publication . Article . Conference object . 2015

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

Nicholas J. Timpson; Neil M Davies; Peter A. Flach; George Davey Smith; Louise A C Millard; Kate Tilling;
Open Access
  • Published: 16 Nov 2015
  • Country: United Kingdom
Abstract
<p>Observational cohort studies can provide rich datasets with a diverse range of phenotypic variables. However, hypothesis-driven epidemiological analyses by definition only test particular hypotheses chosen by researchers. Furthermore, observational analyses may not provide robust evidence of causality, as they are susceptible to confounding, reverse causation and measurement error. Using body mass index (BMI) as an exemplar, we demonstrate a novel extension to the phenome-wide association study (pheWAS) approach, using automated screening with genotypic instruments to screen for causal associations amongst any number of phenotypic outcomes. We used a sample o...
Subjects
free text keywords: Multidisciplinary, Article, /dk/atira/pure/core/keywords/jean_golding, Jean Golding, /dk/atira/pure/researchoutput/pubmedpublicationtype/D016428, Journal Article, /dk/atira/pure/researchoutput/pubmedpublicationtype/D013485, Research Support, Non-U.S. Gov't, Sample size determination, Bonferroni correction, symbols.namesake, symbols, Bioinformatics, False discovery rate, Mendelian Randomization Analysis, Biology, Confounding, Observational study, Mendelian randomization, Instrumental variable, Statistics
Related Organizations
Funded by
WT
Project
  • Funder: Wellcome Trust (WT)
,
EC| DEVHEALTH
Project
DEVHEALTH
UNDERSTANDING HEALTH ACROSS THE LIFECOURSE: AN INTEGRATED DEVELOPMENTAL APPROACH
  • Funder: European Commission (EC)
  • Project Code: 269874
  • Funding stream: FP7 | SP2 | ERC
66 references, page 1 of 5

Cardon L. R. & Bell J. I. Association study designs for complex diseases. Nature Reviews Genetics 2, 91–99, 10.1038/35052543 (2001). [OpenAIRE] [DOI]

Colhoun H. M., McKeigue P. M. & Davey Smith G. Problems of reporting genetic associations with complex outcomes. Lancet 361, 865–872, 10.1016/S0140-6736(03)12715-8 (2003).12642066 [OpenAIRE] [PubMed] [DOI]

McCarthy M. I.et al.Genome-wide association studies for complex traits: consensus, uncertainty and challenges. Nature reviews. Genetics 9, 356–369, 10.1038/nrg2344 (2008). [OpenAIRE] [DOI]

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, 10.1073/Pnas.0903103106 (2009). [OpenAIRE] [DOI]

Davey Smith G. & Ebrahim S. Epidemiology--is it time to call it a day? International journal of epidemiology 30, 1–11, 10.1093/ije/30.1.1 (2001).11171840 [OpenAIRE] [PubMed] [DOI]

Davey Smith G.et al.Clustered environments and randomized genes: A fundamental distinction between conventional and genetic epidemiology. Plos Med 4, 1985–1992, 10.1371/journal.pmed.0040352 (2007). [OpenAIRE] [DOI]

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, 10.1136/bmj.325.7378.1437 (2002).12493654 [OpenAIRE] [PubMed] [DOI]

Davey Smith G. & Ebrahim S. ‘Mendelian randomization’: can genetic epidemiology contribute to understanding environmental determinants of disease? International journal of epidemiology 32, 1–22, 10.1093/ije/dyg070 (2003).12689998 [OpenAIRE] [PubMed] [DOI]

Davey Smith G. & Hemani G. Mendelian randomization: genetic anchors for causal inference in epidemiological studies. Human molecular genetics 23, R89–R98, 10.1093/hmg/ddu328 (2014).25064373 [OpenAIRE] [PubMed] [DOI]

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, 10.1007/S12263-010-0181-Y (2011).21437028 [OpenAIRE] [PubMed] [DOI]

Didelez V. & Sheehan N. Mendelian randomization as an instrumental variable approach to causal inference. Stat Methods Med Res 16, 309–330, 10.1177/0962280206077743 (2007).17715159 [OpenAIRE] [PubMed] [DOI]

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, 10.1002/Sim.3034 (2008).17886233 [PubMed] [DOI]

VanderWeele T. J., Tchetgen Tchetgen E. J., Cornelis M. & Kraft P. Methodological challenges in mendelian randomization. Epidemiology 25, 427–43 5, 10.1097/EDE.0000000000000081 (2014).24681576 [OpenAIRE] [PubMed] [DOI]

Hernan M. A. & Robins J. M. Instruments for causal inference: an epidemiologist’s dream? Epidemiology 17, 360–372, 10.1097/01.ede.0000222409.00878.37 (2006).16755261 [OpenAIRE] [PubMed] [DOI]

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, 10.1093/ije/dys003 (2012).22421054 [OpenAIRE] [PubMed] [DOI]

66 references, page 1 of 5
Abstract
<p>Observational cohort studies can provide rich datasets with a diverse range of phenotypic variables. However, hypothesis-driven epidemiological analyses by definition only test particular hypotheses chosen by researchers. Furthermore, observational analyses may not provide robust evidence of causality, as they are susceptible to confounding, reverse causation and measurement error. Using body mass index (BMI) as an exemplar, we demonstrate a novel extension to the phenome-wide association study (pheWAS) approach, using automated screening with genotypic instruments to screen for causal associations amongst any number of phenotypic outcomes. We used a sample o...
Subjects
free text keywords: Multidisciplinary, Article, /dk/atira/pure/core/keywords/jean_golding, Jean Golding, /dk/atira/pure/researchoutput/pubmedpublicationtype/D016428, Journal Article, /dk/atira/pure/researchoutput/pubmedpublicationtype/D013485, Research Support, Non-U.S. Gov't, Sample size determination, Bonferroni correction, symbols.namesake, symbols, Bioinformatics, False discovery rate, Mendelian Randomization Analysis, Biology, Confounding, Observational study, Mendelian randomization, Instrumental variable, Statistics
Related Organizations
Funded by
WT
Project
  • Funder: Wellcome Trust (WT)
,
EC| DEVHEALTH
Project
DEVHEALTH
UNDERSTANDING HEALTH ACROSS THE LIFECOURSE: AN INTEGRATED DEVELOPMENTAL APPROACH
  • Funder: European Commission (EC)
  • Project Code: 269874
  • Funding stream: FP7 | SP2 | ERC
66 references, page 1 of 5

Cardon L. R. & Bell J. I. Association study designs for complex diseases. Nature Reviews Genetics 2, 91–99, 10.1038/35052543 (2001). [OpenAIRE] [DOI]

Colhoun H. M., McKeigue P. M. & Davey Smith G. Problems of reporting genetic associations with complex outcomes. Lancet 361, 865–872, 10.1016/S0140-6736(03)12715-8 (2003).12642066 [OpenAIRE] [PubMed] [DOI]

McCarthy M. I.et al.Genome-wide association studies for complex traits: consensus, uncertainty and challenges. Nature reviews. Genetics 9, 356–369, 10.1038/nrg2344 (2008). [OpenAIRE] [DOI]

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, 10.1073/Pnas.0903103106 (2009). [OpenAIRE] [DOI]

Davey Smith G. & Ebrahim S. Epidemiology--is it time to call it a day? International journal of epidemiology 30, 1–11, 10.1093/ije/30.1.1 (2001).11171840 [OpenAIRE] [PubMed] [DOI]

Davey Smith G.et al.Clustered environments and randomized genes: A fundamental distinction between conventional and genetic epidemiology. Plos Med 4, 1985–1992, 10.1371/journal.pmed.0040352 (2007). [OpenAIRE] [DOI]

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, 10.1136/bmj.325.7378.1437 (2002).12493654 [OpenAIRE] [PubMed] [DOI]

Davey Smith G. & Ebrahim S. ‘Mendelian randomization’: can genetic epidemiology contribute to understanding environmental determinants of disease? International journal of epidemiology 32, 1–22, 10.1093/ije/dyg070 (2003).12689998 [OpenAIRE] [PubMed] [DOI]

Davey Smith G. & Hemani G. Mendelian randomization: genetic anchors for causal inference in epidemiological studies. Human molecular genetics 23, R89–R98, 10.1093/hmg/ddu328 (2014).25064373 [OpenAIRE] [PubMed] [DOI]

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, 10.1007/S12263-010-0181-Y (2011).21437028 [OpenAIRE] [PubMed] [DOI]

Didelez V. & Sheehan N. Mendelian randomization as an instrumental variable approach to causal inference. Stat Methods Med Res 16, 309–330, 10.1177/0962280206077743 (2007).17715159 [OpenAIRE] [PubMed] [DOI]

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, 10.1002/Sim.3034 (2008).17886233 [PubMed] [DOI]

VanderWeele T. J., Tchetgen Tchetgen E. J., Cornelis M. & Kraft P. Methodological challenges in mendelian randomization. Epidemiology 25, 427–43 5, 10.1097/EDE.0000000000000081 (2014).24681576 [OpenAIRE] [PubMed] [DOI]

Hernan M. A. & Robins J. M. Instruments for causal inference: an epidemiologist’s dream? Epidemiology 17, 360–372, 10.1097/01.ede.0000222409.00878.37 (2006).16755261 [OpenAIRE] [PubMed] [DOI]

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, 10.1093/ije/dys003 (2012).22421054 [OpenAIRE] [PubMed] [DOI]

66 references, page 1 of 5
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