MR-pheWAS with stratification and interaction: Searching for the causal effects of smoking heaviness identified an effect on facial aging

English version

Autoři: Louise A. C. Millard ^aff001; Marcus R. Munafò ^aff001; Kate Tilling ^aff001; Robyn E. Wootton ^aff001; George Davey Smith ^aff001
Působiště autorů: MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, United Kingdom ^aff001; Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom ^aff002; Intelligent Systems Laboratory, Department of Computer Science, University of Bristol, Bristol, United Kingdom ^aff003; UK Centre for Tobacco and Alcohol Studies, School of Experimental Psychology, University of Bristol, Bristol, United Kingdom ^aff004
Vyšlo v časopise: MR-pheWAS with stratification and interaction: Searching for the causal effects of smoking heaviness identified an effect on facial aging. PLoS Genet 15(10): e32767. doi:10.1371/journal.pgen.1008353
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pgen.1008353

Souhrn

Mendelian randomization (MR) is an established approach to evaluate the effect of an exposure on an outcome. The gene-by-environment (GxE) study design can be used to determine whether the genetic instrument affects the outcome through pathways other than via the exposure of interest (horizontal pleiotropy). MR phenome-wide association studies (MR-pheWAS) search for the effects of an exposure, and can be conducted in UK Biobank using the PHESANT package. In this proof-of-principle study, we introduce the novel GxE MR-pheWAS approach, that combines MR-pheWAS with the use of GxE interactions. This method aims to identify the presence of effects of an exposure while simultaneously investigating horizontal pleiotropy. We systematically test for the presence of causal effects of smoking heaviness–stratifying on smoking status (ever versus never)–as an exemplar. If a genetic variant is associated with smoking heaviness (but not smoking initiation), and this variant affects an outcome (at least partially) via tobacco intake, we would expect the effect of the variant on the outcome to differ in ever versus never smokers. We used PHESANT to test for the presence of effects of smoking heaviness, instrumented by genetic variant rs16969968, among never and ever smokers respectively, in UK Biobank. We ranked results by the strength of interaction between ever and never smokers. We replicated previously established effects of smoking heaviness, including detrimental effects on lung function. Novel results included a detrimental effect of heavier smoking on facial aging. We have demonstrated how GxE MR-pheWAS can be used to identify potential effects of an exposure, while simultaneously assessing whether results may be biased by horizontal pleiotropy.

Klíčová slova:

Phenotypes – Genetic engineering – Face – Aging – Smoking habits – Data processing – Aging and cancer – Colliders

Zdroje

1. Davey Smith G, Ebrahim S. “Mendelian randomization”: can genetic epidemiology contribute to understanding environmental determinants of disease? Int J Epidemiol. 2003;32(1):1–22. doi: 10.1093/ije/dyg070 12689998

2. Davey Smith G, Hemani G. Mendelian randomization: genetic anchors for causal inference in epidemiological studies. Hum Mol Genet. 2014;23(R1):R89–98. doi: 10.1093/hmg/ddu328 25064373

3. Hemani G, Bowden J, Davey Smith G. Evaluating the potential role of pleiotropy in Mendelian randomization studies. Hum Mol Genet. 2018;27(R2):R195–208. doi: 10.1093/hmg/ddy163 29771313

4. Davey Smith G, Timpson N, Ebrahim S. Strengthening causal inference in cardiovascular epidemiology through Mendelian randomization. Ann Med. 2008;40(7):524–41. doi: 10.1080/07853890802010709 18608114

5. Millard LAC, Davies NM, Timpson NJ, Tilling K, Flach PA, Davey Smith G. MR-PheWAS: hypothesis prioritization among potential causal effects of body mass index on many outcomes, using Mendelian randomization. Sci Rep. 2015;5 : 16645. doi: 10.1038/srep16645 26568383

6. Zheng J, Baird D, Borges M-C, Bowden J, Hemani G, Haycock P, et al. Recent Developments in Mendelian Randomization Studies. Curr Epidemiol Reports. 2017;4(4):330–45.

7. Taylor AE, Davies NM, Ware JJ, VanderWeele T, Davey Smith G, Munafò MR. Mendelian randomization in health research: Using appropriate genetic variants and avoiding biased estimates. Econ Hum Biol. 2014;13(100):99–106.

8. Chen L, Davey Smith G, Harbord RM, Lewis SJ. Alcohol Intake and Blood Pressure: A Systematic Review Implementing a Mendelian Randomization Approach. PLOS Med. 2008;5(3):e52. doi: 10.1371/journal.pmed.0050052 18318597

9. Burgess S, Timpson NJ, Ebrahim S, Davey Smith G. Mendelian randomization: where are we now and where are we going? Int J Epidemiol. 2015;44(2):379–88. doi: 10.1093/ije/dyv108 26085674

10. Freathy RM, Kazeem GR, Morris RW, Johnson PCD, Paternoster L, Ebrahim S, et al. Genetic variation at CHRNA5-CHRNA3-CHRNB4 interacts with smoking status to influence body mass index. Int J Epidemiol. 2011;40(6):1617–28. doi: 10.1093/ije/dyr077 21593077

11. Davey Smith G. Use of genetic markers and gene-diet interactions for interrogating population-level causal influences of diet on health. Genes Nutr. 2011;6(1):27–43. doi: 10.1007/s12263-010-0181-y 21437028

12. Davey Smith G. Mendelian Randomization for Strengthening Causal Inference in Observational Studies: Application to Gene × Environment Interactions. Perspect Psychol Sci. 2010;5(5):527–45. doi: 10.1177/1745691610383505 26162196

13. Ottman R, Rao DC. An epidemiologic approach to gene-environment interaction. Genet Epidemiol. 1990;7(3):177–85. doi: 10.1002/gepi.1370070302 2369997

14. Munafò MR, Tilling K, Taylor AE, Evans DM, Davey Smith G. Collider Scope: When selection bias can substantially influence observed associations. Int J Epidemiol. 2017;47(1):226–35.

15. Hughes RA, Davies NM, Davey Smith G, Tilling K. Selection bias when estimating average treatment effects using one-sample instrumental variable analysis. Epidemiology. 2019;30(3):350–57.

16. Morris RW, Taylor AE, Fluharty ME, Bjørngaard JH, Åsvold BO, Elvestad Gabrielsen M, et al. Heavier smoking may lead to a relative increase in waist circumference: evidence for a causal relationship from a Mendelian randomisation meta-analysis. The CARTA consortium. BMJ Open. 2015;5(8).

17. Ware JJ, van den Bree MBM, Munafò MR. Association of the CHRNA5-A3-B4 Gene Cluster With Heaviness of Smoking: A Meta-Analysis. Nicotine Tob Res. 2011;13(12):1167–75. doi: 10.1093/ntr/ntr118 22071378

18. Ware JJ, van den Bree M, Munafò MR. From Men to Mice: CHRNA5/CHRNA3, Smoking Behavior and Disease. Nicotine Tob Res. 2012;14(11):1291–9. doi: 10.1093/ntr/nts106 22544838

19. Taylor A, Fluharty M. Investigating the possible causal association of smoking with depression and anxiety using Mendelian randomisation meta-analysis: the CARTA consortium. BMJ Open. 2014;4(10):e006141. doi: 10.1136/bmjopen-2014-006141 25293386

20. Taylor AE, Morris RW, Fluharty ME, Bjorngaard JH, Åsvold BO, Gabrielsen ME, et al. Stratification by Smoking Status Reveals an Association of CHRNA5-A3-B4 Genotype with Body Mass Index in Never Smokers. PLOS Genet. 2014;10(12):e1004799. doi: 10.1371/journal.pgen.1004799 25474695

21. Winsløw UC, Rode L, Nordestgaard BG. High tobacco consumption lowers body weight: a Mendelian randomization study of the Copenhagen General Population Study. Int J Epidemiol. 2015;44(2):540–50. doi: 10.1093/ije/dyu276 25777141

22. Thompson WD. Effect modification and the limits of biological inference from epidemiologic data. J Clin Epidemiol. 1991;44(3):221–32. doi: 10.1016/0895-4356(91)90033-6 1999681

23. Millard LAC, Davies NMM, Gaunt TR, Davey Smith G, Tilling K, Smith GD, et al. Software Application Profile: PHESANT: a tool for performing automated phenome scans in UK Biobank. Int J Epidemiol. 2017;47(1):29–35.

24. Rassen JA, Schneeweiss S, Glynn RJ, Mittleman MA, Brookhart MA. Instrumental Variable Analysis for Estimation of Treatment Effects With Dichotomous Outcomes. Am J Epidemiol. 2008;169(3):273–84. doi: 10.1093/aje/kwn299 19033525

25. Wilk JB, Shrine NRG, Loehr LR, Zhao JH, Manichaikul A, Lopez LM, et al. Genome-Wide Association Studies Identify CHRNA5/3 and HTR4 in the Development of Airflow Obstruction. Am J Respir Crit Care Med. 2012;186(7):622–32. doi: 10.1164/rccm.201202-0366OC 22837378

26. SA DH, Wensveen CA, Bastiaens MT, Kielich CJ, Berkhout MJ, Westendorp RG, et al. Relation between smoking and skin cancer. J Clin Oncol. 2001;19(1):231–8. doi: 10.1200/JCO.2001.19.1.231 11134217

27. Gibson M, Munafo MR, Taylor A, Treur JL. Evidence for genetic correlations and bidirectional, causal effects between smoking and sleep behaviours. bioRxiv. 2018;

28. Kadunce DP, Burr R, Gress R, Kanner R, Lyon JL, Zone JJ. Cigarette Smoking: Risk Factor for Premature Facial Wrinkling. Ann Intern Med. 1991 May 15;114(10):840–4. doi: 10.7326/0003-4819-114-10-840 2014944

29. Skinner AL, Woods A, Stone CJ, Penton-Voak I, Munafò MR. Smoking status and attractiveness among exemplar and prototypical identical twins discordant for smoking. R Soc Open Sci. 2017;4(12).

30. Lawlor DA, Tilling K, Davey Smith G. Triangulation in aetiological epidemiology. Int J Epidemiol. 2016;45(6):1866–86. doi: 10.1093/ije/dyw314 28108528

31. Munafò MR, Davey Smith G. Robust research needs many lines of evidence. Nature. 2018;553(7689):399–401. doi: 10.1038/d41586-018-01023-3 29368721

32. Liu JZ, Erlich Y, Pickrell JK. Case–control association mapping by proxy using family history of disease. Nat Genet. 2017;49(3):325–31. doi: 10.1038/ng.3766 28092683

33. Brumpton B, Sanderson E, Hartwig FP, Harrison S, Vie GÅ, Cho Y, et al. Within-family studies for Mendelian randomization: avoiding dynastic, assortative mating, and population stratification biases. bioRxiv. 2019;602516.

34. Keller MC. Gene × Environment Interaction Studies Have Not Properly Controlled for Potential Confounders: The Problem and the (Simple) Solution. Biol Psychiatry. 2014;75(1):18–24. doi: 10.1016/j.biopsych.2013.09.006 24135711

35. Swanson JM. The UK Biobank and selection bias. Lancet. 2012;380(9837):110.

36. Fry A, Littlejohns TJ, Sudlow C, Doherty N, Adamska L, Sprosen T, et al. Comparison of Sociodemographic and Health-Related Characteristics of UK Biobank Participants With Those of the General Population. Am J Epidemiol. 2017;186(9):1026–1034. doi: 10.1093/aje/kwx246 28641372

37. Labrecque JA, Swanson SA. Interpretation and Potential Biases of Mendelian Randomization Estimates With Time-Varying Exposures. Am J Epidemiol. 2018;188(1):231–8.

38. Spiller W, Slichter D, Bowden J, Davey Smith G. Detecting and correcting for bias in Mendelian randomization analyses using gene-by-environment interactions. Int J Epidemiol. 2018;dyy204.

39. Liu M, Jiang Y, Wedow R, Li Y, Brazel DM, Chen F, et al. Association studies of up to 1.2 million individuals yield new insights into the genetic etiology of tobacco and alcohol use. Nat Genet. 2019;51(2):237–44. doi: 10.1038/s41588-018-0307-5 30643251

40. Labrecque J, Swanson SA. Understanding the Assumptions Underlying Instrumental Variable Analyses: a Brief Review of Falsification Strategies and Related Tools. Curr Epidemiol Reports. 2018;5(3):214–20.

41. Allen N, Sudlow C, Downey P, Peakman T, Danesh J, Elliott P, et al. UK Biobank: Current status and what it means for epidemiology. Heal Policy Technol. 2012;1(3):123–6.

42. Sudlow C, Gallacher J, Allen N, Beral V, Burton P, Danesh J, et al. UK Biobank: An Open Access Resource for Identifying the Causes of a Wide Range of Complex Diseases of Middle and Old Age. PLoS Med. 2015;12(3):e1001779. doi: 10.1371/journal.pmed.1001779 25826379

43. Bycroft C, Freeman C, Petkova D, Band G, Elliott LT, Sharp K, et al. Genome-wide genetic data on ~500,000 UK Biobank participants. bioRxiv. 2017;166298.

44. Rosenbaum PR. The Consequences of Adjustment for a Concomitant Variable that Has Been Affected by the Treatment. J R Stat Soc Ser A. 1984;147(5):656–66.

45. Benjamini Y, Hochberg Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. J R Stat Soc Ser B. 1995;57(1):289–300.

46. Murcray CE, Lewinger JP, Gauderman WJ. Gene-Environment Interaction in Genome-Wide Association Studies. Am J Epidemiol. 2009;169(2):219–26. doi: 10.1093/aje/kwn353 19022827

47. Leffondré K, Abrahamowicz M, Xiao Y, Siemiatycki J. Modelling smoking history using a comprehensive smoking index: application to lung cancer. Stat Med. 2006;25(24):4132–46. doi: 10.1002/sim.2680 16998807

48. Wootton RE, Richmond RC, Stuijfzand BG, Lawn RB, Sallis HM, Taylor GMJ, et al. Evidence for causal effects of lifetime smoking on risk for depression and schizophrenia: A Mendelian randomisation study. Psychological Medicine. 2019; in press.