Genetic risk prediction for type-2 diabetes.

PRS models were built based on the summary statistics from a meta-analysis of DIAGRAM consortium and GERA data (17,802 cases and 105,109 controls in total) and validated in independent 1500 cases and 1500 controls in GERA. (A) Prediction R² (observational scale) for 1D PRS with or without winner’s curse correction. “NO”: no winner’s correction for association coefficients; “Lasso”: regression coefficients were modified by a lasso-type correction; “MLE”: association coefficients were modified by maximizing a likelihood function conditioning on selection. (B) Quantile-quantile plot for −log₁₀(P) for high priority (HP) SNPs vs. low priority (LP) SNPs. SNPs were pruned to have pairwise r² ≤ 0.1. Here, the HP SNPs were eSNPs/meSNPs in adipose tissue or SNPs related with the H3K4me3 mark in pancreatic islet cell line with data downloaded from the ROADMAP project. The HP SNPs were strongly enriched in the discovery data. (C) Prediction R² for 2D PRS with lasso-type winner’s curse correction. The SNP set was the same to (B). The best prediction (R² = 3.53%) was achieved when we included HP SNPs using criterion P ≤ 0.03 and LP SNPs with P ≤ 0.005. (D) The prediction R², the area under the curve (AUC) and the significances for testing whether an alternative PRS was better than the standard 1D.