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Cited by 8Alzheimer's disease research and treatmentsDementia and Cognitive Impairment ResearchGenetic Associations and Epidemiology

Consensus meta-analysis of genome-wide association studies for Alzheimer’s disease and related dementias

Open access
EADB, Bellenguez, Céline, Castillo Morales, Atahualpa, Amin, Najaf, van der Lee, Sven J., Muntaner, Manon, Parveen, Kayenat, Küçükali, Fahri +517 more·2026·Nature Genetics
DOI 10.1038/s41588-026-02583-1PMC PMC13263136
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Abstract

To better characterize the genetic architecture underlying Alzheimer’s disease (AD) and related dementias (ADRD), we performed a meta-analysis of European-ancestry genome-wide association studies in 128,681 cases or proxy cases of ADRD and 849,833 (proxy) controls. We identified 91 genetic loci associated with ADRD risk, of which 16 are new and 56 are specifically detected in clinically diagnosed AD cases. We also provide a list of 18 loci (15 new) requiring further external validation. A polygenic score combining the effects of ADRD loci other than APOE was primarily associated with AD rather than non-AD pathology. Individuals in the tenth decile of the score exhibited a twofold increased risk of presenting with Braak neurofibrillary tangles stage of >4 and moderate-to-severe neuritic amyloid plaque pathology at death compared to individuals in the median score group. In conclusion, our study validated a large number of loci associated with the risk of clinically diagnosed AD, while further investigations are required to confirm the impact of the other loci on AD clinical diagnosis and of each locus on AD pathology.

AI snapshot

Written from the paper’s full text.

TL;DR

This consensus meta-analysis of European-ancestry ADRD GWAS identified 91 genome-wide significant loci, including 16 new loci, and found that an ADRD polygenic score excluding APOE was primarily associated with AD rather than non-AD neuropathology. The study also highlights 18 loci requiring external validation and shows that many signals depend on whether proxy or biobank cases are included.

Key findings

  • The meta-analysis included 128,681 ADRD cases or proxy cases and 849,833 (proxy) controls, and identified 91 genome-wide significant loci; 16 were new in European-ancestry samples, and 56 were specifically detected in clinically diagnosed AD cases.
  • An additional 18 loci (15 new) were classified as requiring further external validation, including 5 tier 2 loci and 13 loci detected only in sensitivity meta-analyses.
  • A polygenic score combining ADRD loci other than APOE was primarily associated with AD pathology: in ADC/NACC, individuals in the tenth decile had 2.05-fold (95% CI = 1.47–2.85) increased risk for Braak stage >4 and 1.96-fold (95% CI = 1.39–2.78) increased risk for moderate-to-severe neuritic amyloid plaque pathology versus the median quintile.
  • The score explained limited variance: Nagelkerke’s pseudo-R² was 3.98% for Braak stage and 4.37% for CERAD score, with liability-scale estimates of 2.43–3.6% and 3.32–4.93%, respectively.

Methods

  • Consensus meta-analysis of European-ancestry GWAS data from 52 studies, including 72,721 AD cases, 55,960 proxy ADRD cases, 614,267 controls and 235,566 proxy controls, with a rough effective sample size of 230,631.
  • Main analysis used fixed-effects inverse-variance-weighted meta-analysis in METAL; sensitivity analyses excluded proxy samples (no-proxy) or large biobanks (no-biobank), and conditional/joint analyses used GCTA COJO.
  • Polygenic scores based on tier 1 main and secondary signals excluding APOE were tested for association with 11 neuropathology endophenotypes in the ACT (n = 677) and ADC/NACC (n = 5,808) datasets; genetic correlations used LDSC, and enrichment/pathway analyses used FUMA and MAGMA.

Limitations

  • The paper states that genetic correlation with ADRD could not be reliably assessed for most of the 11 neuropathology endophenotypes due to limited study sample sizes and/or low heritability of these traits.
  • The paper states that the PGS explained low variance and that larger sample sizes are required to assess whether the LATE-NC association is due to co-occurrence with AD neuropathology; it also notes that several loci require external validation.
  • Evident from the text: the meta-analysis was restricted to European-ancestry samples, and the inclusion of proxy or biobank cases can blur the distinction between AD and non-AD dementia signals, as the paper itself discusses.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.1038/s41588-026-02583-1

Cites

61 of 61 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Claims the paper makes

30 of 32 cited sources checked · 9 supported · 5 flagged · accuracy not yet measured

  • Premisecitation

    More than 90 genetic loci are associated with Alzheimer's disease and related dementia (ADRD).

    “More than 90 genetic loci are associated with Alzheimer’s disease and related dementia (ADRD).”
    Quote in the paperSubstantial claim7 of 7 sources checked
    • cites [1] Lambert, J. C. et al. Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer’s disease. Nat. Genet. 45, 1452–1458 (2013). source

      The Abstract (page not provided) reports that 19 loci in addition to APOE reached genome-wide significance in the combined analysis, including 11 newly associated loci. That count is contrary to the attributed figure of more than 90; the paper studies late-onset Alzheimer’s disease rather than the broader ADRD category. (LLM, conf=0.85)

      Checked by a reasoning model — may be wrong.

      Experimental — accuracy not yet measured
    • cites [2] Kunkle, B. W. et al. Genetic meta-analysis of diagnosed Alzheimer’s disease identifies new risk loci and implicates Aβ, tau, immunity and lipid processing. Nat. Genet. 51, 414–430 (2019). source

      The Results section, “Candidate gene prioritization at genome-wide loci” (page unavailable), reports 24 Alzheimer’s disease association signals excluding APOE. It supports the existence of associated loci, but not the claim that more than 90 loci are associated with Alzheimer’s disease and related dementia as a broader category. (LLM, conf=0.86)

      Experimental — accuracy not yet measured
    • cites [3] Jansen, I. E. et al. Genome-wide meta-analysis identifies new loci and functional pathways influencing Alzheimer’s disease risk. Nat. Genet. 51, 404–413 (2019). source

      The Results (Genome-wide meta-analysis for AD status; page not provided) report 94 lead SNPs located in 29 distinct loci, not more than 90 loci. The Abstract likewise reports 29 risk loci for Alzheimer’s disease; the paper does not present the 94 SNPs as loci associated with ADRD. (LLM, conf=0.90)

      Checked by a reasoning model — may be wrong.

      Experimental — accuracy not yet measured
    • cites [4] De Rojas, I. et al. Common variants in Alzheimer’s disease and risk stratification by polygenic risk scores. Nat. Commun. 12, 3417 (2021). source

      The Introduction (page unavailable) says prior GWAS had identified 30 genomic loci modifying Alzheimer’s disease risk. The Discussion (page unavailable) reports six additional loci, not more than 90. The paper’s count also concerns Alzheimer’s disease rather than the broader ADRD category. (LLM, conf=0.82)

      Checked by a reasoning model — may be wrong.

      Experimental — accuracy not yet measured
    • cites [5] Wightman, D. P. et al. A genome-wide association study with 1,126,563 individuals identifies new risk loci for Alzheimer’s disease. Nat. Genet. 53, 1276–1282 (2021). source

      The Genome-wide inferences section reports 38 independent loci associated with late-onset Alzheimer’s disease, not more than 90. The paper does not report a count of more than 90 loci for Alzheimer’s disease and related dementia. (LLM, conf=0.88)

      Checked by a reasoning model — may be wrong.

      Experimental — accuracy not yet measured
    • cites [6] Bellenguez, C. et al. New insights into the genetic etiology of Alzheimer’s disease and related dementias. Nat. Genet. 54, 412–436 (2022). source

      The Discussion (page unavailable) reports 75 independent loci associated with Alzheimer’s disease and related dementias: 33 previously reported and 42 new. That is contrary to the cited claim of more than 90 loci. (LLM, conf=0.90)

      Checked by a reasoning model — may be wrong.

      Experimental — accuracy not yet measured
    • cites [7] Lambert, J.-C., Ramirez, A., Grenier-Boley, B. & Bellenguez, C. Step by step: towards a better understanding of the genetic architecture of Alzheimer’s disease. Mol. Psychiatry 28, 2716–2727 (2023). source

      The Introduction reports 97 GWAS-catalog loci with genome-wide-significant associations with Alzheimer’s disease risk in populations of European ancestry. However, the authors classify seven as “not validated,” leaving 90 in their figure of classified loci. The passage addresses Alzheimer’s disease risk, not the broader category of Alzheimer’s disease and related dementia (ADRD). (LLM, conf=0.83)

      Experimental — accuracy not yet measured
  • Findingcitation

    In the SRC gene, loss-of-function variants and missense variants with a REVEL score >50 were jointly associated with AD risk (OR = 4.23, 95% CI = 2.04-8.79, P = 1.06 × 10−4) in the ADES-ADSP summary statistics.

    “In the SRC gene, loss-of-function variants and missense variants with a REVEL score of >50 were jointly associated with AD risk (odds ratio (OR) = 4.23, 95% confidence interval (CI) = 2.04–8.79, P = 1.06 × 10−4; Supplementary Table 8) in the Alzheimer Disease European Sequencing (ADES) - Alzheimer’s Disease Sequencing Project (ADSP) summary statistics10.”
    Quote in the paperSubstantial claim1 of 1 sources checked
    • cites [10] Holstege, H. et al. Exome sequencing identifies rare damaging variants in ATP8B4 and ABCA1 as risk factors for Alzheimer’s disease. Nat. Genet. 54, 1786–1794 (2022). source

      The Main section describes testing combined loss-of-function and REVEL-scored missense variants, but the passages found do not report a result for SRC. Searches for SRC, ADES–ADSP, OR 4.23, the stated confidence interval, and P = 1.06 × 10−4 found no passage supporting the specific association. (LLM, conf=0.76)

      Experimental — accuracy not yet measured
  • Findingown data

    Four loci identified in the two previous largest ADRD GWAS meta-analyses (HAVCR2, SLC2A4RG/LIME1, FOXF1 and NTN5) were not GWS in the EADB-IGAP-PGC meta-analysis.

    “four loci identified in the two previous largest ADRD GWAS meta-analyses on European-ancestry samples5,6 (HAVCR2, SLC2A4RG/LIME1, FOXF1 and NTN5) were not GWS in the EADB–IGAP–PGC meta-analysis”
    Quote in the paperNot a substantial claim2 of 2 sources checked
    • cites [5] Wightman, D. P. et al. A genome-wide association study with 1,126,563 individuals identifies new risk loci for Alzheimer’s disease. Nat. Genet. 53, 1276–1282 (2021). source
    • cites [6] Bellenguez, C. et al. New insights into the genetic etiology of Alzheimer’s disease and related dementias. Nat. Genet. 54, 412–436 (2022). source

      In the GWAS meta-analysis section, Table 2 reports genome-wide significant signals at FOXF1 and SLC2A4RG, supporting their identification in this earlier study. The cited paper reports its own EADB stage I and II analyses, not the claimed EADB–IGAP–PGC result; the passages do not establish that any of the four loci failed to reach genome-wide significance in that separate meta-analysis. (LLM, conf=0.82)

      Experimental — accuracy not yet measured
  • Interpretationcitation

    This is in accordance with previous reports of biases observed with genome-wide summary statistics of studies including proxy cases.

    “This is in accordance with previous reports of biases observed with genome-wide summary statistics of studies including proxy cases14–16.”
    Quote in the paperNot a substantial claim2 of 3 sources checked
    • cites [14] Wu, Y. et al. Pervasive biases in proxy genome-wide association studies based on parental history of Alzheimer’s disease. Nat. Genet. 56, 2696–2703 (2024). source
    • cites [15] Liu, H. et al. Mendelian randomization highlights significant difference and genetic heterogeneity in clinically diagnosed Alzheimer’s disease GWAS and self-report proxy phenotype GWAX. Alzheimers Res. Ther. 14, 17 (2022). source
    • cites [16] European Alzheimer’s & Dementia Biobank Mendelian Randomization (EADB-MR) Collaboration. Genetic associations between modifiable risk factors and Alzheimer disease. JAMA Netw. Open 6, e2313734 (2023).

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • Findingown data

    12 known ADRD loci are now GWS after focusing on clinically diagnosed AD cases compared with refs. 3,4,6 (NCK2, MME, ANKH/OTULIN, ABCA1, BLNK, IGH, ATP8B4, SNX1/CIAO2A, IL34/MTSS2, SIGLEC11, RBCK1 and APP).

    “while 12 known ADRD loci are now GWS after focusing on clinically diagnosed AD cases compared with refs. 3,4,6 (NCK2, MME, ANKH/OTULIN, ABCA1, BLNK, IGH, ATP8B4, SNX1/CIAO2A, IL34/MTSS2, SIGLEC11, RBCK1 and APP).”
    Quote in the paperSubstantial claim3 of 3 sources checked
    • cites [3] Jansen, I. E. et al. Genome-wide meta-analysis identifies new loci and functional pathways influencing Alzheimer’s disease risk. Nat. Genet. 51, 404–413 (2019). source

      The Results section (‘Genome-wide meta-analysis for AD status’; page unavailable) reports a clinically diagnosed AD analysis with 18 genome-wide-significant loci, so this paper can serve as a prior-study comparator. It does not report that the 12 loci named in the citing sentence became genome-wide significant after a later analysis restricted to clinically diagnosed cases. (LLM, conf=0.78)

      Experimental — accuracy not yet measured
    • cites [4] De Rojas, I. et al. Common variants in Alzheimer’s disease and risk stratification by polygenic risk scores. Nat. Commun. 12, 3417 (2021). source

      The Results (Meta-GWAS of AD; page not provided) report genome-wide significant associations at APP and confirm an IL-34 variant, supporting part of the locus list. They do not report the claimed set of 12 loci becoming genome-wide significant after restricting the analysis to clinically diagnosed AD cases; the study’s discovery analysis also included by-proxy AD data. (LLM, conf=0.87)

      Experimental — accuracy not yet measured
    • cites [6] Bellenguez, C. et al. New insights into the genetic etiology of Alzheimer’s disease and related dementias. Nat. Genet. 54, 412–436 (2022). source

      The GWAS meta-analysis (Table 2; page unavailable) reports genome-wide-significant associations at several listed loci, including NCK2, MME, ANKH, ABCA1, BLNK, IGH and IL34. It does not report that all 12 listed loci became genome-wide significant after restricting the analysis to clinically diagnosed AD cases; instead, the GWAS meta-analysis says that this restriction did not substantially change the magnitude of associations. (LLM, conf=0.86)

      Experimental — accuracy not yet measured
  • Classified as probably not a substantial claim. 25
  • May combine two claims. 16

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    ADGRL3 was recently identified in a multi-ancestry GWAS.

    “although ADGRL3 was recently identified in a multi-ancestry GWAS8”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites [8] Dalmasso, M. C. et al. The first genome-wide association study in the Argentinian and Chilean populations identifies shared genetics with Europeans in Alzheimer’s disease. Alzheimers Dement. 20, 1298–1308 (2024). source

      The Results section (page unavailable) describes the Argentinian and Chilean GWAS and lists loci from its meta-analysis, but does not mention ADGRL3. Searches for “ADGRL3” and “multi-ancestry GWAS identified ADGRL3” found no passage in our copy establishing the claimed identification. (LLM, conf=0.74)

      Experimental — accuracy not yet measured

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    TRIB1 was identified in a recent multi-ancestry GWAS.

    “although TRIB1 was identified in a recent multi-ancestry GWAS8”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites [8] Dalmasso, M. C. et al. The first genome-wide association study in the Argentinian and Chilean populations identifies shared genetics with Europeans in Alzheimer’s disease. Alzheimers Dement. 20, 1298–1308 (2024). source

  • May combine two claims.

    Premisecitation

    The C16orf95 locus has been associated with phosphorylated tau levels in cerebrospinal fluid and with ventricular volume.

    “the C16orf95 locus has been associated with phosphorylated tau levels in cerebrospinal fluid as well as with ventricular volume (Supplementary Note)11,12.”
    Quote in the paperNot a substantial claim2 of 2 sources checked
    • cites [11] Jansen, I. E. et al. Genome-wide meta-analysis for Alzheimer’s disease cerebrospinal fluid biomarkers. Acta Neuropathol. 144, 821–842 (2022). source
    • cites [12] Vojinovic, D. et al. Genome-wide association study of 23,500 individuals identifies 7 loci associated with brain ventricular volume. Nat. Commun. 9, 3945 (2018). source

      Table 1 (Results, Genome-wide association results; page not provided) identifies a ventricular-volume-associated variant at 16q24.2 annotated to FOXL1 and C16orf95. The Discussion associates cerebrospinal-fluid tau/phosphorylated tau with a different locus, 3q28, and does not establish that association for C16orf95. (LLM, conf=0.88)

      Experimental — accuracy not yet measured

  • Classified as probably not a substantial claim.

    Study design and conductcitation

    Pathway analyses using MAGMA corrected for the number of variants in each gene, LD between variants and LD between genes.

    “Pathway analyses were performed using MAGMA (v1.08)41,42, with correction for the number of variants in each gene, LD between variants and LD between genes.”
    Quote in the paperSubstantial claim2 of 2 sources checked
    • cites [41] De Leeuw, C. A., Mooij, J. M., Heskes, T. & Posthuma, D. MAGMA: generalized gene-set analysis of GWAS data. PLoS Comput. Biol. 11, e1004219 (2015). source
    • cites [42] Yurko, R., Roeder, K., Devlin, B. & G’Sell, M. H-MAGMA, inheriting a shaky statistical foundation, yields excess false positives. Ann. Hum. Genet. 85, 97–100 (2021). source

      The main text says MAGMA uses reference LD data and describes adjustment for LD-induced covariance among SNP-level p-values. It does not say that pathway analyses correct for the number of variants in each gene or for LD between genes. Although it mentions a corrected testing strategy in v1.08, it does not specify those corrections. (LLM, conf=0.82)

      Experimental — accuracy not yet measured

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductcitation

    In deCODE, correction for inflation of test statistics due to relatedness and population stratification was performed using the intercept estimate (1.30) from linkage disequilibrium score regression.

    “In deCODE, correction for inflation of test statistics due to relatedness and population stratification was performed using the intercept estimate (1.30) from linkage disequilibrium (LD) score regression21.”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites [21] Bulik-Sullivan, B. K. et al. LD score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295 (2015). source

      The Introduction explains that the LD Score regression intercept estimates inflation from confounding biases, including relatedness and population stratification; the Discussion proposes using it to correct test statistics. Our copy does not report a deCODE analysis, an intercept of 1.30, or that this correction was performed in deCODE. (LLM, conf=0.88)

      Experimental — accuracy not yet measured

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Study design and conductcitation

    The index variant rs7481951 was not available in FUMA and was replaced by rs10833712, its best tag variant (r2 = 0.827) according to TopLD.

    “The index variant rs7481951 was not available in FUMA and was replaced by rs10833712, its best tag variant (r2 = 0.827) according to TopLD45.”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites [45] Huang, L. et al. TOP-LD: a tool to explore linkage disequilibrium with TOPMed whole-genome sequence data. Am. J. Hum. Genet. 109, 1175–1181 (2022). source

      The Main text (page not available) describes TOP-LD as a tool that reports LD proxies and their R² values, supporting its identification as the tool used. It does not report either rs7481951 or rs10833712, their alleged R² of 0.827, or whether the index variant was unavailable in FUMA. (LLM, conf=0.84)

      Experimental — accuracy not yet measured

  • May combine two claims.

    Findingown data

    The five loci with decreased significance were classified as tier 2 and require external validation.

    “Those loci require external validation and were thus classified as ‘tier 2’.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    All 13 loci identified exclusively in sensitivity meta-analyses were new and classified as tier 2.

    “All 13 of these loci were new and classified as ‘tier 2’.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    Of the five tier 2 loci, three were known (SEC61G/EGFR, SPPL2A/USP8/USP50, KAT8/BCKDK) and two were new in European-ancestry samples (TRIB1 and AXIN1).

    “Three were known (SEC61G/EGFR, SPPL2A/USP8/USP50, KAT8/BCKDK) and two were new in European-ancestry samples (TRIB1 and AXIN1)”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    The variance explained on the liability scale varied between 2.43% and 3.6% for Braak stage, and between 3.32% and 4.93% for CERAD score, depending on the population prevalence considered.

    “while the variance explained on the liability scale varied between 2.43% and 3.6% for Braak stage, and between 3.32% and 4.93% for CERAD score, depending on the population prevalence considered.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    The variance explained by the main PGS remained low: Nagelkerke's pseudo-R-squared was 3.98% for Braak stage and 4.37% for CERAD score.

    “the variance explained by the score remained low—Nagelkerke’s pseudo-R-squared (R2) was 3.98% and 4.37% for Braak stage and CERAD score, respectively”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim.

    Interpretationown data

    This may indicate common pathological pathways underlying conversion from the pathology to AD or Lewy body dementia.

    “This may indicate common pathological pathways underlying conversion from the pathology to AD or Lewy body dementia.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Premisereasoning

    Including large biobank samples increases the power to identify AD loci but can blur the distinction between AD and non-AD dementia signals because the AD phenotype definition is less specific in those samples.

    “This strategy increases the power to identify AD loci but also can blur the distinction between AD and non-AD dementia signals, as the AD phenotype definition is less specific in those samples.”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim.

    Study design and conductown data

    Models for the association between PGS and NPEs were adjusted for age at death, sex, number of APOE ε4 and ε2 alleles, ten PCs and centers.

    “Models were adjusted for age at death, sex, number of APOE ε4 and ε2 alleles, ten PCs and centers.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Study design and conductown data

    Variants analyzed in fewer than 70% effective number of cases were excluded from the conditional analyses, which led to the exclusion of the ADGRL3 locus.

    “Only variants analyzed in ≥70% effective number of cases were considered, leading to the exclusion of the ADGRL3 locus from these analyses.”
    Quote in the paperNot a substantial claim

  • Classified as probably not a substantial claim.

    Study design and conductreasoning

    An association between PGS and NPEs was considered significant if the P value was <2.27 × 10−3, corresponding to a Bonferroni correction for 22 tests (11 NPEs analyzed in two studies).

    “An association was considered significant if the P value was <2.27 × 10−3, corresponding to a Bonferroni correction for 22 tests (11 NPEs analyzed in two studies).”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductnone given

    The appropriate review boards from the participating consortia reviewed and approved the study protocol.

    “The appropriate review boards from the ADGC, Bonn, CHARGE, EADB, EADI, GERAD, GR@ACE/DEGESCO and PGC-ALZ reviewed and approved the study protocol.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductown data

    Six models were tested in the single-cell enrichment analysis: one primary model and five sensitivity models corresponding to the primary, common-only, no-APOE, larger-window, no-proxy and no-biobank gene-level summary statistics.

    “We tested six models—one primary model and five sensitivity models—corresponding to the primary, common-only, no-APOE, larger-window, no-proxy and no-biobank gene-level summary statistics.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductnone given

    Sensitivity of the genetic correlation results was assessed using the main and no-biobank ADRD summary statistics.

    “However, we assessed the sensitivity of the results using the main and no-biobank ADRD summary statistics.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Study design and conductnone given

    The strict stepwise conditional analysis was restricted to variants with imputation quality >0.8 in the EADB-core dataset and analyzed in ≥90% effective number of cases.

    “The strict stepwise conditional analysis was performed on variants with imputation quality >0.8 in the EADB-core dataset and analyzed in ≥90% effective number of cases.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Study design and conductown data

    Sensitivity analyses for the PGS-NPE associations were conducted by additionally adjusting for (1) AD diagnosis, (2) the three AD NPEs, (3) both AD diagnosis and the three AD NPEs, or (4) AD NPEs and LATE-NC.

    “Sensitivity analyses were conducted by additionally adjusting for the following: (1) AD diagnosis, coded in three categories (not impaired, AD/mild cognitive impairment and unknown/other dementia); (2) the three AD NPEs; (3) both AD diagnosis and the three AD NPEs or (4) AD NPEs and LATE-NC.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Study design and conductown data

    Several sensitivity meta-analyses were conducted excluding the UKBB-proxy study, excluding large biobanks, and removing per-study variant filtering on effective allele count.

    “excluding the UKBB, FinnGen, deCODE and HUNT studies, corresponding to large biobanks using International Classification of Diseases codes to identify AD cases (no-biobank meta-analysis; Supplementary Table 1)”
    Quote in the paperNot a substantial claim

  • Classified as probably not a substantial claim.

    Study design and conductnone given

    Classical quality control protocols were applied to samples and autosomal variants in each study.

    “Classical quality control protocols were applied to samples and autosomal variants in each study”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

    Study design and conductnone given

    The samples included in the NPE GWAS largely overlap with the samples included in the ADRD meta-analysis.

    “The samples included in the NPE GWAS largely overlap with the ones included in the ADRD meta-analysis.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductown data

    Sensitivity analyses were performed to assess the sensitivity of the approximate stepwise conditional analysis results to LD and imputation quality, using a strict stepwise conditional analysis and an exact conditional analysis.

    “To assess the sensitivity of the results of the approximate stepwise conditional analysis to LD and imputation quality, we performed (1) a strict stepwise conditional analysis and (2) an exact conditional analysis.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Study design and conductnone given

    Written informed consent was obtained from all study participants or, for those with substantial cognitive impairment, from a caregiver, legal guardian or other proxy.

    “Written informed consent was obtained from all study participants or, for those with substantial cognitive impairment, from a caregiver, legal guardian or other proxy.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductnone given

    The effect size estimated for chr4:993555:G:T was used for the tag variant chr4:973547:G:T.

    “The effect size estimated for chr4:993555:G:T was considered for the tag variant chr4:973547:G:T.”
    Quote in the paperNot a substantial claim

  • Classified as probably not a substantial claim.

    Study design and conductreasoning

    Genetic correlation was considered significant at P < 3.13 × 10−3, corresponding to a Bonferroni correction for the 16 phenotypes for which genetic correlation could be computed.

    “Correlation was considered significant at P < 3.13 × 10−3, corresponding to a Bonferroni correction for the 16 phenotypes for which genetic correlation could be computed.”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim.

    Study design and conductreasoning

    In each joint analysis, one index variant from each pair in LD (r2 > 0.75 in the EADB-core dataset) was excluded to avoid collinearity.

    “From each joint analysis, we excluded one index variant from each pair in LD (r2 > 0.75 in the EADB-core dataset, as computed by PLINK 2.0) to avoid collinearity.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Study design and conductown data

    The sensitivity of results for GWS lead variants from the main meta-analysis was assessed after adjustment for age and sex, and after adjustment for age, sex and the number of APOE ε4 and ε2 alleles.

    “Additionally, we assessed the sensitivity of results for GWS lead variants from the main meta-analysis after adjustment for age and sex, and after adjustment for age, sex and the number of APOE ε4 and ε2 alleles”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Study design and conductown data

    Variants with positions >80 Mb on chromosome 11 were inadvertently missing from the UKBB-diagnosed summary statistics, resulting in a minor loss of power (~6% effective sample size) in a small genomic region (~2%) for the no-proxy sensitivity analyses; this had minimal impact on the results.

    “Variants with positions of >80 Mb on chromosome 11 were inadvertently missing from the UKBB-diagnosed summary statistics, resulting in a minor loss of power (~6% effective sample size) in a small genomic region (~2%) for the no-proxy sensitivity analyses; this had minimal impact on the results”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Study design and conductnone given

    The genetic correlation analysis was restricted to HapMap 3 variants and excluded variants in the APOE locus, A/T or C/G alleles, variants with MAF of <1%, with duplicated rsID and indels.

    “The analysis was restricted to HapMap 3 variants and excluded variants in the APOE locus, A/T or C/G alleles, variants with MAF of <1%, with duplicated rsID and indels.”
    Quote in the paperNot a substantial claim

  • Classified as probably not a substantial claim.

    Study design and conductreasoning

    A significance threshold of P < 9.8 × 10−4, corresponding to a Bonferroni correction for 51 tests, was used for the rare-variant burden comparison.

    “We considered a significance threshold of P < 9.8 × 10−4, corresponding to a Bonferroni correction for 51 tests.”
    Quote in the paperSubstantial claim
33 of 33

  • The paper relies on this without support.

    The latest ADRD GWASs partly overlapped, used different imputation panels or analytical approaches, and some results were discordant across studies.

    “Although the latest GWASs on ADRD partly overlapped, they used different imputation panels or analytical approaches and some results were discordant across studies.”

  • The paper relies on this without support.

    Prior ADRD GWASs all included, to varying extents, large biobank samples using International Classification of Diseases codes to identify AD cases, proxy ADRD cases, or both.

    “They all included, to varying extents, large biobank samples using International Classification of Diseases codes to identify AD cases, proxy ADRD cases (that is, individuals reporting at least one parent or sibling with dementia) or both.”
2 of 2

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Numerous SNPs in the CASS4 locus have been linked to AD.

    “Although numerous singlenucleotide polymorphisms (SNPs) in the CASS4 locus have been linked to AD (Beck et al., 2014; Bellenguez et al., 2022; EADB et al., 2026; Lambert et al., 2013; Lin et al., 2017; Schwartzentruber et al., 2021)”

    From Alzheimer’s genetic risk factor CASS4 controls microglial focal adhesion biology and amyloidosis (2026) · cites this paper as EADB et al., 2026

    The Main section reports 91 loci associated with Alzheimer’s disease and related dementias, but the passages retrieved for “CASS4” and “CASS4 SNPs” do not identify that locus or describe SNP associations within it. Our copy therefore does not establish the CASS4-specific claim. (LLM, conf=0.70)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

Each contradiction between two passages listed here was confirmed by an independent check.

  • AUC comparison gives conflicting descriptions of the baseline model

    Minor

    The results describe the comparison model as containing only age at death, sex and APOE alleles. The Methods say the null model also included principal components and centers.

1 of 1

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Authors
EADB, Bellenguez, C., Castillo Morales, A., Amin, N., van der Lee, S. J., Muntaner, M., Parveen, K., Küçükali, F., Grenier-Boley, B., Heikkinen, S., de Rojas, I., Dalmasso, M. C., Kleineidam, L., Peters, O., Schneider, A., Dichgans, M., Rujescu, D., Scherbaum, N., Deckert, J., Riedel-Heller, S., Hausner, L., Molina-Porcel, L., Düzel, E., Grimmer, T., Wiltfang, J., Heilmann-Heimbach, S., Moebus, S., Schmid, M., Tegos, T., Scarmeas, N., Dols-Icardo, O., Moreno, F., Pérez-Tur, J., Bullido, M. J., Pastor, P., Sánchez-Valle, R., Álvarez, V., Boada, M., García-González, P., Puerta, R., Mir, P., Real, L. M., Piñol-Ripoll, G., García-Alberca, J. M., Rodriguez-Rodriguez, E., Soininen, H., de Mendonça, A., Mehrabian, S., Hort, J., Vyhnalek, M., Sandau, N., Luo, J., Thomassen, J. Q., Pijnenburg, Y. A. L., van der Flier, W., Seelaar, H., Ramakers, I., Papma, J., Hulsman, M., Biessels, G.-J., Graff, C., Thonberg, H., Ullgren, A., Papenberg, G., Giedraitis, V., Löwenmark, M., Kilander, L., Williams, J., Holmans, P., Le Borgne, J., Palmal, S., Nicolas, A., Amouyel, P., Boland, A., Deleuze, J.-F., Nicolas, G., Dufouil, C., Pasquier, F., Hanon, O., Debette, S., Grünblatt, E., Popp, J., Galimberti, D., Arosio, B., Mecocci, P., Solfrizzi, V., Parnetti, L., Squassina, A., Tremolizzo, L., Borroni, B., Wagner, M., Nacmias, B., Spallazzi, M., Seripa, D., Rainero, I., Daniele, A., Bossù, P., Masullo, C., Rossi, G., Jessen, F., Holstege, H., Mather, K., Fernandez, M. V., Kehoe, P. G., Tsolaki, M., van Duijn, C., Frikke-Schmidt, R., Ghidoni, R., Sánchez-Juan, P., Sleegers, K., Ingelsson, M., Hiltunen, M., Sims, R., Andreassen, O., Ruiz, A., Ramirez, A., Lambert, J.-C., EADI, Bonn, ADGC, Naj, A. C., Rajabli, F., Benchek, P., Shade, L. M. P., Qiao, Q., Kushch, N., Sha, J., Bazemore, K., Zhu, C., Lee, W.-P., Haut, J., Hamilton-Nelson, K. L., Wheeler, N. R., Zhao, Y., Farrell, J. J., Grunin, M. A., Leung, Y. Y., Kuksa, P. P., Li, D., da Fonseca, E. L., Mez, J. B., Palmer, E. L., Pillai, J., Sherva, R. M., Song, Y. E., Zhang, X., Ikeuchi, T., Iqbal, T., Valladares, O., Reyes-Dumeyer, D., Kuzma, A. B., Abner, E., Adams, L. D., Aguirre, A., Albert, M. S., Albin, R. L., Allen, M., Apostolova, L. G., Arnold, S. E., Asthana, S., Atwood, C. S., Auerbach, S., Baldwin, C. T., Barber, R. C., Barnes, L. L., Barral, S., Beach, T. G., Becker, J. T., Beecham, G. W., Beekly, D., Bennett, D. A., Bertelson, J., Bird, T. D., Blacker, D., Boeve, B. F., Bowen, J. D., Boxer, A., Brewer, J., Burns, J. M., Buxbaum, J. D., Cairns, N. J., Cantwell, L. B., Cao, C., Carlson, C. S., Carlsson, C. M., Carney, R. M., Carrasquillo, M. M., Chesselet, M.-F., Chin, N. A., Chui, H. C., Chung, J., Claas, S. A., Craft, S., Crane, P. K., Cribbs, D. H., Crocco, E. A., Cruchaga, C., Cuccaro, M. L., Cullum, M., Darby, E., Davis, B., De Jager, P. L., DeCarli, C., DeToledo, J., Dick, M., Dickson, D. W., Dombroski, B. A., Doody, R. S., Duara, R., Dumitrescu, L. C., Ertekin-Taner, N., Evans, D. A., Faber, K. M., Fairchild, T. J., Fallon, K. B., Farlow, M. R., Fernandez-Hernandez, V., Friedland, R. P., Foroud, T. M., Frosch, M. P., Fulton-Howard, B., Galasko, D. R., Gearing, M., Geschwind, D. H., Ghetti, B., Gilbert, J. R., Go, R. C. P., Goate, A. M., Grabowski, T. J., Graff-Radford, N. R., Gray, N. E., Growdon, J. H., Hakonarson, H., Hall, J., Hamilton, R. L., Harari, O., Hardy, J., Head, E., Henderson, V. W., Hernandez, M., Hohman, T. J., Honig, L. S., Huebinger, R. M., Huentelman, M. J., Hyman, B. T., Hynan, L. S., Ibanez, L., Jarvik, G. P., Jayadev, S., Jin, L.-W., Johnson, K., Johnson, L., Kamboh, M. I., Katsumata, Y., Katz, M. J., Kauwe, J. S., Kaye, J. A., Keene, C. D., Khaleeq, A., Kikuchi, M., Knebl, J., Kowall, N. W., Kramer, J. H., Kukull, W. A., LaFerla, F. M., Lah, J. J., Larson, E. B., Lerner, A., Leverenz, J. B., Levey, A. I., Lieberman, A. P., Lipton, R. B., Logue, M., Lopez, O. L., Lunetta, K. L., Lyketsos, C. G., Mains, D., Marson, D. C., Martin, E. R. R., Martiniuk, F., Mash, D. C., Masliah, E., Massman, P., Masurkar, A., McCormick, W. C., McCurry, S. M., McKee, A. C., Mesulam, M., Miller, B. L., Miller, C. A., Miller, J. W., Montine, T. J., Monuki, E. S., Morris, J. C., Mukherjee, S., Myers, A. J., Nguyen, T., Obisesan, T., O’Bryant, S., Olichney, J. M., Palmer, R., Parisi, J. E., Paulson, H. L., Pavlik, V., Paydarfar, D., Perez, V., Peskind, E., Petersen, R. C., Petrovitch, H., Polk, M., Poon, W. W., Potter, H., Qu, L., Quiceno, M., Quinn, J. F., Raj, A., Raskind, M., Reiman, E. M., Reisberg, B., Reisch, J. S., Ringman, J. M., Roberson, E. D., Rodriguear, M., Rogaeva, E., Rosen, H. J., Rosenberg, R. N., Royall, D. R., Sabbagh, M., Sadovnick, A. D., Sager, M. A., Sano, M., Saykin, A. J., Schneider, J. A., Schneider, L. S., Seeley, W. W., Slifer, S. H., Small, S., Smith, A. G., Sonnen, J. A., George-Hyslop, P. S., Starks, T. D., Stern, R. A., Stevens, A. B., Strittmatter, S. M., Sultzer, D., Swerdlow, R. H., Tanzi, R. E., Tilson, J. L., Troncoso, J. C., Tsuang, D. W., Van Deerlin, V. M., Van Eldik, L. J., Vance, J. M., Vardarajan, B. N., Vassar, R., Vinters, H. V., Vonsattel, J.-P., Weintraub, S., Welsh-Bohmer, K. A., Whitehead, P. L., Wijsman, E. M., Wilhelmsen, K. C., Williams, B., Williamson, J., Wilms, H., Wingo, T. S., Wisniewski, T., Woltjer, R. L., Wright, C. B., Wu, C.-K., Younkin, S. G., Yu, C.-E., Yu, L., Zhu, X., Kunkle, B. W., Bush, W. S., Miyashita, A., Tosto, G., Jun, G. R., Reitz, C., Byrd, G. S., Fardo, D. W., Wang, L.-S., Farrer, L. A., Haines, J. L., Mayeux, R., Pericak-Vance, M. A., Schellenberg, G. D., CHARGE, Fongang, B., Yaqub, A., Sargurupremraj, M., Jian, X., Mishra, A., Bis, J. C., Gireud-Goss, M., Himali, J. J., Zare, H., Guðnason, V., Launer, L., Bressler, J., Grabe, H. J., Ikram, M. A., Psaty, B. M., Longstreth, W. T., Sigurdsson, S., Ghanbari, M., Wolters, F. J., Boerwinkle, E., Beiser, A. S., Sarnowski, C., Mosley, T. H., Lopez, O. L., van Duijn, C., Satizabal, C., Ikram, M. K., Qiong, Y., Fornage, M., Seshadri, S., FinnGen, GERAD, Mahoney, R., Denning, N., Meggy, A., Marshall, R., LeRoux, D., Bresner, C., Escott-Price, V., Morgan, K., Brookes, K., Guetta-Baranes, T., Holmes, C., Windle, G., Burholt, V., Green, E., Macleod, C., Woods, B., Mead, S., Schott, J. M., Fox, N., Love, S., GR@ACE/DEGESCO, Olivé, C., Montrreal, L., Fernández, M. V., Marquié, M., Cano, A., Valero, S., Sotolongo-Grau, O., Pérez-Cordón, A., Espinosa, A., Sanabria, Á., Ortega, G., Rosende-Roca, M., Alegret, M., Tárraga, L., Sáez, M. E., Quintela, I., Carracedo, Á., Real, L. M., Macías, J., Corma-Gómez, A., Pineda, J. A., Mendoza, S., Royo, J. L., Garcia-Ribas, G., García-Madrona, S., Franco-Macías, E., Buiza-Rueda, D., Bernal Sánchez-Arjona, M., Huerto Vilas, R., Arias Pastor, A., Diez-Fairen, M., Alvarez, I., Lage, C., Alcolea, D., Fortea, J., Lleó, A., Frank-García, A., Montes, A. M., Antonell, A., Menéndez-González, M., Lopez de Munain, A., Medina, M., Calero, M., Rábano, A., Pastor, A. B., del Ser, T., Sanchez-Garcia, F., Muñoz-Fernandez, C., Deniz-Naranjo, M. C., PGC-ALZ, Posthuma, D., Andreassen, O. A., Wightman, D. P., Uffelmann, E., Stefansson, H., Walters, G. B., Stefansson, K., Snaedal, J., Eyjólfsdóttir, H., Pedersen, N. L., Reynolds, C. A., Karlsson, I. K., Hägg, S., Zettergren, A., Skoog, I., Kern, S., Waern, M., Blennow, K., Zetterberg, H., Moreno, E., Moksnes, M. R., Hveem, K., Winsvold, B. S., Brumpton, B., Selbæk, G., Fladby, T., Aarsland, D., Djurovic, S., Rongve, A., Bahrami, S., Shadrin, A. A., Saltvedt, I., & Bråthen, G.
Affiliations
  • Inserm (FR)
  • University of Oxford (GB)
  • University of Bonn (DE)
  • University of Antwerp (BE)
  • University of Eastern Finland (FI)
  • German Center for Neurodegenerative Diseases (DE)
  • Martin Luther University Halle-Wittenberg (DE)
  • University of Duisburg-Essen (DE)
  • University of Würzburg (DE)
  • Leipzig University (DE)
  • Heidelberg University (DE)
  • Technical University of Munich (DE)
  • Essen University Hospital (DE)
  • University Hospital Bonn (DE)
  • Aristotle University of Thessaloniki (GR)
  • National and Kapodistrian University of Athens (GR)
  • Consejo Superior de Investigaciones Científicas (ES)
  • Hospital Universitario Central de Asturias (ES)
  • Instituto de Salud Carlos III (ES)
  • Universitat Internacional de Catalunya (ES)
  • Alexandrovska Hospital (BG)
  • Charles University (CZ)
  • Copenhagen University Hospital (DK)
  • Amsterdam Neuroscience (NL)
  • Maastricht University (NL)
  • Karolinska University Hospital (SE)
  • Stockholm University (SE)
  • Uppsala University (SE)
  • Cardiff University (GB)
  • Commissariat à l'Énergie Atomique et aux Énergies Alternatives (FR)
  • Université de Bordeaux (FR)
  • Université Paris Cité (FR)
  • University of Zurich (CH)
  • University of Milan (IT)
  • University of Perugia (IT)
  • Azienda Universitaria Ospedaliera Consorziale - Policlinico Bari (IT)
  • University of Cagliari (IT)
  • University of Milano-Bicocca (IT)
  • Centro San Giovanni di Dio Fatebenefratelli (IT)
  • Don Carlo Gnocchi Foundation (IT)
  • University of Parma (IT)
  • Ospedale Vito Fazzi (IT)
  • University of Turin (IT)
  • Università Cattolica del Sacro Cuore (IT)
  • Fondazione Santa Lucia (IT)
  • Fondazione IRCCS Istituto Neurologico Carlo Besta (IT)
  • University of Cologne (DE)
  • UNSW Sydney (AU)
  • University of Bristol (GB)
  • The University of Texas at San Antonio Health Science Center (US)
  • Bordeaux Population Health (FR)
  • University of Pennsylvania (US)
  • University of Miami (US)
  • Case Western Reserve University (US)
  • University of Kentucky (US)
  • Boston University (US)
  • Cleveland Clinic (US)
  • Niigata University (JP)
  • The University of Texas at Austin (US)
  • Johns Hopkins University (US)
  • University of Michigan (US)
  • Mayo Clinic in Florida (US)
  • Indiana University Indianapolis (US)
  • University of Wisconsin–Madison (US)
  • University of North Texas Health Science Center (US)
  • Rush University Medical Center (US)
  • Columbia University Irving Medical Center (US)
  • Banner Sun Health Research Institute (US)
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  • Harvard University (US)
  • Mayo Clinic (US)
  • Swedish Medical Center (US)
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  • Icahn School of Medicine at Mount Sinai (US)
  • Washington University in St. Louis (US)
  • University of South Florida (US)
  • Fred Hutch Cancer Center (US)
  • Bruce W. Carter VA Medical Center (US)
  • University of Southern California (US)
  • Wake Forest University Health Sciences (US)
  • University of California, Irvine (US)
  • Baylor College of Medicine (US)
  • The University of Texas Southwestern Medical Center (US)
  • University of California, Davis (US)
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  • Oregon Health & Science University (US)
  • Children's Hospital of Philadelphia (US)
  • The Ohio State University (US)
  • UCL Queen Square Institute of Neurology (GB)
  • Stanford Medicine (US)
  • Translational Genomics Research Institute (US)
  • University of California Davis Medical Center (US)
  • Albert Einstein College of Medicine (US)
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  • Barrow Neurological Institute (US)
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  • Texas A&M Health Science Center (US)
  • Yale University (US)
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  • National Institutes of Health (US)
  • Columbia University (US)
  • Erasmus MC (NL)
  • University of Iceland (IS)
  • The University of Texas Health Science Center at Houston (US)
  • Universitätsmedizin Greifswald (DE)
  • Icelandic Heart Association (IS)
  • University of Mississippi Medical Center (US)
  • University of Nottingham (GB)
  • Nottingham Trent University (GB)
  • University of Southampton (GB)
  • Bangor University (GB)
  • University of Auckland (NZ)
  • University of Cambridge (GB)
  • MRC Prion Unit (GB)
  • University College London (GB)
  • Fundación Pública Galega de Medicina Xenómica (ES)
  • Universidade de Santiago de Compostela (ES)
  • Hospital Universitario de Valme (ES)
  • Universidad de Málaga (ES)
  • Institut d'Investigació en Ciències de la Salut Germans Trias i Pujol (ES)
  • Universidad de Cantabria (ES)
  • Universitat Autònoma de Barcelona (ES)
  • Hospital Universitario La Paz (ES)
  • Hospital Clínic de Barcelona (ES)
  • Hospital Universitario de Gran Canaria Doctor Negrín (ES)
  • Karolinska Institutet (SE)
  • University of Colorado Boulder (US)
  • University of Gothenburg (SE)
  • Sahlgrenska University Hospital (SE)
  • University of Science and Technology of China (CN)
  • Norwegian University of Science and Technology (NO)
  • Oslo University Hospital (NO)
  • University of Oslo (NO)
  • King's College London (GB)
  • Fonna Hospital Trust (NO)
Corresponding author
  • Bellenguez, Céline
  • Lambert, Jean-Charles
ORCID
Venue
Nature Genetics
Publisher
Springer Science and Business Media LLC
Published
2026-06
Volume
58
Issue
6
Pages
1214-1225
ISSN
  • 1061-4036 (ISSN-L)
  • 1546-1718
DOI
10.1038/s41588-026-02583-1
PMC
PMC13263136
OpenAlex
W7163386725
Semantic Scholar
45c31b46ec9dd2ffabfd53361142934a275914dd
Cited by
7
References
59
Citations by year
2026: 7
FWCI
13.39
Type
journal article
Language
en
Open access
Licence
Locations
  • Nature Genetics (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • EUR Research Repository (Erasmus University Rotterdam) (publishedVersion, cc-by)
  • ORCA Online Research @Cardiff (Cardiff University) (acceptedVersion)
  • BOA (University of Milano-Bicocca) (submittedVersion, cc-by)
  • Open access LMU (Ludwid Maxmilian's Universitat Munchen) (submittedVersion)
  • DZNE Pub (submittedVersion, cc-by)
  • PubliCatt (Università Cattolica del Sacro Cuore) (submittedVersion, cc-by)
  • DiVA (Linkoping University) (submittedVersion, other-oa)
  • Universitätsbibliographie, Universität Duisburg-Essen (submittedVersion)
  • Research Publications (Maastricht University) (submittedVersion, cc-by)
  • DIGITAL.CSIC (Spanish National Research Council (CSIC)) (submittedVersion, other-oa)
  • IRIS (submittedVersion)
  • PubMed Central (submittedVersion, cc-by)
  • Pure Amsterdam UMC (submittedVersion, cc-by)
  • Research at the University of Copenhagen (University of Copenhagen) (submittedVersion, other-oa)
  • VU Research Portal (submittedVersion, other-oa)
Funders
  • U.S. Department of Veterans Affairs
  • Alzheimer's Association
  • Eli Lilly and Company
  • Pfizer
  • Massachusetts General Hospital
  • Biogen
  • Alnylam Pharmaceuticals
  • Florida Department of Health
  • School of Medicine, Boston University
  • Cure Alzheimer's Fund
  • University of Washington
  • Johns Hopkins University
  • University of Pittsburgh
  • Universidad de Sevilla
  • Universidad de Málaga
  • Wellcome Trust
  • University of Texas Health Science Center at Houston
  • Amarin Corporation
  • McGovern Medical School
  • Siemens Medical Solutions USA
  • Centro de Biología Molecular Severo Ochoa
  • Cerveau Technologies
  • National Institute for Health and Care Research
  • University of Cambridge
  • University College London
  • University of Oxford
  • Institut National de la Santé et de la Recherche Médicale (UMR 1219)
  • Eisai
  • Novo Nordisk
  • Universidad Autónoma de Madrid
  • National and Kapodistrian University of Athens
  • Deutsches Zentrum für Neurodegenerative Erkrankungen
  • Háskóli Íslands
  • Universitat de Barcelona
  • Aristotle University of Thessaloniki
  • Universitat Autònoma de Barcelona
  • H. Lundbeck A/S
  • UK Dementia Research Institute
  • Instituto de Investigación Sanitaria del Principado de Asturias
  • National Institutes of Health
  • Genentech
  • University of Texas Health Science Center at San Antonio
  • Avid Radiopharmaceuticals
  • Medical Research Council
  • Research Committee, Aristotle University of Thessaloniki
  • National Heart, Lung, and Blood Institute
Topics
  • Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
  • Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
Keywords
Alzheimer's disease, related dementias, genetic susceptibility loci, polygenic risk score, apolipoprotein E, European ancestry, Alzheimer's disease pathology, genetic architecture
MeSH terms
Genetic Risk Score, Alzheimer Disease, Dementia, Humans, Genetic Predisposition to Disease, Multifactorial Inheritance, Polymorphism, Single Nucleotide, Genome-Wide Association Study, Genetic Loci

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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-14.

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