Polygenic Susceptibility to Neurodegenerative Diseases and Its Impact on Brain Phenotypes
Poster No:
90
Submission Type:
Abstract Submission
Authors:
Amir Ebneabbasi1, Yuanjun Gu1, Yuankai He2, Rafael Romero-Garcia3, Richard Bethlehem1, Varun Warrier4, Timothy Rittman5
Institutions:
1University of Cambridge, CAMBRIDGE, United Kingdom, 2University of Cambridge, Cambridge, Cambridge, 3University of Seville, Seville, Italy, 4Department of Psychiatry, University of Cambridge, Cambridge, Cambridge, 5University of Cambridge, Cambridge, United Kingdom
First Author:
Co-Author(s):
Introduction:
Neurodegenerative diseases often involve abnormal protein accumulation, such as tau and amyloid-β in Alzheimer's disease (ALZ), α-synuclein in Parkinson's (PD), and TDP-43 in amyotrophic lateral sclerosis (ALS). Vascular dementia (VD) also primarily results from reduced blood flow and ischemic lesions but often co-occurs with other pathologies. Despite neuroimaging studies demonstrating alterations in brain structures in individuals with neurodegenerative conditions, the shared genetic foundations of neuroimaging and neurodegenerative phenotypes remain poorly understood, as does how genetic risk translates into brain alterations. Here, we performed brain-wide association studies (BWAS) to examine how disease-specific polygenic risk scores (PRS) are associated with imaging-derived phenotypes (IDPs) across developmental and ageing stages in young and old healthy cohorts.
Methods:
2. Method
The current study employed two cohorts: the Adolescent Brain Cognitive Development (ABCD) (n = 5,453 healthy subjects; age range 9 to 10) and the UK Biobank (UKB) (n = 60,924 healthy subjects; age range 40 to 70 years). The genetic and neuroimaging data were pre-processed using our previous pipeline (Warrier et al., 2023).
2.1. Genetics
We generated Bayesian-informed PRS using the PRS-CS (Ge et al., 2019) and four external GWAS: ALZ (Wightman et al., 2021), PD (Nalls et al., 2019), ALS (van Rheenen et al., 2021) and VD (Kurki et al., 2023).
2.2. Neuroimaging
We focused on seven cortical macrostructural measures derived from MRI and five cortical microstructural phenotypes extracted from diffusion MRI (Figure 1). These IDPs were calculated across 180 bilaterally averaged regions, utilising the Glasser parcellation.
2.3. Statistics
Linear mixed-effects models were applied to explore the associations between disease-specific PRS and 2160 IDPs, accounting for relevant covariates. Next, we utilised case-control data from the National Alzheimer's Coordinating Center (NACC) and the Parkinson's Progression Markers Initiative (PPMI) to investigate whether PRS-IDP maps in healthy individuals align with disease-related cortical changes. We then employed functional (Yeo et al., 2011), BigBrain (Paquola et al., 2021), and neurotransmitter data (Hansen et al., 2022) for the network, cytoarchitectonic, and molecular decoding of PRS-IDP maps. Finally, two-sample Mendelian randomisation was performed to explore the causal relationships between IDPs and the risk of neurodegenerative disease.
The current study employed two cohorts: the Adolescent Brain Cognitive Development (ABCD) (n = 5,453 healthy subjects; age range 9 to 10) and the UK Biobank (UKB) (n = 60,924 healthy subjects; age range 40 to 70 years). The genetic and neuroimaging data were pre-processed using our previous pipeline (Warrier et al., 2023).
2.1. Genetics
We generated Bayesian-informed PRS using the PRS-CS (Ge et al., 2019) and four external GWAS: ALZ (Wightman et al., 2021), PD (Nalls et al., 2019), ALS (van Rheenen et al., 2021) and VD (Kurki et al., 2023).
2.2. Neuroimaging
We focused on seven cortical macrostructural measures derived from MRI and five cortical microstructural phenotypes extracted from diffusion MRI (Figure 1). These IDPs were calculated across 180 bilaterally averaged regions, utilising the Glasser parcellation.
2.3. Statistics
Linear mixed-effects models were applied to explore the associations between disease-specific PRS and 2160 IDPs, accounting for relevant covariates. Next, we utilised case-control data from the National Alzheimer's Coordinating Center (NACC) and the Parkinson's Progression Markers Initiative (PPMI) to investigate whether PRS-IDP maps in healthy individuals align with disease-related cortical changes. We then employed functional (Yeo et al., 2011), BigBrain (Paquola et al., 2021), and neurotransmitter data (Hansen et al., 2022) for the network, cytoarchitectonic, and molecular decoding of PRS-IDP maps. Finally, two-sample Mendelian randomisation was performed to explore the causal relationships between IDPs and the risk of neurodegenerative disease.
Results:
The associations between polygenic risk scores (PRS) and image-derived phenotypes (IDP) are presented in Figure 1. Only UK Biobank (UKB) results are visualised, as the genetic effects of neurodegenerative diseases are more pronounced in older adults from the UKB compared to the younger population in the ABCD cohort. Figure 2 illustrates the results of the atlas-based spatial co-location analysis. In brief, PRS for Parkinson's disease (PD) shows the strongest association with limbic regions, while Alzheimer's disease (ALZ) is more closely linked to dorsal attention areas. The maps for ALZ and vascular dementia (VasDem) predominantly co-locate with dopaminergic receptors, whereas amyotrophic lateral sclerosis (ALS) is primarily associated with norepinephrine. The disease-specific PRS-IDP maps were positively related to clinical case-control data in PPMI and NACC cohorts. Finally, Mendelian randomisation analysis suggested a causal effect of neurodegenerative risk on brain changes.
Conclusions:
This study represents the most comprehensive investigation to date of the relationship between neurodegenerative genetic risk and brain phenotypes. Our findings, supported by clinical data and atlas-driven spatial analyses, show how genetic risk scores contribute to the brain in UKB. These results could be a basis for developing personalised interventions tailored to individual genetic profiles, potentially advancing clinical care and guiding age-specific health strategies in neurodegenerative diseases.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s) 1
Genetics:
Genetic Association Studies 2
Lifespan Development:
Aging
Early life, Adolescence, Aging
Keywords:
Aging
Degenerative Disease
Development
Movement Disorder
Neurological
Neurotransmitter
Phenotype-Genotype
STRUCTURAL MRI
White Matter
1|2Indicates the priority used for review
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