi.21998 · Cited by 1,624 · see citing works
Self-citationBurgess, S., Dudbridge, F., & Thompson, S. G. (2016) · Statistics in Medicine · doi:10.1002/sim.6835 · Cited by 1,153 · see citing works
Self-citationSanderson, E., Davey Smith, G., Windmeijer, F., & Bowden, J. (2019) · International Journal of Epidemiology · doi:10.1093/ije/dyy262 · Cited by 1,085 · 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
Mendelian randomization of blood lipids for coronary heart disease
Holmes, M. V., Asselbergs, F. W., Palmer, T. M., Drenos, F., Lanktree, M. B., Nelson, C. P., Dale, C. E., Padmanabhan, S., Finan, C., Swerdlow, D. I., et al. (2015) · European Heart Journal · doi:10.1093/eurheartj/eht571 · Cited by 748 · see citing works
Mendelian randomization studies: a review of the approaches used and the quality of reporting
Boef, A. G. C., Dekkers, O. M., & le Cessie, S. (2015) · International Journal of Epidemiology · doi:10.1093/ije/dyv071 · Cited by 610 · see citing works
Burgess, 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
Self-citationSelecting instruments for Mendelian randomization in the wake of genome-wide association studies.
Swerdlow, D. I., Kuchenbaecker, K. B., Shah, S., Sofat, R., Holmes, M. V., White, J., Mindell, J. S., Kivimaki, M., Brunner, E. J., Whittaker, J. C., et al. (2016) · International Journal of Epidemiology · doi:10.1093/ije/dyw088 · Cited by 387 · see citing works
Dongiovanni, P., Stender, S., Pietrelli, A., Mancina, R. M., Cespiati, A., Petta, S., Pelusi, S., Pingitore, P., Badiali, S., Maggioni, M., et al. (2018) · Journal of Internal Medicine · doi:10.1111/joim.12719 · Cited by 367 · see citing works
Staley, J. R., & Burgess, S. (2017) · Genetic Epidemiology · doi:10.1002/gepi.22041 · Cited by 290 · see citing works
Self-citationTaylor, A. E., Jones, H. J., Sallis, H., Euesden, J., Stergiakouli, E., Davies, N. M., Zammit, S., Lawlor, D. A., Munafò, M. R., Davey Smith, G., et al. (2018) · International Journal of Epidemiology · doi:10.1093/ije/dyy060 · Cited by 192 · see citing works
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Davies, N. M., von Hinke Kessler Scholder, S., Farbmacher, H., Burgess, S., Windmeijer, F., & Smith, G. D. (2015) · Statistics in Medicine · doi:10.1002/sim.6358 · Cited by 140 · see citing works
Self-citationPleiotropy-robust Mendelian randomization.
van Kippersluis, H., & Rietveld, C. A. (2018) · International Journal of Epidemiology · doi:10.1093/ije/dyx002 · Cited by 125 · see citing works
Brower, M. A., Hai, Y., Jones, M. R., Guo, X., Chen, Y. D. I., Rotter, J. I., Krauss, R. M., Legro, R. S., Azziz, R., & Goodarzi, M. O. (2019) · Human Reproduction · doi:10.1093/humrep/dey343 · Cited by 115 · see citing works
Spiller, W., Davies, N. M., & Palmer, T. M. (2019) · International Journal of Epidemiology · doi:10.1093/ije/dyy195 · Cited by 116 · see citing works
Spiller, W., Slichter, D., Bowden, J., & Davey Smith, G. (2018) · International Journal of Epidemiology · doi:10.1093/ije/dyy204 · Cited by 90 · see citing works
Minică, C. C., Dolan, C. V., Boomsma, D. I., de Geus, E., & Neale, M. C. (2018) · Behavior Genetics · doi:10.1007/s10519-018-9904-4 · Cited by 80 · see citing works
Emanuelsson, F., Marott, S., Tybjærg-Hansen, A., Nordestgaard, B. G., & Benn, M. (2020) · Diabetes Care · doi:10.2337/dc19-1850 · Cited by 59 · see citing works
Constrained instruments and their application to Mendelian randomization with pleiotropy
Jiang, L., Oualkacha, K., Didelez, V., Ciampi, A., Rosa‐Neto, P., Benedet, A. L., Mathotaarachchi, S., Richards, J. B., Greenwood, C. M. T., & Initiative, A. F. T. A. D. N. (2019) · Genetic Epidemiology · doi:10.1002/gepi.22184 · Cited by 23 · see citing works
Diemer, E. W., Labrecque, J., Tiemeier, H., & Swanson, S. A. (2020) · Epidemiology · doi:10.1097/ede.0000000000001126 · Cited by 19 · see citing works
Spiller, W., Hartwig, F. P., Sanderson, E., Smith, G. D., & Bowden, J. (2020) · doi:10.1101/2020.07.27.20162909 · Cited by 15 · see citing works
- Authors
- Burgess, S., & Thompson, S. G.
- Affiliations
- University of Cambridge (GB)
- Corresponding author
- Burgess, Stephen
- Venue
- International Journal of Epidemiology
- Publisher
- Oxford University Press (OUP)
- Published
- 2013-08
- Volume
- 42
- Issue
- 4
- Pages
- 1134-1144
- ISSN
- 0300-5771 (ISSN-L)
- 1464-3685
- PMC
- PMC3780999
- OpenAlex
- W2162204400
- Cited by
- 452
- References
- 43
- Citations by year
- 2026: 22 · 2025: 32 · 2024: 62 · 2023: 38 · 2022: 41 · 2021: 41 · 2020: 30 · 2019: 34 · 2018: 35 · 2017: 32 · 2016: 30 · 2015: 36 · 2014: 17 · 2013: 1
- FWCI
- 18.58
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- International Journal of Epidemiology (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- HighWire Press Open Archive (submittedVersion)
- Funders
- Wellcome Trust (100114)
- National Institute for Health and Care Research (NF-SI-0512-10165)
- British Heart Foundation (SP/08/007/23628, RG/08/014/24067)
- Medical Research Council (MR/L003120/1)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Keywords
- Mendelian randomization, instrumental variables, weak instrument bias, genetic variants, causal effect estimation, model misspecification, effect modification
- MeSH terms
- Alleles, Humans, Models, Genetic, Risk Factors, Bias, Causality, Mendelian Randomization Analysis
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