Connecting genomic and proteomic signatures of amyloid burden in the brain

Abstract

Background: Alzheimer's disease (AD) has a high heritable component characteristic of complex diseases, yet many of the genetic risk factors remain unknown. We combined genome-wide association studies (GWAS) on amyloid endophenotypes measured in cerebrospinal fluid (CSF) and positron emission tomography (PET) as surrogates of amyloid pathology, which may be helpful to understand the underlying biology of the disease.

Methods: We performed a meta-analysis of GWAS of CSF Aβ42 and PET measures combining six independent cohorts (n=2,076). Due to the opposite effect direction of Aβ phenotypes in CSF and PET measures, only genetic signals in the opposite direction were considered for analysis (n=376,599). Polygenic risk scores (PRS) were calculated and evaluated for AD status and amyloid endophenotypes. We then searched the CSF proteome signature of brain amyloidosis using SOMAscan proteomic data (Ace cohort, n=1,008) and connected it with GWAS results of loci modulating amyloidosis. Finally, we compared our results with a large meta-analysis using publicly available datasets in CSF (n=13,409) and PET (n=13,116). This combined approach enabled the identification of overlapping genes and proteins associated with amyloid burden and the assessment of their biological significance using enrichment analyses.

Results: After filtering the meta-GWAS, we observed genome-wide significance in the rs429358-APOE locus and nine suggestive hits were annotated. We replicated the APOE loci using the large CSF-PET meta-GWAS and identified multiple AD-associated genes as well as the novel GADL1 locus. Additionally, we found a significant association between the AD PRS and amyloid levels, whereas no significant association was found between any Aβ PRS with AD risk. CSF SOMAscan analysis identified 1,387 FDR-significant proteins associated with CSF Aβ42 levels. The overlap among GWAS loci and proteins associated with amyloid burden was very poor (n=35). The enrichment analysis of overlapping hits strongly suggested several signalling pathways connecting amyloidosis with the anchored component of the plasma membrane, synapse physiology and mental disorders that were replicated in the large CSF-PET meta-analysis.

Conclusions: The strategy of combining CSF and PET amyloid endophenotypes GWAS with CSF proteome analyses might be effective for identifying signals associated with the AD pathological process and elucidate causative molecular mechanisms behind the amyloid mobilization in AD.

Keywords: Aβ42; CSF biomarkers; GWAS; PET tomography; Proteome.

Figure 1.. Plots of the Aβ burden meta-analysis combining data of CSF-PET endophenotypes.

A) (upper) Manhattan plot of our CSF-PET meta-analysis (n=2,076). Results were filtered according effect size direction and dataset missingness. Suggestive independent markers were annotated with the nearest gene name. Mapped genes coloured in grey represent those that were not replicated in the PAD CSF-PET meta-GWAS. (lower) Manhattan plot of the PAD CSF-PET meta-analysis filtered (n=23,532). Genome-wide significant independent markers were annotated with the nearest gene name. The Y-axis was restricted to visualize suggestive signals. The genome-wide significance threshold was set to P<5e-08 (red line) and the suggestive threshold was set to P<1e-05 (blue line). B) Venn diagram representing the overlap between the top 500 ranking of independent genetic markers comparing the PAD and our amyloid burden meta-analysis. C) Venn diagram representing the overlap between the top 500 ranking of independent genes in the PAD and our gene-based analysis.

Figure 2.. Forest plot of the meta-analysis association between the AD PRS.

A) CSF Aβ42, and B) Aβ PET endophenotypes. The significance threshold was set to 0.05.

Figure 3.. Forest plot of the meta-analysis association between the AD PRS and dementia status as case-control.

In ACE (305 cases and 703 controls, 30.25%), ADNI1 (94 cases and 285 controls, 24.80%) and ADNI2GO cohorts (27 cases and 385 controls, 6.55%).

Figure 4.. Forest plot of the association between the AD, Aβ PRS and case-control status.

PRS for AD (76 SNPs from Bellenguez et al., 2022) and Aβ42 (30 SNPs from Jansen et al., 2022, 9 SNPs from our meta-analysis). The GR@ACE cohort included 8110 cases and 9640 controls.

Figure 5.. Associations between CSF SOMAscan and CSF Aβ42 levels.

A) Vulcano plot only considering proteins with good inter-assay correlation (n=2,682), significant proteins (FDR < 1.864e-05) were highlighted in red (n=1,387). B) Top 10 results of the enrichment analysis of significant protein associations with CSF Aβ42 levels using the WebGestalt tool.

Figure 6.. Overlapping loci/proteins in genomic and proteomic analysis.

A) Venn diagram of the top 500 ranking of CSF Aβ42-associated proteins in the SOMAscan panel (orange), our gene-based MAGMA analysis (red), GWAS of CSF Aβ42 (Jansen et al., 2022) (dark blue) and our amyloid burden meta-analysis of filtered CSF-PET endophenotypes (light blue). B) Venn diagram of the top 500 ranking of CSF Aβ42-associated proteins in the SOMAscan panel (orange), PAD gene-based MAGMA meta-analysis (red) and PAD amyloid burden meta-analysis of filtered CSF-PET endophenotypes (light blue). C) Top 10 enrichment analysis results of the overlapping proteins between our genomic and proteomic analyses. C) Top 10 enrichment analysis results of the overlapping proteins between proteomic and PAD genomic analyses. The analysis was done using the WebGestalt tool.