Improving Machine Learning Prediction of ADHD Using Gene Set Polygenic Risk Scores and Risk Scores From Genetically Correlated Phenotypes.

Abstract

Polygenic risk scores (PRSs), which sum the effects of SNPs throughout the genome to measure risk afforded by common genetic variants, have improved our ability to estimate disorder risk for Attention-Deficit/Hyperactivity Disorder (ADHD) but the accuracy of risk prediction is rarely investigated. In a study of 10,887 participants across nine cohorts, we performed gene set analysis of GWAS data to select gene sets associated with ADHD within a training subset. For each gene set, we generated gene set polygenic risk scores (gsPRSs), which sum the effects of SNPs for each selected gene set. We created gsPRS for ADHD and for phenotypes that are genetically correlated with ADHD. These gsPRS were added to the standard PRS as input to machine learning models predicting ADHD. On the test subset, a random forest (RF) model using PRSs from ADHD and genetically correlated phenotypes and an optimized group of 20 gsPRS had an area under the receiving operating characteristic curve (AUC) of 0.72 (95% CI: 0.70-0.74). This AUC was a statistically significant improvement over logistic regression models and RF models using only PRS from ADHD and genetically correlated phenotypes. Summing risk at the gene set level and incorporating genetic risk from disorders with high genetic correlations with ADHD improved the accuracy of predicting ADHD. Learning curves suggest that additional improvements would be expected with larger study sizes. Our study suggests that better accounting of genetic risk and the genetic context of allelic differences results in more predictive models.