Background Early detection of individuals at risk for Alzheimer’s disease (AD) is highly important. Amyloid accumulation is an early pathological AD event, but the genetic association with known AD risk variants beyond the APOE4 effect is largely unknown. We investigated the association between different AD polygenic risk scores (PRS) and amyloid accumulation in the Flemish Prevent AD Cohort KU Leuven (F-PACK). Methods We calculated PRS with and without the APOE region in 90 cognitively healthy F-PACK participants (baseline age 67.8 (52–80) years, 41 APOE4 carriers), with baseline and follow-up amyloid-PET (time interval 6.1 (3.4–10.9) years). Individuals were genotyped using Illumina GSA and imputed. PRS were calculated using three p-value thresholds (pT) for variant inclusion: 5 × 10−8, 1 × 10−5, and 0.1, based on the stage 1 summary statistics from Kunkle et al. (Nat Genet 51:414–30, 2019). Linear regression models determined if these PRS predicted amyloid accumulation. Results A score based on PRS excluding the APOE region at pT = 5 × 10−8 plus the weighted sum of the two major APOE variants (rs429358 and rs7412) was significantly associated with amyloid accumulation (p = 0.0126). The two major APOE variants were also significantly associated with amyloid accumulation (p = 0.0496). The other PRS were not significant. Conclusions Specific PRS are associated with amyloid accumulation in the asymptomatic phase of AD.

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RNA analysis of post-mortem tissues, or thanatotranscriptomics, has become a topic of interest in forensic science due to the essential information it can provide in forensic investigations. Several studies have previously investigated the effect of death on gene transcription, but it has never been conducted with samples of the same individual. For the first time, a longitudinal mRNA expression analysis study was performed with post-mortem human blood samples from individuals with a known time of death. The results reveal that, after death, two clearly differentiated groups of up- and down-regulated genes can be detected. Pathway analysis suggests active processes that promote cell survival and DNA damage repair, rather than passive degradation, are the source of early post-mortem changes of gene expression in blood. In addition, a generalized linear model with an elastic net restriction predicted post-mortem interval with a root mean square error of 4.75 h. In conclusion, we demonstrate that post-mortem gene expression data can be used as biomarkers to estimate the post-mortem interval though further validation using independent sample sets is required before use in forensic casework.