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A. de Mendonça

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Open access Sep 2026

APOE-stratified genome-wide association analyses provide insights into the genetic etiology of Alzheimers's disease.

Among the more than 90 identified genetic risk loci for late-onset Alzheimer's disease (AD) and related dementias, the apolipoprotein E (APOE) gene ɛ2/ɛ3/ɛ4 polymorphisms remain the longstanding benchmark for genetic disease risk with a consistently large effect across studies1-10. Despite this massive signal, the exact mechanisms by which ɛ4 increases and ɛ2 decreases dementia risk remain poorly understood. Notably, recent trials of anti-amyloid therapies suggest less efficacy and higher risks of severe side effects in ε4 carriers11-13, hampering the treatment of those with the highest unmet need. To improve our understanding of the genetic architecture of AD in the context of its main genetic driver, we performed genome-wide association studies (GWASs) stratified by ε4 and ε2 carrier status. HP1BP3, SLC50A1, PTPRC, NPAS3, DDHD1, CHST9, SMYD2, PRAMEF1 and GFRA1 emerged as new genomic signals for AD risk, appearing only when stratified by APOE carrier status. DDHD1 appeared especially promising, showing protective effects in ε4 carriers, being identified as an expression quantitative trait locus and being involved in rare neuronal diseases. Such APOE-stratified insights may help understand and overcome side effects, inform clinical trial enrollment strategies, and create the scientific basis for targeted, mechanism-driven therapies in neurodegenerative diseases.

J. Thomassen, H. Leonard, Brittany Ulms et al. · 0 citations
Open access Jul 2026

Machine-learning MRI stratification of genetic frontotemporal dementia for clinical trial enrichment

BACKGROUND Genetic frontotemporal dementia (FTD) shows large differences in symptom profiles, brain atrophy patterns, and progression rate, making clinical trials difficult to design and power. There is a need for biomarkers that can model disease progression, identify biologically distinct groups, and support efficient trial enrichment. METHODS We applied contrastive trajectory inference (cTI), a machine-learning method, to structural MRI, white matter hyperintensity, and demographic data from 736 participants in the GENFI cohort, including non-carriers and carriers of C9orf72, GRN, or MAPT mutations. cTI produced an individual “genetic FTD progression score” (0–1) and grouped mutation carriers into data-driven subtypes. We tested construct validity using correlations between progression score and cognitive/functional measures, examined subtype differences in brain–behavior coupling, plasma neurofilament light (NfL), and longitudinal decline, and compared cTI-based trial enrichment against age, cortical thickness and NfL using analytic and simulation-based power analyses. RESULTS Genetic FTD progression scores correlated strongly with global dementia severity and multiple cognitive domains (all p < 0.001), confirming robust clinical scoring. Two mutation-carrier subtypes emerged: a Progressive Track (Subtype 2) with strong associations between progression score and cognitive/functional impairment, rising NfL, and faster longitudinal decline; and a Dissociated Track (Subtype 3) with comparable levels of structural variation but weak or absent clinical and NfL changes, suggesting relative biological stability. Baseline subtype membership added prognostic value for future decline in processing speed and language beyond baseline severity. Notably, for C9orf72 and GRN, cTI-informed enrichment reduced required recruited sample size per arm by about 61–75% compared with unenriched designs, and outperformed enrichment using age, cortical thickness or NfL in both analytic and simulation-based power analyses. CONCLUSIONS Machine-learning stratification of genetic FTD reveals a progressive and a dissociated disease track and provides individualized progression scores that closely track clinical status. cTI progression scores offer a powerful tool for trial enrichment, enabling smaller, more efficient prevention and early-intervention trials than conventional MRI or NfL markers alone.

M. Soltaninejad, Y. Iturria-medina, A. Bouzigues et al. · 0 citations
Open access Aug 2026

Exome analysis of 22,319 individuals links extremely rare copy-number variants and 22q11.21 dosage to Alzheimer risk.

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. · 1 citation
Open access Aug 2026

Educational attainment and sex modulate clinical outcomes in genetic frontotemporal dementia

Sex and educational attainment significantly affect the development and maintenance of cognitive reserve in individuals with genetic FTD, and underscore the importance of identifying disease-modifying interventions since the presymptomatic stages of the disease.

E. Premi, Damiano Archetti, A. Redolfi et al. · 0 citations

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