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Using multiple genetic variants as instrumental variables for modifiable risk factors
Open accessAbstract
Mendelian randomisation analyses use genetic variants as instrumental variables (IVs) to estimate causal effects of modifiable risk factors on disease outcomes. Genetic variants typically explain a small proportion of the variability in risk factors; hence Mendelian randomisation analyses can require large sample sizes. However, an increasing number of genetic variants have been found to be robustly associated with disease-related outcomes in genome-wide association studies. Use of multiple instruments can improve the precision of IV estimates, and also permit examination of underlying IV assumptions. We discuss the use of multiple genetic variants in Mendelian randomisation analyses with continuous outcome variables where all relationships are assumed to be linear. We describe possible violations of IV assumptions, and how multiple instrument analyses can be used to identify them. We present an example using four adiposity-associated genetic variants as IVs for the causal effect of fat mass on bone density, using data on 5509 children enrolled in the ALSPAC birth cohort study. We also use simulation studies to examine the effect of different sets of IVs on precision and bias. When each instrument independently explains variability in the risk factor, use of multiple instruments increases the precision of IV estimates. However, inclusion of weak instruments could increase finite sample bias. Missing data on multiple genetic variants can diminish the available sample size, compared with single instrument analyses. In simulations with additive genotype-risk factor effects, IV estimates using a weighted allele score had similar properties to estimates using multiple instruments. Under the correct conditions, multiple instrument analyses are a promising approach for Mendelian randomisation studies. Further research is required into multiple imputation methods to address missing data issues in IV estimation.
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“All three of these SNPs have previously been shown to be strong instruments for MR analysis as measured by F-statistics > 10 [8,18]”
From Associations of genetically determined iron status across the phenome: A mendelian randomization study (2019) · cites this paper as [18]
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“Interpretation of results from MR studies relies on four conditions.12,24”
From Mendelian randomization (2022) · cites this paper as [24]
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“The association between SBP genetic variants and the rate of rise in SBP over time is consistent with a possible causal effect of SBP on the usual age-related rise in SBP. Novelty and Significance”
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He, Y., Timofeeva, M. N., Farrington, S. M., Vaughan-Shaw, P. G., Svinti, V., Walker, M., Zgaga, L., Meng, X., Li, X., Spiliopoulou, A., et al. (2018) · BMC Medicine · doi:10.1186/s12916-018-1119-2 · Cited by 78
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“MR analysis using genetic instruments with two-stage least squares regression can be biased when both the variance explained by the instruments and the sample size are small.(45) Even though our sample size was limited, the CRP-scores had F-values >35 and explained 2 to 3% of the interindividual variance in CRP concen…”
MethodGenetic Evidence for a Causal Role of Obesity in Diabetic Kidney Disease
Todd, J. N., Dahlström, E. H., Salem, R. M., Sandholm, N., Forsblom, C. M., the FinnDiane Study Group, McKnight, A. J., Maxwell, A. P., Brennan, E. P., Sadlier, D. M., et al. (2015) · Diabetes · doi:10.2337/db15-0254 · Cited by 75
“13 pleiotropy in the sense of a given individual variant affecting multiple phenotypes (31; 32), although systematic pleiotropic effects across many loci could still theoretically be present.”
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Age at puberty and risk of asthma: A Mendelian randomisation study
Minelli, C., van der Plaat, D. A., Leynaert, B., Granell, R., Amaral, A. F. S., Pereira, M., Mahmoud, O., Potts, J., Sheehan, N. A., Bowden, J., et al. (2018) · PLoS Medicine · doi:10.1371/journal.pmed.1002634 · Cited by 70
“The instrument strength of each SNP for both early and late menarche was assessed using the F statistic, a function of the magnitude and precision of the SNP’s effect on menarche [17].”
MethodObesity and peripheral arterial disease: A Mendelian Randomization analysis
Huang, Y. F., Xu, M., Xie, L., Wang, T., Huang, X., Lv, X., Chen, Y., Ding, L., Lin, L., Wang, W., et al. (2015) · Atherosclerosis · doi:10.1016/j.atherosclerosis.2015.12.034 · Cited by 71
Formalising recall by genotype as an efficient approach to detailed phenotyping and causal inference
Corbin, L. J., Tan, V. Y., Hughes, D. A., Wade, K. H., Paul, D. S., Tansey, K. E., Butcher, F. C., Dudbridge, F., Howson, J. M. M., Jallow, M. W., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-03109-y · Cited by 72
Marouli, E., Fabiola Del Greco, M., Astley, C. M., Yang, J., Ahmad, S., Berndt, S. I., Caulfield, M. J., Εvangelou, Ε., McKnight, B., Medina‐Gómez, C., et al. (2019) · Communications Biology · doi:10.1038/s42003-019-0361-2 · Cited by 68
Lawlor, D. A., Harbord, R. M., Tybjærg‐Hansen, A., Palmer, T., Zacho, J., Benn, M., Timpson, N. J., Smith, G. D., & Nordestgaard, B. G. (2011) · Journal of Internal Medicine · doi:10.1111/j.1365-2796.2011.02343.x · Cited by 70
Self-citationGill, D., Benyamin, B., Moore, L. S. P., Monori, G., Zhou, A., Koskeridis, F., Evangelou, E., Laffan, M., Walker, A. P., Tsilidis, K. K., et al. (2019) · PLOS Medicine · doi:10.1371/journal.pmed.1002833 · Cited by 63
Does higher education protect against obesity? Evidence using Mendelian randomization
Böckerman, P., Viinikainen, J., Pulkki-Råbäck, L., Hakulinen, C. A., Pitkänen, N., Lehtimäki, T. J., Pehkonen, J., & Raitakari, O. T. (2017) · Preventive Medicine · doi:10.1016/j.ypmed.2017.06.015 · Cited by 69
“A larger data set is needed to provide more power and obtain more tightly estimated effects of education on BMI in the IV models (Palmer et al., 2012).”
BackgroundMendelian randomisation applied to drug development in cardiovascular disease: a review
Mokry, L. E., Ahmad, O. S., Forgetta, V., Thanassoulis, G., & Richards, J. B. (2014) · Journal of Medical Genetics · doi:10.1136/jmedgenet-2014-102438 · Cited by 67
Yeung, S. L. A., Jiang, C. Q., Cheng, K. K., Liu, B., Zhang, W. S., Lam, T., Leung, G. M., & Schooling, C. M. (2012) · American Journal of Epidemiology · doi:10.1093/aje/kwr462 · Cited by 67
Silverwood, R. J., Holmes, M. V., Dale, C. E., Lawlor, D. A., Whittaker, J. C., Smith, G. D., Leon, D. A., Palmer, T., Keating, B. J., Zuccolo, L., et al. (2014) · International Journal of Epidemiology · doi:10.1093/ije/dyu187 · Cited by 66
“More robust inference of non-linear causal effects may be possible from polychotomous or continuous instruments, such as gene scores constructed from multiple SNPs.(28,29) Such instruments will allow the identification of non-linear models with many parameters, though IV estimation of parametric non-linear models has…”
BackgroundSelf-citationCausal associations between modifiable risk factors and intervertebral disc degeneration
Guo, W., Li, B., Zhao, J., Li, X., & Wang, L. (2023) · The Spine Journal · doi:10.1016/j.spinee.2023.10.021 · Cited by 73
Osteoporosis and sarcopenia-related traits: A bi-directional Mendelian randomization study
Liu, C., Liu, N., Xia, Y., Zhao, Z., Xiao, T., & Li, H. (2022) · Frontiers in Endocrinology · doi:10.3389/fendo.2022.975647 · Cited by 66
The role of obesity, type 2 diabetes, and metabolic factors in gout: A Mendelian randomization study
Yang, Y., Xian, W., Wu, D., Huo, Z., Hong, S., Li, Y., & Xiao, H. (2022) · Frontiers in Endocrinology · doi:10.3389/fendo.2022.917056 · Cited by 67
“SNPs with instrument strengths (F) larger than 10 were selected (23).”
BackgroundMendelian randomization: the challenge of unobserved environmental confounds
Koellinger, P., & de Vlaming, R. (2019) · International Journal of Epidemiology · doi:10.1093/ije/dyz138 · Cited by 61
“In fact, the bias induced by weak instruments in combination with even just slight violations of cj 1⁄4 0 and dj 1⁄4 0 may be much worse than the bias in a naive ordinary regression of Y on X.(26,27)”
BackgroundEfstathiadou, A., Gill, D., McGrane, F., Quinn, T. J., & Dawson, J. (2019) · Journal of the American Heart Association · doi:10.1161/jaha.119.012738 · Cited by 63
“The strength of each instrument was evaluated using F-statistic values, which were in turn calculated based on R(2), the proportion of phenotypic variance explained by each SNP.(18,19) Genetic association estimates of SNP-outcome relationships were extracted from different data sets.”
BackgroundMethodInfluentialNi, J., Zhou, W., Cen, H., Chen, G., Huang, J., Yin, K.-J., & Sui, C. (2021) · Osteoarthritis and Cartilage · doi:10.1016/j.joca.2021.11.021 · Cited by 66
Shen, L., Walter, S., Melles, R. B., Glymour, M. M., & Jorgenson, E. (2015) · American Journal of Epidemiology · doi:10.1093/aje/kwv204 · Cited by 62
“Interpretation of instrumental variable estimates as causal effects relies on strong assumptions, as do any efforts at causal inference from observational data (36).”
BackgroundSkaaby, T., Husemoen, L. L. N., Martinussen, T., Thyssen, J. P., Melgaard, M., Thuesen, B. H., Pisinger, C. H., Jørgensen, T., Johansen, J. D., Menné, T., et al. (2013) · PLoS ONE · doi:10.1371/journal.pone.0057647 · Cited by 60
“The strength of the instrument is assessed in table 2 [25].”
BackgroundAhmad, O. S., Leong, A., Miller, J. A., Morris, J. A., Forgetta, V., Mujammami, M. H., & Richards, J. B. (2016) · Journal of Bone and Mineral Research · doi:10.1002/jbmr.3063 · Cited by 62
“MR estimates using multiple variants might be subject to weak instrument bias.((8,12)) However, in two-sample MR studies, such as ours, such bias is toward the null.”
BackgroundMethodInsight into rheumatological cause and effect through the use of Mendelian randomization
Robinson, P. C., Choi, H. K., Do, R., & Merriman, T. R. (2016) · Nature Reviews Rheumatology · doi:10.1038/nrrheum.2016.102 · Cited by 59
Liu, B., Lyu, L., Zhou, W., Song, J., Ye, D., Mao, Y., Chen, G., & Sun, X. (2023) · BMC Medicine · doi:10.1186/s12916-023-02736-7 · Cited by 59
“The F-statistics of IVs used in the present study ranged from 11 to 789, suggesting the sufficient strength of IVs [21].”
MethodYang, Q., Borges, M. C., Sanderson, E. C. M., Magnus, M. C., Kilpi, F., Collings, P. J., Soares, A. G., West, J., Magnus, P. M., Wright, J., et al. (2022) · PLoS Medicine · doi:10.1371/journal.pmed.1004090 · Cited by 58
“The 81 SNPs explained approximately 0.42% of the variance of insomnia among the 208,140 UKB women included in this study (S4 Table), and the mean F-statistic of the 81 SNPs was 11.”
MethodInfluentialDiabetes and intervertebral disc degeneration: A Mendelian randomization study
Jin, P., Xing, Y., Xiao, B., Wei, Y., Yan, K., Zhao, J., & Tian, W. (2023) · Frontiers in Endocrinology · doi:10.3389/fendo.2023.1100874 · Cited by 59
“presence of unobserved confounding variables (29).”
Mendelian Randomization Analysis to Examine for a Causal Effect of Urate on Bone Mineral Density
Dalbeth, N., Topless, R., Major, T. J., Cadzow, M. J., Bolland, M. J., & Merriman, T. R. (2014) · Journal of Bone and Mineral Research · doi:10.1002/jbmr.2434 · Cited by 57
“(15–17) This method has also been used to demonstrate that fat mass (adiposity) has a causal effect on BMD.(18) We used Mendelian randomization analysis to examine whether urate has a causal effect on BMD.”
MethodInfluentialMendelian Randomization: Application to Cardiovascular Disease
Timpson, N. J., Wade, K. H., & Smith, G. D. (2011) · Current Hypertension Reports · doi:10.1007/s11906-011-0242-7 · Cited by 60
“For example, the use of multiple, independent instruments in single analyses can both aid power (multiple variants in sum explaining more variance in risk factor exposure then any one instrument) and overcome some of the restrictions in inference coming from possible pleiotropy [102, 103].”
BackgroundKrishnamoorthy, S., Li, G. H., & Cheung, C. (2022) · Journal of Medical Virology · doi:10.1002/jmv.28162 · Cited by 57
“By including multiple instruments, especially within the cis region of a probe, the likelihood of horizontal pleiotropy is diminished, and the statistical power of the analysis increases.(17,18) Detailed information of the SMR and multi‐SNPs SMR has been described previously.”
BackgroundLeong, A., Porneala, B., Dupuis, J., Florez, J. C., & Meigs, J. B. (2016) · Diabetes Care · doi:10.2337/dc15-2080 · Cited by 57
“an instrumental variable analysis (38).”
Mendelian Randomization Studies of Cancer Risk: a Literature Review
Pierce, B. L., Kraft, P., & Zhang, C. (2018) · Current Epidemiology Reports · doi:10.1007/s40471-018-0144-1 · Cited by 55
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“The second is a reintroduced confounding through pleiotropy [12, 14, 15].”
BackgroundMethodEstimating peer effects in longitudinal dyadic data using instrumental variables
O’Malley, A. J., Elwert, F., Rosenquist, J. N., Zaslavsky, A. M., & Christakis, N. A. (2014) · Biometrics · doi:10.1111/biom.12172 · Cited by 52
“Several recent methodological papers discuss Mendelian randomization as IVs (Didelez, Meng, and Sheehan, 2010; Vansteelandt et al., 2011; Palmer et al., 2012) but none consider peer effects.”
BackgroundRosoff, D. B., Kaminsky, Z. A., McIntosh, A. M., Smith, G. D., & Lohoff, F. W. (2020) · Translational Psychiatry · doi:10.1038/s41398-020-01047-2 · Cited by 62
Liu, B., Ye, D., Yang, H., Song, J., Sun, X., He, Z., Mao, Y., & Hao, G. (2023) · BMC Gastroenterology · doi:10.1186/s12876-023-02791-7 · Cited by 55
“After extracted relevant information such as effect allele, effect size including β-value, standard error and P-value for each SNP, we calculated the proportion of variation explained (R2) and F-statistics to quantify the instrument strength, with the following equation: R2 = 2 × MAF × (1 − MAF) × β2, F = R2 (n-k-1) /…”
MethodLiu, W., Yang, C., Lei, F., Huang, X., Cai, J., Chen, S., She, Z., & Li, H. (2024) · EBioMedicine · doi:10.1016/j.ebiom.2023.104964 · Cited by 55
“32 All the SNPs used as instrumental variables were listed in Supplemental Tables S2 – S4.”
MethodLawlor, D. A., Wade, K. H., Borges, M. C., Palmer, T., Hartwig, F. P., Hemani, G., & Bowden, J. (2019) · OSF Preprints (OSF Preprints) · doi:10.31219/osf.io/6yzs7 · Cited by 52
Self-citationWang, Q., Shi, Q., Lu, J., Wang, Z., & Hou, J. (2022) · International Journal of Cancer · doi:10.1002/ijc.34214 · Cited by 51
“of the variance of trait explained by the SNP and N is the sample size of the GWAS of SNPs with the trait.(22) The R(2) values were estimated using the formula R(2) = 2 EAF (1 EAF) β(2), where EAF is the”
MethodXu, L., & Hao, Y. T. (2017) · Scientific Reports · doi:10.1038/s41598-017-01073-z · Cited by 51
Georgiou, A. N., Ntritsos, G., Papadimitriou, N., Dimou, N. L., & Εvangelou, Ε. (2020) · Inflammatory Bowel Diseases · doi:10.1093/ibd/izaa152 · Cited by 52
Chen, M., Wang, Z., Xu, H., Teng, P., Li, W., & Ma, L. (2024) · Journal of Translational Medicine · doi:10.1186/s12967-024-04956-8 · Cited by 53
“The risk of weak instrument bias in MR analysis is relatively low when the F-statistics ≥ 10 [35].”
BackgroundLipid and metabolic syndrome traits in coronary artery disease: a Mendelian randomization study
Thomas, D. G., Wei, Y., & Tall, A. R. (2020) · Journal of Lipid Research · doi:10.1194/jlr.p120001000 · Cited by 53
“All analyses were performed on anonymized summary statistics from published GWASs with appropriate institutional review board approval (20–24).”
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A multi-ancestry genetic study of pain intensity in 598,339 veterans
Toikumo, S., Vickers‐Smith, R., Jinwala, Z., Xu, H., Saini, D., Hartwell, E. E., Pavicic, M., Sullivan, K. A., Xu, K., Jacobson, D., et al. (2024) · Nature Medicine · doi:10.1038/s41591-024-02839-5 · Cited by 48
von Hinke, S., Smith, G. D., Lawlor, D. A., Propper, C., & Windmeijer, F. (2012) · Economics & Human Biology · doi:10.1016/j.ehb.2012.04.015 · Cited by 49
“…sets predict the same causal effect, this is very unlikely to be due to some common pleiotropy or LD across the different sets of variants, assuming that the different variants are located on different chromosomes and affect the trait via different pathways (Davey Smith, 2011; Palmer et al., 2012).”
BackgroundSheehan, N. A., & Didelez, V. (2019) · Human Genetics · doi:10.1007/s00439-019-02027-3 · Cited by 47
Cecelja, M., Keehn, L., Ye, L., Spector, T. D., Hughes, A. D., & Chowienczyk, P. (2020) · European Heart Journal · doi:10.1093/eurheartj/ehaa238 · Cited by 48
“Although PWV is a haemodynamic determinant of the pulsatile components of BP, it is influenced by BP via the non-linear elastic properties of wall of the artery which result in a functional stiffening of the artery when distended by a higher BP. Long-term effect of steady state or pulsatile BP components may also lead…”
BackgroundInfluentialAtrial fibrillation and left atrial size and function: a Mendelian randomization study
van de Vegte, Y. J., Siland, J. E., Rienstra, M., & van der Harst, P. (2021) · Scientific Reports · doi:10.1038/s41598-021-87859-8 · Cited by 49
Ye, X., Liu, B., Bai, Y., Cao, Y., Lin, S.-R., Lyu, L., Meng, H., Dai, Y., Ye, D., Pan, W., et al. (2023) · Journal of Translational Medicine · doi:10.1186/s12967-023-04559-9 · Cited by 45
“2) [28].”
Contemporary Considerations for Constructing a Genetic Risk Score: An Empirical Approach
Goldstein, B. A., Yang, L., Salfati, E., & Assimes, T. L. (2015) · Genetic Epidemiology · doi:10.1002/gepi.21912 · Cited by 44
“, 2009], Mendelian randomization studies [Palmer et al., 2012], and risk prediction [Weijmans et al.”
BackgroundBonilla, C., Lewis, S. J., Martin, R. M., Donovan, J., Hamdy, F. C., Neal, D. E., Eeles, R. A., Easton, D. F., Kote‐Jarai, Z., Olama, A. A. A., et al. (2016) · BMC Medicine · doi:10.1186/s12916-016-0602-x · Cited by 46
“Scores are used instead of individual genetic variants because they are likely to explain a larger proportion of trait variability and therefore represent stronger proxies for the exposure [12].”
BackgroundMethodAge at menarche and lung function: a Mendelian randomization study
Gill, D., Sheehan, N. A., Wielscher, M., Shrine, N. R. G., Amaral, A. F. S., Thompson, J. R., Granell, R., Leynaert, B., Real, F. G., Hall, I. P., et al. (2017) · European Journal of Epidemiology · doi:10.1007/s10654-017-0272-9 · Cited by 46
“We assessed instrument strength for the 122 SNPs, a function of magnitude and precision of their genetic effect, using the F statistic [26].”
MethodBlood lipid genetic scores, the HMGCR gene and cancer risk: a Mendelian randomization study
Orho‐Melander, M., Hindy, G., Borgquist, S., Schulz, C., Manjer, J., Melander, O., & Stocks, T. (2017) · International Journal of Epidemiology · doi:10.1093/ije/dyx237 · Cited by 46
Said, M. A., van de Vegte, Y. J., Verweij, N., & van der Harst, P. (2020) · Journal of the American Heart Association · doi:10.1161/jaha.120.016808 · Cited by 47
“Caffeine from coffee 0 (0-0) 15 (6-60) 120 (85-120) 180 (170-180) 240 (240-240) 300 (255-300) 360 (340-360) 480 (425-595)”
InfluentialCorrection: Serum Iron Levels and the Risk of Parkinson Disease: A Mendelian Randomization Study
Pichler, I., Fabiola Del Greco, M., Gögele, M., Lill, C. M., Bertram, L., Do, C. B., Eriksson, N., Foroud, T., Myers, R. H., Nalls, M. A., et al. (2013) · PLoS Medicine · doi:10.1371/annotation/c4d81646-0c0e-4a3e-9425-b220bae2d8b6 · Cited by 47
Mendelian Randomization: Principles and its usage in Lp(a) research
Lamina, C. (2022) · Atherosclerosis · doi:10.1016/j.atherosclerosis.2022.04.013 · Cited by 47
Genetically Determined Platelet Count and Risk of Cardiovascular Disease
Gill, D., Monori, G., Georgakis, M. K., Tzoulaki, I., & Laffan, M. (2018) · Arteriosclerosis Thrombosis and Vascular Biology · doi:10.1161/atvbaha.118.311804 · Cited by 45
Zhao, G., Lu, Z., Sun, Y., Kang, Z., Feng, X., Liao, Y., Sun, J., Zhang, Y., Huang, Y., & Yue, W. (2023) · Translational Psychiatry · doi:10.1038/s41398-023-02492-5 · Cited by 45
“When the F-statistic was less than 10, the genetic variant was considered weak, which may bias the results [25].”
BackgroundSerum iron level and kidney function: a Mendelian randomization study
Fabiola Del Greco, M., Foco, L., Pichler, I., Eller, P., Eller, K., Benyamin, B., Whitfield, J. B., Pramstaller, P. P., Thompson, J. R., Pattaro, C., et al. (2016) · Nephrology Dialysis Transplantation · doi:10.1093/ndt/gfw215 · Cited by 41
“The strength of each instrument was evaluated using the F statistic, which is a function of the magnitude and precision of the genetic effect on the biomarker: F 1⁄4 R(2)(N 2)/(1 R(2)), where R(2) is the proportion of the variance of iron (or ferritin) explained by the genetic variant and N is the sample size of the g…”
MethodLi, M., Lin, J., Liang, S., Chen, Z., Bai, Y., Long, X., Huang, S., & Mo, Z. (2021) · Aging · doi:10.18632/aging.203384 · Cited by 41
“To satisfy the first MR assumption, we applied an Fstatistic to evaluate the strength of each selected SNP, and an F-statistic > 10 suggests that the SNP is sufficiently strong to lessen any potential bias [17].”
MethodGut microbiota and atopic dermatitis: a two-sample Mendelian randomization study
Xue, Y., Zhang, L., Chen, Y., Wang, H., & Xie, J. (2023) · Frontiers in Medicine · doi:10.3389/fmed.2023.1174331 · Cited by 43
“In this formula, β identifies the estimated effect of the genetic variant and EAF represents the effect allele frequency, and then F = R2 (N-k-1)/ k(1-R2) was calculated based on the sample size (N), the number of included SNPs (k), and R2 (34).”
MethodThe relationship of vitamin D status to risk of cardiovascular disease and mortality.
Skaaby, T. (2015) · PubMed · Cited by 42
Sleep duration and risk of diabetes: Observational and Mendelian randomization studies
Wang, J., Kwok, M. K., Yeung, S. L. A., Li, A. M., Lam, H. S., Leung, J. Y. Y., Hui, L. L., Leung, G. M., & Schooling, C. M. (2018) · Preventive Medicine · doi:10.1016/j.ypmed.2018.11.019 · Cited by 43
Harris, H. R., Cushing‐Haugen, K. L., Webb, P. M., Nagle, C. M., Jordan, S. J., Australian Ovarian Cancer Study Group, Risch, H. A., Rossing, M. A., Doherty, J. A., Goodman, M. T., et al. (2019) · International Journal of Epidemiology · doi:10.1093/ije/dyz113 · Cited by 42
Child height, health and human capital: Evidence using genetic markers
von Hinke, S., Smith, G. D., Lawlor, D. A., Propper, C., & Windmeijer, F. (2012) · European Economic Review · doi:10.1016/j.euroecorev.2012.09.009 · Cited by 45
Xu, W., Zhang, F., Shi, Y., Chen, Y., Shi, B., & Yu, G. (2022) · Frontiers in Medicine · doi:10.3389/fmed.2022.989950 · Cited by 41
“The proportion of trait variance explained by genetic tools (R2) and tool strength (F-statistics) was calculated using R2 =(2*MAF*(1−MAF)*β2)/(SE2*N) and F = R2(N−k−1)/(k*(−1−R2)) (MAF = minor allele frequency, β = effect size, SE = standard error, N = sample size, k = number of instrumental variables) (32).”
MethodCausal Relevance of Blood Lipid Fractions in the Development of Carotid Atherosclerosis
Shah, S. H., Casas, J. P., Drenos, F., Whittaker, J. C., Deanfield, J. E., Swerdlow, D. I., Holmes, M. V., Mika Kivimaki, B., Langenberg, C., Wareham, N. J., et al. (2012) · Circulation Cardiovascular Genetics · doi:10.1161/circgenetics.112.963140 · Cited by 45
“Using 2 different scores containing only partially overlapping SNPs gives us confidence that the results are not biased by the SNP set used, as the 2 instruments are unlikely to be influenced by the same pleiotropy or linkage disequilibrium-induced confounding, and their consistency provides strong evidence against th…”
ResultLi, S., Gao, L., Zhang, X., He, J.-W., Fu, W., Liu, Y., Hu, Y.-Q., & Zhang, Z. (2016) · Scientific Reports · doi:10.1038/srep33202 · Cited by 41
“Although no recognized formulas for calculating sample sizes were available for MR analyses using multiple genetic variants, the results showed that a 20% decrease in SE would lead to a 56% increase in the practicable sample size 14 .”
InfluentialIdentifying and treating subclinical thyroid dysfunction in pregnancy: emerging controversies
Velasco, I., & Taylor, P. N. (2017) · European Journal of Endocrinology · doi:10.1530/eje-17-0598 · Cited by 42
Causal Link Between Vitamin D and Total Testosterone in Men: A Mendelian Randomization Analysis
Chen, C., Zhai, H., Cheng, J., Weng, P., Chen, Y., Li, Q., Wang, C., Xia, F., Wang, N., & Lu, Y. (2019) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2018-01874 · Cited by 42
Li, Y., Miao, Y., & Zhang, Q. (2022) · SLEEP · doi:10.1093/sleep/zsac298 · Cited by 40
Instrumental Variable Analysis in Epidemiologic Studies: An Overview of the Estimation Methods
Uddin, M. J. (2015) · Pharmaceutica Analytica Acta · doi:10.4172/2153-2435.1000353 · Cited by 40
“Typically, standard errors (SEs) of the IV estimate from the second-stage model are too small [24,30,44,45].”
BackgroundMethodYao, Z., Guo, F., Tan, Y., Zhang, Y., Geng, Y., Yang, G., & Wang, S. (2024) · Frontiers in Immunology · doi:10.3389/fimmu.2024.1334772 · Cited by 43
Evidence triangulation in health research
Gutierrez, S., Glymour, M. M., & Smith, G. D. (2025) · European Journal of Epidemiology · doi:10.1007/s10654-024-01194-6 · Cited by 52
“…its early applications, numerous extensions of MR methods have allowed for estimates that are more robust to possible violations of assumptions and address more flexible substantive questions such as effects at different stages of the lifecourse and non-linear dose–response effects [15, 19, 25–29].”
Gilbert, R., Bonilla, C., Metcalfe, C., Lewis, S. J., Evans, D. M., Fraser, W. D., Kemp, J. P., Donovan, J., Hamdy, F. C., Neal, D. E., et al. (2014) · Cancer Causes & Control · doi:10.1007/s10552-014-0500-5 · Cited by 41
“Multiple variant allele scores were created based on SNPs found to be associated with vitamin D status in prior studies [29] and in the current study [30].”
BackgroundMethodGenomic insights in ascending aortic size and distensibility
Benjamins, J. W., Yeung, M. W., van de Vegte, Y. J., Said, M. A., van der Linden, T., Ties, D., Juárez‐Orozco, L. E., Verweij, N., & van der Harst, P. (2021) · EBioMedicine · doi:10.1016/j.ebiom.2021.103783 · Cited by 37
Mendelian randomization: the use of genes in instrumental variable analyses
von Hinke, S., Smith, G. D., Lawlor, D. A., Propper, C., & Windmeijer, F. (2011) · Health Economics · doi:10.1002/hec.1746 · Cited by 40
Zhao, J., Liang, R., Song, Q., Song, S., Yue, J., & Wu, C. (2023) · Precision Clinical Medicine · doi:10.1093/pcmedi/pbad010 · Cited by 40
Liu, Y., Lai, H., Zhang, R., Xia, L. P., & Liu, L. (2023) · International Journal of Epidemiology · doi:10.1093/ije/dyad090 · Cited by 37
Genetic insights into resting heart rate and its role in cardiovascular disease
van de Vegte, Y. J., Eppinga, R. N., van der Ende, M. Y., Hagemeijer, Y. P., Mahendran, Y., Salfati, E., Smith, A. V., Tan, V. Y., Arking, D. E., Ντάλλα, Ι., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-39521-2 · Cited by 41
Said, M. A., Yeung, M. W., van de Vegte, Y. J., Benjamins, J. W., Dullaart, R. P. F., Ruotsalainen, S., Ripatti, S., Natarajan, P., Juarez-Orozco, L. E., Verweij, N., et al. (2021) · Arteriosclerosis, Thrombosis, and Vascular Biology · doi:10.1161/atvbaha.120.315300 · Cited by 39
Habitual Coffee Consumption Increases Risk of Primary Open-Angle Glaucoma
Li, X., Cheng, S., Cheng, J., Wang, M., Zhong, Y., & Yu, A. (2022) · Ophthalmology · doi:10.1016/j.ophtha.2022.04.027 · Cited by 38
Zhang, X., Geng, T., Li, N., Wu, L., Wang, Y., Zheng, D., Guo, B., & Wang, B. (2022) · Nutrients · doi:10.3390/nu15010069 · Cited by 35
“For each selected IV, the mean F-statistic was calculated to evaluate the strength with the approximation method described previously [27,28].”
MethodType 2 diabetes is causally associated with depression: a Mendelian randomization analysis
Xuan, L., Zhao, Z., Xu, J. Y., Hou, Y., Wang, T., Li, M., Lu, J., Xu, Y., Chen, Y., Qi, L., et al. (2018) · Frontiers of Medicine · doi:10.1007/s11684-018-0671-7 · Cited by 34
Li, H., Hu, Q., Zhang, Q., Su, G., Xiao, H., Li, B., Shen, W., Qiu, X. Y., Lv, W., & Deng, H. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.00415 · Cited by 35
Levin, M. G., Klarin, D. M., Walker, V. M., Gill, D., Lynch, J. A., Hellwege, J. N., Keaton, J. M., Lee, K. M., Assimes, T. L., Natarajan, P., et al. (2021) · Arteriosclerosis Thrombosis and Vascular Biology · doi:10.1161/atvbaha.120.315482 · Cited by 35
Smoking and caffeine consumption: a genetic analysis of their association
Treur, J. L., Taylor, A. E., Ware, J. J., Nivard, M. G., Neale, M. C., McMahon, G., Hottenga, J., Baselmans, B. M. L., Boomsma, D. I., Munafo, M. R., et al. (2016) · Addiction Biology · doi:10.1111/adb.12391 · Cited by 35
“Mendelian randomization (MR) analysis can be employed to test causality (Davey Smith & Ebrahim 2003; Palmer et al .”
Georgakis, M. K., Malik, R., Gill, D., Franceschini, N., Sudlow, C. L. M., & Dichgans, M. (2019) · medRxiv · doi:10.1101/19007682 · Cited by 36
“We selected and weighted genetic instruments for genetic predisposition to IL-6 signaling on the basis of their associations with CRP levels, because elevated CRP levels are a well-described downstream effect of IL-6 signaling (Figure 1).13, 20, 34 Genetic association estimates with circulating CRP levels were obtaine…”
BackgroundMethodInfluentialIntegrative analysis of clinical and epigenetic biomarkers of mortality
Huan, T., Nguyen, S., Colicino, E., Ochoa‐Rosales, C., Hill, W. D., Brody, J. A., Soerensen, M., Zhang, Y., Baldassari, A., Elhadad, M. A., et al. (2022) · Aging Cell · doi:10.1111/acel.13608 · Cited by 25
“IV assumption (Palmer et al., 2012 ).”
Mendelian Randomization and Estimation of Treatment Efficacy for Chronic Diseases
Schooling, C. M., Freeman, G., & Cowling, B. J. (2013) · American Journal of Epidemiology · doi:10.1093/aje/kws344 · Cited by 34
Rheumatoid arthritis and osteoporosis: a bi-directional Mendelian randomization study
Liu, Y., Liu, Y., Chen, Z., Li, H., & Xiao, T. (2021) · Aging · doi:10.18632/aging.203029 · Cited by 36
“To ensure that the selected IVs have enough power for detecting the causal effect of exposure on the outcome, we calculated the F statistic of selected IVs with an online tool (https://sb452.shinyapps.io/overlap) [48].”
Groot, H. E., van de Vegte, Y. J., Verweij, N., Lipšic, E., Karper, J. C., & van der Harst, P. (2020) · Scientific Reports · doi:10.1038/s41598-020-70724-5 · Cited by 35
Su, Q., Jin, C., Bo, Z., Yang, Y., Wang, J., Wang, J., Zhou, J., Chen, Y., Zeng, H., Chen, G., et al. (2023) · Frontiers in Microbiology · doi:10.3389/fmicb.2023.1181328 · Cited by 35
“Then, SNPs with F-statistics [formula: R2/K × (N − K − 1)/(1 − R2)] < 10 were removed (Palmer et al., 2012).”
Genetically Predicted Circulating Levels of Cytokines and the Risk of Cancer
Song, J., Li, A., Qian, Y., Liu, B., Lv, L., Ye, D., Sun, X., & Mao, Y. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.886144 · Cited by 33
Zheng, J., Ni, C., Zhang, Y., Huang, J., Hukportie, D. N., Liang, B., & Tang, S. (2023) · BMC Medicine · doi:10.1186/s12916-023-02816-8 · Cited by 33
Xie, N., Wang, Z., Shu, Q., Liang, X., Wang, J., Wu, K., Nie, Y., Shi, Y., Fan, D., fan, D., et al. (2023) · Nutrients · doi:10.3390/nu15132937 · Cited by 34
Metadata
- Authors
- Palmer, T. M., Lawlor, D. A., Harbord, R. M., Sheehan, N. A., Tobias, J. H., Timpson, N. J., Smith, G. D., & Sterne, J. A.
- Affiliations
- University of Bristol (GB)
- University of Leicester (GB)
- School of Clinical Sciences, University of Bristol, Bristol, UK
- Corresponding author
- Palmer, Tom M
- ORCID
- Venue
- Statistical Methods in Medical Research
- Publisher
- SAGE Publications
- Published
- 2011-01-07
- Volume
- 21
- Issue
- 3
- Pages
- 223-242
- ISSN
- 0962-2802 (ISSN-L)
- 1477-0334
- PMC
- PMC3917707
- OpenAlex
- W2151096860
- Cited by
- 1,289
- References
- 76
- Citations by year
- 2026: 92 · 2025: 240 · 2024: 394 · 2023: 164 · 2022: 83 · 2021: 67 · 2020: 44 · 2019: 33 · 2018: 22 · 2017: 20 · 2016: 27 · 2015: 44 · 2014: 20 · 2013: 19 · 2012: 14
- FWCI
- 11.61
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Statistical Methods in Medical Research (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Adelaide Research & Scholarship (AR&S) (University of Adelaide) (submittedVersion)
- Lancaster EPrints (Lancaster University) (submittedVersion)
- Figshare (submittedVersion, cc-by)
- Leicester Research Archive (University of Leicester) (submittedVersion, cc-by)
- Bristol Research (University of Bristol) (submittedVersion, cc-by)
- Funders
- Wellcome Trust (G0600705, 092731)
- University of Bristol (G0600705)
- Medical Research Council (G0600705)
- Economic and Social Research Council (ES/E00234X/1)
- 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, modifiable risk factors, weak instruments, finite-sample bias, fat mass, bone mineral density, children, causal effect estimation, continuous outcomes
- MeSH terms
- Female, Genotype, Humans, Male, Risk Factors, Genetic Variation, Mendelian Randomization Analysis
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