Epigenetic prediction of complex traits and death

Daniel L. McCartney, Robert F. Hillary, Anna J. Stevenson, Stuart J. Ritchie, Rosie M. Walker, Qian Zhang, Stewart W. Morris, Mairead L. Bermingham, Archie Campbell, Alison D. Murray, Heather C. Whalley, Catharine R. Gale, David J. Porteous, Chris S. Haley, Allan F. McRae, Naomi R. Wray, Peter M. Visscher, Andrew M. McIntosh, Kathryn L. Evans, Ian J. Deary & 1 others Riccardo E. Marioni

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Abstract

BACKGROUND: Genome-wide DNA methylation (DNAm) profiling has allowed for the development of molecular predictors for a multitude of traits and diseases. Such predictors may be more accurate than the self-reported phenotypes and could have clinical applications.

RESULTS: Here, penalized regression models are used to develop DNAm predictors for ten modifiable health and lifestyle factors in a cohort of 5087 individuals. Using an independent test cohort comprising 895 individuals, the proportion of phenotypic variance explained in each trait is examined for DNAm-based and genetic predictors. Receiver operator characteristic curves are generated to investigate the predictive performance of DNAm-based predictors, using dichotomized phenotypes. The relationship between DNAm scores and all-cause mortality (n = 212 events) is assessed via Cox proportional hazards models. DNAm predictors for smoking, alcohol, education, and waist-to-hip ratio are shown to predict mortality in multivariate models. The predictors show moderate discrimination of obesity, alcohol consumption, and HDL cholesterol. There is excellent discrimination of current smoking status, poorer discrimination of college-educated individuals and those with high total cholesterol, LDL with remnant cholesterol, and total:HDL cholesterol ratios.

CONCLUSIONS: DNAm predictors correlate with lifestyle factors that are associated with health and mortality. They may supplement DNAm-based predictors of age to identify the lifestyle profiles of individuals and predict disease risk.

Original languageEnglish
Article number136
JournalGenome Biology
Volume19
DOIs
Publication statusPublished - 27 Sep 2018

Fingerprint

methylation
DNA methylation
DNA Methylation
Epigenomics
epigenetics
death
DNA
prediction
lifestyle
Life Style
smoking
high density lipoprotein cholesterol
mortality
HDL Cholesterol
Mortality
phenotype
alcohol
Smoking
Phenotype
waist-to-hip ratio

Keywords

  • Ageing
  • DNA methylation
  • Mortality
  • Polygenic scores
  • Prediction

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics
  • Genetics
  • Cell Biology

Cite this

McCartney, D. L., Hillary, R. F., Stevenson, A. J., Ritchie, S. J., Walker, R. M., Zhang, Q., ... Marioni, R. E. (2018). Epigenetic prediction of complex traits and death. Genome Biology, 19, [136]. https://doi.org/10.1186/s13059-018-1514-1

Epigenetic prediction of complex traits and death. / McCartney, Daniel L.; Hillary, Robert F.; Stevenson, Anna J.; Ritchie, Stuart J.; Walker, Rosie M.; Zhang, Qian; Morris, Stewart W.; Bermingham, Mairead L.; Campbell, Archie; Murray, Alison D.; Whalley, Heather C.; Gale, Catharine R.; Porteous, David J.; Haley, Chris S.; McRae, Allan F.; Wray, Naomi R.; Visscher, Peter M.; McIntosh, Andrew M.; Evans, Kathryn L.; Deary, Ian J.; Marioni, Riccardo E. (Corresponding Author).

In: Genome Biology, Vol. 19, 136, 27.09.2018.

Research output: Contribution to journalArticle

McCartney, DL, Hillary, RF, Stevenson, AJ, Ritchie, SJ, Walker, RM, Zhang, Q, Morris, SW, Bermingham, ML, Campbell, A, Murray, AD, Whalley, HC, Gale, CR, Porteous, DJ, Haley, CS, McRae, AF, Wray, NR, Visscher, PM, McIntosh, AM, Evans, KL, Deary, IJ & Marioni, RE 2018, 'Epigenetic prediction of complex traits and death', Genome Biology, vol. 19, 136. https://doi.org/10.1186/s13059-018-1514-1
McCartney DL, Hillary RF, Stevenson AJ, Ritchie SJ, Walker RM, Zhang Q et al. Epigenetic prediction of complex traits and death. Genome Biology. 2018 Sep 27;19. 136. https://doi.org/10.1186/s13059-018-1514-1
McCartney, Daniel L. ; Hillary, Robert F. ; Stevenson, Anna J. ; Ritchie, Stuart J. ; Walker, Rosie M. ; Zhang, Qian ; Morris, Stewart W. ; Bermingham, Mairead L. ; Campbell, Archie ; Murray, Alison D. ; Whalley, Heather C. ; Gale, Catharine R. ; Porteous, David J. ; Haley, Chris S. ; McRae, Allan F. ; Wray, Naomi R. ; Visscher, Peter M. ; McIntosh, Andrew M. ; Evans, Kathryn L. ; Deary, Ian J. ; Marioni, Riccardo E. / Epigenetic prediction of complex traits and death. In: Genome Biology. 2018 ; Vol. 19.
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abstract = "BACKGROUND: Genome-wide DNA methylation (DNAm) profiling has allowed for the development of molecular predictors for a multitude of traits and diseases. Such predictors may be more accurate than the self-reported phenotypes and could have clinical applications.RESULTS: Here, penalized regression models are used to develop DNAm predictors for ten modifiable health and lifestyle factors in a cohort of 5087 individuals. Using an independent test cohort comprising 895 individuals, the proportion of phenotypic variance explained in each trait is examined for DNAm-based and genetic predictors. Receiver operator characteristic curves are generated to investigate the predictive performance of DNAm-based predictors, using dichotomized phenotypes. The relationship between DNAm scores and all-cause mortality (n = 212 events) is assessed via Cox proportional hazards models. DNAm predictors for smoking, alcohol, education, and waist-to-hip ratio are shown to predict mortality in multivariate models. The predictors show moderate discrimination of obesity, alcohol consumption, and HDL cholesterol. There is excellent discrimination of current smoking status, poorer discrimination of college-educated individuals and those with high total cholesterol, LDL with remnant cholesterol, and total:HDL cholesterol ratios.CONCLUSIONS: DNAm predictors correlate with lifestyle factors that are associated with health and mortality. They may supplement DNAm-based predictors of age to identify the lifestyle profiles of individuals and predict disease risk.",
keywords = "Ageing, DNA methylation, Mortality, Polygenic scores, Prediction",
author = "McCartney, {Daniel L.} and Hillary, {Robert F.} and Stevenson, {Anna J.} and Ritchie, {Stuart J.} and Walker, {Rosie M.} and Qian Zhang and Morris, {Stewart W.} and Bermingham, {Mairead L.} and Archie Campbell and Murray, {Alison D.} and Whalley, {Heather C.} and Gale, {Catharine R.} and Porteous, {David J.} and Haley, {Chris S.} and McRae, {Allan F.} and Wray, {Naomi R.} and Visscher, {Peter M.} and McIntosh, {Andrew M.} and Evans, {Kathryn L.} and Deary, {Ian J.} and Marioni, {Riccardo E.}",
note = "Funding: GS received core support from the Chief Scientist Office of the Scottish Government Health Directorates (CZD/16/6) and the Scottish Funding Council (HR03006). Genotyping and DNA methylation profiling of the GS samples was carried out by the Genetics Core Laboratory at the Wellcome Trust Clinical Research Facility, Edinburgh, Scotland and was funded by the Medical Research Council UK and the Wellcome Trust (Wellcome Trust Strategic Award “STratifying Resilience and Depression Longitudinally” ((STRADL) Reference 104036/Z/14/Z)). The LBC1936 is supported by Age UK (Disconnected Mind program) and the Medical Research Council (MR/M01311/1). Methylation typing was supported by Centre for Cognitive Ageing and Cognitive Epidemiology (Pilot Fund award), Age UK, The Wellcome Trust Institutional Strategic Support Fund, The University of Edinburgh, and The University of Queensland. This work was conducted in the Centre for Cognitive Ageing and Cognitive Epidemiology, which is supported by the Medical Research Council and Biotechnology and Biological Sciences Research Council (MR/K026992/1), and which supports IJD. DLM and REM are supported by Alzheimer’s Research UK major project grant ARUK-PG2017B-10. This research was supported by Australian National Health and Medical Research Council (grants 1010374, 1046880, and 1113400) and by the Australian Research Council (DP160102400). PMV, NRW, and AFM are supported by the NHMRC Fellowship Scheme (1078037, 1078901, and 1083656). RFH and AJS are supported by funding from the Wellcome Trust 4-year PhD in Translational Neuroscience – training the next generation of basic neuroscientists to embrace clinical research [108890/Z/15/Z].",
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T1 - Epigenetic prediction of complex traits and death

AU - McCartney, Daniel L.

AU - Hillary, Robert F.

AU - Stevenson, Anna J.

AU - Ritchie, Stuart J.

AU - Walker, Rosie M.

AU - Zhang, Qian

AU - Morris, Stewart W.

AU - Bermingham, Mairead L.

AU - Campbell, Archie

AU - Murray, Alison D.

AU - Whalley, Heather C.

AU - Gale, Catharine R.

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AU - Haley, Chris S.

AU - McRae, Allan F.

AU - Wray, Naomi R.

AU - Visscher, Peter M.

AU - McIntosh, Andrew M.

AU - Evans, Kathryn L.

AU - Deary, Ian J.

AU - Marioni, Riccardo E.

N1 - Funding: GS received core support from the Chief Scientist Office of the Scottish Government Health Directorates (CZD/16/6) and the Scottish Funding Council (HR03006). Genotyping and DNA methylation profiling of the GS samples was carried out by the Genetics Core Laboratory at the Wellcome Trust Clinical Research Facility, Edinburgh, Scotland and was funded by the Medical Research Council UK and the Wellcome Trust (Wellcome Trust Strategic Award “STratifying Resilience and Depression Longitudinally” ((STRADL) Reference 104036/Z/14/Z)). The LBC1936 is supported by Age UK (Disconnected Mind program) and the Medical Research Council (MR/M01311/1). Methylation typing was supported by Centre for Cognitive Ageing and Cognitive Epidemiology (Pilot Fund award), Age UK, The Wellcome Trust Institutional Strategic Support Fund, The University of Edinburgh, and The University of Queensland. This work was conducted in the Centre for Cognitive Ageing and Cognitive Epidemiology, which is supported by the Medical Research Council and Biotechnology and Biological Sciences Research Council (MR/K026992/1), and which supports IJD. DLM and REM are supported by Alzheimer’s Research UK major project grant ARUK-PG2017B-10. This research was supported by Australian National Health and Medical Research Council (grants 1010374, 1046880, and 1113400) and by the Australian Research Council (DP160102400). PMV, NRW, and AFM are supported by the NHMRC Fellowship Scheme (1078037, 1078901, and 1083656). RFH and AJS are supported by funding from the Wellcome Trust 4-year PhD in Translational Neuroscience – training the next generation of basic neuroscientists to embrace clinical research [108890/Z/15/Z].

PY - 2018/9/27

Y1 - 2018/9/27

N2 - BACKGROUND: Genome-wide DNA methylation (DNAm) profiling has allowed for the development of molecular predictors for a multitude of traits and diseases. Such predictors may be more accurate than the self-reported phenotypes and could have clinical applications.RESULTS: Here, penalized regression models are used to develop DNAm predictors for ten modifiable health and lifestyle factors in a cohort of 5087 individuals. Using an independent test cohort comprising 895 individuals, the proportion of phenotypic variance explained in each trait is examined for DNAm-based and genetic predictors. Receiver operator characteristic curves are generated to investigate the predictive performance of DNAm-based predictors, using dichotomized phenotypes. The relationship between DNAm scores and all-cause mortality (n = 212 events) is assessed via Cox proportional hazards models. DNAm predictors for smoking, alcohol, education, and waist-to-hip ratio are shown to predict mortality in multivariate models. The predictors show moderate discrimination of obesity, alcohol consumption, and HDL cholesterol. There is excellent discrimination of current smoking status, poorer discrimination of college-educated individuals and those with high total cholesterol, LDL with remnant cholesterol, and total:HDL cholesterol ratios.CONCLUSIONS: DNAm predictors correlate with lifestyle factors that are associated with health and mortality. They may supplement DNAm-based predictors of age to identify the lifestyle profiles of individuals and predict disease risk.

AB - BACKGROUND: Genome-wide DNA methylation (DNAm) profiling has allowed for the development of molecular predictors for a multitude of traits and diseases. Such predictors may be more accurate than the self-reported phenotypes and could have clinical applications.RESULTS: Here, penalized regression models are used to develop DNAm predictors for ten modifiable health and lifestyle factors in a cohort of 5087 individuals. Using an independent test cohort comprising 895 individuals, the proportion of phenotypic variance explained in each trait is examined for DNAm-based and genetic predictors. Receiver operator characteristic curves are generated to investigate the predictive performance of DNAm-based predictors, using dichotomized phenotypes. The relationship between DNAm scores and all-cause mortality (n = 212 events) is assessed via Cox proportional hazards models. DNAm predictors for smoking, alcohol, education, and waist-to-hip ratio are shown to predict mortality in multivariate models. The predictors show moderate discrimination of obesity, alcohol consumption, and HDL cholesterol. There is excellent discrimination of current smoking status, poorer discrimination of college-educated individuals and those with high total cholesterol, LDL with remnant cholesterol, and total:HDL cholesterol ratios.CONCLUSIONS: DNAm predictors correlate with lifestyle factors that are associated with health and mortality. They may supplement DNAm-based predictors of age to identify the lifestyle profiles of individuals and predict disease risk.

KW - Ageing

KW - DNA methylation

KW - Mortality

KW - Polygenic scores

KW - Prediction

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