LD-informed deep learning for Alzheimer's gene loci detection using WGS data.

Introduction: The exponential growth of genomic datasets necessitates advanced analytical tools to effectively identify genetic loci from large-scale high throughput sequencing data. This study presents Deep-Block, a multi-stage deep learning framework that incorporates biological knowledge into its AI architecture to identify genetic regions as significantly associated with Alzheimer's disease (AD). The framework employs a three-stage approach: (1) genome segmentation based on linkage disequilibrium (LD) patterns, (2) selection of relevant LD blocks using sparse attention mechanisms, and (3) application of TabNet and Random Forest algorithms to quantify single nucleotide polymorphism (SNP) feature importance, thereby identifying genetic factors contributing to AD risk.

miR-129-5p as a biomarker for pathology and cognitive decline in Alzheimer's disease.

Background: Alzheimer's dementia (AD) pathogenesis involves complex mechanisms, including microRNA (miRNA) dysregulation. Integrative network and machine learning analysis of miRNA can provide insights into AD pathology and prognostic/diagnostic biomarkers.

Methods: We performed co-expression network analysis to identify network modules associated with AD, its neuropathology markers, and cognition using brain tissue miRNA profiles from the Religious Orders Study and Rush Memory and Aging Project (ROS/MAP) (N = 702) as a discovery dataset.

Circular-SWAT for deep learning based diagnostic classification of Alzheimer's disease: application to metabolome data.

Background: Deep learning has shown potential in various scientific domains but faces challenges when applied to complex, high-dimensional multi-omics data. Alzheimer's Disease (AD) is a neurodegenerative disorder that lacks targeted therapeutic options. This study introduces the Circular-Sliding Window Association Test (c-SWAT) to improve the classification accuracy in predicting AD using serum-based metabolomics data, specifically lipidomics.

Transcriptional risk scores in Alzheimer's disease: From pathology to cognition.

Introduction: Our previously developed blood-based transcriptional risk scores (TRS) showed associations with diagnosis and neuroimaging biomarkers for Alzheimer's disease (AD). Here, we developed brain-based TRS.

Methods: We integrated AD genome-wide association study summary and expression quantitative trait locus data to prioritize target genes using Mendelian randomization.

Circulating lipid profiles are associated with cross-sectional and longitudinal changes of central biomarkers for Alzheimer's disease.

Investigating the association of lipidome profiles with central Alzheimer's disease (AD) biomarkers, including amyloid/tau/neurodegeneration (A/T/N), can provide a holistic view between the lipidome and AD. We performed cross-sectional and longitudinal association analysis of serum lipidome profiles with AD biomarkers in the Alzheimer's Disease Neuroimaging Initiative cohort (N=1,395). We identified lipid species, classes, and network modules that were significantly associated with cross-sectional and longitudinal changes of A/T/N biomarkers for AD.

Novel Alzheimer's disease locus identified by genome-wide association analysis of cerebral tau deposition on PET.

Determining the genetic architecture of Alzheimer's disease (AD) pathologies can enhance mechanistic understanding and inform precision medicine strategies. Here, we performed a genome-wide association study of cortical tau quantified by positron emission tomography in 3,136 participants from 12 independent studies. The locus was associated with tau deposition.

Altered cerebral blood flow in older adults with Alzheimer's disease: a systematic review.

The prevalence of Alzheimer's disease is projected to reach 13 million in the U.S. by 2050.

Integrative Co-methylation Network Analysis Identifies Novel DNA Methylation Signatures and Their Target Genes in Alzheimer's Disease.

Background: DNA methylation is a key epigenetic marker, and its alternations may be involved in Alzheimer's disease (AD). CpGs sharing similar biological functions or pathways tend to be co-methylated.

Methods: We performed an integrative network-based DNA methylation analysis on 2 independent cohorts (N = 941) using brain DNA methylation profiles and RNA-sequencing as well as AD pathology data.

Immunity gene variant: Relation to cognition and Alzheimer's disease pathology.

Introduction: We investigated single-nucleotide polymorphisms (SNPs) in , an innate immunity gene and modulator of amyloid beta in Alzheimer's disease (AD), for association with cognition and AD biomarkers.

Methods: We used data from the Alzheimer's Disease Neuroimaging Initiative (ADNI;  = 1565) and AddNeuroMed ( = 633) as discovery and replication samples, respectively. We performed gene-based association analysis of SNPs in with cognitive performance and SNP-based association analysis with cognitive decline and amyloid, tau, and neurodegeneration biomarkers for AD.

Deep learning-based identification of genetic variants: application to Alzheimer's disease classification.

Deep learning is a promising tool that uses nonlinear transformations to extract features from high-dimensional data. Deep learning is challenging in genome-wide association studies (GWAS) with high-dimensional genomic data. Here we propose a novel three-step approach (SWAT-CNN) for identification of genetic variants using deep learning to identify phenotype-related single nucleotide polymorphisms (SNPs) that can be applied to develop accurate disease classification models.

Dysregulated expression levels of APH1B in peripheral blood are associated with brain atrophy and amyloid-β deposition in Alzheimer's disease.

Background: The interaction between the brain and periphery might play a crucial role in the development of Alzheimer's disease (AD).

Methods: Using blood transcriptomic profile data from two independent AD cohorts, we performed expression quantitative trait locus (cis-eQTL) analysis of 29 significant genetic loci from a recent large-scale genome-wide association study to investigate the effects of the AD genetic variants on gene expression levels and identify their potential target genes. We then performed differential gene expression analysis of identified AD target genes and linear regression analysis to evaluate the association of differentially expressed genes with neuroimaging biomarkers.

BMI1 is associated with CS8F amyloid-β and rates of cognitive decline in Alzheimer's disease.

Background: Accumulating evidence suggests that BMI1 confers protective effects against Alzheimer's disease (AD). However, the mechanism remains elusive. Based on recent pathophysiological evidence, we sought for the first time to identify genetic variants in BMI1 as associated with AD biomarkers, including amyloid-β.

Estrogen-Dependent Upregulation of Expression in Nucleus Accumbens Is Associated With Genetic Predisposition of Sex-Specific QTL for Alcohol Consumption on Rat Chromosome 4.

Humans show sex differences related to alcohol use disorders (AUD). Animal model research has the potential to provide important insight into how sex differences affect alcohol consumption, particularly because female animals frequently drink more than males. In previous work, inbred strains of the selectively bred alcohol-preferring (P) and non-preferring (NP) rat lines revealed a highly significant quantitative trait locus (QTL) on rat chromosome 4, with a logarithm of the odds score of 9.

Fine mapping quantitative trait loci that influence alcohol preference behavior in the High and Low Alcohol Preferring (HAP and LAP) mice.

The High Alcohol Preferring (HAP1) and Low Alcohol Preferring (LAP1) mouse lines were selectively bred for differences in alcohol intake. The HAP1 and LAP1 mice are essentially non-inbred lines that originated from an outbred colony of HS/Ibg mice, a heterogeneous stock developed from intercrossing 8 inbred strains of mice. In a former genomewide SNP association study, we identified quantitative trait loci (QTL) on chromosomes 1, 3, 5, and 9 (Bice et al.

Fine mapping and expression of candidate genes within the chromosome 10 QTL region of the high and low alcohol-drinking rats.

The high and low alcohol-drinking (HAD and LAD) rats were selectively bred for differences in alcohol intake. The HAD/LAD rats originated from the N/Nih heterogeneous stock developed from intercrossing eight inbred rat strains. The HAD×LAD F2 were genotyped, and a powerful analytical approach, using ancestral recombination and F2 recombination, was used to narrow a quantitative trait loci (QTL) for alcohol drinking to a 2-cM region on distal chromosome 10 that was in common in the HAD1/LAD1 and HAD2/LAD2 analyses.

Genomewide SNP screen to detect quantitative trait loci for alcohol preference in the high alcohol preferring and low alcohol preferring mice.

Background: The high and low alcohol preferring (HAP1 and LAP1) mouse lines were selectively bred for differences in alcohol intake. The HAP1 and LAP1 mice are essentially noninbred lines that originated from the outbred colony of HS/Ibg mice, a heterogeneous stock developed from intercrossing 8 inbred strains of mice.

Methods: A total of 867 informative SNPs were genotyped in 989 HAP1 x LAP1 F2, 68 F1s, 14 parents (6 LAP1, 8 HAP1), as well as the 8 inbred strains of mice crossed to generate the HS/Ibg colony.

Drd2 expression in the high alcohol-preferring and low alcohol-preferring mice.

The high alcohol-preferring (HAP) and low alcohol-preferring (LAP) mice were selectively bred for differences in alcohol preference and consumption. Recently, a large-effect QTL was identified on chromosome 9. The peak for this QTL is near the Drd2 (dopamine receptor 2) locus.

Candidate genes and their regulatory elements: alcohol preference and tolerance.

QTL analysis of behavioral traits and mouse brain gene expression studies were combined to identify candidate genes involved in the traits of alcohol preference and acute functional alcohol tolerance. The systematic application of normalization and statistical analysis of differential gene expression, behavioral and expression QTL location, and informatics methodologies resulted in identification of 8 candidate genes for the trait of alcohol preference and 22 candidate genes for acute functional tolerance. Pathway analysis, combined with clustering by ontology, indicated the importance of transcriptional regulation and DNA and protein binding elements in the acute functional tolerance trait, and protein kinases and intracellular signal transduction elements in the alcohol preference trait.