Genetic and Epigenetic Signatures for Cognitive Resilience in Neuropathologically Defined Alzheimer’s Disease Brains.

Background: Not all people with neuropathological evidence of Alzheimer’s disease (AD) manifest clinical symptoms in life (cognitive resilience). We aimed to identify genetic and epigenetic signatures of cognitive resilience, utilizing data from brain donors with neuropathological evidence of AD who were either symptomatic or asymptomatic in life.

Method: Among brain donors with neuropathologically‐confirmed AD (364 asymptomatic/cognitively resilient and 490 symptomatic) from the Boston University AD Research Center, Framingham Heart Study—where we generated our own data—as well as the Religious Orders Study and Rush Memory and Aging Project, we utilized genome‐wide genetic array data, genome‐wide DNA methylation array data and RNA sequencing data.

PreSiBOGNN: Multi‐Modal Graph Neural Network for Prediction of Cognitive Improvement in Medication Usage in UK Biobank.

Background: The prohibitive costs of drug development for Alzheimer’s Disease (AD) emphasize the need for alternative in silico drug repositioning strategies. Graph learning algorithms, capable of learning intrinsic features from complex network structures, can leverage existing databases of biological interactions to improve predictions in drug efficacy. We developed a novel machine learning framework, the PreSiBOGNN, that integrates muti‐modal information to predict cognitive improvement at the subject level for precision medicine in AD.

Unsupervised Learning‐based Drug Repositioning for Alzheimer’s Disease Targeting Brain Cell Type Specific Genetic Subtypes.

Background: Alzheimer’s disease (AD) presents challenges with its complex neurodegenerative mechanisms, leading to a high failure rate in clinical trials. While drug repositioning offers a cost‐effective solution, the lack of a subtype‐driven strategy hinders success. Previously, we defined genetic subtypes and their prioritized genes for each genetic subtype (Sahelijo et al.

Brain Cell-based Genetic Subtyping and Drug Repositioning for Alzheimer Disease.

Alzheimer's Disease (AD) is characterized by its complex and heterogeneous etiology and gradual progression, leading to high drug failure rates in late-stage clinical trials. In order to better stratify individuals at risk for AD and discern potential therapeutic targets we employed a novel procedure utilizing cell-based co-regulated gene networks and polygenic risk scores (cbPRSs). After defining genetic subtypes using extremes of cbPRS distributions, we evaluated correlations of the genetic subtypes with previously defined AD subtypes defined on the basis of domain-specific cognitive functioning and neuroimaging biomarkers.

APOE genotype-specific methylation patterns are linked to Alzheimer disease pathology and estrogen response.

The joint effects of APOE genotype and DNA methylation on Alzheimer disease (AD) risk is relatively unknown. We conducted genome-wide methylation analyses using 2,021 samples in blood (91 AD cases, 329 mild cognitive impairment, 1,391 controls) and 697 samples in brain (417 AD cases, 280 controls). We identified differentially methylated levels in AD compared to controls in an APOE genotype-specific manner at 25 cytosine-phosphate-guanine (CpG) sites in brain and 36 CpG sites in blood.

Alzheimer's disease heterogeneity explained by polygenic risk scores derived from brain transcriptomic profiles.

Introduction: Alzheimer's disease (AD) is heterogeneous, both clinically and neuropathologically. We investigated whether polygenic risk scores (PRSs) integrated with transcriptome profiles from AD brains can explain AD clinical heterogeneity.

Methods: We conducted co-expression network analysis and identified gene sets (modules) that were preserved in three AD transcriptome datasets and associated with AD-related neuropathological traits including neuritic plaques (NPs) and neurofibrillary tangles (NFTs).

African ancestry GWAS of dementia in a large military cohort identifies significant risk loci.

While genome wide association studies (GWASs) of Alzheimer's Disease (AD) in European (EUR) ancestry cohorts have identified approximately 83 potentially independent AD risk loci, progress in non-European populations has lagged. In this study, data from the Million Veteran Program (MVP), a biobank which includes genetic data from more than 650,000 US Veteran participants, was used to examine dementia genetics in an African descent (AFR) cohort. A GWAS of Alzheimer's disease and related dementias (ADRD), an expanded AD phenotype including dementias such as vascular and non-specific dementia that included 4012 cases and 18,435 controls age 60+ in AFR MVP participants was performed.

The Mutational Signature Comprehensive Analysis Toolkit (musicatk) for the Discovery, Prediction, and Exploration of Mutational Signatures.

Mutational signatures are patterns of somatic alterations in the genome caused by carcinogenic exposures or aberrant cellular processes. To provide a comprehensive workflow for preprocessing, analysis, and visualization of mutational signatures, we created the Mutational Signature Comprehensive Analysis Toolkit (musicatk) package. musicatk enables users to select different schemas for counting mutation types and to easily combine count tables from different schemas.