Seshadri, S

Abstract Histopathological assessment has served as the gold standard for diagnosing Alzheimer’s disease (AD). Emerging technological advancements, including the development of amyloid positron emission tomography (PET), have enabled early detection of amyloid pathology, one of the neuropathological hallmarks of AD. Genome-wide association study (GWAS) across cohorts of aging and AD, leveraging different measurements of amyloid burden, may facilitate the identification of novel genetic variants that drive the earliest neuropathological changes in AD. This study presents the largest GWAS of brain amyloidosis to date, leveraging amyloid β (Aβ) measured by in vivo amyloid PET and postmortem histopathology from 13,555 individuals of European ancestry. Amyloid positivity was defined as moderate or frequent neuritic plaques according to the Consortium to Establish a Registry for Alzheimer’s Disease (CERAD) staging scores for each postmortem cohort. A Gaussian mixture model (GMM) was applied to each amyloid PET cohort to identify the cohort and tracer-specific cut-offs that differentiate amyloid positive and negative populations. In silico and ex vivo analyses further characterized implicated loci, including interrogating the association between bulk and single-nucleus gene expression profiles and AD-related traits. Genetic covariance analysis assessed the extent amyloid PET and postmortem measures reflect the shared genetic architecture of brain amyloidosis. Our combined amyloidosis GWAS identified three established AD risk loci: BIN1 (rs6733839, OR = 1.20, 95% CI 1.14-1.26, P = 1.32 × 10−11), CR1 (rs4844610, OR = 1.24, 95% CI = 1.16-1.32, P = 4.21 × 10−10), APOE (rs429358, OR = 4.01, 95% CI = 3.66-4.38, P = 4.54 × 10−201), and a newly identified brain amyloidosis-associated variant on chromosome 17 (rs35635959, OR = 1.18, 95% CI = 1.12-1.25, P = 1.47 × 10−8). SuSiE fine-mapping identified a single credible set of 15 putative causal variants with rs35635959 as the lead variant. Subsequent eQTL and SuSiE-based colocalization analyses prioritized rs35635959 as a strong eQTL for TUBG2, encoding tubulin gamma 2, which is involved in microtubule organization and synaptic plasticity. Further cell-type-specific characterization of this gene in neurons from dorsolateral prefrontal cortex tissue indicated that decreased TUBG2 expression was associated with increased Aβ burden and AD case status (PFDR < 0.045). Furthermore, our study is the first to report a modest genetic covariance (covariance=0.17, P < 6.54 × 10−8) between the genetic architecture of amyloid burden captured by different modalities. While APOE showed a strong association with both amyloid endophenotypes, the observed genetic covariance was not substantially attenuated after excluding variants within the APOE region (covariance=0.16, P < 1.32 × 10−7). Our results highlight the benefits of leveraging compatible, harmonized AD endophenotypes to increase power to uncover new molecular insights into the etiology of AD neuropathology. Wang et al. present the largest GWAS of brain amyloidosis to date, analysing 13,555 individuals using amyloid PET and postmortem Aβ measures. They identify a novel amyloidosis-associated variant on chromosome 17, demonstrate genetic covariance between modalities, and highlight complex traits sharing genetic architecture with Aβ burden.

Abstract Rationale The impacts of COVID-19 vaccination and SAR-CoV-2 infection on brain tissues and structures remain controversial. This study examines whether COVID-19-related exposures are associated with measurable changes in structural magnetic resonance imaging (MRI) using data from the US general population-based Collaborative Cohort of Cohorts for COVID-19 Research (C4R) Study. Methods The C4R study is an observational meta-cohort study of 14 NIH-funded population-based cohorts, with directly measured data on COVID-19-related exposures, including vaccination and infection. For this analysis, we included participants with C4R data and paired brain MRI scans from both before and after the onset of the COVID-19 pandemic (defined as March 1, 2020) from two cohorts: the Atherosclerosis Risk in Communities study (ARIC) and Framingham Heart Study (FHS). MRI features examined as outcomes were white matter volume (WMV), gray matter volume (GMV), total brain volume (BV), hippocampal volume (HV), and white matter hyperintensity volume (WMH), a marker of microvascular disease. Exposures included SARS-CoV-2 infections identified via questionnaires, serosurveys, and medical record reviews from 2020-2025. Infections resulting in hospitalization were classified as severe. Time-to-recovery, symptoms of long COVID, and vaccination status were self-reported by questionnaire. Analysis of covariance (ANCOVA) estimated associations between COVID-19-related exposures and pandemic MRI-measured outcomes, with adjustment for: the corresponding pre-pandemic MRI measure, the time between the two MRI scans, age, sex, systolic blood pressure, diabetes, and cardiovascular disease. Results Of 270 participants with available data, mean (SD) age was 80.6 years (10.6); 43.0% were male; and the mean (SD) interval between paired MRI scans was 4.2 (2.0) years. Most (90%) were vaccinated (of whom 6.7% vaccinated before infection) and 7.9% reported SARS-CoV-2 infection (of which 13.6% were severe infections) prior to their pandemic MRI (conducted March 2020 - June 2022). In fully adjusted models, there was no statistically significant association for change in WMV, GMV, BV, HV, or WMH with SAR-CoV2 infection status, infection severity, vaccination status, or vaccination timing (Table, P > 0.05 for all comparisons). Self-reported symptoms of long COVID and vaccine manufacturer were also not associated with MRI outcomes. Conclusions Based on repeated (pre-pandemic and pandemic era) structural MRI scans, we found no evidence that SARS-CoV-2 infection or COVID-19 vaccination were associated with changes in brain structure or brain microvascular disease. These findings are consistent with prior data supporting COVID-19 vaccine safety and suggest resilience of brain structures following SARS-CoV-2 exposures. Nonetheless, further research is warranted using more sensitive measures and assessing longer term effects. This abstract is funded by: OT2HL156812