Copy-number variants (CNVs) are major contributors to human disease. In Alzheimer disease (AD), APP duplications cause autosomal-dominant forms, but the role of CNVs in non-monogenic AD remains poorly characterized. We analyzed rare CNVs (frequency <1%) from 22,319 exomes (4,150 early-onset AD [EOAD, ≤65 years], 8,519 late-onset AD [LOAD], 9,650 unaffected control subjects) using harmonized calling and quality control. After identifying 17 individuals with a pathogenic CNV, we performed exome-wide and gene-set burden analyses. EOAD-affected individuals showed increased burdens of rare CNVs affecting coding genes, particularly deletions in AD-related genes. Integrated loss-of-function (LoF) analysis gathering short truncating variants with deletions showed that ABCA1 (odds ratio [OR] = 5.77 [95% confidence interval 2.25; 17.06], p = 0.0002) and ABCA7 deletions contribute to this deletion burden (OR = 2.29 [1.44; 3.65], p = 0.0006), while CTSB LoF alleles appear as candidates (OR = 5.03 [1.50; 20.71], p = 0.0089). We then performed exome-wide gene-level dosage analysis and highlighted 18 genes across five loci with a false discovery rate of <10%, including the 22q11.21 central region, where deletions were restricted to EOAD (including one de novo event) and duplications were enriched in control individuals, with intermediate frequencies in LOAD. We narrowed this locus to the SCARF2-KLHL22-MED15 region after integrating short truncating variants. Replication in 33,977 affected individuals and 362,322 control subjects confirmed association for 22q11.21 dosage with exome-wide significance (ORSCARF2 = 0.34 [0.21; 0.53]; mega-p value = 5.52 × 10-7). SCARF2 overexpression significantly increased amyloid-β uptake, congruent with duplication-associated decreased AD risk. We conclude that rare coding CNVs in a proportion of AD-associated genes and 22q11.21 deletions, including some found in DiGeorge syndrome, increase AD risk. Conversely, we identify 22q11.21 duplication as a strong AD-risk-decreasing factor.
O. Quenez, Catherine Schramm, K. Cassinari et al.· American Journal of Human Ge...· 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