Authors: Danai Chasioti, Tanner Y. Jacobson, Kwangsik Nho, Shannon L. Risacher, Sujuan Gao, Jingwen Yan, Andrew J. Saykin
Published: 2020-12-07
DOI: 10.1002/alz.046766
Source: Full article
AbstractBackgroundPrecision medicine is designed to resolve heterogeneity in complex diseases such as Alzheimer’s (AD) to deliver timely and tailored treatment. Using ADNI data, we developed profiling based on genetically driven endophenotype‐specific polygenic risk scores (PRS) and assessed participant clustering on these profiles.MethodWe used ADNI genetic, clinical and biomarker data for PRS development. We performed cross‐sectional and longitudinal analysis in order to identify individual profiles at the baseline and to track disease progression. We derived four ADRD endophenotype components including Amyloid (A), Tau (T), Neurodegeneration (N) and Vascular (V), by applying PCA on 10 phenotypic variables. Bootstrapping was used for SNP selection and weight re‐estimation. APOE SNPs (rs429358, rs7412) and nearby genes were excluded. We performed K‐means clustering on the 4 derived A/T/N/V PRSs and cluster assignment was computed for 839 individuals of the validation set (excluding AD at the baseline). Baseline levels of cognition and biomarkers were estimated using linear regression adjusting for age, sex, education and (ICV when necessary) (Table 1, Figure 2). Longitudinal trajectories were estimated using linear mixed models (LMM) (Table 2, Figure 3). The performance of the LMM was assessed using marginal R2 (Table 3).ResultCluster assignment placed 306 individuals in Cluster1 and 533 in Cluster2. Both were balanced in terms of APOE e4 status, and demographics. Cluster1 showed elevated genetic risk (Figure 1) that was manifest at baseline by higher levels of amyloid (PET, CSF) and reduced temporal lobe thickness and memory (Table 1, Figure 2). Longitudinally, significant effect differences among the clusters were observed in angular gyrus, temporal lobe, white mater hyperintensity (WMHI), and clinical/cognitive measures (ADNI_EF and FAQ) (Table 2, Figure 3). Among APOE e4 non‐carriers, Cluster1 had significantly worse scores compared to Cluster2 (ADNI_EF and WMHI).ConclusionUsing clusters derived from endophenotype specific genetic information we were able to identify distinct progression profiles with predictive value beyond APOE alone. Endophenotype‐based PRS may be useful for therapeutic enrichment and targeting strategies in development of personalized therapeutics. Validation in independent samples is a critical next step.