Using Genomic Context Informed Genotype Data and Within-model Ancestry Adjustment to Classify Type 2 Diabetes

Abstract

Despite high heritability estimates, complex genetic disorders have proven difficult to predict with genetic data. Genomic research has documented polygenic inheritance, cross-disorder genetic correlations, and enrichment of risk by functional genomic annotation, but the vast potential of that combined knowledge has not yet been leveraged to build optimal risk models. Additional methods are likely required to progress genetic risk models of complex genetic disorders towards clinical utility. We developed a framework that uses annotations providing genomic context alongside genotype data as input to convolutional neural networks to predict disorder risk. We validated models in a matched-pairs type 2 diabetes dataset. A neural network using genotype data (AUC: 0.66) and a convolutional neural network using context-informed genotype data (AUC: 0.65) both significantly outperformed polygenic risk score approaches in classifying type-2 diabetes. Adversarial ancestry tasks eliminated the predictability of ancestry without changing model performance.