Despite the identification of numerous genetic risk variants for Alzheimer's disease (AD), mechanisms through which these variants act remain unclear. Identifying specific proteins levels affected by genetic variation can provide valuable insights into the underlying biological pathways implicated in AD. To gain more insight into effects of genetic variation on AD-related processes, we conducted a genome-wide protein pQTL study using untargeted TMT mass spectrometry in cerebrospinal fluid (CSF) of 2,215 proteins across 487 individuals. Replication was assessed in the independent EMIF-AD MBD cohort of 242 individuals. We identified 399 independent CSF pQTL signals (PBonferroni < 2.26 × 10⁻11) associated with 222 proteins, 69% of which were novel. Findings included gene-protein links such as RPS23P10/HSPA6 with CSF FCGR2A, BIN2 with CSF GALNT6, APOE with CSF HS3ST1, and the HLA-region with CSF HLA-DPB1 and PLXDC2. We replicated 230 of 270 gene-protein associations. A proteome-wide association study identified genetically predicted CSF protein levels to be associated with AD, including SIRPA, PLXDC2, and GALNT6. Many AD pQTLs in CSF were enriched in neuroimmune activation, suggesting a genetic basis for neuroimmune dysregulation in AD. This study highlights how genetic variation shapes protein expression in the central nervous system, offering mechanistic insight into AD.
L. Reus, Chen-Yang Jiang, N. Vilor-Tejedor et al.· Molecular Neurodegeneration...· 0 citations
Background Genome-wide association studies (GWAS) have identified thousands of loci associated with complex traits and diseases, yet translating these signals into biological insight remains challenging. Most associated variants are non-coding and reside in linkage disequilibrium (LD) blocks, where multiple correlated variants jointly contribute to association signals. These clusters, or haplotypes, may capture shared regulatory and functional contexts. Interpreting GWAS signals thus requires approaches that integrate regulatory, functional, and cross-trait evidence, while preserving the broader haplotypic context of disease-associated loci. At the same time, the rapid growth of publicly available GWAS summary statistics has enabled large-scale cross-trait analyses, but also introduced redundancy across closely related phenotypes. Efficient interpretation of GWAS data therefore requires tools that integrate heterogeneous data sources while preserving genomic and biological contexts. Results We present snpXplorer, an interactive web platform for haplotype-aware exploration and annotation of GWAS data. The platform incorporates >10,000 GWAS datasets from OpenGWAS and enables multi-scale analysis across variants, haplotypes, genes, and traits. Key features include (i) a haplotype-based representation of association signals derived from LD structure, (ii) a unified variant annotation framework integrating clinical annotations (ClinVar), allele frequencies (gnomAD), functional predictions (CADD, AlphaGenome), quantitative trait loci (GTEx), structural variation, and GWAS associations, and (iii) cross-trait exploration using semantic similarity-based clustering of phenotypes. Use cases centered on Alzheimer’s disease illustrate this utility: for example, at the TMEM106B locus, snpXplorer identified a haplotype linked to eleven distinct traits, revealing synergistic pleiotropy across neurological and behavioral phenotypes alongside antagonistic pleiotropy with height. Conclusions snpXplorer allows users to browse, filter, and inspect variant-, haplotype-, gene- and trait-level evidence, lowering the barrier to biological interpretation of GWAS results. Compared with existing tools that focus on specific aspects of GWAS interpretation, the strength of snpXplorer is that it reduces the need for fragmented queries across databases.
N. Tesi, G. Green, A. Salazar et al.· bioRxiv· 0 citations
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