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Am J Hum Genet. 2015 Jul 2;97(1):75-85. doi: 10.1016/j.ajhg.2015.05.014. Epub 2015 Jun 25.

Improving Phenotypic Prediction by Combining Genetic and Epigenetic Associations.

Author information

1
Queensland Brain Institute, University of Queensland, Brisbane 4072, Australia; University of Queensland Diamantina Institute, Translational Research Institute, University of Queensland, Brisbane, QLD 4072, Australia.
2
Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen 9713 AV, the Netherlands.
3
Queensland Brain Institute, University of Queensland, Brisbane 4072, Australia; Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh EH8 9JZ, UK; Medical Genetics Section, Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh EH4 2XU, UK.
4
Queensland Brain Institute, University of Queensland, Brisbane 4072, Australia.
5
Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh EH8 9JZ, UK; Medical Genetics Section, Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh EH4 2XU, UK.
6
Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh EH8 9JZ, UK.
7
Queensland Institute of Medical Research Berghofer Medical Research Institute, Brisbane, QLD 4029, Australia.
8
Framingham Heart Study and Boston University School of Medicine, Boston, MA 01702, USA; Department of Cardiology, Boston Children's Hospital, Boston, MA 02115, USA; Population Studies Branch, National Heart, Lung, and Blood Institute, NIH, Bethesda, MD 20892-7936, USA.
9
Department of Biostatistics, Boston University, Boston, MA 02118, USA.
10
Hebrew Senior Life, Harvard Medical School, Boston, MA 02131, USA.
11
Departments of Epidemiology and Biostatistics, T.H. Chan School of Public Health, Harvard University, Boston, MA 02115, USA.
12
Population Studies Branch, National Heart, Lung, and Blood Institute, NIH, Bethesda, MD 20892-7936, USA.
13
Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh EH8 9JZ, UK; Department of Psychology, University of Edinburgh, Edinburgh EH8 9JZ, UK.
14
Queensland Brain Institute, University of Queensland, Brisbane 4072, Australia; University of Queensland Diamantina Institute, Translational Research Institute, University of Queensland, Brisbane, QLD 4072, Australia; Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh EH8 9JZ, UK. Electronic address: peter.visscher@uq.edu.au.

Abstract

We tested whether DNA-methylation profiles account for inter-individual variation in body mass index (BMI) and height and whether they predict these phenotypes over and above genetic factors. Genetic predictors were derived from published summary results from the largest genome-wide association studies on BMI (n ∼ 350,000) and height (n ∼ 250,000) to date. We derived methylation predictors by estimating probe-trait effects in discovery samples and tested them in external samples. Methylation profiles associated with BMI in older individuals from the Lothian Birth Cohorts (LBCs, n = 1,366) explained 4.9% of the variation in BMI in Dutch adults from the LifeLines DEEP study (n = 750) but did not account for any BMI variation in adolescents from the Brisbane Systems Genetic Study (BSGS, n = 403). Methylation profiles based on the Dutch sample explained 4.9% and 3.6% of the variation in BMI in the LBCs and BSGS, respectively. Methylation profiles predicted BMI independently of genetic profiles in an additive manner: 7%, 8%, and 14% of variance of BMI in the LBCs were explained by the methylation predictor, the genetic predictor, and a model containing both, respectively. The corresponding percentages for LifeLines DEEP were 5%, 9%, and 13%, respectively, suggesting that the methylation profiles represent environmental effects. The differential effects of the BMI methylation profiles by age support previous observations of age modulation of genetic contributions. In contrast, methylation profiles accounted for almost no variation in height, consistent with a mainly genetic contribution to inter-individual variation. The BMI results suggest that combining genetic and epigenetic information might have greater utility for complex-trait prediction.

PMID:
26119815
PMCID:
PMC4572498
DOI:
10.1016/j.ajhg.2015.05.014
[Indexed for MEDLINE]
Free PMC Article

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