Discovering genetic biomarkers for Alzheimer’s disease using 2D‐CNN and GWAS

Background To identify candidate neuroimaging and genetic biomarkers for Alzheimer’s Disease (AD) and other brain diseases, especially for little‐known brain disorders, in a data‐driven way, we advocate an approach which incorporates an adaptive classifier ensemble model by combining Convolutional Neural Network (CNN) and Ensemble Learning with Genetic Algorithm (GA), i.e., the CNN‐EL‐GA method, into Genome‐Wide Association Studies (GWAS). Method In the CNN‐EL‐GA method, a large number of base classifiers, i.e., CNN models, were trained utilizing a set of sagittal, coronal, or transversal magnetic resonance imaging slices, and the CNN models with strong discriminability were then selected and integrated into a single classifier ensemble with the GA for classifying AD. While the generalization capability of the acquired classifier ensemble was maximized, the intersection points were decided by the most discriminative slices among the sagittal, coronal, and transversal slice sets. The gray matter volumes of the top ten discriminative brain regions which contained the most intersection points were utilized to carry out GWAS together with the genotypic data. The pipeline of the proposed approach incorporating the CNN‐EL‐GA method into GWAS for discovering candidate genetic biomarkers of AD is shown in Figure 1. Result Six genes of PCDH11X/Y, TPTE2, LOC107985902, MUC16 and LINC01621 as well as Single Nucleotide Polymorphisms, e.g., rs36088804, rs34640393, rs2451078, rs10496214, rs17016520, rs2591597, rs9352767 and rs5941380, were identified. Conclusion This approach overcomes the limitations associated with subjective factors while adaptively achieving more robust and effective candidate biomarkers in a data‐driven way. The approach is promising to advance the discovery of effective candidate genetic biomarkers for brain disorders.