Improving phenotypic prediction by combining genetic and epigenetic associations

Shah, Sonia, Bonder, Marc J., Marioni, Riccardo E., Zhu, Zhihong, McRae, Allan F., Zhernakova, Alexandra, Harris, Sarah E., Liewald, Dave, Henders, Anjali K., Mendelson, Michael M., Liu, Chunyu, Joehanes, Roby, Liang, Liming, Levy, Daniel, Martin, Nicholas G., Starr, John M., Wijmenga, Cisca, Wray, Naomi R., Yang, Jian, Montgomery, Grant W., Franke, Luke, Deary, Ian J., Visscher, Peter M. and BIOS Consortium (2015) Improving phenotypic prediction by combining genetic and epigenetic associations. American Journal of Human Genetics, 97 1: 75-85. doi:10.1016/j.ajhg.2015.05.014

Author Shah, Sonia
Bonder, Marc J.
Marioni, Riccardo E.
Zhu, Zhihong
McRae, Allan F.
Zhernakova, Alexandra
Harris, Sarah E.
Liewald, Dave
Henders, Anjali K.
Mendelson, Michael M.
Liu, Chunyu
Joehanes, Roby
Liang, Liming
Levy, Daniel
Martin, Nicholas G.
Starr, John M.
Wijmenga, Cisca
Wray, Naomi R.
Yang, Jian
Montgomery, Grant W.
Franke, Luke
Deary, Ian J.
Visscher, Peter M.
BIOS Consortium
Title Improving phenotypic prediction by combining genetic and epigenetic associations
Journal name American Journal of Human Genetics   Check publisher's open access policy
ISSN 1537-6605
Publication date 2015-07-02
Year available 2015
Sub-type Article (original research)
DOI 10.1016/j.ajhg.2015.05.014
Open Access Status DOI
Volume 97
Issue 1
Start page 75
End page 85
Total pages 11
Place of publication Cambridge, MA, United States
Publisher Cell Press [Elsevier]
Language eng
Subject 1311 Genetics
2716 Genetics (clinical)
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.
Keyword Body-Mass Index
Dna Methylation
Genotype Imputation
Wide Association
Q-Index Code C1
Q-Index Status Confirmed Code
Grant ID BB/F019394/1
Institutional Status UQ

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