P1‐465: Role of collagen VI in Alzheimer's disease: Potential mechanisms of protection

Dubal, Dena B.; Cheng, Jason S.; Legleiter, Justin; Wilkins, Sarah E.; Bonaldo, Paolo; Yu, Gui-Qiu; Mucke, Lennart

doi:10.1016/j.jalz.2008.05.1048

Cited by 1 publication

(1 citation statement)

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“…FBXO24 encodes a subunit of the F-box proteins that control proteasomal degradation (Omolaoye et al, 2022) and have been linked to the dysfunction of the ubiquitin-protease system, an accepted cause for AD (Watanabe et al, 2013). PCOLCE-AS1 is likely related to proteases involved in procollagen processing (Podvin et al, 2021), and interestingly, collagen VI was found to counteract Aβ-induced neurotoxicity (Dubal et al, 2008). Furthermore, all four of these genes are located within the ZCWPW1 locus (Efthymiou & Goate, 2017), a region that is implicated to contain major functional AD genes (Gao et al, 2016).…”

Section: Resultsmentioning

confidence: 99%

S-BEAM: A Semi-Supervised Ensemble Approach to Rank Potential Causal Variants and Their Target Genes in Microglia for Alzheimer’s Disease

Khaire

Wen

et al. 2022

Preprint

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Alzheimer's disease (AD) is the leading cause of death among individuals over 65. Despite many AD genetic variants detected by large genome-wide association studies (GWAS) a limited number of causal genes have been confirmed. Conventional machine learning techniques integrate functional annotation data and GWAS signals to assign variants functional relevance probabilities. Yet, a large proportion of genetic variation lies in the non-coding genome, where unsupervised and semi-supervised techniques have demonstrated a greater advantage. Furthermore, cell-type specific approaches are needed to better understand disease etiology. Studying AD from a microglia-specific lens is more likely to reveal causal variants involved in immune pathways. Therefore, in this study, we developed a semi-supervised ensemble approach using microglia-specific data to prioritize non-coding variants and their target genes that play roles in immune-related AD mechanisms. We designed a transductive positive-unlabeled and negative-unlabeled learning model that employs a bagging technique to learn from unlabeled variants, generating multiple predicted probabilities of variant risk. Using a combined homogeneous-heterogeneous ensemble framework, we aggregated the predictions. We applied our model to AD variant data, identifying 11 risk variants acting in well-known AD genes, such as TSPAN14, INPP5D, and MS4A2. These results validated our model's performance and demonstrated a need to study these genes in the context of microglial pathways. We also proposed further experimental study for 37 potential causal variants associated with less-known genes. Our work has utility in predicting AD relevant genes and variants functioning in microglia and can be generalized for application to other complex diseases.

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