Connecting genomic and proteomic signatures of amyloid burden in the brain
Authors: Puerta Raquel , de Rojas Itziar , García-González Pablo , Olivé Clàudia , Sotolongo-Grau Oscar , García-Sánchez Ainhoa , García-Gutiérrez Fernando , Montrreal Laura , Tartari Juan Pablo , Sanabria Ángela , Pytel Vanesa , Lage Carmen , Quintela Inés , Aguilera Nuria , Rodriguez-Rodriguez Eloy , Alarcón-Martín Emilio , Orellana Adelina , Pastor Pau , Pérez-Tur Jordi , Piñol-Ripoll Gerard , López de Munian Adolfo , García-Alberca Jose María , Royo Jose Luís , Bullido María Jesús , Álvarez Victoria , Real Luis Miguel , Anchuelo Arturo Corbatón , Gómez-Garre Dulcenombre , Martínez Larrad María Teresa , Franco-Macías Emilio , Mir Pablo , Medina Miguel , Sánchez-Valle Raquel , Icardo Oriol Dols- , Sáez María Eugenia , Carracedo Ángel , Tárraga Lluís , Alegret Montse , Valero Sergi , Marquié Marta , Boada Mercè , Juan Pascual Sánchez , Cavazos Jose Enrique , Cabrera Alfredo , Cano Amanda , Ruiz Agustín ,   Alzheimer’s Disease Neuroimaging Initiative
Published: September 2024

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 oflocimodulating 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 locusand nine suggestive hits were annotated. We replicated theAPOE lociusing the large CSF-PET meta-GWAS and identified multiple AD-associated genes as well as the novelGADL1locus. 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 GWASlociand 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.