Distinguishing genetic correlation from causation across 52 diseases and complex traits
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Abstract
Mendelian randomization, a method to infer causal relationships, is confounded by genetic correlations reflecting shared etiology. We developed a model in which a latent causal variable mediates the genetic correlation; trait 1 is partially genetically causal for trait 2 if it is strongly genetically correlated with the latent causal variable, quantified using the genetic causality proportion. We fit this model using mixed fourth moments [Formula: see text] and [Formula: see text] of marginal effect sizes for each trait; if trait 1 is causal for trait 2, then SNPs affecting trait 1 (large [Formula: see text]) will have correlated effects on trait 2 (large α 1 α 2), but not vice versa. In simulations, our method avoided false positives due to genetic correlations, unlike Mendelian randomization. Across 52 traits (average n = 331,000), we identified 30 causal relationships with high genetic causality proportion estimates. Novel findings included a causal effect of low-density lipoprotein on bone mineral density, consistent with clinical trials of statins in osteoporosis.
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Fully causal relationships were deduced for significant trait pairs with gĉp ≥ 0.70; otherwise partial causality was considered.
“Fully causal relationships were deduced for significant trait pairs with gĉp estimates ≥ 0.70; otherwise gĉp estimates were considered evidence for partial causality.43”
From Bi-ancestral depression GWAS in the Million Veteran Program and meta-analysis in >1.2 million individuals highlight new therapeutic directions (2021) · cites this paper as [43]
Genetic causality proportions were interpreted only when both traits had heritability z-scores ≥ 7.
“Genetic causality proportions (gĉp) were interpreted only when the heritability z-score of both traits was ≥ 7, as determined by LCV, not LDSC.43”
From Bi-ancestral depression GWAS in the Million Veteran Program and meta-analysis in >1.2 million individuals highlight new therapeutic directions (2021) · cites this paper as [43]
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Sudlow, C., Gallacher, J., Allen, N., Beral, V., Burton, P., Danesh, J., Downey, P., Elliott, P., Green, J., Landray, M., et al. (2015) · PLOS Medicine · doi:10.1371/journal.pmed.1001779 · Cited by 12,728 · see citing works
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
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
Independent component analysis, A new concept?
Comon, P. (1994) · Signal Processing · doi:10.1016/0165-1684(94)90029-9 · Cited by 8,150 · see citing works
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
Mendelian Randomization Analysis With Multiple Genetic Variants Using Summarized Data
Burgess, S., Butterworth, A., & Thompson, S. G. (2013) · Genetic Epidemiology · doi:10.1002/gepi.21758 · Cited by 6,050 · 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
LD Score regression distinguishes confounding from polygenicity in genome-wide association studies
Schizophrenia Working Group of the Psychiatric Genomics Consortium, Bulik-Sullivan, B. K., Loh, P.-R., Finucane, H. K., Ripke, S., Yang, J., Patterson, N., Daly, M. J., Price, A. L., & Neale, B. M. (2015) · Nature Genetics · doi:10.1038/ng.3211 · Cited by 5,559 · 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
Interpreting findings from Mendelian randomization using the MR-Egger method.
Burgess, S., & Thompson, S. G. (2017) · European Journal of Epidemiology · doi:10.1007/s10654-017-0255-x · Cited by 4,571 · see citing works
An atlas of genetic correlations across human diseases and traits
ReproGen Consortium, Bulik-Sullivan, B., Psychiatric Genomics Consortium, Genetic Consortium for Anorexia Nervosa of the Wellcome Trust Case Control Consortium 3, Finucane, H. K., Anttila, V., Gusev, A., Day, F. R., Loh, P.-R., Duncan, L., et al. (2015) · Nature Genetics · doi:10.1038/ng.3406 · Cited by 4,255 · see citing works
Self-citationGenetic effects on gene expression across human tissues
GTEx Consortium (2017) · Nature · doi:10.1038/nature24277 · Cited by 4,036 · 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
Integration 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
Sequence Variations in <i>PCSK9,</i> Low LDL, and Protection against Coronary Heart Disease
Cohen, J. C., Boerwinkle, E., Mosley, T. H., & Hobbs, H. H. (2006) · New England Journal of Medicine · doi:10.1056/nejmoa054013 · Cited by 3,181 · see citing works
Integrative approaches for large-scale transcriptome-wide association studies
Gusev, A., Ko, A., Shi, H., Bhatia, G., Chung, W., Penninx, B. W. J. H., Jansen, R., de Geus, E. J. C., Boomsma, D. I., Wright, F. A., et al. (2016) · Nature Genetics · doi:10.1038/ng.3506 · Cited by 2,479 · see citing works
Self-citationThyroid hormone and the cardiovascular system.
Klein, I., & Ojamaa, K. (2001) · New England Journal of Medicine · doi:10.1056/nejm200102153440707 · Cited by 2,300 · see citing works
PRICE, G. R. (1970) · Nature · doi:10.1038/227520a0 · Cited by 1,964 · see citing works
Large-scale association analysis identifies 13 new susceptibility loci for coronary artery disease
Cardiogenics, Schunkert, H., the CARDIoGRAM Consortium, König, I. R., Kathiresan, S., Reilly, M. P., Assimes, T. L., Holm, H., Preuss, M., Stewart, A. F. R., et al. (2011) · Nature Genetics · doi:10.1038/ng.784 · Cited by 1,866 · see citing works
A gene-based association method for mapping traits using reference transcriptome data
Gamazon, E. R., Wheeler, H. E., Shah, K. P., Mozaffari, S. V., Aquino-Michaels, K., Carroll, R. J., Eyler, A. E., Denny, J. C., Nicolae, D. L., Cox, N. J., et al. (2015) · Nature Genetics · doi:10.1038/ng.3367 · Cited by 1,762 · see citing works
The essentials of factor analysis
Child, D. (1970) · Medical Entomology and Zoology · Cited by 1,671 · 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
Vimaleswaran, K. S., Berry, D. J., Lu, C., Tikkanen, E., Pilz, S., Hiraki, L. T., Cooper, J. D., Dastani, Z., Li, R., Houston, D. K., et al. (2013) · PLoS Medicine · doi:10.1371/journal.pmed.1001383 · Cited by 1,046 · see citing works
The UK10K project identifies rare variants in health and disease
The UK10K Consortium, Writing group, Walter, K., Min, J. L., Huang, J., Crooks, L., Memari, Y., McCarthy, S., Perry, J. R. B., Xu, C., et al. (2015) · Nature · doi:10.1038/nature14962 · Cited by 1,040 · 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
Illustrating bias due to conditioning on a collider
Cole, S. R., Platt, R. W., Schisterman, E. F., Chu, H., Westreich, D., Richardson, D., & Poole, C. (2010) · International Journal of Epidemiology · doi:10.1093/ije/dyp334 · Cited by 796 · see citing works
Mixed model association for biobank-scale data sets
Loh, P.-R., Kichaev, G., Gazal, S., Schoech, A. P., & Price, A. L. (2017) · doi:10.1101/194944 · Cited by 788 · see citing works
Self-citationMixed-model association for biobank-scale datasets
Loh, P.-R., Kichaev, G., Gazal, S., Schoech, A. P., & Price, A. L. (2018) · Nature Genetics · doi:10.1038/s41588-018-0144-6 · Cited by 758 · see citing works
Self-citationStudy, T. L. C., Yang, J., Bakshi, A., Zhu, Z., Hemani, G., Vinkhuyzen, A. A. E., Lee, S. H., Robinson, M. R., Perry, J. R. B., Nolte, I. M., et al. (2015) · Nature Genetics · doi:10.1038/ng.3390 · Cited by 745 · see citing works
Mendelian randomization in cardiometabolic disease: challenges in evaluating causality
Holmes, M. V., Ala-Korpela, M., & Smith, G. D. (2017) · Nature Reviews Cardiology · doi:10.1038/nrcardio.2017.78 · Cited by 679 · see citing works
The Essentials of Factor Analysis.
Williams, J. S., & Child, D. (1974) · Contemporary Sociology A Journal of Reviews · doi:10.2307/2061984 · Cited by 618 · see citing works
Genome-wide genetic data on ~500,000 UK Biobank participantsPreprint
Bycroft, C., Freeman, C., Petkova, D., Band, G., Elliott, L. T., Sharp, K., Motyer, A., Vukcevic, D., Delaneau, O., O’Connell, J., et al. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/166298 · Cited by 606 · see citing works
Methodological Challenges in Mendelian Randomization
VanderWeele, T. J., Tchetgen Tchetgen, E. J., Cornelis, M., & Kraft, P. (2014) · Epidemiology · doi:10.1097/ede.0000000000000081 · Cited by 555 · see citing works
Keller, M. C., & Miller, G. F. (2006) · Behavioral and Brain Sciences · doi:10.1017/s0140525x06009095 · Cited by 545 · 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
So Many Correlated Tests, So Little Time! Rapid Adjustment of P Values for Multiple Correlated Tests
Conneely, K. N., & Boehnke, M. (2007) · The American Journal of Human Genetics · doi:10.1086/522036 · Cited by 446 · see citing works
Vitamin D and Risk of Multiple Sclerosis: A Mendelian Randomization Study
Mokry, L. E., Ross, S., Ahmad, O. S., Forgetta, V., Smith, G. D., Leong, A., Greenwood, C. M. T., Thanassoulis, G., & Richards, J. B. (2015) · PLOS Medicine · doi:10.1371/journal.pmed.1001866 · Cited by 445 · see citing works
Paaby, A. B., & Rockman, M. V. (2012) · Trends in Genetics · doi:10.1016/j.tig.2012.10.010 · Cited by 423 · see citing works
Monzani, F., Caraccio, N., Kozàkowà, M., Dardano, A., Vittone, F., Virdis, A., Taddei, S., Palombo, C., & Ferrannini, E. (2004) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2003-031669 · Cited by 399 · see citing works
Meier, C., Staub, J.-J., Roth, C.-B., Guglielmetti, M., Kunz, M., Miserez, A. R., Drewe, J., Huber, P., Herzog, R. F. K., & Müller-Stich, B. P. (2001) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jcem.86.10.7973 · Cited by 397 · see citing works
Razvi, S., Ingoe, L., Keeka, G., Oates, C., McMillan, C., & Weaver, J. U. (2007) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2006-1869 · Cited by 389 · see citing works
Davies, G., Marioni, R. E., Liewald, D. C., Hill, W. D., Hagenaars, S. P., Harris, S. E., Ritchie, S. J., Luciano, M., Fawns-Ritchie, C., Lyall, D., et al. (2016) · Molecular Psychiatry · doi:10.1038/mp.2016.45 · Cited by 373 · see citing works
Height, body mass index, and socioeconomic status: mendelian randomisation study in UK Biobank
Tyrrell, J., Jones, S. E., Beaumont, R. N., Astley, C. M., Lovell, R., Yaghootkar, H., Tuke, M. A., Ruth, K. S., Freathy, R. M., Hirschhorn, J. N., et al. (2016) · BMJ · doi:10.1136/bmj.i582 · Cited by 372 · see citing works
Monzani, F., Di Bello, V., Caraccio, N., Bertini, A., Giorgi, D., Giusti, C., & Ferrannini, E. (2001) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jcem.86.3.7291 · Cited by 308 · see citing works
Grais, I. M., & Sowers, J. R. (2014) · The American Journal of Medicine · doi:10.1016/j.amjmed.2014.03.009 · Cited by 299 · see citing works
Polymorphism of the Androgen Receptor Gene is Associated with Male Pattern Baldness
Ellis, J. A., Stebbing, M. S., & Harrap, S. B. (2001) · Journal of Investigative Dermatology · doi:10.1046/j.1523-1747.2001.01261.x · Cited by 298 · see citing works
Adjusting for Heritable Covariates Can Bias Effect Estimates in Genome-Wide Association Studies
Aschard, H., Vilhjálmsson, B. J., Joshi, A. D., Price, A. L., & Kraft, P. (2015) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2014.12.021 · Cited by 287 · see citing works
Self-citationNordestgaard, B. G., Palmer, T., Benn, M., Zacho, J., Tybjærg‐Hansen, A., Smith, G. D., & Timpson, N. J. (2012) · PLoS Medicine · doi:10.1371/journal.pmed.1001212 · Cited by 280 · see citing works
Kang, H., Zhang, A., Cai, T. T., & Small, D. S. (2016) · Journal of the American Statistical Association · doi:10.1080/01621459.2014.994705 · Cited by 265 · see citing works
Lyall, D. M., Celis-Morales, C., Ward, J., Iliodromiti, S., Anderson, J. J., Gill, J. M. R., Smith, D. J., Ntuk, U. E., Mackay, D. F., Holmes, M. V., et al. (2017) · JAMA Cardiology · doi:10.1001/jamacardio.2016.5804 · Cited by 235 · see citing works
Holmes, M. V., Lange, L. A., Palmer, T., Lanktree, M. B., North, K. E., Almoguera, B., Buxbaum, S., Chandrupatla, H. R., Elbers, C. C., Guo, Y., et al. (2014) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2013.12.014 · Cited by 216 · see citing works
Thyroid function and risk of type 2 diabetes: a population-based prospective cohort study
Chaker, L., Ligthart, S., Korevaar, T. I. M., Albert Hofman, D., Franco, O. H., Peeters, R. P., & Dehghan, A. (2016) · BMC Medicine · doi:10.1186/s12916-016-0693-4 · Cited by 194 · see citing works
Clarke, T.-K., Lupton, M. K., Fernandez-Pujals, A. M., Starr, J., Davies, G., Cox, S., Pattie, A., Liewald, D. C., Hall, L. S., MacIntyre, D. J., et al. (2016) · Molecular Psychiatry · doi:10.1038/mp.2015.12 · Cited by 179 · see citing works
Evidence of directional and stabilizing selection in contemporary humans
Sanjak, J. S., Sidorenko, J., Robinson, M. R., Thornton, K., & Visscher, P. M. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1707227114 · Cited by 179 · see citing works
Longitudinal association of body mass index with lung function: The CARDIA Study
Thyagarajan, B., Jacobs, D. R., Apostol, G. G., Smith, L. J., Jensen, R. L., Crapo, R. O., Barr, R. G., Lewis, C. E., & Williams, O. D. (2008) · Respiratory Research · doi:10.1186/1465-9921-9-31 · Cited by 166 · see citing works
HDL Cholesterol and Risk of Type 2 Diabetes: A Mendelian Randomization Study
Haase, C. L., Tybjærg‐Hansen, A., Nordestgaard, B. G., & Frikke‐Schmidt, R. (2015) · Diabetes · doi:10.2337/db14-1603 · Cited by 162 · see citing works
Schoech, A. P., Jordan, D. M., Loh, P.-R., Gazal, S., O’Connor, L. J., Balick, D. J., Palamara, P. F., Finucane, H. K., Sunyaev, S. R., & Price, A. L. (2019) · Nature Communications · doi:10.1038/s41467-019-08424-6 · Cited by 157 · see citing works
Self-citationPlasma 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
Adiposity as a cause of cardiovascular disease: a Mendelian randomization study
Hägg, S., Fall, T., Ploner, A., Mägi, R., Fischer, K., Draisma, H. H. M., Kals, M., de Vries, P. S., Dehghan, A., Willems, S. M., et al. (2015) · International Journal of Epidemiology · doi:10.1093/ije/dyv094 · Cited by 142 · see citing works
Welsh, P., Polisecki, E., Robertson, M., Jahn, S., Buckley, B. M., de Craen, A. J. M., Ford, I., Jukema, J. W., Macfarlane, P. W., Packard, C. J., et al. (2010) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2009-1064 · Cited by 140 · see citing works
Identification and Inference With Many Invalid Instruments
Kolesár, M., Chetty, R., Friedman, J. N., Glaeser, E. L., & Imbens, G. W. (2014) · Journal of Business and Economic Statistics · doi:10.1080/07350015.2014.978175 · Cited by 140 · see citing works
Hemani, 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
Ross, S. A., Gerstein, H. C., Eikelboom, J. W., Anand, S. S., Yusuf, S., & Paré, G. (2015) · European Heart Journal · doi:10.1093/eurheartj/ehv083 · Cited by 128 · see citing works
Smith, G. D., Sterne, J. A. C., Fraser, A., Tynelius, P., Lawlor, D. A., & Rasmussen, F. E. (2009) · BMJ · doi:10.1136/bmj.b5043 · Cited by 127 · see citing works
Estimating Effects and Making Predictions from Genome-Wide Marker Data
Goddard, M. E., Wray, N. R., Verbyla, K., & Visscher, P. M. (2009) · Statistical Science · doi:10.1214/09-sts306 · Cited by 121 · see citing works
Effects of Statins on Bone Mineral Density and Fracture Risk
Wang, Z., Li, Y., Zhou, F., et al. (2016) · Medicine · doi:10.1097/md.0000000000003042 · Cited by 72 · see citing works
Skaaby, T., Taylor, A. E., Thuesen, B. H., Jacobsen, R. K., Friedrich, N., Møllehave, L. T., Hansen, S., Larsen, S. C., Völker, U., Nauck, M. A., et al. (2017) · Allergy · doi:10.1111/all.13242 · Cited by 65 · see citing works
Galinsky, K. J., Loh, P., Mallick, S., Patterson, N. J., & Price, A. L. (2016) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2016.09.014 · Cited by 51 · see citing works
Self-citationNagasaki, T., Inaba, M., Yamada, S., Shirakawa, K., Nagata, Y., Kumeda, Y., Hiura, Y., Tahara, H., Ishimura, E., & Nishizawà, Y. (2008) · European Journal of Endocrinology · doi:10.1530/eje-08-0742 · Cited by 47 · see citing works
Acute Thyroid Hormone Withdrawal in Athyreotic Patients Results in a State of Insulin Resistance
Brenta, G., Celi, F. S., Pisarev, M. A., Schnitman, M., Sinay, I. R., & Arias, P. (2009) · Thyroid · doi:10.1089/thy.2009.0108 · Cited by 46 · see citing works
Genome-Wide Meta-Analysis of Cotinine Levels in Cigarette Smokers Identifies Locus at 4q13.2
Ware, J. J., Chen, X., Vink, J. M., Loukola, A., Minică, C. C., Pool, R., Milaneschi, Y., Mangino, M., Menni, C., Chen, J., et al. (2016) · Scientific Reports · doi:10.1038/srep20092 · Cited by 42 · see citing works
Thyroid function and ischemic heart disease: a Mendelian randomization study
Zhao, J. V., & Schooling, C. M. (2017) · Scientific Reports · doi:10.1038/s41598-017-07592-z · Cited by 34 · see citing works
Decreased Bone Mineral Density in Subjects Carrying Familial Defective Apolipoprotein B-100
Yerges‐Armstrong, L. M., Shen, H., Ryan, K. A., Streeten, E. A., Shuldiner, A. R., & Mitchell, B. D. (2013) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2013-2471 · Cited by 31 · see citing works
Schoech, A. P., Jordan, D. M., Loh, P., et al. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/188086 · Cited by 31 · see citing works
Reproductive fitness and genetic risk of psychiatric disorders in the general population
Mullins, N., Ingason, A., Porter, H. F., Euesden, J., Gillett, A. C., Ólafsson, S., Gudbjartsson, D. F., Lewis, C. M., Sigurðsson, E., Sæmundsen, E., et al. (2017) · Nature Communications · doi:10.1038/ncomms15833 · Cited by 28 · see citing works
Correcting subtle stratification in summary association statisticsPreprint
Bhatia, G., Furlotte, N. A., Loh, P., Liu, X., Finucane, H. K., Gusev, A., & Price, A. L. (2016) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/076133 · Cited by 4 · see citing works
Self-citationO’Rourke, C. J., Murphy, T. M., Hollywood, D. P., et al. (2012) · Trends in Genetics · doi:10.1016/j.tig.2012.10.015 · Cited by 1 · see citing works
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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
Guidelines 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
The genetic architecture of the human cerebral cortex
Grasby, K. L., Jahanshad, N., Painter, J. N., Colodro-Conde, L., Bralten, J., Hibar, D. P., Lind, P. A., Pizzagalli, F., Ching, C. R. K., McMahon, M. A. B., et al. (2020) · Science · doi:10.1126/science.aay6690 · Cited by 876 · see citing works
Levey, D. F., Stein, M. B., Wendt, F. R., Pathak, G. A., Zhou, H., Aslan, M., Quaden, R., Harrington, K. M., Nuñez, Y. Z., Overstreet, C., et al. (2021) · Nature Neuroscience · doi:10.1038/s41593-021-00860-2 · Cited by 422 · see citing works
Pathophysiology-based subphenotyping of individuals at elevated risk for type 2 diabetes
Wagner, R., Heni, M., Tabák, A. G., Machann, J., Schick, F., Randrianarisoa, E., Hrabě de Angelis, M., Birkenfeld, A. L., Stefan, N., Peter, A., et al. (2021) · Nature Medicine · doi:10.1038/s41591-020-1116-9 · Cited by 415 · see citing works
Porcu, E., Rüeger, S., Lepik, K., eQTLGen Consortium, Agbessi, M., Ahsan, H., Alves, I., Andiappan, A., Arindrarto, W., Awadalla, P., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-10936-0 · Cited by 322 · see citing works
Deconstructing a Syndrome: Genomic Insights Into PCOS Causal Mechanisms and Classification
Dapas, M., & Dunaif, A. (2022) · Endocrine Reviews · doi:10.1210/endrev/bnac001 · Cited by 300 · see citing works
Extreme Polygenicity of Complex Traits Is Explained by Negative Selection
O'Connor, L. J., Schoech, A. P., Hormozdiari, F., Gazal, S., Patterson, N., & Price, A. L. (2019) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2019.07.003 · Cited by 268 · see citing works
Self-citationHatoum, A. S., Colbert, S. M. C., Johnson, E. C., Huggett, S. B., Deak, J. D., Pathak, G. A., Jennings, M. V., Paul, S. E., Karcher, N. R., Hansen, I., et al. (2023) · Nature Mental Health · doi:10.1038/s44220-023-00034-y · Cited by 241 · 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
Qi, G., & Chatterjee, N. (2019) · Nature Communications · doi:10.1038/s41467-019-09432-2 · Cited by 207 · see citing works
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Zeng, J., Xue, A., Jiang, L., Lloyd-Jones, L. R., Wu, Y., Wang, H., Zheng, Z., Yengo, L., Kemper, K. E., Goddard, M. E., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-21446-3 · Cited by 144 · see citing works
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Yang, Q., Sanderson, E., Tilling, K., Borges, M. C., & Lawlor, D. A. (2019) · doi:10.1101/19009605 · Cited by 85 · see citing works
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Bettencourt, C., Skene, N., Bandres‐Ciga, S., Anderson, E., Winchester, L. M., Foote, I. F., Schwartzentruber, J., Botia, J. A., Nalls, M., Singleton, A., et al. (2023) · Alzheimer's & Dementia · doi:10.1002/alz.13427 · Cited by 30 · see citing works
Haworth, S., Kho, P. F., Holgerson, P. L., Hwang, L.-D., Timpson, N. J., Rentería, M. E., Johansson, I., & Cuellar-Partida, G. (2021) · European Journal of Human Genetics · doi:10.1038/s41431-020-00734-4 · Cited by 29 · see citing works
Hatoum, A. S., Morrison, C. L., Mitchell, E. C., Lam, M., Benca-Bachman, C. E., Reineberg, A. E., Palmer, R. H. C., Evans, L. M., Keller, M. C., & Friedman, N. P. (2019) · doi:10.1101/674515 · Cited by 115 · 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
How humans can contribute to Mendelian randomization analyses.
Burgess, S., & Davey Smith, G. (2019) · International Journal of Epidemiology · doi:10.1093/ije/dyz152 · Cited by 20 · 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
Liu, R., Shang, X., Fu, Y., Wang, Y., Wang, P., & Yan, S. (2024) · Molecular Immunology · doi:10.1016/j.molimm.2024.02.002 · Cited by 13 · see citing works
- Authors
- O’Connor, L. J., & Price, A. L.
- Affiliations
- Harvard University (US)
- Broad Institute (US)
- Corresponding author
- O’Connor, Luke J.
- Price, Alkes L.
- Venue
- Nature Genetics
- Published
- 2018-10-29
- Volume
- 50
- Issue
- 12
- Pages
- 1728-1734
- ISSN
- 1061-4036 (ISSN-L)
- 1546-1718
- PMC
- PMC6684375
- OpenAlex
- W2950958620
- Cited by
- 394
- References
- 78
- Citations by year
- 2026: 27 · 2025: 42 · 2024: 85 · 2023: 50 · 2022: 53 · 2021: 53 · 2020: 39 · 2019: 35 · 2018: 9 · 2017: 1
- FWCI
- 20.52
- Type
- journal article
- Language
- en
- Update notices
- correction (2018-11-06)
- Open access
- green
- doi.org
- Licence
- cc-by-nc
- www.springer.com/tdm (tdm)
- Locations
- Nature Genetics (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health (16549, CA194393, MH107649)
- National Institute of Mental Health (R01MH107649, R01MH101244)
- National Human Genome Research Institute (T32HG002295)
- National Cancer Institute (U01CA194393)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic and phenotypic traits in livestock
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
- Keywords
- genetic correlation, genetic causality, shared etiology, complex traits, disease characteristics, low-density lipoprotein, bone mineral density, statins, osteoporosis, causal inference
- MeSH terms
- Autistic Disorder, Computer Simulation, Disease, Genotype, Humans, Hypothyroidism, Models, Theoretical, Myocardial Infarction, Osteoporosis, Phenotype, Bone Density, Linkage Disequilibrium, Causality, Genetic Predisposition to Disease, Multifactorial Inheritance, Polymorphism, Single Nucleotide
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