Multimodal genome‐wide meta‐analysis of brain amyloidosis reveals heterogeneity across CSF, PET, and pathological amyloid measures: Genetics/genetic factors of Alzheimer's disease. (7th December 2020)

• Record Type: Journal Article
• Authors: Archer, Derek B.; Mahoney, Emily R.; Dumitrescu, Logan; Jefferson, Angela L.; Jagust, William J.; Resnick, Susan M.; Bilgel, Murat; Johnson, Sterling C.; Engelman, Corinne D.; Cruchaga, Carlos; Zetterberg, Henrik; Blennow, Kaj; Deming, Yuetiva; Sperling, Reisa A.; Johnson, Keith A.; Buckley, Rachel F.; Larson, Eric B.; Mayeux, Richard; Bennett, David A.; Schneider, Julie A.; Kukull, Walter A.; Keene, C. Dirk; Montine, Thomas J.; Beecham, Gary W.; Schellenberg, Gerard D.; Hohman, Timothy J.
• Is Part Of: Alzheimer's & dementia. Volume 16(2020)Supplement 3
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-12-07
• Subjects: Alzheimer's disease -- Periodicals; Alzheimer Disease -- Periodicals; Dementia -- Periodicals; Démence; Maladie d'Alzheimer; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 616.83
• DOI: 10.1002/alz.046009
• Languages: English
• ISSNs: 1552-5260

Abstract

Abstract: Background: Amyloidosis in Alzheimer's disease (AD) is a common feature that can be measured via cerebrospinal fluid (CSF), positron emission tomography (PET), and autopsy measures of neuritic plaques. While individual genome‐wide association studies (GWAS) have identified more than 40 loci as potential biological drivers of AD, fewer drivers of amyloidosis have been identified due to the small sample sizes in endophenotype analyses. Combining unimodal measures of amyloid into a larger multi‐modal analysis could provide new insight into mechanisms driving amyloidosis. The goal of this study was to conduct a large meta‐analysis of CSF‐, PET‐, and pathology‐derived metrics of amyloid positivity. Method: 11, 941 non‐Hispanic white participants (CSF = 2505, PET = 3976, Autopsy = 5460) from 14 cohorts were analyzed. To facilitate cross‐modality harmonization, a binarized amyloid status (negative/positive) was determined on an individual cohort basis using two approaches: a Gaussian mixture model clustering algorithm for CSF/PET measurements and CERAD thresholds (CERAD>sparse or CERAD ≤ sparse) for autopsy measures of amyloid pathology. In total, our analysis included 5, 634 amyloid‐negative and 6, 307 amyloid‐positive individuals. GWAS using a logistic regression model covarying for age and sex were performed in each of the 14 cohorts, and a meta‐analysis was performed within each modality. These meta‐analyses were followed with a multi‐modal meta‐analysis. The standard genome‐wide threshold of statistical significance (p < 5 × 10 −8 ) was applied. Additionally, we performed candidate analysis for the 39 previously published clinical AD or amyloid risk loci. Result: Aside from variants within the APOE region, our multi‐modal meta‐analysis did not identify any genome‐wide loci (Figure 1). A lack of convergent GWAS signals was also observed when stratifying by age, sex, and APOE carrier status. However, our candidate gene analysis demonstrated some consistency across modalities. Two prior loci were significant in 2/3 modalities, including ABCA7 (rs4147929; p = 1.98 × 10 −4 ) and CLU (rs4236671; p = 0.001). Conclusion: In the largest GWAS of brain amyloidosis, we found a lack of convergence across modalities, suggesting substantial heterogeneity across measures and cohorts. Candidate analyses highlight some consistent signals in ABCA7 and CLU . Results suggest continued emphasis on increasing sample sizes for endophenotype analyses and on standardized methodologies to reduce sample heterogeneity to improve statistical power.