Using matched CMC genotype and gene expression data, we developed DLPFC genetically regulated gene expression (GREX) predictor models. We systematically compared four approaches to building predictors15,16 within a cross-validation framework. Elastic net regression had a higher distribution of cross-validation R2 (RCV2) and higher mean RCV2 values (Supplementary Figure 1, 2a) than all other methods. We therefore used elastic net regression to build our prediction models. We compared prediction models created using elastic net regression on SVA-corrected and uncorrected data14. The distribution of Rcv2 values for the SVA-based models was significantly higher than for the un-corrected data14,18 (ks-test; p < 2.2 × 10−16; Supplementary figure 1b,c). In total, 10,929 genes were predicted with elastic net cross-validation Rcv2 > 0.01 in the SVA-corrected data and were included in the final predictor database (mean Rcv2 = 0.076).
