Detecting pleiotropy in Mendelian randomisation studies with summary data and a continuous outcome

Statistics in Medicine

Volumn 34, Issue 21, 2015, Pages 2926-2940

  Greco M, Fabiola Del ^a   Minelli, Cosetta ^b   Sheehan, Nuala A ^c   Thompson, John R ^c  

^a University of Lubeck   (Italy)

^b IMPERIAL COLLEGE LONDON   (United Kingdom)

^c UNIVERSITY OF LEICESTER   (United Kingdom)

Author keywords

Heterogeneity Q test; I2 index; Instrumental variables; Mendelian randomisation; Pleiotropy

Indexed keywords

GLUCOSE;

ACCURACY; ADULT; ADULTHOOD; ARTICLE; BETWEEN INSTRUMENT HETEROGENEITY; BIRTH WEIGHT; COMPARATIVE STUDY; CONTINUOUS OUTCOME; CONTROLLED STUDY; DATA PROCESSING; ERROR; GENE FREQUENCY; GENETIC ASSOCIATION; GENETIC HETEROGENEITY; GENETIC VARIABILITY; GLUCOSE BLOOD LEVEL; HUMAN; MATHEMATICAL COMPUTING; MENDELIAN RANDOMIZATION ANALYSIS; PHENOTYPE; PLEIOTROPY; SAMPLE SIZE; SARGAN TEST; SELECTION BIAS; SENSITIVITY ANALYSIS; SIMULATION; STATISTICAL ANALYSIS; STATISTICAL BIAS; STATISTICAL DISTRIBUTION; STATISTICAL PARAMETERS; STATISTICAL POWER; SUMMARY DATA; TYPE 1 ERROR; BIOLOGICAL MODEL; COMPUTER SIMULATION; DIABETES MELLITUS; EPIDEMIOLOGY; GENETIC VARIATION; GENETICS; META ANALYSIS (TOPIC); REGRESSION ANALYSIS; SENSITIVITY AND SPECIFICITY;

CAUSALITY; COMPUTER SIMULATION; DIABETES MELLITUS; GENETIC PLEIOTROPY; GENETIC VARIATION; HUMANS; MENDELIAN RANDOMIZATION ANALYSIS; META-ANALYSIS AS TOPIC; MODELS, GENETIC; PHENOTYPE; REGRESSION ANALYSIS; SAMPLE SIZE; SENSITIVITY AND SPECIFICITY;

EID: 84938292262     PISSN: 02776715     EISSN: 10970258     Source Type: Journal    
DOI: 10.1002/sim.6522     Document Type: Article

References (40)

1. Davey-Smith G, Ebrahim S. 'Mendelian randomization': can genetic epidemiology contribute to understanding environmental determinants of disease. International Journal of Epidemiology 2003; 32:1-22.
2. Davey-Smith G, Ebrahim S. Mendelian randomization: prospects, potentials, and limitations. International Journal of Epidemiology 2004; 333:30-42.
3. Thomas D, Conti D. Commentary: the concept of Mendelian randomization. International Journal of Epidemiology 2004; 33:21-25.
4. Wooldridge JM. Econometric Analysis of Cross Section and Panel Data. MIT Press: Cambridge, 2002.
5. Didelez V, Sheehan N. Mendelian randomization as an instrumental variable approach to causal inference. Statistical Methods in Medical Research 2007; 16:309-330.
6. Lawlor DA, Harbord RM, Sterne JAC, Timpson N, Davey-Smith G. Mendelian randomization: using genes as instruments for making causal inferences in epidemiology. Statistics in Medicine 2008; 27:1133-1163.
7. Didelez V, Meng S, Sheehan N. Assumptions of IV methods for observational epidemiology. Statistical Science 2010; 25:22-40.
8. Bound J, Jaeger D, Baker R. Problems with instrumental variables estimation when the correlation between the instruments and the endogenous explanatory variable is weak. Journal of the American Statistical Association 1995; 90:443-450.
9. Bowden RJ, Turkington DA. Instrumental Variables, University Press: Cambridge, 2007.
10. Pierce B, Ahsan H, VanderWeele T. Power and instrument strength requirements for Mendelian randomization studies using multiple genetic variants. International Journal of Epidemiology 2011; 40:740-752.
11. Sheehan NA, Didelez V. Commentary:can 'many weak' instruments ever be 'strong'?. International Journal of Epidemiology 2011; 40(3): 752-754.
12. Palmer TM, Lawlor DS, Harbord RM, Sheehan NA, Tobias JH, Timpson NJ, Davey-Smith G, Sterne JA. Using multiple genetic variants as instrumental variables for modifiable risk factors. Statistical Methods in Medical Research 2012; 21:223-242.
13. Sargan JD. The estimation of economic relationships using instrumental variables. Econometrica 1958; 26:393-415.
14. Cochran WG. The combination of estimates from different experiments. Biometrics 1954; 10:101-129.
15. Hardy RJ, Thompson SG. Detecting and describing heterogeneity in meta-analysis. Statistics in Medicine 1998; 17:841-856.
16. Higgins JP, Thompson SG. Quantifying heterogeneity in a meta-analysis. Statistics in Medicine 2002; 21:1539-1558.
17. Higgins JP, Thompson SG, Deeks JJ, Altman DG. Measuring inconsistency in meta-analyses. BMJ 2003; 327:557-560.
18. Bautista LE, Smeeth L, Hingorani AD, Casas JP. Estimation of bias in nongenetic observational studies using "mendelian triangulation". Annals of Epidemiology 2006; 16:675-680.
19. Thomas DC, Lawlor DA, Thompson JR. Re: estimation of bias in nongenetic observational studies using "mendelian triangulation" by Bautista et al. Annals of Epidemiology 2007; 17:511-513.
20. Ehret G (The International Consortium for Blood Pressure Genome-wide Association Studies) . Genetic variants in novel pathways influence blood pressure and cardiovascular disease risk. Nature 2011; 478:103-109.
21. Burgess S, Butterworth A, Thompson SG. Mendelian randomization analysis with multiple genetic variants using summarized data. Genetic Epidemiology 2013; 37:658-665.
22. Lange K. Mathematical and Statistical Methods for Genetic Analysis 2ndedn. Springer: New York, 2003.
23. Falconer DS, Mackay TFC Introduction to Quantitative Genetics 4thedn. Longman: Harlow, Essex, UK, 1996..
24. Petitti DB. Approaches to heterogeneity in meta-analyses. Statistics in Medicine 2001; 20:3625-3633.
25. Barker DJP. Mother, Babies, and Health in Later Life 2ndedn. Churchill Livingstone: Edinburgh, 1998.
26. Whincup PH, Kaye SJ, Owen CG, Huxley R, Cook DG, Anazawa S, Barrett-Connor E, SK Bhargava, Birgisdottir BE, Carlsson S, de Rooij SR, Dyck RF, Eriksson JG, Falkner B, Fall C, Forsén T, Grill V, Gudnason V, Hulman S, Hyppönen E, Jeffreys M, Lawlor DA, Leon DA, J Minami, Mishra G, Osmond C, Power C, Rich-Edwards JW, Roseboom TJ, Sachdev HS, Syddall H, Thorsdottir I, Vanhala M, Wadsworth M, Yarbrough DE. Birth weight and risk of type 2 diabetes. A systematic review. JAMA 2008; 300:2886-2896.
27. Sayers SM, Mott SA, Mann KD, Pearce MS, Singh GR. Birth weight and fasting glucose and insulin levels: results from the Aboriginal Birth Cohort Study. Medical Journal of Australia 2013; 199:112-116.
28. Horikoshi M, Yaghootkar H, Mook-Kanamori DO, Sovio U, Taal HR, Hennig BJ, Bradfield JP, B St Pourcain, Evans DM, Charoen P, Kaakinen M, Cousminer DL, Lehtimäki T, Kreiner-Møller E, Warrington NM, Bustamante M, Feenstra B, Berry DJ, Thiering E, Pfab T, Barton SJ, Shields BM, Kerkhof M, van Leeuwen EM, Fulford AJ, Kutalik Z, Zhao JH, den Hoed M, Mahajan A, Lindi V, Goh LK, Hottenga JJ, Wu Y, Raitakari OT, Harder MN, Meirhaeghe A, Ntalla I, Salem RM, Jameson KA, Zhou K, Monies DM, Lagou V, Kirin M, Heikkinen J, Adair LS, Alkuraya FS, A Al-Odaib, Amouyel P, Andersson EA, Bennett AJ, Blakemore AI, Buxton JL, Dallongeville J, Das S, de Geus EJ, Estivill X, Flexeder C, Froguel P, Geller F, Godfrey KM, Gottrand F, Groves CJ, Hansen T, Hirschhorn JN, Hofman A, Hollegaard MV, Hougaard DM, Hyppönen E, Inskip HM, Isaacs A, Jørgensen T, Kanaka-Gantenbein C, Kemp JP, Kiess W, Kilpeläinen TO, Klopp N, BA Knight, Kuzawa CW, McMahon G, Newnham JP, Niinikoski H, Oostra BA, Pedersen L, Postma DS, Ring SM, Rivadeneira F, Robertson NR, Sebert S, Simell O, Slowinski T, Tiesler CM, Tönjes A, Vaag A, Viikari JS, Vink JM, Vissing NH, Wareham NJ, Willemsen G, Witte DR, Zhang H, Zhao J; Meta-Analyses of Glucose- and Insulin-related traits Consortium (MAGIC), Wilson JF, Stumvoll M, AM Prentice, Meyer BF, Pearson ER, Boreham CA, Cooper C, Gillman MW, Dedoussis GV, Moreno LA, Pedersen O, Saarinen M, Mohlke KL, Boomsma DI, Saw SM, Lakka TA, Körner A, Loos RJ, Ong KK, Vollenweider P, van Duijn CM, Koppelman GH, Hattersley AT, Holloway JW, Hocher B, Heinrich J, Power C, Melbye M, Guxens M, Pennell CE, Bønnelykke K, Bisgaard H, Eriksson JG, Widén E, Hakonarson H, Uitterlinden AG, Pouta A, Lawlor DA, Smith GD, Frayling TM, MI McCarthy, Grant SF, Jaddoe VW, Jarvelin MR, Timpson NJ, Prokopenko I, Freathy RM; Early Growth Genetics (EGG) Consortium. New loci associated with birth weight identify genetic links between intrauterine growth and adult height and metabolism. Nature Genetics 2013; 45:76-82.
29. Dupuis J, Langenberg C, Prokopenko I, Saxena R, Kesaniemi YA, Kivimaki M, Knight B, S Koskinen, Kovacs P, Kyvik KO, Lathrop GM, Lawlor DA, Le Bacquer O, Lecoeur C, Li Y, V Lyssenko, Mahley R, Mangino M, Manning AK, Martínez-Larrad MT, McAteer JB, McCulloch LJ, R McPherson, Meisinger C, Melzer D, Meyre D, Mitchell BD, Morken MA, Mukherjee S, Naitza S, N Narisu, Neville MJ, Oostra BA, Orrù M, Pakyz R, Palmer CN, Paolisso G, Pattaro C, Pearson D, Peden JF, Pedersen NL, Perola M, Pfeiffer AF, Pichler I, Polasek O, Posthuma D, Potter SC, Pouta A, Province MA, Psaty BM, Rathmann W, Rayner NW, Rice K, Ripatti S, Rivadeneira F, Roden M, Rolandsson O, Sandbaek A, Sandhu M, Sanna S, Sayer AA, Scheet P, Scott LJ, U Seedorf, Sharp SJ, Shields B, Sigurethsson G, Sijbrands EJ, Silveira A, Simpson L, Singleton A, NL Smith, Sovio U, Swift A, Syddall H, Syvänen AC, Tanaka T, Thorand B, Tichet J, Tönjes A, Tuomi T, Uitterlinden AG, van Dijk KW, van Hoek M, Varma D, Visvikis-Siest S, Vitart V, N Vogelzangs, Waeber G, Wagner PJ, Walley A, Walters GB, Ward KL, Watkins H, Weedon MN, Wild SH, Willemsen G, Witteman JC, Yarnell JW, Zeggini E, Zelenika D, Zethelius B, Zhai G, Zhao JH, Zillikens MC; DIAGRAM Consortium; GIANT Consortium; Global BPgen Consortium, Borecki IB, Loos RJ, Meneton P, Magnusson PK, Nathan DM, Williams GH, Hattersley AT, Silander K, V Salomaa, Smith GD, Bornstein SR, Schwarz P, Spranger J, Karpe F, Shuldiner AR, Cooper C, Dedoussis GV, Serrano-Ríos M, Morris AD, Lind L, Palmer LJ, Hu FB, Franks PW, Ebrahim S, Marmot M, Kao WH, Pankow JS, Sampson MJ, Kuusisto J, Laakso M, Hansen T, Pedersen O, Pramstaller PP, Wichmann HE, Illig T, Rudan I, Wright AF, Stumvoll M, Campbell H, Wilson JF; Anders Hamsten on behalf of Procardis Consortium; MAGIC investigators, Bergman RN, Buchanan TA, Collins FS, Mohlke KL, Tuomilehto J, Valle TT, Altshuler D, Rotter JI, Siscovick DS, Penninx BW, Boomsma DI, Deloukas P, Spector TD, Frayling TM, Ferrucci L, Kong A, Thorsteinsdottir U, K Stefansson, van Duijn CM, Aulchenko YS, Cao A, Scuteri A, Schlessinger D, Uda M, Ruokonen A, Jarvelin MR, Waterworth DM, Vollenweider P, Peltonen L, Mooser V, Abecasis GR, Wareham NJ, Sladek R, Froguel P, Watanabe RM, Meigs JB, Groop L, Boehnke M, McCarthy MI, Florez JC, Barroso I. New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk. Nature Genetics 2010; 42:105-116.
30. Freathy RM, Bennett AJ, Ring SM, Shields B, Groves CJ, Timpson NJ, Weedon MN, Zeggini E, Lindgren CM, Lango H, Perry JR, Pouta A, Ruokonen A, Hyppönen E, Power C, Elliott P, DP Strachan, Järvelin MR, Smith GD, McCarthy MI, Frayling TM, Hattersley AT. Type 2 diabetes risk alleles are associated with reduced size at birth. Diabetes 2009; 58:1428-1433.
31. Zhao J, Li M, Bradfield JP, Wang K, Zhang H, Sleiman P, Kim CE, Annaiah K, Glaberson W, Glessner JT, Otieno FG, Thomas KA, Garris M, Hou C, Frackelton EC, RM Chiavacci, Berkowitz RI, Hakonarson H, Grant SFA. Examination of type 2 diabetes loci implicates CDKAL1 as a birth weight gene. Diabetes 2009; 58:2414-2418.
32. Freathy RM, Mook-Kanamori DO, Sovio U, Prokopenko I, Timpson NJ, Berry DJ, Warrington NM, Widen E, Hottenga JJ, Kaakinen M, Lange LA, Bradfield JP, Kerkhof M, Marsh JA, Mägi R, Chen CM, Lyon HN, Kirin M, Adair LS, Aulchenko YS, Bennett AJ, Borja JB, Bouatia-Naji N, Charoen P, Coin LJ, Cousminer DL, de Geus EJ, Deloukas P, Elliott P, Evans DM, Froguel P; Genetic Investigation of ANthropometric Traits (GIANT) Consortium, Glaser B, Groves CJ, Hartikainen AL, Hassanali N, Hirschhorn JN, Hofman A, Holly JM, Hyppönen E, Kanoni S, Knight BA, Laitinen J, Lindgren CM; Meta-Analyses of Glucose and Insulin-related traits Consortium, McArdle WL, O'Reilly PF, CE Pennell, Postma DS, Pouta A, Ramasamy A, Rayner NW, Ring SM, Rivadeneira F, Shields BM, Strachan DP, Surakka I, Taanila A, Tiesler C, Uitterlinden AG, van Duijn CM; Wellcome Trust Case Control Consortium, Wijga AH, Willemsen G, Zhang H, Zhao J, Wilson JF, Steegers EA, AT Hattersley, Eriksson JG, Peltonen L, Mohlke KL, Grant SF, Hakonarson H, Koppelman GH, GV Dedoussis, Heinrich J, Gillman MW, Palmer LJ, Frayling TM, Boomsma DI, Davey Smith G, C Power, Jaddoe VW, Jarvelin MR; Early Growth Genetics (EGG) Consortium, McCarthy MI. Variants in ADCY5 and near CCNL1 are associated with fetal growth and birth weight. Nature Genetics 2010; 42:430-435.
33. Kivimaki M, Magnussen CG, Juonala M, Kahonen M, Kettunen J, Loo BM, Lehtimäki T, Viikari J, Raitakari OT. Conventional and Mendelian randomization analyses suggest no association between lipoprotein(a) and early atherosclerosis: the Young Finns Study. International Journal of Epidemiology 2011; 40:470-478.
34. De Silva NM, Freathy RM, Palmer TM, Donnelly LA, Luan J, Gaunt T, Langenberg C, MN Weedon, Shields B, Knight BA, Ward KJ, Sandhu MS, Harbord RM, McCarthy MI, Davey Smith G, Ebrahim S, Hattersley AT, Wareham N, Lawlor DA, Morris AD, Palmer CNA, TM Frayling . Mendelian randomization studies do not support a role for raised circulating triglyceride levels influencing type 2 diabetes, glucose levels, or insulin resistance. Diabetes 2011; 60:1008-1018.
35. Ken-Dror G, Humphries SE, Kumari M, Kivimaki M, Drenos F. A genetic instrument for Mendelian randomization of fibrinogen. European Journal of Epidemiology 2012; 27:267-279.
36. 2 index. Psychological Methods 2006; 11:193-206.
37. Ioannidis JP, Patsopoulos NA, Evangelou E. Uncertainty in heterogeneity estimates in meta-analysis. BMJ 2007a; 335:914-916.
38. Ioannidis JP, Patsopoulos NA, Evangelou E. Heterogeneity in meta-analyses of genome-wide association investigations. Plos One 2007b; 2:e841.
39. Mittlböck M, Heinzl H. A simulation study comparing properties of heterogeneity measures in meta-analysis. Statistics in Medicine 2010; 37:4321-4333.
40. Thorlund K, Imberger G, Johnston BC, Walsh M, Awad T, L Thabane, Gluud C, Devereaux PJ, Wetterslev J. Estimates and their 95% confidence interval in large meta-analyses. Plos One 2012; 7: e39471.