Additional file 2 of Multi-method genome- and epigenome-wide studies of inflammatory protein levels in healthy older adults

Additional file 2: Supplementary Tables. The association of pre-adjusted protein levels with biological and technical covariates. Protein levels were adjusted for age, sex, array plate and four genetic principal components (population structure) prior to analyses. Significant associations are emboldened. (Table S1). pQTLs associated with inflammatory biomarker levels from Bayesian penalised regression model (Posterior Inclusion Probability > 95%). (Table S2). All pQTLs associated with inflammatory biomarker levels from ordinary least squares regression model (P 95%). (Table S12). CpGs associated with inflammatory protein biomarkers as identified by linear model (limma) at P < 5.14 × 10− 10. (Table S13). CpGs associated with inflammatory protein biomarkers as identified by mixed linear model (OSCA) at P < 5.14 × 10− 10. (Table S14). Estimate of variance explained for blood protein levels by DNA methylation as well as proportion of explained attributable to different prior mixtures - BayesR+. (Table S15). Comparison of variance in protein levels explained by genome-wide DNA methylation data by mixed linear model (OSCA) and Bayesian penalised regression model (BayesR+). (Table S16). Variance in circulating inflammatory protein biomarker levels explained by common genetic and methylation data (joint and conditional estimates from BayesR+). Ordered by combined variance explained by genetic and epigenetic data - smallest to largest. Significant results from t-tests comparing distributions for variance explained by methylation or genetics alone versus combined estimate are emboldened. (Table S17). Genetic and epigenetic factors identified by BayesR+ when conditioning on all SNPs and CpGs together. (Table S18). Mendelian Randomisation analyses to assess whether proteins with concordantly identified genetic signals are causally associated with Alzheimer’s disease risk. (Table S19).