Transcriptomic Profiling of Brain Amyloidosis and Alzheimer’s Disease Phenotypes Identifies Novel Gene Targets from Peripheral Blood Gene Expression Data

Background In an effort towards pre‐symptomatic risk assessment, precision medicine and biomarker development in AD, we evaluated the efficacy of characterizing and clustering peripheral blood transcriptomic data with respect to brain amyloidosis. Method 356 ADNI participants with blood gene expression, Florbetapir SUVR, neurodegeneration measures and CSF measures (CN = 120, EMCI = 130, LMCI = 72 and AD = 34) were identified (Table‐1). Differential expressed genes from ∼50,000 transcripts (p<0.001) were identified. Pairwise euclidean distance between genes was used to construct a gene‐gene distance matrix followed by K‐means clustering (Figure‐1). Gene enrichment analysis was performed on each cluster to identify biological processes associated with the cluster. “Eigengene”; a data vector representing the whole cluster was obtained for each cluster using Principal Component Analyses. The top driver genes were identified from each cluster. We used the tool MAGMA (Multi‐marker Analysis of GenoMic Annotation) to analyze variants in the driver genes. Voxel‐wise multiple regression in SPM12 with age and sex as covariates was used to visualize the pattern of association of driver gene expression with brain amyloidosis. The association of the transcripts identified through our analyses to amyloid SUVR, and disease diagnosis were validated in the external ImaGene(n = 160) dataset. Result We identified five clusters with distinct biological processes highly relevant in AD. All five clusters were significantly associated with Florbetapir SUVR and CSF Abeta measures (p<0.05) (Figure‐2). 28 driver genes were identified. Six driver genes (BAIAP3, E2F2, PSMF1, SMOX, UBE20 and RNF11) had negative association with amyloidosis in the lateral temporal, lateral parietal and temporo‐ and parieto‐occipital areas, in addition to regions of the frontal lobe (Figure‐3). One of the driver genes, KANK2, and overall, 29 of the differentially expressed genes had SNPs significantly associated with AD and MCI phenotype (Table‐2). The top driver genes showed similar correlation to amyloid SUVR in the ImaGene dataset (Figure‐4B). A logistic regression model with 28 driver genes, and age and sex as covariates predicted amnestic MCI diagnosis in the external dataset with an AUC of 0.82(Figure‐4A). Conclusion Using a data driven approach we identified novel gene targets from peripheral blood which directly correlate to amyloid‐related pathogenesis and AD phenotype