MR-PheWAS: Hypothesis prioritization among potential causal effects of body mass index on many outcomes, using Mendelian randomization

Scientific Reports

Volumn 5, Issue , 2015, Pages

  Millard, Louise A C ^a   Davies, Neil M ^a   Timpson, Nic J ^a   Tilling, Kate ^a   Flach, Peter A ^a   Smith, George Davey ^a  

^a UNIVERSITY OF BRISTOL   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ALPHA KETOGLUTARATE DEPENDENT DIOXYGENASE FTO; FTO PROTEIN, HUMAN; LEPTIN; LIPID; PROTEIN;

ALLELE; BLOOD; BLOOD PRESSURE; BODY MASS; CHILD; COHORT ANALYSIS; FEMALE; GENETICS; GENOME-WIDE ASSOCIATION STUDY; GENOTYPE; HUMAN; LONGITUDINAL STUDY; MALE; MENDELIAN RANDOMIZATION ANALYSIS; PHENOTYPE; SINGLE NUCLEOTIDE POLYMORPHISM;

ALLELES; ALPHA-KETOGLUTARATE-DEPENDENT DIOXYGENASE FTO; BLOOD PRESSURE; BODY MASS INDEX; CHILD; COHORT STUDIES; FEMALE; GENOME-WIDE ASSOCIATION STUDY; GENOTYPE; HUMANS; LEPTIN; LIPIDS; LONGITUDINAL STUDIES; MALE; MENDELIAN RANDOMIZATION ANALYSIS; PHENOTYPE; POLYMORPHISM, SINGLE NUCLEOTIDE; PROTEINS;

EID: 84947060384     PISSN: None     EISSN: 20452322     Source Type: Journal    
DOI: 10.1038/srep16645     Document Type: Article

References (66)

1. Cardon, L. R. & Bell, J. I. Association study designs for complex diseases. Nature Reviews Genetics 2, 91-99, doi: 10.1038/35052543 (2001).
2. Colhoun, H. M., McKeigue, P. M. & Davey Smith, G. Problems of reporting genetic associations with complex outcomes. Lancet 361, 865-872, doi: 10.1016/S0140-6736(03)12715-8 (2003).
3. McCarthy, M. I. et al. Genome-wide association studies for complex traits: consensus, uncertainty and challenges. Nature reviews. Genetics 9, 356-369, doi: 10.1038/nrg2344 (2008).
4. Hindorff, L. A. et al. Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. P Natl Acad Sci USA 106, 9362-9367, doi: 10.1073/Pnas.0903103106 (2009).
5. Davey Smith, G. & Ebrahim, S. Epidemiology - is it time to call it a day? International journal of epidemiology 30, 1-11, doi: 10.1093/ije/30.1.1 (2001).
6. Davey Smith, G. et al. Clustered environments and randomized genes: A fundamental distinction between conventional and genetic epidemiology. Plos Med 4, 1985-1992, doi: 10.1371/journal.pmed.0040352 (2007).
7. Davey Smith, G. & Ebrahim, S. Data dredging, bias, or confounding - They can all get you into the BMJ and the Friday papers. Brit Med J 325, 1437-1438, doi: 10.1136/bmj.325.7378.1437 (2002).
8. Davey Smith, G. & Ebrahim, S. 'Mendelian randomization': can genetic epidemiology contribute to understanding environmental determinants of disease? International journal of epidemiology 32, 1-22, doi: 10.1093/ije/dyg070 (2003).
9. Davey Smith, G. & Hemani, G. Mendelian randomization: genetic anchors for causal inference in epidemiological studies. Human molecular genetics 23, R89-R98, doi: 10.1093/hmg/ddu328 (2014).
10. Davey Smith, G. Use of genetic markers and gene-diet interactions for interrogating population-level causal influences of diet on health. Genes Nutr 6, 27-43, doi: 10.1007/S12263-010-0181-Y (2011).
11. Didelez, V. & Sheehan, N. Mendelian randomization as an instrumental variable approach to causal inference. Stat Methods Med Res 16, 309-330, doi: 10.1177/0962280206077743 (2007).
12. Lawlor, D. A., Harbord, R. M., Sterne, J. A. C., Timpson, N. & Davey Smith, G. Mendelian randomization: Using genes as instruments for making causal inferences in epidemiology. Statistics in medicine 27, 1133-1163, doi: 10.1002/Sim.3034 (2008).
13. VanderWeele, T. J., Tchetgen Tchetgen, E. J., Cornelis, M. & Kraft, P. Methodological challenges in mendelian randomization. Epidemiology 25, 427-435, doi: 10.1097/EDE.0000000000000081 (2014).
14. Hernan, M. A. & Robins, J. M. Instruments for causal inference: an epidemiologist's dream? Epidemiology 17, 360-372, doi: 10.1097/01.ede.0000222409.00878.37 (2006).
15. Patel, C. J., Cullen, M. R., Ioannidis, J. P. & Butte, A. J. Systematic evaluation of environmental factors: persistent pollutants and nutrients correlated with serum lipid levels. International journal of epidemiology 41, 828-843, doi: 10.1093/ije/dys003 (2012).
16. Tzoulaki, I. et al. A nutrient-wide association study on blood pressure. Circulation 126, 2456-2464, doi: 10.1161/CIRCULATIONAHA.112.114058 (2012).
17. Patel, C. J., Chen, R. & Butte, A. J. Data-driven integration of epidemiological and toxicological data to select candidate interacting genes and environmental factors in association with disease. Bioinformatics 28, i121-126, doi: 10.1093/bioinformatics/bts229 (2012).
18. Pendergrass, S. & Ritchie, M. D. Phenome-Wide Association Studies: Leveraging Comprehensive Phenotypic and Genotypic Data for Discovery. Current Genetic Medicine Reports 3, 92-100, doi: 10.1007/s40142-015-0067-9 (2015).
19. Davey Smith, G. Random allocation in observational data: how small but robust effects could facilitate hypothesis-free causal inference. Epidemiology 22, 460-463; discussion 467-468, doi: 10.1097/EDE.0b013e31821d0426 (2011).
20. Evans, D. M. et al. Mining the Human Phenome Using Allelic Scores That Index Biological Intermediates. Plos Genet 9, doi: 10.1371/journal.pgen.1003919 (2013).
21. Wurtz, P. et al. Metabolic signatures of adiposity in young adults: mendelian randomization analysis and effects of weight change. Plos Med 11, e1001765, doi: 10.1371/journal.pmed.1001765 (2014).
22. Oude Luttikhuis, H. et al. Interventions for treating obesity in children. Cochrane Database Syst Rev CD001872, doi: 10.1002/14651858.CD001872.pub2 (2009).
23. Shaw, K., Gennat, H., O'Rourke, P. & Del Mar, C. Exercise for overweight or obesity. Cochrane Database Syst Rev CD003817, doi: 10.1002/14651858.CD003817.pub3 (2006).
24. Timpson, N. J. et al. Does Greater Adiposity Increase Blood Pressure and Hypertension Risk? Mendelian Randomization Using the FTO/MC4R Genotype. Hypertension 54, 84-U131, doi: 10.1161/Hypertensionaha.109.130005 (2009).
25. Mumby, H. S. et al. Mendelian Randomisation Study of Childhood BMI and Early Menarche. J Obes 2011, 180729, doi: 10.1155/2011/180729 (2011).
26. Brennan, P. et al. Obesity and cancer: Mendelian randomization approach utilizing the FTO genotype. International journal of epidemiology 38, 971-975, doi: 10.1093/ije/dyp162 (2009).
27. Kivimaki, M. et al. Lifetime body mass index and later atherosclerosis risk in young adults: examining causal links using Mendelian randomization in the Cardiovascular Risk in Young Finns study. Eur Heart J 29, 2552-2560, doi: 10.1093/eurheartj/ehn252 (2008).
28. Welsh, P. et al. Unraveling the Directional Link between Adiposity and Inflammation: A Bidirectional Mendelian Randomization Approach. J Clin Endocr Metab 95, 93-99, doi: 10.1210/Jc.2009-1064 (2010).
29. Hubacek, J. A. et al. The FTO gene polymorphism is associated with end-stage renal disease: two large independent case-control studies in general population. Nephrol Dial Transpl 27, 1030-1035, doi: 10.1093/Ndt/Gfr418 (2012).
30. Timpson, N. J., Sayers, A., Davey Smith, G. & Tobias, J. H. How Does Body Fat Influence Bone Mass in Childhood? A Mendelian Randomization Approach. J Bone Miner Res 24, 522-533, doi: 10.1359/Jbmr.081109 (2009).
31. Freathy, R. M. et al. Common variation in the FTO gene alters diabetes-related metabolic traits to the extent expected given its effect on BMI. Diabetes 57, 1419-1426, doi: 10.2337/db07-1466 (2008).
32. Frayling, T. M. et al. A common variant in the FTO gene is associated with body mass index and predisposes to childhood and adult obesity. Science 316, 889-894, doi: 10.1126/science.1141634 (2007).
33. Nordestgaard, B. G. et al. The effect of elevated body mass index on ischemic heart disease risk: causal estimates from a Mendelian randomisation approach. Plos Med 9, e1001212, doi: 10.1371/journal.pmed.1001212 (2012).
34. Timpson, N. J. et al. C-reactive protein levels and body mass index: elucidating direction of causation through reciprocal Mendelian randomization. Int J Obes (Lond) 35, 300-308, doi: 10.1038/ijo.2010.137 (2011).
35. Lyngdoh, T. et al. Serum uric acid and adiposity: deciphering causality using a bidirectional Mendelian randomization approach. PloS one 7, e39321, doi: 10.1371/journal.pone.0039321 (2012).
36. Palmer, T. M. et al. Association of plasma uric acid with ischaemic heart disease and blood pressure: mendelian randomisation analysis of two large cohorts. Brit Med J 347, f4262, doi: 10.1136/bmj.f4262 (2013).
37. Vimaleswaran, K. S. et al. Causal Relationship between Obesity and Vitamin D Status: Bi-Directional Mendelian Randomization Analysis of Multiple Cohorts. Plos Med 10, doi: 10.1371/journal.pmed.1001383 (2013).
38. Thakkinstian, A. et al. Causal relationship between body mass index and fetuin-A level in the asian population: a bidirectional mendelian randomization study. Clinical endocrinology 81, 197-203, doi: 10.1111/cen.12303 (2014).
39. Sovio, U. et al. Association between Common Variation at the FTO Locus and Changes in Body Mass Index from Infancy to Late Childhood: The Complex Nature of Genetic Association through Growth and Development. Plos Genet 7, doi: 10.1371/Journal.Pgen.1001307 (2011).
40. Ioannidis, J. P. How to make more published research true. Plos Med 11, e1001747, doi: 10.1371/journal.pmed.1001747 (2014).
41. Heini, A. F. et al. Association of leptin and hunger-satiety ratings in obese women. International Journal of Obesity 22, 1084-1087, doi: 10.1038/sj.ijo.0800731 (1998).
42. Fall, T. et al. The role of adiposity in cardiometabolic traits: a Mendelian randomization analysis. Plos Med 10, e1001474, doi: 10.1371/journal.pmed.1001474 (2013).
43. Holmes, M. V. et al. Causal effects of body mass index on cardiometabolic traits and events: a Mendelian randomization analysis. Am J Hum Genet 94, 198-208, doi: 10.1016/j.ajhg.2013.12.014 (2014).
44. Ong, K. K. et al. Infancy Weight Gain Predicts Childhood Body Fat and Age at Menarche in Girls. J Clin Endocr Metab 94, 1527-1532, doi: 10.1210/Jc.2008-2489 (2009).
45. Howe, L. D. et al. Socioeconomic disparities in trajectories of adiposity across childhood. International Journal of Pediatric Obesity 6, E144-E153, doi: 10.3109/17477166.2010.500387 (2011).
46. Matijasevich, A. et al. Socioeconomic position and overweight among adolescents: data from birth cohort studies in Brazil and the UK. BMC Public Health 9, 105, doi: 10.1186/1471-2458-9-105 (2009).
47. Ness, A. R. et al. The social patterning of fat and lean mass in a contemporary cohort of children. Int J Pediatr Obes 1, 59-61, doi: 10.1080/17477160600569339 (2006).
48. Bowden, J., Davey Smith, G. & Burgess, S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. International journal of epidemiology, doi: 10.1093/ije/dyv080 (2015).
49. Ioannidis, J. P. A. Why most discovered true associations are inflated. Epidemiology 19, 640-648, doi: 10.1097/EDE.0b013e31818131e7 (2008).
50. Ioannidis, J. P. A. Why most published research findings are false. Plos Med 2, 696-701, doi: 10.1371/journal.pmed.0020124 (2005).
51. Rothman, K. J. No adjustments are needed for multiple comparisons. Epidemiology 1, 43-46 (1990).
52. Fewell, Z., Davey Smith, G. & Sterne, J. A. The impact of residual and unmeasured confounding in epidemiologic studies: a simulation study. Am J Epidemiol 166, 646-655, doi: 10.1093/aje/kwm165 (2007).
53. Holmes, M. V. et al. Mendelian randomization of blood lipids for coronary heart disease. Eur Heart J, doi: 10.1093/eurheartj/eht571 (2014).
54. Fraser, A. et al. Cohort Profile: The Avon Longitudinal Study of Parents and Children: ALSPAC mothers cohort. International journal of epidemiology 42, 97-110, doi: 10.1093/ije/dys066 (2013).
55. Golding, J., Pembrey, M. & Jones, R. ALSPAC-the Avon Longitudinal Study of Parents and Children. I. Study methodology. Paediatr Perinat Epidemiol 15, 74-87, doi: 10.1046/j.1365-3016.2001.00325.x (2001).
56. Boyd, A. et al. Cohort Profile: the 'children of the 90s'-the index offspring of the Avon Longitudinal Study of Parents and Children. International journal of epidemiology 42, 111-127, doi: 10.1093/ije/dys064 (2013).
57. Speliotes, E. K. et al. Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat Genet 42, 937-948, doi: 10.1038/ng.686 (2010).
58. Burgess, S. T., S. Use of allele scores as instrumental variables for Mendelian randomization. International journal of epidemiology 42, 1134-1144, doi: 10.1093/Ije/Dyt093 (2013).
59. Booth, J. N. et al. Obesity impairs academic attainment in adolescence: findings from ALSPAC, a UK cohort. Int J Obes (Lond), doi: 10.1038/ijo.2014.40 (2014).
60. Hancox, R. J. et al. Associations between birth weight, early childhood weight gain and adult lung function. Thorax 64, 228-232, doi: 10.1136/Thx.2008.103978 (2009).
61. Dennison, B. A., Erb, T. A. & Jenkins, P. L. Television viewing and television in bedroom associated with overweight risk among low-income preschool children. Pediatrics 109, 1028-1035, doi: 10.1542/Peds.109.6.1028 (2002).
62. StataCorp, L. STATA (Release 11)(Computer Software). StataCorp LP, College Station, TX, USA (2009).
63. Stock, J. H., Wright, J. H. & Yogo, M. A survey of weak instruments and weak identification in generalized method of moments. J Bus Econ Stat 20, 518-529, doi: 10.1198/073500102288618658 (2002).
64. Palmer, T. M. et al. Using multiple genetic variants as instrumental variables for modifiable risk factors. Stat Methods Med Res 21, 223-242, doi: 10.1177/0962280210394459 (2012).
65. White, I. R., Royston, P. & Wood, A. M. Multiple imputation using chained equations: Issues and guidance for practice. Statistics in medicine 30, 377-399, doi: 10.1002/sim.4067 (2011).
66. Locke, A. E. et al. Genetic studies of body mass index yield new insights for obesity biology. Nature 518, 197-206, doi: 10.1038/nature14177 (2015).