Improving Phenotypic Prediction by Combining Genetic and Epigenetic Associations

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.

Figure 1

BMI and Height Prediction The plots depict how much of the variance in the sex- and age-adjusted BMI and height phenotypes (adjusted R²) was explained by the methylation-profile score, the genetic-profile score, an additive model including both scores (methylation + genetic), and an interaction model (methylation × genetic). The methylation score in the LBCs is based on selected probes and effects sizes from the LifeLines DEEP MWAS, and vice versa. The genetic-profile scores are based on results from the GIANT meta-GWAS.

Figure 2

BMI Prediction in BSGS Adolescents The plots show how much of the variance in the sex- and age-adjusted BMI phenotype (adjusted R²) was explained by the methylation-profile score, the genetic-profile score, an additive model including both scores (methylation + genetic), and an interaction model (methylation × genetic). The GWAS scores are based on results from the GIANT meta-GWAS. Methylation scores are based on probe selection and weights derived from the LBCs MWAS or the LifeLines DEEP MWAS (upper panel) or probe selection from the Framingham discovery with weights derived from the LBCs or LifeLines DEEP studies (lower panel).