Improving Phenotypic Prediction by Combining Genetic and Epigenetic Associations

S Shah, MJ Bonder, RE Marioni, ZH Zhu, AF Mcrae, A Zhernakova, SE Harris, D Liewald, AK Henders, MM Mendelson, CY Liu, R Joehanes, LM Liang, D Levy, NG Martin, JM Starr, C Wijmenga, NR Wray, Jiaqi Yang, GW MontgomeryL Franke, IJ Deary, PM Visscher

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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 similar to 350,000) and height (n similar to 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 Life Lines 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 140/0 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 Life Lines DEEP were 5%, 9%, and 13%, respectively, suggesting that the methylation profiles represent environmental effects. The differential effects of the BM! 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.
Original languageUndefined/Unknown
Pages (from-to)75-85
Number of pages11
JournalAmerican Journal of Human Genetics
Issue number1
Publication statusPublished - 2015
Externally publishedYes

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