Pathway Enrichment of Longitudinal AD Endophenotypes Identifies Potential Therapeutic Targets for Modifying Disease Trajectory

Abstract Background Alzheimer’s disease (AD) is characterized by longitudinal changes of biomarker endophenotypes over the course of the disease prodrome, onset, and progression. The genetic pathways that influence these heterogenous changes in longitudinal endophenotype trajectories may provide insight into disease mechanisms and represent potential therapeutic targets. Methods Longitudinal endophenotypes from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) were selected: amyloid‐β (Amyloid PET and CSF), total tau and phosphorylated tau (CSF), glucose metabolism (FDG PET), neurodegeneration (atrophy on MRI), and cognition (composite scores for memory and executive functioning). Genome‐wide association analysis for the selected longitudinal endophenotypes was performed using Linear Mixed Modelling (LMM; LME4 R package), with (Time x Subject) as a random effect and age as the time variable. Gene‐based association analysis was performed using MAGMA on SNP P values from the LMM. The SNP to gene assignment was performed in two steps to select SNPs with a functional relation to each target gene: SNPs within gene transcription start and end positions, and SNPs that have significant eQTLs in brain tissue from the MetaBrain eQTL project. Gene‐based analysis results were then processed for gene‐set enrichment with MAGMA and the C2 curated gene set collection from the Gene Set Enrichment Analysis (GSEA) Molecular Signatures Database (MSigDB). Results Pathway enrichment analysis identified 19 pathways (Figure 1) as significantly associated with longitudinal trajectories of AD endophenotypes. These pathways fall into six groups, with each pathway group having stronger association with different types of endophenotypes. Immune and cytoskeletal pathways largely associated with changes in amyloid trajectory. Metabolic pathways associated strongly with changes in amyloid and tau trajectories. Glycosylation pathways were associated with changes in brain atrophy. Pathways related to cell and neuronal signaling associated with changes in cognition, tau, and amyloid trajectories. Cell growth and survival was associated with changes in neurodegeneration trajectory (structural atrophy and hypometabolism). Conclusions Pathway enrichment analysis of genetic variation associated with longitudinal changes of AD endophenotypes identified pathways that uniquely associate with trajectories of key AD biomarkers and cognition. These pathways may provide insight into AD pathological mechanisms and constitute new potential therapeutic targets to modify disease trajectory.

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Journal
Alzheimer s & Dementia
Published
2024-12-01
DOI
https://doi.org/10.1002/alz.095746
Primary Topic
Computational Drug Discovery Methods
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article
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article

Pathway Enrichment of Longitudinal AD Endophenotypes Identifies Potential Therapeutic Targets for Modifying Disease Trajectory

Shannon L. Risacher, Andrew J. Saykin, Sujuan Gao, Thea Rosewood et al.
Alzheimer s & Dementia
Computational Drug Discovery Methods
article

Pathway Enrichment of Longitudinal AD Endophenotypes Identifies Potential Therapeutic Targets for Modifying Disease Trajectory

Shannon L. Risacher, Andrew J. Saykin, Sujuan Gao, Thea Rosewood, Kwangsik Nho, Shiwei Liu
article en

Abstract

Abstract Background Alzheimer’s disease (AD) is characterized by longitudinal changes of biomarker endophenotypes over the course of the disease prodrome, onset, and progression. The genetic pathways that influence these heterogenous changes in longitudinal endophenotype trajectories may provide insight into disease mechanisms and represent potential therapeutic targets. Methods Longitudinal endophenotypes from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) were selected: amyloid‐β (Amyloid PET and CSF), total tau and phosphorylated tau (CSF), glucose metabolism (FDG PET), neurodegeneration (atrophy on MRI), and cognition (composite scores for memory and executive functioning). Genome‐wide association analysis for the selected longitudinal endophenotypes was performed using Linear Mixed Modelling (LMM; LME4 R package), with (Time x Subject) as a random effect and age as the time variable. Gene‐based association analysis was performed using MAGMA on SNP P values from the LMM. The SNP to gene assignment was performed in two steps to select SNPs with a functional relation to each target gene: SNPs within gene transcription start and end positions, and SNPs that have significant eQTLs in brain tissue from the MetaBrain eQTL project. Gene‐based analysis results were then processed for gene‐set enrichment with MAGMA and the C2 curated gene set collection from the Gene Set Enrichment Analysis (GSEA) Molecular Signatures Database (MSigDB). Results Pathway enrichment analysis identified 19 pathways (Figure 1) as significantly associated with longitudinal trajectories of AD endophenotypes. These pathways fall into six groups, with each pathway group having stronger association with different types of endophenotypes. Immune and cytoskeletal pathways largely associated with changes in amyloid trajectory. Metabolic pathways associated strongly with changes in amyloid and tau trajectories. Glycosylation pathways were associated with changes in brain atrophy. Pathways related to cell and neuronal signaling associated with changes in cognition, tau, and amyloid trajectories. Cell growth and survival was associated with changes in neurodegeneration trajectory (structural atrophy and hypometabolism). Conclusions Pathway enrichment analysis of genetic variation associated with longitudinal changes of AD endophenotypes identified pathways that uniquely associate with trajectories of key AD biomarkers and cognition. These pathways may provide insight into AD pathological mechanisms and constitute new potential therapeutic targets to modify disease trajectory.

Alzheimer s & DementiaVol. 20(S8)
University of Indianapolis (US), Indiana University School of Medicine, Indiana University – Purdue University Indianapolis (US), Indiana University (US)
Good health and well-being
Openalex Percentile: Top 70%
Computational Drug Discovery Methods
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