Genetic modifiers of APOE-ε4-associated cognitive decline

Domain: chemistry
Published: 2026-02-20
Editorial outcome: minor_revision

Abstract
The APOE-ε4 allele is the strongest genetic risk factor for late-onset Alzheimer’s disease. However, APOE-ε4 is not deterministic, highlighting the need to identify additional genetic and environmental factors. APOE-ε4 has been linked to accelerated cognitive decline, so we sought to investigate genetic factors that modify APOE-ε4–associated cognitive decline. We conduct cross-ancestry APOE-ε4-stratified and interaction GWAS using harmonized cognitive data from 32,778 participants, including 29,354 non-Hispanic White and 3,424 non-Hispanic Black individuals. Our primary outcome is late-life cognition, measured using harmonized composite scores for memory, executive function, and language, modeled as continuous traits reflecting both normative cognitive aging and disease-related decline. We identify two genome-wide significant loci in APOE-ε4 carriers, reaching genome-wide significance for executive function. These loci also demonstrate nominal associations across the other domains, suggesting broad effects on cognition. In non-carriers, we identify a genome-wide significant association at ITGB8 restricted to executive function, and another locus associated with language. We further link these loci to SEMA6D, GRIN3A, and ITGB8 through expression and methylation databases. Post-GWAS analyses implicate additional genes including SLCO1A2, and DNAH11. Genetic correlation analyses reveal differences by APOE-ε4 status for immune-related traits, suggesting immune-related predispositions may exacerbate cognitive risk in APOE-ε4 carriers.

Introduction
Late-onset Alzheimer’s disease (AD) is a progressive neurodegenerative disorder affecting over 40 million people worldwide1. Characterized by hallmark neuropathological features such as amyloid-beta plaques and tau tangles, clinical AD manifests as cognitive decline across domains, including memory, language, and executive function2. The heritability of sporadic, late-onset AD is estimated at 60–80% from twin studies3, underscoring the significant genetic contribution to disease susceptibility. AD is polygenic, with the apolipoprotein E ε4 (APOE-ε4) allele recognized as the strongest genetic risk factor identified to date4,5. Found in approximately 10–25% of the global population, APOE-ε4 is associated with a 3-fold increase in AD risk for individuals carrying one allele and up to a 12-fold increase for those carrying two alleles in European ancestry populations. APOE-ε4 carriers tend to experience earlier disease onset and faster cognitive decline compared to non-carriers (individuals without any copies of the ε4 allele)6–8.

Despite its significant impact, APOE-ε4-associated cognitive outcomes are heterogeneous, influenced by genetic ancestry and biological sex9–14. Non-European populations often exhibit attenuated effects of APOE-ε415–17, yet may have higher AD prevalence overall18. This ancestry-related heterogeneity provides additional motivation for assessing genetic interactions that may help explain differences in APOE-ε4 effects across populations. This underscores the necessity of ancestry-informed research to better explain how APOE-ε4 influences AD risk and cognitive trajectories across populations.

APOE-ε4 interacts with additional genetic factors at different points along the AD neuropathological cascade, influencing the effect of known-AD risk genes (e.g., ABCA7, BIN1, CLU, CR1, PICALM, and TREM2)19–23. For example, variants in TREM2 influence the microglial response to amyloid-beta plaques in the presence of APOE-ε4, altering severity and disease progression23. Therefore, characterizing genetic interactions with APOE-ε4 is essential for understanding the full genetic architecture of AD.

The role of common genetic variation interacting with APOE-ε4 to influence cognitive outcomes remains incompletely understood, especially across ancestral groups, partly due to limited sample sizes of carriers in previous studies. Here, we leveraged harmonized cognitive phenotypes and genetic data from 32,778 participants across nine well-characterized aging and AD cohorts to identify genetic modifiers of APOE-ε4-associated cognitive outcomes. Among these participants, 12,477 (38%) were APOE-ε4 carriers. Building on prior work establishing associations between APOE-ε4 and these cognitive endophenotypes24, we hypothesized distinct genetic risk architectures between carriers and non-carriers. By identifying such genetic factors, we aim to provide the most comprehensive analysis to date of common genetic variants modifying APOE-ε4-associated cognitive decline.

Results
Demographics/Study population
APOE-ε4 carriers and non-carriers had similar age and sex distributions and were predominantly self-reported as non-Hispanic White (NHW). Despite these similarities, APOE-ε4 carriers had lower mean cognitive scores across memory, executive functioning, and language compared to non-carriers, consistent with the established relationship between APOE-ε4 and cognitive decline. Among carriers, 15% were APOE-ε4 homozygotes. Sample sizes varied slightly by cognitive outcomes due to data availability. Among participants with longitudinal data, 9.10% converted from cognitively normal (CN) to mild cognitive impairment (MCI), 4.71% from CN to AD, and 6.36% from MCI to AD. Participant characteristics are summarized in Table 1 and detailed by cognitive domain in Data S1.

APOE-ε4 associations with cognitive endophenotypes
As previously reported by our group24, robust associations were observed between the APOE-ε4 allele and baseline executive functioning (β=−0.19, p=2.27×10−199), baseline language (β=−0.20, p=4.17×10−201), and baseline memory (β=−0.39, p=2.23×10−308). Similar associations were observed when using longitudinal trajectories, referred to simply as decline, across executive functioning decline (β=−0.03, p=6.13×10−194), language decline (β=−0.04, p=5.19×10−272), and memory decline (β=−0.06, p=2.23×10−308).

Cross-ancestry meta-analysis
Our cross-ancestry APOE-ε4-stratified and interaction genome-wide meta-analyses identified five genome-wide significant (p<5×10−8) loci (Table 2). Each locus is described in its own subsection below, with follow-up linking the associated variants to SEMA6D, GRIN3A, ITGB8, SLCO1A2, and SLC25A21. Comprehensive results of primary and sensitivity analyses, stratified analyses by self-reported race, and replication results from the Framingham Heart Study (FHS) are included in Tables S2, S3 and S9. Allele frequencies for the identified interacting variants were similar across ancestry groups (Table 2).

A novel locus in APOE-ε4 carriers linked to SEMA6D
We identified a novel locus on chromosome 15 (lead SNP: rs2959641; MAF=0.47) significantly associated with better baseline executive functioning among APOE-ε4 carriers (β =0.05, p =4.04×10−8), with a nominal ε4 interaction (Fig. 2A; β =0.04, p =4.77×10−4). This carrier-specific association extended nominally to baseline language (β =0.03, p =5.18×10−4). No significant effects were observed for baseline memory (β =0.006, p =0.14; β =0.01, p =0.033), although no longitudinal associations were observed for this locus.

Sensitivity analyses demonstrated stable effect sizes and directionality, with slightly attenuated p values (Data S2). Replication in FHS supported the baseline language association (β =0.05, p =0.0495; β =0.06, p =0.03), aligning with the discovery analysis, but did not replicate for executive function (β =0.01, p =0.66; Data S3).

The significant variants upstream of SEMA6D were strong methylation quantitative trait loci (mQTLs) for CpG sites cg13300915 (β=−0.21, p=1.06×10−6) and cg27090409 (β=0.19, p=5.84×10−6), suggesting regulatory effects on methylation (Fig. 2B). Moreover, rs2959641 resides within a brain-specific enhancer region identified by ChIP-Seq data (Roadmap Epigenomics Consortium), with evidence of physical interaction with the enhancer region of SEMA6D from neuron-specific PLAC-seq data (Fig. 2C)25.

A novel locus within GRIN3A among APOE-ε4 Carriers
An additional genome-wide significant locus on chromosome 9 (lead SNP: rs28609108; MAF=0.05) was associated with better baseline executive function among APOE-ε4 carriers with a suggestively significant APOE-ε4 interaction (β =0.10, p =2.32×10−8; β =0.091, p =2.47×10−5), but not in non-carriers (β =0.01, p =0.43). This locus also showed nominal associations with baseline language (β =0.03, p =0.04) and baseline memory (β =0.03, p =0.02), but not with longitudinal outcomes.

Sensitivity analyses supported the stability of these findings. Interestingly, analysis of participants aged 60 and older showed a stronger association with baseline executive function (β =0.12, p =8.67×10−10; β =0.11, p =1.54×10−6). This region did not replicate in FHS for executive function (p’s>0.4). This locus mapped to an intron in GRIN3A, with variants in this region serving as expression quantitative trait loci (eQTL) for GRIN3A in pituitary gland tissue (p.FDR=1.40×10−7).

A novel locus near ITGB8 among APOE-ε4 non-carriers
Specific to APOE-ε4 non-carriers, we identified a locus on chromosome 7 (lead SNP: rs73072750; MAF=0.11) associated with worse executive functioning at baseline, with a nominally significant interaction effect (β =−0.06, p =4.67×10−10; β =0.07, p =8.55×10−6). In addition, we observed a nominally significant interaction effect for baseline memory (β =0.02, p =0.06; β =0.03, p =0.01). No significant associations in this region were observed for decline outcomes. These associations were consistent across both sensitivity analyses but did not replicate for executive function, memory, or language (Data S2 and S3). This locus mapped to the intronic region of a long non-coding RNA, LOC101927668, is a cis-junction eQTL for ITGB8 (BrainSeq: p.FDR=0.01), suggesting that the variant may influence splicing near ITGB8, potentially affecting gene expression.

SLCO1A2 variation and executive function decline
In non-carriers, we identified a novel locus on chromosome 6 (lead SNP: rs61926244; MAF=0.14) associated with executive functioning decline (β =−0.002, p =1.98×10−8; β =0.002, p =1.36×10−4; β =0.03, p =4.49×10−3) and memory decline (β =−9.29×10−4; p =0.15; β =8.63×10−4, p =0.0003, p.FDR =0.01). No significant effects were observed for language decline (β =−2.20×10−5, p =0.96; β =9.62×10−4, p =0.21; β =7.94×10−4, p =0.38). This variant is a cis-eQTL for SLCO1A2 in cerebellar tissue (BrainSeq; p.FDR=0.002). The association remained significantly associated with executive function decline across both sensitivity analyses, but was not significant for any other outcome.

Interaction between APOE-ε4 and a novel locus on Chromosome 14
The interaction analysis identified a novel locus on chromosome 14 (lead SNP: rs1631933; MAF=0.37), mapping proximally to SLC25A21, whereby rs1631933 modified the association between APOE-ε4 and executive function decline (β =−5.4×10−4, p =0.02; β =0.002, p =2.4×10−7; β =0.002, p =2.9×10−8). The interaction was driven by a strong positive association among APOE-ε4 carriers (p =2.4×10−7) and a modest negative association among APOE-ε4 non-carriers (p =0.02). A similar direction of effect was observed for memory decline (β =−3.8×10−4, p =0.40; β =0.001, p =0.04; β =0.002, p =0.05), although not for language decline (β =2.50×10−5, p =0.94; β =7.3×10−4, p =0.17; β =8.7×10−4, p =0.17). There was no interaction effect on baseline outcomes. We were unable to replicate this interaction effect in the FHS dataset (p’s>0.2).

Shared genetic architecture with complex traits
Our genetic correlation analyses examined the shared genetic architecture with baseline cognitive performance among APOE-ε4 carriers and non-carriers (Data S7). Figure 3 shows genetic correlations between baseline cognitive outcomes and complex traits, highlighting statistical differences between APOE-ε4 carriers and non-carriers.

In non-carriers, we observed positive genetic correlations with smoking cessation (genetic-correlation =0.23, p =0.01, p.FDR =0.02) and multiple sclerosis (genetic-correlation =0.14, p =0.03, p.FDR =0.07), and a negative correlation with hippocampal volume (HV; genetic-correlation =−0.27, p =0.02, p.FDR =0.06). Conversely, APOE-ε4 carriers exhibited the opposite pattern, with a negative genetic correlation with multiple sclerosis (genetic-correlation =−0.20, p =0.03, p.FDR =0.10) and a positive correlation with hippocampal volume (HV; genetic-correlation =0.26, p =0.02, p.FDR =0.10).

For baseline language, non-carriers showed a positive genetic correlation with smoking cessation (genetic-correlation =0.29, p =0.002, p.FDR =0.02) and primary sclerosing cholangitis (genetic-correlation =0.21, p =0.038, p.FDR =0.10), but were insignificant for Tourette syndrome (genetic-correlation =0.20, p =0.05, p.FDR =0.13), multiple sclerosis (genetic-correlation =−0.05, p =0.46, p.FDR =0.59), and hippocampal volume (genetic-correlation =−0.22, p =0.08, p.FDR =0.17). In contrast, APOE-ε4 carriers had negative genetic correlations with Tourette syndrome (genetic-correlation =−0.21, p =0.04, p.FDR =0.15), smoking cessation (genetic-correlation =−0.24, p =0.02, p.FDR =0.11), primary sclerosing cholangitis (genetic-correlation =−0.18, p =0.01, p.FDR =0.0495), and multiple sclerosis (genetic-correlation =−0.48, p =0.0005, p.FDR =0.01), but a positive correlation with brain structure HV (genetic-correlation =0.30, p =0.02, p.FDR =0.11).

For baseline memory, significant genetic correlations were observed only in APOE-ε4 carriers. There was a positive genetic correlation with celiac disease (genetic-correlation =0.53, p =0.0003, p.FDR =0.01). No significant genetic correlations with baseline memory were observed among non-carriers.

Gene- and pathway-level analyses
Our stratified analysis identified no significant (p.FDR<0.05) or suggestive (p.FDR<0.10) gene-level associations for APOE-ε4 carriers or non-carriers. In the interaction analysis involving SNP × APOE-ε4-carrier status, several genes had suggestive significance levels. For memory decline, associations were observed with DNAH11 (Chr7; p.FDR=0.095) and GRID1 (Chr10; p.FDR=0.095). Additionally, we report CLSPN (Chr1; p.FDR=0.099) specific to baseline language. These associations did not extend to other domains. DNAH11 did not reach false-discovery rate (FDR) significance in our primary analyses but passed FDR correction (Chr7; p.FDR=0.04) in a sensitivity analysis excluding individuals with comorbid conditions.

Several pathways were identified in our primary analysis, with each significant in one domain. In non-carriers, we observed an association with executive function decline and a regulation of entry of bacterium into host cell pathway (β =1.10, p.FDR =0.03; β =0.36, p.FDR =0.96; β =0.30, p.FDR =0.91). No pathways were significant among APOE-ε4 carriers. Pathway analyses of ε4-interaction meta-analysis results showed an association between baseline memory for the FC gamma receptor FCGR-dependent phagocytosis pathway, whereby the pathway showed positive enrichment in non-carriers and negative enrichment in carriers (β =0.07, p.FDR =0.95; β =−0.43, p.FDR =1.00; β =0.42, p.FDR =0.02). This association was also significant with exclusion of participants with relevant comorbid conditions (β =0.19, p.FDR =0.93; β =−0.50, p.FDR =1.00; β =0.41, p.FDR =0.04). All gene and pathway results are presented across each domain in Tables S5 and S6.

Associations with established AD-risk genes
From a curated list of 77 AD-associated SNPs from the Alzheimer’s Disease Sequencing Project (ADSP), 48 variants met inclusion criteria for the primary meta-analysis, having sufficient representation across cohorts. Here we report the top associations (p<0.005) to assess potential nominal interactions with APOE-ε4 status. Among APOE-ε4 carriers, nominal associations were identified with BIN1, USP6NL, CTSB, ABCA7, HLA-DQA1, and CD2AP. In non-carriers, associations included PTK2B, CLU, RBCK1, and USP6NL. Interaction analysis highlighted associations with BIN1, RBCK1, SPPL2A, and CLU.

After applying Bonferroni correction (α=0.001) for multiple testing, BIN1, PTK2B, CLU, HLA-DQA1, CTSB, ABCA7, and USP6NL remained significantly associated with baseline cognitive outcomes. Comprehensive results for all tested AD-risk gene associations, stratified by cognitive domain and APOE-ε4 status, are provided in Data S10.

Methods
Ethics statement
All participants provided informed consent in their respective cohort studies, and research was carried out in accordance with Institutional Review Board-approved protocols. Secondary analyses of all data were approved by the Vanderbilt University Medical Center Institutional Review Board and comply with all relevant ethical regulations. Sex was recorded by the parent studies, primarily via self-report and/or clinical records, and was considered in the design and analysis of the study (included as a covariate). Compensation, when provided, was administered by the original cohorts.

Participant information
This cross-ancestry study integrates harmonized cognitive data from nine cohorts focused on aging and AD, with a total participant count of 32,778. Of these participants, 29,354 self-reported as NHW (10,969 APOE-ε4 carriers—37%) and 3,424 self-reported non-Hispanic black (NHB) individuals (1508 APOE-ε4 carriers—44%). Table 1 presents an overview of participant characteristics by APOE-ε4 carrier status, while Data S1 provides more detailed participant characteristics by cohort. For brevity, we list the cohorts included in this study here; further details are provided in the supplement. These cohorts are: The Amyloid Treatment in Asymptomatic Alzheimer’s Disease (A4)43, the Adult Changes in Thought (ACT)44 study, the Alzheimer’s Disease Neuroimaging Initiative (ADNI—phases 1, 2, GO, and 3)45, the Baltimore Longitudinal Study of Aging (BLSA)46, the Biomarkers of Cognitive Decline Among Normal Individuals (BIOCARD)47 study, the National Alzheimer’s Coordinating Center (NACC)48,49, the interrelated longitudinal studies of the Religious Orders Study, the Rush Memory and Aging Project, and Minority Aging Research Study (ROS/MAP/MARS)50,51, the Knight Alzheimer’s Disease Research Center (Knight-ADRC)52, and the Wisconsin Registry for Alzheimer’s Prevention (WRAP)53,54.

Cognitive harmonization across cohort studies
This study leverages harmonized cognitive data from the ADSP Phenotype Harmonization Consortium, derived from the 9 cohorts of cognitive aging and AD described above. The harmonization of these cognitive scores allows for measures of cognitive performance to be analyzed across cohorts by ensuring scores are on a shared scale55,56. In brief, item-level neuropsychological responses were reviewed and mapped to domains, and domain-specific models were used to derive harmonized composite scores on a shared scale across cohorts that represent performance in cognitive domains, including memory, language, and executive function. It is important to note, however, that the cognitive scores are presented on an arbitrary metric (generally ranging from −3 to 3) and cannot be directly compared across cognitive domains (e.g., one unit of memory is not equivalent to one unit of language). The harmonized measures of executive function, language, and memory were leveraged in the current study.

Genetic data quality control and imputation
Raw genetic data were collected by cohort across a variety of genotyping arrays, and this information is available upon request. For cohorts that used multiple genotype chips, such as those with different phases or designs, datasets were first harmonized by addressing overlapping samples and removing duplicates from the chip with lower coverage. Quality control (QC) was then conducted separately by chip, after which the datasets were merged across chips. Duplicate samples were removed, cryptic relatedness was re-evaluated, and a final PCA was performed to identify and remove ancestry outliers. In cases where cohorts were genotyped using a single chip, no internal merging was required.

These cohort-specific datasets were processed through a standardized QC and imputation pipeline. Variant-level filtering excluded variants with a genotyping rate below 95%, a minor allele frequency (MAF) below 1%, or deviation from Hardy-Weinberg Equilibrium (p<1×10−6). Sample-level filtering removed individuals with discordance between self-reported and genetically determined sex, more than 1% missing genotyped data, or evidence of relatedness (measured by identity-by-descent or cryptic relatedness).

Principal component analysis (PCA) was performed separately within the NHW and NHB groups. To detect genetic ancestry outliers, principal components were projected onto the 1000 Genomes Project super populations to verify alignment between self-reported race and genetically inferred ancestry.

Genotype imputation was performed using the Trans-Omics for Precision Medicine (TOPMed) imputation server with the GrCH38 TOPMed reference panel57. Post-imputation QC excluded variants with low imputation quality (r²<0.8), duplicated or multi-allelic variants, and variants with MAF<1%. Iterative outlier removal was conducted based on 5 standard deviations from principal component distributions and visual inspection.

APOE genotyping
Each cohort conducted APOE genotyping using methods tailored to their specific protocols and available resources. In the A4 study, APOE genotyping was directly conducted as part of the screening protocol58. Determination of APOE genotypes in NACC and ACT was based on analysis of single-nucleotide variants rs7412 and rs42935859. For ADNI and ROS/MAP/MARS, APOE genotyping was conducted using pyrosequencing of codons 112 and 1583560. The BIOCARD study involved restriction of endonuclease digestion of PCR amplified DNA, carried out by Athena Diagnostics7. The BLSA cohort underwent two methodological phases: initial genotyping was performed using PCR amplification, followed by HhaI restriction isotyping61, and later transition to the TaqMan assay62. The Knight-ADRC cohort applied TaqMan assays for APOE genotyping, focused on rs429358 and rs741263. For WRAP, genotyping was conducted by Athena Diagnostics, Worcester, MA, and the Atwood Lab, Madison, WI53.

Baseline and cognitive decline endophenotypes
To generate the cognitive endophenotypes used in the GWAS, we first established baseline measures by extracting the first available cognitive score for each participant across three domains: executive function, language, and memory. To generate cognitive decline endophenotypes, we employed a null linear mixed-effects regression model to estimate cognitive trajectories. This model allowed both slope and intercept to vary for each participant and required participants to have data from at least two visits. We extracted slopes from the model for each participant for each cognitive domain, and this represented each participant’s rate of decline in cognitive performance. This approach has been successfully implemented by our group in previous GWAS focused on baseline memory and memory decline64,65. In total, we produced six endophenotypes: baseline executive function, baseline language, baseline memory, executive function decline, language decline, and memory decline. To ensure a normal distribution of cognitive measures, outliers based on cognitive scores were removed. Additionally, if a participant was an outlier in any domain, that individual was excluded from all other GWAS.

Genome-wide association analysis
For each of the six endophenotypes (detailed above), the following cohort-by-cohort approach was used to conduct GWAS:
Prior to conducting GWAS, we implemented a few additional QC steps. First, each cohort was restricted to self-reported race/ethnicity (i.e., NHW or NHB). We use genetic ancestry information to identify and exclude statistical outliers based on population PCs. The final population PCs were leveraged as covariates in all statistical models to minimize potential confounding effects of population stratification. From this point on, we will call analyses conducted in NHW or NHB as ancestry-stratified and analyses conducted across both NHW and NHB as cross-ancestry to align with our use of genetic information in group membership.

For APOE-ε4 stratified GWAS, participants in each ancestry group were further stratified by APOE-ε4 carrier status, defined as having 1 or 2 copies of ε4. Additional GWAS inclusion criteria included a baseline age of 50 years or older. Individual cohort GWAS were conducted with each cognitive endophenotype as an individual outcome. All GWAS implemented PLINK (v1.9, https://www.cog-genomics.org/plink/1.9)66, linear association model with additive allele coding, covarying for age, sex, and the first five genetic ancestry principal components. In addition to conducting APOE-ε4-stratified GWAS, we also performed APOE-ε4-interaction analyses, including a SNP×APOE-ε4-status interaction term of interest in the model.

Ancestry-stratified and cross-ancestry genome-wide meta-analysis
In order to increase our statistical power, we conducted fixed effects meta-analysis across cohorts (GWAMA, v.2.2.2)67. We first performed ancestry-stratified meta-analyses among NHW and NHB. To qualify for analysis, GWAS were required to have a minimum of 50 individuals. The resulting meta-analyses were filtered to include variants present in at least seven cohorts for NHW and at least two cohorts for NHB. To better understand the genetic interplay of APOE-ε4 and common variants on cognition across ancestries, we performed a cross-ancestry meta-analysis, inputting the NHW and NHB meta-analyses into this meta-analysis. As a result, the NHW analyses included the A4, ACT, ADNI, BIOCARD, BLSA, NACC, ROS/MAP/MARS, Knight-ADRC, and WRAP cohorts. NHB analyses included the BLSA, NACC, and ROS/MAP/MARS cohorts. To ensure findings are representative of both ancestries, we included only variants that were present in both NHW and NHB groups. Results from the primary and sensitivity analyses are provided in Data S2, while replication results are presented in Data S3.

Replication analyses
Replication of all genome-wide significant variants was sought through an independent cohort, the FHS68 (n=3497, n carriers =800–23%). This cohort, initially established to study cardiovascular disease, expanded in the 1990s to include research on aging and neurological conditions, such as AD. Cognitive domains, memory, executive function, and language, were assessed both at baseline and longitudinally. Cognitive domains (memory, executive function, and language) were assessed both at baseline and longitudinally, using the same approach55,69. The replication analyses used the same analytical models and covariates as in the discovery GWAS analyses.

Sensitivity analyses
Three sensitivity analyses were conducted, following an identical procedure to the primary analysis. The exact numbers for individuals excluded for each sensitivity analysis varied slightly across the endophenotypes; detailed information by endophenotype is provided in Data S1. These analyses were designed to assess the robustness of our findings by addressing potential confounding factors. The first sensitivity analysis excluded individuals with a history of comorbid conditions, which were classified based on information provided by their respective cohorts. These comorbidities included various neurodegenerative diseases (e.g., Parkinson’s disease, frontotemporal dementia), neuropsychiatric disorders (e.g., autism, bipolar disorder, schizophrenia), traumatic brain injury with extended loss of consciousness (5+min), recent cancer, or other severe conditions (see Data S8), as such conditions may influence the observed associations. The second sensitivity analysis excluded individuals under age 60 to assess whether primary associations were robust to age-related differences in disease risk. The third sensitivity analysis included education as a covariate, due to educational attainment’s strong association with cognition and AD, helping to isolate the effects of genetic factors.

Expression and methylation quantitative trait locus analyses
To investigate the functional implications of significant genetic variants on gene expression, we consulted the following eQTL databases: GTEx (https://gtexportal.org)70,71, eQTLGen Consortium (https://www.eqtlgen.org)72, BrainSeq (http://eqtl.brainseq.org)73, and the xQTL database (https://mostafavilab.stat.ubc.ca/xqtl/)74. Additionally, we used FUMA75 for SNP annotation and functional mapping, including annotation of chromatin interactions and tissue-specific regulatory elements. HaploReg v4.2 (https://pubs.broadinstitute.org/mammals/haploreg/haploreg.php)76 was used to annotate non-coding variants with chromatin state information and to predict the regulatory potential of associated SNPs. Detailed QTL evidence for each significant SNP, as obtained from these databases, is provided in Data S4. The QTL significance threshold was set a priori at a FDR adjusted p value of p.FDR<0.05, whereby FDR was defined based on the database’s FDR adjustment protocols.

Gene- and pathway-level analyses
We performed APOE-ε4-stratified gene- and pathway-based analyses using the Multi-marker Analysis of GenoMic Annotation (MAGMA v1.09)77 software on both ancestry-stratified and the cross-ancestry meta-analysis results. MAGMA aggregates SNP-level association signals to the gene level while accounting for linkage disequilibrium, and then tests whether sets of genes (pathways) are associated with the phenotype. We annotated SNPs to genes with NCBI build-38 gene locations, and pathway annotations were compiled from the Molecular Signatures Database (v7.0) gene sets78. All gene- and pathway-based tests were corrected for multiple comparisons with FDR significance set at p.FDR<0.05 and suggestive significance set at p.FDR<0.10. Gene-level results are provided in Data S5, and pathway-level results are detailed in Data S6.

Genetic correlation analysis
We next conducted genetic correlation tests between our meta-analyses and a previously published set of 65 previously published GWAS of complex traits. Genetic correlation analyses were restricted to the NHW sample due to the limited availability of GWAS in non-Europeans samples. The genetic correlation tests aimed to uncover if there is shared genetic architecture between genetic susceptibility to APOE-ε4-modulated cognitive performance and genetic risk for relevant complex traits. Genetic correlation estimates were calculated with the Genetic Covariance Analyzer (GNOVA)79, which leverages GWAS summary statistics to estimate genetic correlation across traits to expand discovery of the distinct and shared genetic architecture of cognitive decline and complex traits. To calculate genetic correlations, SNP-associations were placed on a z-score scale to allow for cross-trait comparison, and then correlation values were calculated between each trait, adjusting for genome-wide LD inflation with ancestry-matched LD scores. Analyses were adjusted for multiple comparisons using the FDR approach. Genetic correlation results are provided in Data S7.

Reporting summary
Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability
All harmonized cognitive data utilized in our genome-wide association studies (GWAS) analyses, as well as the summary statistics derived from these analyses, are publicly accessible on the National Institute on Aging Genetics of Alzheimer’s Disease Data Storage Site (NIAGADS) website. For access to the harmonized cognitive data and summary statistics, please visit https://dss.niagads.org/. The Framingham Heart Study (FHS) data used for replication analyses are available to approved researchers through dbGaP under accession phs000007.v33.p14 [https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000007.v33.p14]. Researchers may also request access directly through the Framingham Heart Study [https://www.framinghamheartstudy.org/fhs-for-researchers/] or the FHS Brain Aging Program [https://www.bumc.bu.edu/fhs-bap/contact/]. A data use proposal must be submitted and approved by study investigators, and a data sharing fee may apply. Source data are provided with this paper.

Code availability
Code used for data processing, statistical analyses, and figure generation has been deposited in a public GitHub repository and is available at: https://github.com/VUMC-VMAC/APOE_Stratified_GWAS. A permanent archived version corresponding to the exact code used for this analysis is available through Zenodo80 under the DOI: 10.5281/zenodo.17833353. The code is released under an MIT License. There are no access restrictions.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This is a very nice manuscript and I don't have many suggestions to improve it (in other words, I think it is already pretty great!). The authors performed APOE-e4 stratified GWAS. The methods for the genetic data preparation and GWAS adequately explained. Replication analysis was perfumed. Follow up analysis was appropriate. The manuscript is well written, everything is clear. A few very minor comments:\n- Please add information about accessing FHS data to the data availability statement.\n- Figure 3: there are error bars for \"insignificant correlations\", but there's no information about which correlations were estimated. Carriers or non-carriers? the readers has to be infer it which take some effort.\n- If word limit allows, it would be nice to refer to the ancestry-related heterogeneity in APOE alleles associations with AD and cognitive outcomes, which is (in my opinion) a stronger argument for assessing interaction than 'APOE-e4 is not deterministic\", as many pathogenic, even highly pathogenic, variants are not deterministic.\n- It will be nice to report frequencies of the interacting variants across populations to see if they can explain some of the heterogeneity in APOE alleles associations across populations defined by genetic ancestry.\nVery nice work!", "reviewer_2": "”Genetic modifiers of APOE-ε4-associated cognitive decline”, constitutes a cross-ancestry APOE-stratified and interaction\nGenome-wide association study on cognitive level and decline. The primary goal was to identify gene variants that can\nmodify APOE ε4- associated cognitive decline. Results reveal two variants specific to APOE ε4 carriers and one specific to\nnon-carriers, with effects on level or decline in executive functioning and language. One variant was also showing an\ninteraction with APOE in the replication sample, however only on language. No SNPs were identified in relation to level or\ndecline in memory. The work builds upon previous publications from the same group, using the same data and similar\nanalyses, but instead examining sex-stratified GWAS (Eissman et al., Alz Dem 2024). I generally find methods and analyses\nrobust and the paper well written. However, given that most of the results did not replicate in the independent samples,\nstrong conclusions are somewhat difficult to draw.\nSpecific comments:\n1. What are the authors conclusions and comments on the lack of replication?\n2. I find the study population rather diverse, spanning over both AD/dementia and normal cognitive ability/normal cognitive\naging. About 2/3 of participants are reported to be cognitively normal. In the list of co-morbidities, you list eg. frontotemporal\ndementia, but not AD and vascular dementia, presumably as those diseases were part of the study group? How many\nparticipants had an AD diagnosis or other dementias, and MCI, at baseline vs follow up? Please add this to a table, eg.\nTable 1. How did dementia diagnostics compare between APOE genotype and ethnic groups?\n3. Related, I think it could be helpful if you clarify to readers whether you are interested in gene*APOE interactions on\ncognition, AD or normal aging. In the beginning of the discussion, you say “Our findings offer new insights into the genetic\narchitecture underlying APOE effects on cognition”, but genetic effects on cognition is likely to differ depending on age and\ndiagnostics. In the abstract, you say that three loci were identified, but does not mention in relation to what cognitive\nphenotype.\n4. Among the cognitive tests included, memory is the one that is closest related to AD. What are the authors comments on\nthe fact that no significant findings were made for memory? Why do you think most APOE dependent genetic effects were\nrelated to executive functions? Does this e.g. reflect differences in test reliability?\n5. Please also add information about number of visits, preferably to Table 1.", "reviewer_3": "The manuscript ‘Genetic modifiers of APOE-ε4-associated cognitive decline' by A.G. Contreras et. al., is a complex genome-\nwide association study (GWAS)-based assessment of modifiers of APOE-ε4 in the context of cognitive decline using large\ndatabases to probe for candidate genes. It is well established that 1 copy of APOE-ε4 confers ~3-fold and 2 copies ~10-12\nfold increased risk of developing Alzheimer’s disease (AD) over the lifespan. However, every individual APOE-ε4 carrier\ndoes not develop AD, so assessing genetic modifiers is timely and relevant. This is also prescient, since APOE-ε4 confers\nrisk for other disorders that can shorten the lifespan, including immunological disturbances, which may or may not influence\nAD pathogenesis.\nConducting this kind of large-scale genomics assessment requires an enormous amount of patient input data (e.g., APOE-ε4\ncarrier status plus multiple measures of cognition- namely memory and executive function). The multisite, collaborative\nresearch team, with extensive experience in this type of functional genomics experimental design, generated an assessment\nusing 9 primary cohorts with harmonized cognitive data for a cross-ancestry (in this case White and Black databases)\nassessment. Specifically, a non-Hispanic White (NHW; n=29,354) cohort was compared/contrasted to a non-Hispanic Black\n(NHB; n=3,424) cohort. Several hits were validated, including SEMA6D and GRIN3A in APOE-ε4 carriers and ITGB8 in non-\nAPOE-ε4 carriers, among other relevant findings of modifiers based upon cognitive performance and APOE carrier status.\nValidation was conducted using data obtained from the Framingham Heart Study (n=3,497). Identification of several\npotential APOE-ε4 candidate modifiers that pass stringent statistical and validation assessments are important to advancing\nthe field.\nThe manuscript is well written, referenced, and the figures are helpful to understand the identification, and potential impact,\nof the APOE-ε4 modifier gene candidates. It is interesting that both APOE-ε4 modifiers and non-APOE-ε4 modifiers were\nfound in the context of cognitive decline. The Reviewer appreciated the effort made by the authors to describe, not just list,\nthe top hit candidate genes, and put the information into the context of what is known about influence on AD (or cognitive\ndecline) pathogenesis, especially in relation to immune function. This in depth description will be helpful to interested\nreadership.\nThere are several key issues to consider for improvement of the submission.\n1. Sex effects. Given the known impact of APOE-ε4 on AD pathogenesis in terms of sex, it is surprising that sex was not part\nof the analyses (or sub analyses)- other than reporting the cohorts were largely sex balanced. This seems to be a critical\nmissing piece of the present submission. The authors should include sex in a primary analysis (rather than a covariate) to\nsee if there are genetic modifiers that are sex-dependent (or not). This would greatly increase the impact of the findings in\nthis Reviewer’s opinion. If the study does not have enough power to conduct sex-based analyses, this is an issue- that\nshould be addressed specifically in a caveats/pitfalls/limitations section.\n2. Sample size discrepancies. The authors should expand the pitfalls/limitations upon the large discrepancies in terms of\nparticipants in the NHW and NHB cohorts (nearly a tenfold difference), and how this may skew data analyses and\ninterpretations. This is not trivial. Further, more information, as well as caveats, about using the Framingham Heart Study as\na validation cohort is recommended. What is the racial breakdown of the study participants used for validation? How does\nthis relate to the NHW and NHB cohorts? In other words, please justify and explain in greater detail why the Framingham\nHeart Study was applied, and if alternative validation strategies could have been employed.\n3. Expanded limitations section. The Reviewer acknowledges the existing limitations present in the Discussion. However,\nthe authors are urged to re-evaluate their data, gene modifier findings (especially the impact these potential gene modifiers\nmay have (or not), and limitations for a more in-depth assessment of what has gone right (and gone wrong) in terms of their\nstudy design, results, and implications."}, "author_response": "Author Response: We thank Reviewer 1 for their thoughtful and positive feedback on\nour manuscript. We appreciate the constructive suggestions provided and have\naddressed each point in detail below.\nComment 1-1: “Please add information about accessing FHS data to the data\navailability statement”\nAuthor Response: We thank the reviewer for this helpful suggestion. We have added\ninformation about how to access the Framingham Heart Study (FHS) data to the Data\nAvailability section.\nChanges made:\nThe Data Availability section now states:\n“The Framingham Heart Study (FHS) data used for replication analyses are available to\napproved researchers through dbGaP (phs000007.v33.p14):\nhttps://www.ncbi.nlm.nih.gov/projects/gap/cgi-\nbin/study.cgi?study_id=phs000007.v33.p14. Researchers may also request access\ndirectly through the Framingham Heart Study\n(https://www.framinghamheartstudy.org/fhs-for-researchers/) or the FHS Brain Aging\nProgram (https://www.bumc.bu.edu/fhs-bap/contact/). A data use proposal must be\nsubmitted and approved by study investigators, and a data sharing fee may apply.”\nComment 1-2: “Figure 3: there are error bars for \"insignificant correlations\", but there's\nno information about which correlations were estimated. Carriers or non-carriers? the\nreaders has to be infer it which take some effort”\nAuthor Response: We appreciate this helpful clarification request. To improve figure\nclarity, we have updated the figure legend to explicitly state that the top bar always\nrepresents non-carriers and the bottom bar represents APOE-ε4 carriers. We also\nclarified that traits are only included if at least one of the two groups shows a statistically\nsignificant correlation.\nChanges made:\nThe Figure 3 legend now states:\n“This figure presents the genetic correlations between complex traits and baseline\ncognitive outcomes (executive function, language, and memory) that statistically differed\nby APOE-ε4 status. Traits are included if at least one APOE-ε4 stratum demonstrated a\nstatistically significant correlation, defined as FDR < 0.05. For each trait, the top bar\nrepresents non-carriers and the bottom bar represents APOE-ε4 carriers. Statistically\nsignificant correlations (FDR < 0.05 and p < 0.05) are shown in color: purple for non-\ncarriers and green for carriers. Insignificant correlations (p > 0.05) are displayed in gray.\nError bars represent 95% confidence intervals for the genetic correlation estimates.”\nComment 1-3: “If word limit allows, it would be nice to refer to the ancestry-related\nheterogeneity in APOE alleles associations with AD and cognitive outcomes, which is\n(in my opinion) a stronger argument for assessing interaction than 'APOE-e4 is not\ndeterministic\", as many pathogenic, even highly pathogenic, variants are not\ndeterministic.”\nAuthor Response: We thank the reviewer for this point. We have now emphasized the\nancestry-related heterogeneity in APOE associations in the Introduction and positioned\nit as a motivation for assessing interactions.\nChanges made: Added this to the introduction: “This ancestry-related heterogeneity\nprovides additional motivation for assessing genetic interactions that may help explain\ndifferences in APOE-ε4 effects across populations.”\nComment 1-4: “It will be nice to report frequencies of the interacting variants across\npopulations to see if they can explain some of the heterogeneity in APOE alleles\nassociations across populations defined by genetic ancestry.”\nAuthor Response: We thank the reviewer for this thoughtful suggestion. The allele\nfrequencies for the interacting variants are included in Table 2 and were generally\nsimilar across ancestry groups, suggesting they do not explain the ancestry-related\nheterogeneity in APOE associations. To make this clearer to readers, we have added a\nsentence to the Results section explicitly summarizing this point.\nChanges made: We added a clarifying sentence to the Results section: “Allele\nfrequencies for the identified interacting variants were similar across ancestry groups\n(Supplemental Table 2).”\nAuthor Response: We thank the reviewer for their thoughtful evaluation of our work\nand for their positive assessment of the methods, analyses, and writing. We appreciate\nthe feedback provided and have addressed each point in detail below.\nComment 2-1: “What are the authors conclusions and comments on the lack of\nreplication”\nAuthor Response: In our discovery analysis, we observed an APOE-specific\nassociation with executive function that survived correction for multiple comparisons,\nand an association with language performance that fell just below our genome-wide\nthreshold. Importantly, we did not observe statistical evidence of domain-specificity as\nthe confidence intervals for these associations overlapped. When we moved to\nreplication, we replicated the APOE-specific association with language including the\nsame direction of effect in the same APOE-stratified group, with an effect size within the\n95% confidence interval of our discovery analysis. Again, we did not observe evidence\nof\nevidence\nof a domain-specific effect, but the association with executive function did not reach\nstatistical significance. Together, the collective evidence from the discovery and\nreplication analysis suggests that the minor allele rs2959641is associated with better\ncognitive performance among APOE-ε4 carriers. Given the variability in the cognitive\ndomain that is most strongly associated across datasets, we suggest that future work\nwill need to clarify whether this association is domain-specific.\nChanges Made: We expanded the Discussion to include a section interpreting the\nlimitations of the replication results in more detail, emphasizing the lack of strict domain\nspecificity, the consistency of the language association in APOE-ε4 carriers, and the\nneed for future work to clarify domain-specific versus cross-domain effects.\n“Third, our findings showed a lack of strict domain specificity: while significant\nassociations emerged for executive function and language, no genome-wide significant\nloci were detected for memory. This may reflect APOE-ε4’s strong main effect on\nmemory, leaving less residual variance for modification, but it is also notable that\nconfidence intervals overlapped across domains and several top variants showed\nnominal associations with memory. This pattern suggests that identified loci may reflect\ncross-domain influences on late-life cognition, though differences in test reliability and\ncohort harmonization could also contribute.”\nComment 2-2: “I find the study population rather diverse, spanning over both\nAD/dementia and normal cognitive ability/normal cognitive aging. About 2/3 of\nparticipants are reported to be cognitively normal. In the list of co-morbidities, you list\neg. frontotemporal dementia, but not AD and vascular dementia, presumably as those\ndiseases were part of the study group? How many participants had an AD diagnosis or\nother dementias, and MCI, at baseline vs follow up? Please add this to a table, eg.\nTable 1. How did dementia diagnostics compare between APOE genotype and ethnic\ngroups?”\nAuthor Response: We thank the reviewer for this insightful suggestion. We have now\nadded the diagnostic breakdown across Alzheimer’s disease, mild cognitive impairment\n(MCI), and cognitively normal participants stratified by APOE-ε4 carrier status to Table\n1. To further address the reviewer’s point, we have also provided comparison of\ndementia diagnoses across ethnic groups in Supplemental Table 2.\nChanges made: Updated the Demographics/Study Population subsection in Results:\nAmong participants with longitudinal data, 9.10% converted from cognitively normal\n(CN) to mild cognitive impairment (MCI), 4.71% from CN to AD, and 6.36% from MCI to\nAD. Table 1 now reflects the total diagnostic breakdown and across carrier status.\nAdditionally, Supplemental Table 1 has been updated to reflect N for Cognitively\nNormal, MCI, and AD across ethnic groups and all domains.\nComment 2-3: “Related, I think it could be helpful if you clarify to readers whether you\nare interested in gene*APOE interactions on cognition, AD or normal aging. In the\nbeginning of the discussion, you say “Our findings offer new insights into the genetic\narchitecture underlying APOE effects on cognition”, but genetic effects on cognition is\nlikely to differ depending on age and diagnostics\nAuthor Response: We thank the reviewer for this comment. Our study is focused on\ngene×APOE interactions on late-life cognitive outcomes, primarily memory, executive\nfunction and language, rather than clinical diagnosis of AD. While APOE-ε4 is a strong\ngenetic risk factor for AD, its cognitive effects extend into normal aging. We now clarify\nin both the Abstract and Discussion that our primary phenotypes reflect cognitive\nperformance and decline, which are continuous traits capturing both pathological and\nnormative aging processes. This approach increases power to detect modifier loci and\nallows for transdiagnostic insights into the mechanisms underlying APOE effects.\nChanges made:\nWe revised the beginning of the Discussion:\n“The APOE-ε4 allele remains among the strongest genetic risk factors for Alzheimer's\ndisease (AD), yet how APOE interacts with other genetic factors to influence cognitive\noutcomes is incompletely understood. Here, we conducted the largest APOE-ε4-\nstratified genome-wide analysis of late-life cognitive performance and decline to date,\npooling harmonized cognitive data from over 32,000 individuals across nine cohorts.\nOur findings offer new insights into the genetic architecture underlying APOE-ε4 effects\non cognition across aging and disease.”\nWe revised the abstract:\n“Our primary outcome was late-life cognition, measured using harmonized composite\nscores for memory, executive function, and language, modeled as continuous traits\nreflecting both normative cognitive aging and disease-related decline. We identified two\ngenome-wide significant loci in APOE-ε4 carriers, one for executive function decline and\none for executive function level, and one locus in non-carriers for language. These loci\nwere further linked through expression and methylation databases to SEMA6D,\nGRIN3A, and ITGB8.”\nComment 2-4: “In the abstract, you say that three loci were identified, but does not\nmention in relation to what cognitive phenotype”\nAuthor Response: We now clarify that our primary outcome was late-life cognition,\nmeasured using harmonized composite scores for memory, executive function, and\nlanguage. We have revised the abstract to specify which cognitive domain each locus\nwas associated with.\nChanges made:\n“Our primary outcome was late-life cognition, measured using harmonized composite\nscores for memory, executive function, and language, modeled as continuous traits\nreflecting both normative cognitive aging and disease-related decline. We identified two\ngenome-wide significant loci in APOE-ε4 carriers, reaching genome-wide significance\nfor executive function. These loci also demonstrated nominal associations across the\nother domains, suggesting broad effects on cognition. In non-carriers, we identified a\ngenome-wide significant association at ITGB8 restricted to executive function, and\nanother locus associated with language. These loci were further linked through\nexpression and methylation databases to SEMA6D, GRIN3A, and ITGB8.”\nComment 2-5: “Among the cognitive tests included, memory is the one that is closest\nrelated to AD. What are the authors comments on the fact that no significant findings\nwere made for memory? Why do you think most APOE dependent genetic effects were\nrelated to executive functions? Does this e.g. reflect differences in test reliability?”\nAuthor Response: We agree that memory is a core domain affected in AD and an\nimportant phenotype in genetic studies. Interestingly, APOE-ε4’s main effect on\ncognition is strongest for memory. One possibility is that the strong main effect of APOE\non memory leaves less residual variance for genetic modification, whereas executive\nfunction and language may be more sensitive to modifier effects given weaker direct\neffects of APOE and greater inter-individual variability. Importantly, most of our\nassociations do not demonstrate strict domain specificity, with overlapping confidence\nintervals across executive function, language, and memory. Although no genome-wide\nsignificant loci were detected for memory, several top variants showed nominal or\nsuggestive associations with memory, suggesting that these effects may extend across\ndomains. We now note this in the Discussion, and also acknowledge that differences in\ntest reliability and measurement heterogeneity across cohorts could contribute to\ndomain-specific detection.\nChanges made:\nClarified and expanded the Strengths/Limitations section of the Discussion.\n“Second, despite harmonization efforts, residual differences in cognitive assessments\nand follow-up frequency across cohorts may contribute to phenotype heterogeneity.\nThird, our findings showed a lack of strict domain specificity: while significant\nassociations emerged for executive function and language, no genome-wide significant\nloci were detected for memory. This may reflect APOE-ε4’s strong main effect on\nmemory, leaving less residual variance for modification, but it is also notable that\nconfidence intervals overlapped across domains and several top variants showed\nnominal associations with memory. This pattern suggests that identified loci may reflect\ncross-domain influences on late-life cognition, though differences in test reliability and\ncohort harmonization could also contribute.”\nComment 2-6: “Please also add information about number of visits, preferably to Table\n1.\nAuthor Response: We have now included the mean and standard deviation of follow-\nup visits per participant in Table 1, both overall and stratified by APOE-ε4 carrier status.\nChanges made: Table 1 was updated to report the mean (SD) number of follow-up\nvisits per participant, shown overall and by APOE-ε4 carrier status.\nAuthor Response: We thank the reviewer for their thoughtful and thorough evaluation\nof our manuscript, and for their positive comments regarding the study design, writing,\nand interpretation of results. We appreciate the recognition of the value of our APOE-\nstratified GWAS approach in identifying candidate genetic modifiers of cognitive decline,\nand we are encouraged that the biological framing of our top loci was found to be\ninformative.\nWe also thank the reviewer for raising several important points that have helped us\nfurther strengthen the manuscript. In the responses below, we address each of the\nreviewer’s specific suggestions in detail, including requests for expanded discussion of\nsex effects, ancestry-specific limitations, replication caveats, and the interpretation of\ndomain-specific cognitive findings. Where appropriate, we have revised the manuscript\nto incorporate additional analyses, clarify methodological decisions, and expand the\nlimitations section to reflect the reviewer's feedback.\nComment 3-1: “Sex effects. Given the known impact of APOE-ε4 on AD pathogenesis\nin terms of sex, it is surprising that sex was not part of the analyses (or sub analyses)-\nother than reporting the cohorts were largely sex balanced. This seems to be a critical\nmissing piece of the present submission. The authors should include sex in a primary\nanalysis (rather than a covariate) to see if there are genetic modifiers that are sex-\ndependent (or not). This would greatly increase the impact of the findings in this\nReviewer’s opinion. If the study does not have enough power to conduct sex-based\nanalyses, this is an issue- that should be addressed specifically in a\ncaveats/pitfalls/limitations section.”\nAuthor Response: We thank the reviewer for highlighting the importance of sex as a\npotential modifier of APOE-related cognitive decline. We fully agree that sex-stratified\nand sex-interaction analyses are biologically meaningful and warranted in the context of\nAPOE-ε4 effects. In the present study, however, our primary focus was on identifying\ngenetic modifiers of APOE-ε4 status across cognitive domains in a cross-ancestry,\ngenotype-stratified design. Incorporating sex as an additional stratification or interaction\nfactor would substantially expand the analytic scope and number of multiple-testing\ncomparisons, requiring a separate analytical framework and power considerations.\nThat said, we recognize the reviewer’s concern and have added a statement to the\nLimitations section noting that sex effects were modeled as covariates in all analyses,\nand that dedicated sex-specific analyses are beyond the scope of this study.\nImportantly, we are currently pursuing a complementary, in-depth analysis of sex-\ndependent genetic modifiers of APOE effects on cognition, which will be reported in a\nseparate manuscript.\nChanges made:\nUpdated and expanded the limitation section of the Discussion.\n“Fourth, while sex was included as a covariate, we did not examine sex-specific genetic\nmodifiers, and our analyses were not powered to evaluate interactions with sex;\ndedicated analyses of sex-dependent effects will be the focus of future work.\nComment 3-2: “Sample size discrepancies. The authors should expand the\npitfalls/limitations upon the large discrepancies in terms of participants in the NHW and\nNHB cohorts (nearly a tenfold difference), and how this may skew data analyses and\ninterpretations. This is not trivial. Further, more information, as well as caveats, about\nusing the Framingham Heart Study as a validation cohort is recommended. What is the\nracial breakdown of the study participants used for validation? How does this relate to\nthe NHW and NHB cohorts? In other words, please justify and explain in greater detail\nwhy the Framingham Heart Study was applied, and if alternative validation strategies\ncould have been employed.”\nAuthor Response: We thank the reviewer for raising these important points. We\nacknowledge the sample size imbalance between NHW and NHB participants in our\ndiscovery sample, which reflects broader disparities in cohort availability and\nlongitudinal cognitive phenotyping in genomics research. While we employed stratified\nmodels and used methods designed to mitigate confounding due to unequal\nrepresentation, this imbalance inevitably results in differential statistical power and\nreduced sensitivity for detecting ancestry-specific associations in the NHB subgroup.\nWe now explicitly discuss this limitation and its implications for interpretation in the\nrevised Discussion.\nRegarding replication, the Framingham Heart Study (FHS) cohort is composed almost\nentirely of NHW participants. We selected FHS due to its high-quality longitudinal\ncognitive phenotypes, availability of genome-wide genotype data with APOE status, and\nestablished utility for genetic validation studies. At the time of analysis, it remained one\nof the few independent cohorts with comparable cognitive measures and APOE-\nstratified genotypes. However, we agree with the reviewer that this limits our ability to\nvalidate findings in underrepresented populations and have added a caveat to that\neffect.\nChanges made:\nClarified and updated the limitations section:\n“First, while our cohort includes diverse ancestry groups, statistical power was\nsubstantially lower in non-European ancestry participants, and the replication cohort\n(FHS) included individuals primarily of European descent, limiting generalizability.”\nComment 3-3: “Expand limitations section. The Reviewer acknowledges the existing\nlimitations present in the Discussion. However, the authors are urged to re-evaluate\ntheir data, gene modifier findings (especially the impact these potential gene modifiers\nmay have (or not), and limitations for a more in-depth assessment of what has gone\nright (and gone wrong) in terms of their study design, results, and implications.”\nAuthor Response: We thank the reviewer for this suggestion and have expanded the\nDiscussion to provide a more in-depth assessment of the strengths and limitations of\nour study. In particular, we now highlight the reduced power to detect interaction effects\nin the smaller NHB subgroup, the impact of phenotype heterogeneity across cohorts,\nthe lack of strict domain specificity in our findings, and the limits of power for sex-\nspecific and rare APOE genotype analyses. We also note that while our top loci\nhighlight promising APOE-dependent modifiers, their biological relevance remains to be\nvalidated in future studies. Finally, we emphasize that certain extensions, such as sex-\nor polygenic interaction models, were beyond the scope of the present work but are\nbeing pursued in follow-up analyses.\nChanges made: We expanded the Limitations section of the Discussion to explicitly\naddress ancestry imbalances and replication caveats, phenotype harmonization, lack of\ndomain specificity, limited power for sex and rare genotype analyses, and the need for\nfuture validation.\n“Key strengths of our study include well-harmonized longitudinal cognitive data across\nnine cohorts and the use of APOE-ε4-stratified models, which enhance power to detect\nmodifying genetic effects. However, several limitations should be noted. First, while our\ncohort includes diverse ancestry groups, statistical power was substantially lower in\nnon-European ancestry participants, and the replication cohort (FHS) included\nindividuals primarily of European descent, limiting generalizability. Second, despite\nharmonization efforts, residual differences in cognitive assessments and follow-up\nfrequency across cohorts may contribute to phenotype heterogeneity. Third, our findings\nshowed a lack of strict domain specificity: while significant associations emerged for\nexecutive function and language, no genome-wide significant loci were detected for\nmemory. This may reflect APOE-ε4’s strong main effect on\nmemory, leaving less residual variance for modification, but it is also notable that\nconfidence intervals overlapped across domains and several top variants showed\nnominal associations with memory. This pattern suggests that identified loci may reflect\ncross-domain influences on late-life cognition, though differences in test reliability and\ncohort harmonization could also contribute. Fourth, while sex was included as a covariate,\nwe did not examine sex-specific genetic modifiers, and our analyses were not powered to\nevaluate interactions with sex; dedicated analyses of sex-dependent effects will be the\nfocus of future work. Finally, our analyses were not powered to evaluate rarer APOE\ngenotypes individually (e.g., ε2/ε2, ε2/ε3), and genetic correlation analyses were limited to\nEuropean-descent data. Taken together, these factors shape both the opportunities and\nlimits of our study: while we identify promising loci, further validation and mechanistic\nwork are needed to determine their functional relevance and generalizability.”"}, {"round": 2, "reviewer_comments": {"reviewer_1": "I'm sorry for the delay in review. The manuscript looks great, thank you for addressing my comments!", "reviewer_2": "I am pleased with the authors thorough responses to both mine and the other reviewers comments, and the changes made\nto the manuscript. The manuscript has improved substantially. I have no further concerns or comments. Thank you for your\nwork!", "reviewer_3": "I have read the revised submission and the authors' responses to the initial criqitues. I find the revision to suitably address\nthe principal inquiries of the Review panel. Although the Reviewer would like to see an analysis by sex, the rationale\nprovided by the research team indicating a lack of power for this assessment is adequate. The pitfalls and limitations section\nis well written. Overall, I believe the manuscript will appeal to the readership of Nature Communications."}, "author_response": ""}]

Decision letter