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Article

APOE Genotype-Stratified Meta-Analysis of Cognitive Decline Reveals Novel Loci for Language and Global Cognitive Function in Older Adults

by
Vibha Acharya
1,
Kang-Hsien Fan
1,
Beth E. Snitz
2,
Mary Ganguli
3,4,
Steven T. DeKosky
5,
Oscar L. Lopez
2,
Eleanor Feingold
1 and
M. Ilyas Kamboh
1,3,*
1
Department of Human Genetics, School of Public Health, University of Pittsburgh, Pittsburgh, PA 15261, USA
2
Department of Neurology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA
3
Department of Psychiatry, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA
4
Department of Epidemiology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA
5
McKnight Brain Institute and Department of Neurology, College of Medicine, University of Florida, Gainesville, FL 32610, USA
*
Author to whom correspondence should be addressed.
Int. J. Mol. Sci. 2025, 26(14), 6940; https://doi.org/10.3390/ijms26146940
Submission received: 27 May 2025 / Revised: 28 June 2025 / Accepted: 16 July 2025 / Published: 19 July 2025
(This article belongs to the Section Molecular Genetics and Genomics)
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Abstract

Apolipoprotein E (APOE) allele 4 (APOE4), one of the robust genetic risk factors for AD, has also been associated with cognitive decline in terms of memory, executive function, language, and global cognitive function. APOE genotype-stratified analysis can help to identify additional genetic loci which might be masked due to a strong effect of APOE4. We conducted a genome-wide meta-analysis in APOE2 carriers, APOE4 carriers, and APOE 3/3 homozygote groups among 2969 non-Hispanic Whites aged ≥ 65 years using slopes of decline over time across five cognitive domains (attention, language, executive function, memory, and visuospatial function) and global cognitive function. We identified novel genome-wide significant associations for decline in global cognitive function in the intergenic region between RNU7-66P/RNA5SP208 at rs116379916 (p = 1.44 × 10−9) in the APOE 3/3 group and for decline in language in the intergenic region between LINC0221/DTWD2 at rs13187183 (p = 3.79 × 10−8) in APOE4 carriers. A previously reported locus for decline in attention near RASEF at rs6559700 (p = 9.95 × 10−9) was found to be confined to the APOE 3/3 group. We also found two sub-threshold significant associations in the APOE 2 group for decline in attention (IL1RL2/rs77127114; p = 8.64 × 10−8) and decline in language (YTHDC2/KCNN2, rs116191836; p = 5.66 × 10−8). Our study points to potential biological pathways pertaining to specific domains within each APOE genotype group, and the findings suggest that immune-related pathways, plasma levels of polysaturated fatty acids, and bitter taste receptors may play roles in cognitive decline. Our findings enhance the understanding of cognitive aging and provide a framework for future studies.

1. Introduction

Older age is characterized by a decline across several cognitive domains, including memory, executive functioning, and language. Age-related cognitive decline begins in early adulthood and becomes more prominent in older age depending on the interplay between genetic and environmental factors [1]. With the population aging worldwide, cognitive impairment poses a growing public health concern. Studies have shown that cognitive aging has a substantial genetic component that increases with age [2,3,4]; thus, understanding the genetic architecture of cognitive aging can help us to identify the underlying biology and devise novel therapeutics.
In the past decade, numerous genetic studies have been conducted on both general cognitive ability and domain-specific abilities to unravel the genetic component of cognitive endophenotypes, leading to the discovery of hundreds of genetic loci [5,6,7]. However, these genetic loci account for only a fraction of heritability across the cognitive phenotypes, and yet a substantial proportion of genetic variance remains unaccounted for [5,8]. It is also possible that genetic loci with large effect sizes, such as APOE, obscure genetic loci with weaker effect sizes. Hence, stratified genetic studies might aid in discovering novel genetic signals and explain missing heritability.
Apolipoprotein E (APOE) is one of the major genetic risk factors for late-onset sporadic Alzheimer’s disease (AD). While there are several polymorphisms in the APOE gene, two SNPs, rs429358 at codon 112 and rs7412 at codon 158, constitute the common three-allele polymorphism (APOE2 / E3 /E4), resulting in six genotypes (APOE 2/2, 2/3, 2/4, 3/3, 3/4, and 4/4). This polymorphism has a differential effect on AD risk. Compared to the common APOE3 allele, APOE2 is known to impart a protective effect, and APOE4 increases the risk of AD and other dementia [9,10,11]. APOE4 has also been associated with age-related cognitive decline in several cognitive domains, such as memory, executive function, language, and global cognitive function across multiple studies [8,12,13]. APOE4 carriers are also at elevated risk for cognitive decline, possibly due to increased susceptibility to blood–brain barrier breakdown [14,15,16]. Similarly, APOE2 individuals are found to be relatively protected from cognitive decline [13] and aging [17]. It is possible that several other genetic loci impart the differential effect across different APOE genotypes, which could be obscured in non-stratified analysis. Previous studies focusing on APOE subgroups have identified novel genetic loci associated with AD, demonstrating that an APOE-stratified analysis can help to discover new associations [18,19,20]. To better understand the genetic architecture of cognitive decline, we conducted genome-wide meta-analyses on older adults in three APOE genotype groups: APOE2 carriers (APOE 2/2, 2/3), APOE3 homozygotes (APOE 3/3), and APOE4 carriers (APOE 3/4, 4/4). We excluded individuals with the APOE 2/4 genotype due to the presence of both risk and protective alleles.

2. Results

The study comprised 3021 individuals derived from three longitudinal cohorts, including Gingko Evaluation of Memory (GEM), the Monongahela-Youghiogheny Healthy Aging Team (MYHAT), and the Monongahela Valley Independent Elders Survey (MoVIES). The demographic details across each APOE subgroup in each study cohort are shown in Table 1. There was a relatively higher number of women in the MYHAT and MoVIES cohorts across all APOE subgroups as compared to the GEM cohort, which had a slightly higher proportion of men in all APOE groups. The mean age of the participants was similar in APOE subgroups across the studies. GEM individuals were slightly better educated than MYHAT and MoVIES across all the three APOE groups. Cognitive phenotypes from 2969 individuals were analyzed, of which 413, 1995, and 561 belonged to the APOE2 carrier, APOE 3/3 homozygous, and APOE4 carrier groups, respectively. Fifty-two individuals with the APOE 2/4 genotype were excluded from analysis in total.
The QQ plots and the genomic control factor (λ) did not show inflation, indicating that there was no effect of population stratification or cryptic relatedness (Figure S1). We identified three genome-wide significant (GWS) associations. A previously described novel signal for decline in attention [8] on Chr9q21.32 in this sample without APOE genotype stratification was found to be confined to the APOE 3/3 group at a genome-wide significance (GWS) level (top SNP = rs6559700; MAF = 0.09; β = −0.288; p = 9.95 × 10−9) (Figure 1). The SNP was also nominally significant for decline in global cognitive function (β = −0.113; p = 0.024) in the APOE 3/3 genotype group. The Manhattan plots of all the GWAS analyses are presented in the Supplementary Material (Figures S2–S8).
In the APOE 3/3 homozygous group, we identified a novel association for decline in global cognitive function on chromosome 6, in the region between RNU766P and RNA5SP208 (top SNP = rs116379916; MAF = 0.03; β = −0.507; p = 1.44 × 10−9; Figure 2). The SNP was also observed to be nominally associated with all five cognitive domain slopes in the APOE 3/3 homozygous group: memory (β = −0.37; p = 1.1 × 10−5), language (β = −0.33; p = 7.19 × 10−5), attention (β = −0.30; p = 3.73 × 10−4), visuospatial function (β = −0.30; p = 4.95 × 10−4), and executive function (β = −0.23; p = 5.90 × 10−3). The same signal was also observed at a nominal significance in 2 carriers, but in the opposite direction for the decline in global cognitive function (β = 0.57; p = 2.75 × 10−3) and decline in visuospatial function (β = 0.64; p = 1.77 × 10−3). No association of this SNP was found in the APOE4 group for global cognitive decline (β = 0.1637; p = 3.17 × 10−1) or any other cognitive domains (Supplementary Table S1).
We also observed a novel signal associated with the decline in language in APOE4 carriers on chromosome 5 in the intergenic region between LINC02215 and DTWD2 (top SNP = rs13187183; MAF = 0.056 β = 0.693; p = 3.79 × 10−8) (Figure 3; Supplementary Table S1). In the APOE4 group, the top SNP was also nominally associated with global cognitive function (β = 0.56; p = 1.46 × 10−5), memory (β = 0.42; p = 1.35 × 10−3), and executive function (β = 0.27; p = 2.8 × 10−2). Likewise, the top SNP showed a nominal association with the decline in executive function (β = −0.27; p = 3.8 × 10−2), but in the opposite direction, in the APOE2 carrier group (Supplementary Table S1). rs13187183 was found to be an eQTL for HSD17B4 (p = 1.92 × 10−8), DTWD2 (p = 4.8 × 10−3), and DMXL1 (p = 5.2 × 10−3) in blood [21].
We also conducted an additional meta-analysis by using the last CDR (0, 0.5, 1) as an additional covariate on the top genome-wide significant findings to test whether the association was driven by MCI/incident dementia. However, we observed a similar allelic effect and significance: rs6559700 was associated with decline in attention in the APOE 3/3 group (p = 1.38 × 10−8, β = −0.28); rs116379916 was associated with decline in global function in the APOE 3/3 group (p = 2.57 × 10−9, β = −0.46), and rs13187183 was associated with the decline in language (p = 1.025 × 10−8, β = 0.69) in the APOE4 carrier group, indicating that these are not driven by MCI/incident dementia.
In addition to the three GWS associations, we observed three sub-threshold GWS associations, one in the APOE4 carrier group and two in the APOE2 carrier group (Table 2; Figures S9 and S10). In the APOE4 group, an association with the decline in executive function was observed with an intronic SNP on chromosome 8 in CSMD1 (top SNP = rs293879; MAF = 0.33; β = 0.32; p = 8.49 × 10−8). In the APOE2 group, one association with the decline in attention was with an intronic variant of IL1RL1 (top SNP = rs77127114; MAF = 0.04; β = 0.86; p = 8.64 × 10−8) and the other with the decline in language in the intergenic region between YTHDC2 and KCNN2 on chromosome 5 (top SNP = rs116191836; MAF = 0.02; β = 1.41; p = 5.66 × 10−8). Additional variants with p < 1 × 10−6 are shown in Table 2. All SNPs that surpassed the suggestive threshold of p = 1 × 10−5 are listed in Supplementary Tables S2–S19, and their FUMA-based annotation is represented in Supplementary Tables S20–S37.

2.1. Gene-Based Analysis

In gene-based analyses, we observed some genes with a suggestive significance of p < 1 × 10−5 (Supplementary Table S38). ICA1 (p = 5.22 × 10−6) was found to be associated with the decline in language in the APOE2 carrier group. Several SNPs (rs6972885, rs35338938, rs11772202, rs6970637) within the gene were also associated with the decline in language in the APOE2 group. In APOE2 carriers, RAB22A, located on chromosome 20, was found to be associated with the decline in memory (p = 5.56 × 10−6) and global function (p = 8.34 × 10−5). FAM83E was found to be associated with the decline in global cognitive function (p = 7.94 × 10−6) in the APOE 3/3 homozygous group.

2.2. Gene Mapping

We used three techniques to map SNPs to genes: positional, eQTL, and chromatin mapping (Supplementary Tables S39–S56). For the attention domain, there were a total of 65, 118, and 120 genes mapped in the APOE2 carrier, APOE 3/3, and APOE4 carrier groups, respectively. Some of the genes were mapped using all three techniques (Table 3). For example, for the decline in attention, these included ZNF608 and ARID4A in APOE2 carriers, CNN3 and RP4-639F20.1 in APOE 3/3, and USP36 and IRF1 in APOE4 carriers.

2.3. Gene Set Enrichment Analysis

Gene set enrichment analysis was conducted using the FUMA-mapped genes for each cognitive domain within each APOE group (Supplementary Table S57). In the APOE4 group, the executive domain was enriched for biological pathways related to the serine phosphorylation of stat protein and immune function, and in the GWAS catalog, it was enriched for genes implicated in autism and alcohol use. There were several distinct as well as shared pathways associated with attention in each APOE group. The top pathway associated with attention was the serum protein level (sST2) in the APOE2 group; in the APOE3 group, the most significant pathways included prostaglandin secretion and transport. In all three APOE groups, the attention domain was enriched for genes associated with inflammatory and autoimmune-related diseases such as asthma, celiac disease, and lupus. Interestingly, in the APOE4 group, memory was enriched for caffeine consumption, handedness, and low-density lipoprotein and high-density lipoprotein levels and their interaction with diuretics use. In the APOE2 group, visuospatial and executive functions were enriched for the genes associated with plasma levels of omega-6-polyunsaturated fatty acids (linolenic acid, gamma-linolenic acid, dihomo gamma-linolenic acid). Global function was enriched for bitter taste receptors in the APOE2 group, and genes associated with carcinoembryonic antigen levels, a serum biomarker of malignancy, were enriched in the APOE 3/3 homozygous group. Language function in the APOE2 group was enriched for genes implicated in chronic venous diseases.

2.4. Cognitive Ability and AD

We assessed the association of cognition-associated SNPs that surpassed the suggestive significance threshold (p < 1 × 10−5) in our study with AD using a recently published large AD meta-GWAS by Bellenguez et al. [22]. Among the 220 SNPs associated with cognitive decline in the APOE2 group, 217 SNPs were available in recent largest meta-analysis of AD [22] and 24 of them were found to be nominally associated, with p values ranging from 0.047 to 0.014 (Supplementary Table S58). In the APOE 3/3 group, 174 of 180 SNPs associated with cognitive traits were available in AD study [22] of which 12 were nominally associated with AD, with p values ranging from 0.047 to 0.00025 (Supplementary Table S59). Similarly, in the APOE4 group, 231 of 237 SNPs were available in prior AD study [22], and 16 of those were nominally associated with AD, with p values ranging from 0.042 to 0.0055 (Supplementary Table S60).

2.5. Comparison with AD-Associated SNPs

We also evaluated the association of top-reported AD SNPs with cognitive domains in each of the three APOE groups. Ninety-nine lead SNPs were extracted from Kamboh [9] and Bellenguez et al. [22], of which p values were available for 51 SNPs in our cognitive decline data (Supplementary Table S61). AD SNP GRN/rs708382 showed the most significant association with decline in executive function (p = 8.61 × 10−4, β = −0.19) in the APOE4 group. This SNP was also nominally associated with decline in language (p = 9.09 × 10−3, β = −0.19), global cognitive function (p = 2.5 × 10−2, β = −0.15), and visuospatial function (3.9 × 10−2, β = −0.15) in the APOE2 group and with decline in visuospatial function (p = 2.3 × 10−2, β = −0.07) and attention (p = 4.6 × 10−2, β = −0.06) in the APOE 3/3 homozygous group. As reflected by the β-values, the risk allele of GRN/rs708382 was associated with the decline of the above-mentioned domains.

3. Discussion

We conducted an APOE-stratified genome-wide meta-analysis across three APOE groups—APOE2 carriers, APOE 3/3 homozygous, and APOE4 carriers—in older adults aged 65 and above from three prospective cohorts: MYHAT, MoVIES, and GEMS. Using single-variant, gene-based, and gene set analyses across five neurocognitive domains—attention, memory, executive function, language, visuospatial function—and global cognitive function, we identified genes and biological pathways relevant to domain-specific decline within APOE subgroups.
A previously described novel signal for decline in attention on Chr9q21.32 in these cohorts [8] was found to be GWS in the APOE 3/3 group only (rs6559700; p = 9.95 × 10−9). This SNP is an eQTL for RASEF, FRMD3, and IDNK in blood and brain tissues. We identified another novel locus in the APOE 3/3 group associated with decline in global cognitive function in the intergenic region between RNU7-66P and RNA5SP208 on chromosome 6. The top SNP, rs116379916, is in linkage disequilibrium (LD) with rs73449416 and rs9363753, which have previously been reported to be associated with cognitive decline in AD [23] and with educational attainment [24], respectively. RNA5SP208 is a ribosomal pseudogene, and RNU766P represents the U7-small nucleolar pseudogene. Recent studies suggest these pseudogenes might be involved in transcriptional regulation and thus might play a role in health and diseases [25,26]. For example, a pseudogene, ACTBP2, was found to interact with other genes to increase blood–brain permeability in cellular models of AD, suggesting that pseudogenes could modulate gene expression through epigenetic and transcriptional regulation [27]. Other nearby coding genes include EYS and BAI3, also known as adhesion G protein-coupled receptor B3 (ADGRB3). EYS encodes the eye shut homolog gene, a major cause of the recessive condition retinitis pigmentosa. BAI3, a G-protein coupled receptor highly expressed in the brain, modulates synaptic plasticity and is involved in nervous system development, learning, and memory. It has also been implicated in neuropsychiatric illnesses such as schizophrenia and bipolar disorder [28,29,30].
In the APOE4 group, we observed an association of chr5:117976098 in the intergenic region between LINCO2215/DTWD2 with the decline in language. The lead SNP, rs13187183, is an eQTL for HSD17B4, DTWD2, and DMXL1 in blood. HSD17B4 encodes a multifunctional enzyme involved in fatty acid beta-oxidation and lipid metabolism in peroxisomes. Mutations in HSD17B4 are linked to Perrault syndrome, which is associated with hearing loss and intellectual disability, and D-bifunctional protein deficiency, a fatty acid metabolic disorder [31,32]. Compound heterozygous mutations in HSD17B4 also contribute to middle-age-onset spinocerebellar ataxia, with motor, hearing, and speech impairments [33]. Interestingly, an eQTL variant (rs421765) for DTWD2 and DMXL1 has also been associated with severe otitis media (severe ear infection) in Aboriginal Australians [34]. Otitis media frequently leads to conductive hearing loss if not treated promptly. Hearing loss is negatively correlated with language skills in children and older adults [35] and has been associated with cognitive decline across multiple domains, including language and global cognition [36]. As older adults are likely to develop hearing loss with increasing age, it is plausible that genes influencing hearing loss might have a pleotropic effect on decline in language. Recently, age-related hearing loss has been identified as one of the largest modifiable risk factors for dementia, and the use of hearing aids has been found to be associated with reduced cases of dementia [37,38].
We also observed three sub-threshold GWS associations: CSMD1 with decline in executive function in the APOE4 group and IL1RL1 with decline in attention and YTHDC2-KCNN2 with decline in language in the APOE2 group. CSMD1, a transmembrane protein involved in various cellular processes, is highly expressed in the brain and linked to diseases like cancer, lupus, and schizophrenia [39]. CSMD1 has also been reported to be associated with general cognitive ability and executive function in healthy Greek males [40]. IL1RL1, also widely known as suppression of tumorigenicity (ST2), codes for the soluble protein sST2 and interleukin-1 receptor-like 1 cytokine receptor, which is a specific receptor for Interleukin-33 (IL-33). IL-33 is a cytokine expressed by astrocytes, oligodendrocytes, and endothelial cells and is involved in neuroimmune remodulation. Mouse models of AD have shown that IL-33/IL1RL1 signaling can alleviate cognitive impairment in AD by activating microglia to phagocytize amyloid beta [41]. This was also further elucidated in a recent AD GWAS, where another IL1RL1/rs1921622 variant was found to have protective effects in female APOE4 carrier participants by suppressing the soluble receptor that inactivates the microglial response to amyloid beta [42]. Soluble receptor beta plasma levels were found to be elevated in female AD patients in that study. sST2 plasma levels were also found to be elevated in middle-aged Chinese participants with metabolic syndrome [43]. In our study, the top IL1R1/rs77127114 SNP was mapped to several genes of the IL1 family, such as IL18R1, IL18RAP, and IL1R1, and several variants in this region (rs14465596, rs13405222, rs17639215, rs3752659) were not found to be in LD (r2 = 0.12-0.14) with the reported IL1RL1/rs1921622 SNP and were also found to impart protection against decline in attention. IL1RL1/rs1921622 was not associated with a decline in attention (p = 0.1476) in APOE2 carriers in our study. It seems likely that APOE2 carriers have decreased levels of sST2 and thus decreased neuroinflammation, perhaps explaining some of the decreased risk of AD in APOE2 carriers; however, future studies are needed to test this theory. The third sub-threshold association observed in the intergenic region, YTHDC2/KCNN2, has been previously associated with cognition-related traits such as educational attainment [44], decline in visuo-construction [45], and brain shape [46].
Gene-based analysis identified three genes with p < 1 × 10−5: ICA1, RAB22, and FAM83E. ICA1 codes for islet cell autoantigen 69 (ICA69), an autoantigen of diabetes mellitus and Sjögren’s syndrome. It was suggestively associated with the decline in language in APOE2 carriers in both single-variant and gene-based analyses and is involved in modulating APP processing [22]. RAB22 encodes a GTPase protein belonging to the RAB5 subfamily of GTPases, which are localized to early endosomes, and helps in endosomal trafficking [47]. A guanidine exchange factor of RAB5-GTPases, RIN3, was found to upregulate APP processing and the accumulation of toxic amyloid beta in cellular models of AD [48]. FAM83E, associated with global cognitive decline in APOE 3/3 individuals, is a tumor suppressor gene linked to posterior cortical atrophy (PCA), which causes visuospatial and language dysfunction due to parietal and occipital cortex degeneration [49,50].
We used gene mapping (positional, eQTL, and chromatin mapping) to prioritize genes associated with cognitive endophenotypes and prioritized multiple genes using all three mapping methods (Table 3). Some of the selected genes were previously reported to be associated with certain phenotypes. For example, APBB2 was reported to be associated with aging [51], ZNF608 with preclinical AD [52], ARID4A and ARVCF with neuropsychiatric diseases [53,54], CNN3 with epilepsy [55], and BTBD1O with ALS [56]. In the APOE 3/3 group, genes located on chr2q32.2 (ASNSD1, ORMDL1, OSGEPL1, OSGEPL1-AS1, RP11- 455J20.3) were found to be mapped by all three methods to the decline in language. In the APOE2 group, several zinc finger protein-coding genes (ZNF69, ZNF788, ZNF20) were mapped to the decline in visuospatial function. USP36, which codes for deubiquitinating enzyme [57], and IRF1, which regulates microglia, were prioritized for the decline in attention. KAT2A, a transcriptional activator that plays a role in hippocampal memory formation, was mapped to executive function, and regulators of NF-κB and AKT signaling (PPP2R5A, BTBD10) were mapped to the decline in memory in APOE4 carriers.
The gene set enrichment analysis indicates that cognitive aging is a complex process with diverse molecular and biological pathways and has both shared and distinct pathways influencing cognitive domains across APOE groups. For instance, decline in attention was associated with inflammatory and immune-related pathways across all APOE groups, supporting previous studies implicating neuroinflammation in age-related cognitive impairment [58] and neurodegenerative disorders [59]. Neuroinflammation is also considered a risk factor for attention deficit hyperactivity disorder [60], a neurodevelopmental condition characterized by impaired cognitive function in attention and memory. The decline in memory was linked to genes associated with caffeine consumption, but only in APOE4 carriers; caffeine intake has previously been linked to improved cognitive function [61]. Bitter taste receptor (a G protein coupled receptor) activity was overrepresented in global function in the APOE2 group. Bitter receptors are also expressed in the brain [62], as well as in the oral cavity (not limited to the tongue), airways, and alimentary canal, and studies have suggested they exert a neuroprotective effect through immunomodulation [63,64]. Executive and visuospatial function in APOE2 carriers were enriched for genes associated with plasma levels of omega-6 polyunsaturated fatty acids. A recent study found that elevated plasma levels of linolenic acid were suggestively associated with executive function decline in middle-aged healthy participants from Austria [65]. Chronic venous disease-associated genes were linked with decline in language in the APOE2 group, suggesting the role of vascular health or pathology. Some studies suggest that APOE2 carriers might be at risk for cerebral angiopathy, despite APOE2 having a protective effect on aging and AD [66,67].

4. Materials and Methods

4.1. Study Cohorts

We leveraged 3021 individuals from three different longitudinal studies: GEM [68,69], MYHAT [70,71], and MoVIES [72]. Details about these cohorts have been described previously. In brief, MYHAT and MoVIES were longitudinal, observational, population-based cohorts based in the Monongahela Valley of southwestern Pennsylvania, where participants aged 65 years and older were recruited by age-stratified random sampling from voter registration lists. Beginning in 2006, MYHAT recruited 1982 individuals with sufficient hearing and vision to undertake neuropsychological testing, with decisional capacity to provide informed consent, and not living in nursing homes at study entry. The present study includes MYHAT participants who, at baseline, were free of dementia, with Clinical Dementia Rating (CDR) of 0 (cognitively normal) or 0.5 (mild cognitive impairment), followed annually for six years [6]. Of the 745 MYHAT participants included in the analysis, 24 developed incident dementia and had a CDR score (≥1) at the end visit. MoVIES similarly recruited 1681 participants from 1987 to 1989 with the additional requirement of fluency in English and at least 6th grade education. MoVIES participants included in the current study were free of dementia, with a CDR of 0, throughout the study and were followed every two years for a duration of 12 years. The GEM Study was a randomized control trial comprising 3069 volunteers aged 75 years and above to test the effectiveness of Gingko biloba on the prevention of incident dementia [68]. Participants from four communities (Hagertown, Maryland; Pittsburgh, Pennsylvania; Sacramento, California; and Winston-Salem and Greensboro, North Carolina) in the US participated in the clinical trial for a median period of 6.1 years spanning from 2002 to 2008 [69]. Individuals were assessed every six months to check for incident dementia and were followed annually with a battery of neuropsychiatric tests. The current study includes participants who were free of dementia at the end of the study. Written consent was obtained from participants from all three cohorts and secondary analyses were approved by the Institutional Review Board of University of Pittsburgh. In all three cohorts, participants were tested on a range of tests across five cognitive domains: attention, memory, language, visuospatial function, and executive function. Only participants with non-Hispanic White ancestry, with at least two cognitive assessments, and who consented to genotyping were included in the analysis. Written informed consent was obtained from participants in all three cohorts and secondary analyses were approved by the Institutional Review Board of University of Pittsburgh.

4.2. Cognitive Assessment

Neurocognitive tests were focused on five cognitive domains, attention, visuospatial function, executive function, language, and memory, as described previously [6,8]. For each neurocognitive test, the test scores were transformed to z-scores using the baseline mean and standard deviation. For each participant, domain z-scores were derived by averaging the test scores within the specific domain. We also devised a global cognitive function score as the average of all test z-scores in participants who did not miss more than one test score.

4.3. Cognitive Decline Slope

A cognitive trajectory was estimated for each person for each domain and global cognitive function by fitting a linear mixed-effect model with random slope and intercept. Individual-specific slopes were extracted, adjusting for the fixed effect of education, sex, and baseline age, as described previously [8]. Since some individuals showed rapid decline, slopes were ranked [0.1,0.99] and transformed using inverse standard transformation in R version 4.3.1, (accessed on 16 December 2023) (https://www.r-project.org), as described previously [6]. Rank normal transformation helps to conform the data to normality. The cognitive decline slopes for the MoVIES cohort were obtained from the previous analysis [6]. These cognitive decline slopes in each domain and global cognitive functions were used to conduct genome-wide associations in each study cohort.

4.4. Genotyping, Imputation, and Quality Control

DNA was extracted from buffy coats derived from blood, and genome-wide genotyping was conducted using the Illumina Infinium Multi-Ethnic Global, (Illumina, Incorporation, San Diego, CA, USA), the Illumina Omni2.5 (Illumina, Incorporation, San Diego, CA, United States), and the Omni1-Quad Chips (Illumina, Incorporation, San Diego, CA, USA), in the GEM, MYHAT, and MoVIES cohorts, respectively. In the sample-level quality control (QC), individuals were assessed for relatedness within and across cohorts using identity by descent in PLINK [73]; related individuals were excluded from the analyses. Individuals with a genotyping rate < 95% and mismatched sex and race were also excluded from the analyses. For the SNP-level quality measures, SNPs with Hardy–Weinberg equilibrium (p < 1 × 10−6), minor allele frequency (MAF < 0.01), and genotyping rate < 95% were not included in the analysis. Imputation was conducted on the Michigan Imputation server using the Haplotype Reference panel version 1.1, and SNPs with r2 < 0.3 were excluded from the analysis. APOE genotyping was based on the TaqMan assay (Themo Fisher Scientific; Waltham, MA, USA) and has been described previously [6,74].

4.5. Genome-Wide Association and Meta-Analysis

A genome-wide association study (GWAS) was performed on intra-individual domain-specific slopes from five cognitive domains and for global cognitive function using an additive genetic model in PLINK for each study cohort in the three APOE groups. Genetic principal components were calculated using SNPs with an MAF > 5%, and the first four genetic principal components (PCs) were added as covariates in the linear regression model. After conducting a GWAS on each cognitive phenotype in each study cohort, the mean effect across the three cohorts was evaluated by performing a fixed-effect inverse variance-based meta-analysis in METAL (Meta-Analysis of Genome-wide Association; http://www.sph.umich.edu/csg/abecasis/metal/, (accessed on 15 January 2024) [75]. The SNPs that were available across all cohorts were used for meta-analysis. The genome-wide threshold was defined as p < 5 × 10−8, and the suggestive threshold was defined as p ≤ 1 × 10−5.

4.6. Genomic Risk Locus Characterization

Genomic risk loci were defined based on ANNOVAR (https://annovar.openbioinformatics.org/, (accessed on 17 January, 2024) [76] annotation using the SNP2GENE function in Functional Mapping and Annotation of Genetic Associations (FUMA) [77]. Lead SNPs were defined within a genomic risk locus if these surpassed a suggestive threshold of 1 × 10−5 and were in linkage disequilibrium (LD) r2 < 0.1 with other SNPs within a block of 250 kilobases (kb). CADD scores, Regulome DB (RDB) scores, and 15 chromatin states were obtained from FUMA. Based on numerous annotations, CADD-ranked scores represent the deleteriousness of an SNP, with a higher score representing higher deleteriousness. A CADD score of 10 represents the top 10% of the deleterious variants, and a CADD score of 20 represents the top 1% among all reference SNVs.

4.7. Gene Mapping

We also mapped the SNPs to genes based on positional mapping, eQTL mapping, and chromatin mapping. An SNP was mapped to a gene if it resided 10 kilobases upstream or downstream of a gene. Gene-Tissue Expression version 8 (GTEx), as well as other blood and brain tissues available in FUMA, were used at FDR (p) < 1 × 10−3 to identify genes whose expression was associated with the SNPs. Additionally, an SNP was mapped to a region if the locus interacted with another region within 250 base pairs (bp) upstream and 500 bp downstream of the transcription start site in HiC adult cortex [78] at FDR < 1 × 10−6. The GENE2FUNC function in FUMA was used to find the functional relevance of these mapped genes.

4.8. Gene-Based Analysis

MAGMA gene-based and gene set analysis was conducted in FUMA using the SNP2GENE function. SNPs were assigned to 18106 protein-coding genes based on Ensemble build 92 within the 8 kb window upstream and downstream, and genome-wide significance was defined as 0.05/18106 = 2.762 × 10−6. For the MAGMA gene set analysis, gene sets from the Molecular Signature (MsigDB) database were extracted from FUMA, and Bonferroni correction was applied to adjust for multiple testing.

5. Conclusions

APOE-stratified analyses have enabled us to identify novel loci for the decline in language in the APOE4 group and decline in global cognition in the APOE 3/3 group. Our study points to potential biological pathways pertaining to specific domains within each APOE genotype group, and the findings suggest that immune-related pathways, plasma levels of polysaturated fatty acids, and bitter taste receptors may play some roles in cognitive decline. These findings deepen our understanding of the biological mechanism of cognitive aging and offer a foundation for future research. Studies examining cognitive traits within APOE-stratified groups may yield deeper insights into how gene–environment interactions influence cognitive function. For example, the impact of lifestyle factors such as alcohol and coffee consumption, as well as metabolites like omega-6 fatty acids, can be evaluated across different APOE genotypes to assess their roles in cognitive decline.
Our study has several strengths. Given the longitudinal nature of our study cohorts, with participants followed over several years, our study is well-positioned to capture the biological processes underlying cognitive aging, offering advantages over cross-sectional designs. To our knowledge, this is the most comprehensive study conducted to date across neurocognitive domains in APOE subgroups, providing domain-specific findings in each APOE group. However, the study also has some limitations. Since our study is based on non-Hispanic Whites, further studies need to be conducted in other ancestral cohorts to determine whether the allelic effects are similar across different populations. Additionally, we used linear modeling to phenotype cognitive aging and assumed that cognitive aging follows a linear trend; however, cognitive decline could also follow non-linear trends. Although a small sample size owing to stratification into APOE subgroups is another limitation, the stratified analyses have still enabled us to detect association signals obscured in the combined analysis. Thus, our study has elucidated that APOE-stratified analyses can aid in gene discovery, and future cognitive genetic studies can be conducted in other populations following this design to corroborate our findings.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/ijms26146940/s1.

Author Contributions

Conceptualization, M.I.K. and B.E.S.; methodology, K.-H.F.; software, K.-H.F., V.A.; validation, K.-H.F. and V.A.; formal analysis, V.A.; investigation, V.A.; resources, M.I.K.; data curation, K.-H.F. and V.A.; writing—original draft preparation, V.A.; writing—review and editing, V.A.,M.I.K., K.-H.F., B.E.S., M.G., S.T.D., E.F. and O.L.L.; visualization, V.A.; supervision, E.F., K.-H.F. and M.I.K.; project administration, M.I.K., B.E.S., M.G., O.L.L. and S.T.D.; funding acquisition, M.I.K. and M.G. All authors have read and agreed to the published version of the manuscript.

Funding

The study was supported in part by National Institutes of Health (NIH) grants AG030653, AG041718, AG064877, R37 AG023651, AG07562, and P30 AG066468. A subset of samples used in this study was obtained from the National Centralized Repository for Alzheimer’s Disease and Related Dementia (NCRAD), which receives government support under a cooperative agreement grant (U24 AG021886) awarded by the National Institute on Aging. This publication was made possible by grant U01 AT000162 from the National Center for Complementary and Alternative Medicine, NIH.

Institutional Review Board Statement

This study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of University of Pittsburgh (MOD19020042-006; 7 February 2025 and STUDY21030042; 16 April 2021) for studies involving humans.

Informed Consent Statement

Written consent was obtained from participants from all three cohorts: MYHAT, MoVIES, and GEM.

Data Availability Statement

The summary statistics generated during the study is available upon request to the senior author.

Acknowledgments

The authors would like to thank M. Muaaz Aslam, and Ruyu Shi for their assistance in the imputation of MYHAT samples. We also thank contributors who collected samples used in this study, as well as the patients and their families, whose help and participation made this work possible.

Conflicts of Interest

DeKosky is an associate editor of Neurotherapeutics, the official journal of the American Society for Experimental Neurotherapeutics. He also serves on the medical advisory board for Acumen Pharmaceuticals, Cognition Therapeutics, and Vaccinex and on the data safety and monitoring board of Biogen and Prevail Pharmaceuticals. DeKosky is also a consultant at Boxer Capital, Brainstorm Cell Therapeutics, Lundbeck Pharmaceuticals, Amylyx Pharmaceuticals, Reata Pharmaceuticals, and Biogen. Lopez also serves on the consulting committee for Biogen, Novo Nordisk, EISAI, and Lundbeck. The submitted work is unrelated to any of these. All other authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript
ADAlzheimer’s Disease
APOEApolipoprotein E
CDRClinical Dementia Rating
MYHATThe Monongahela-Youghiogheny Healthy Aging Team
MoVIESMonongahela Valley Independent Elders Survey
GEMGingko Evaluation of Memory
LDLinear Disequilibrium
MAFMinor Allele Frequency

References

  1. Lindenberger, U. Human cognitive aging: Corriger la fortune? Science 2014, 346, 572–578. [Google Scholar] [CrossRef] [PubMed]
  2. Mcclearn, G.E. Substantial Genetic Influence on Cognitive Abilities in Twins 80 or More Years Old. Science 1997, 276, 1560–1563. [Google Scholar] [CrossRef] [PubMed]
  3. Reynolds, C.A.; Finkel, D. A Meta-analysis of Heritability of Cognitive Aging: Minding the “Missing Heritability” Gap. Neuropsychol. Rev. 2015, 25, 97–112. [Google Scholar] [CrossRef] [PubMed]
  4. Swan, G.E.; Carmelli, D.; Reed, T.; Harshfield, G.A.; Fabsitz, R.R.; Eslinger, P.J. Heritability of Cognitive Performance in Aging Twins The National Heart, Lung, and Blood Institute Twin Study. Arch. Neurol. 1990, 47, 259–262. [Google Scholar] [CrossRef] [PubMed]
  5. Malanchini, M.; Rimfeld, K.; Allegrini, A.G.; Ritchie, S.J.; Plomin, R. Cognitive ability and education: How behavioural genetic research has advanced our knowledge and understanding of their association. Neurosci. Biobehav. Rev. 2020, 111, 229–245. [Google Scholar] [CrossRef] [PubMed]
  6. Kamboh, M.I.; Fan, K.-H.; Yan, Q.; Beer, J.C.; Snitz, B.E.; Wang, X.; Chang, C.-C.H.; Demirci, F.Y.; Feingold, E.; Ganguli, M. Population-based genome-wide association study of cognitive decline in older adults free of dementia: Identification of a novel locus for the attention domain. Neurobiol. Aging 2019, 84, 239.e15–239.e24. [Google Scholar] [CrossRef] [PubMed]
  7. Warrier, V.; Grasby, K.L.; Uzefovsky, F.; Toro, R.; Smith, P.; Chakrabarti, B.; Khadake, J.; Mawbey-Adamson, E.; Litterman, N.; Hottenga, J.-J.; et al. Genome-wide meta-analysis of cognitive empathy: Heritability, and correlates with sex, neuropsychiatric conditions and cognition. Mol. Psychiatry 2018, 23, 1402–1409. [Google Scholar] [CrossRef] [PubMed]
  8. Acharya, V.; Fan, K.H.; Snitz, B.E.; Ganguli, M.; Dekosky, S.T.; Lopez, O.L.; Feingold, E.; Kamboh, M.I. Meta-analysis of age-related cognitive decline reveals a novel locus for the attention domain and implicates a COVID-19-related gene for global cognitive function. Alzheimer’s Dement. 2023, 19, 5010–5022. [Google Scholar] [CrossRef] [PubMed]
  9. Kamboh, M.I. Genomics and Functional Genomics of Alzheimer’s Disease. Neurotherapeutics 2022, 19, 152–172. [Google Scholar] [CrossRef] [PubMed]
  10. Rasmussen, K.L.; Tybjærg-Hansen, A.; Nordestgaard, B.G.; Frikke-Schmidt, R. Absolute 10-year risk of dementia by age, sex and APOE genotype: A population-based cohort study. Cmaj 2018, 190, E1033–E1041. [Google Scholar] [CrossRef] [PubMed]
  11. Reiman, E.M.; Arboleda-Velasquez, J.F.; Quiroz, Y.T.; Huentelman, M.J.; Beach, T.G.; Caselli, R.J.; Chen, Y.; Su, Y.; Myers, A.J.; Hardy, J.; et al. Exceptionally low likelihood of Alzheimer’s dementia in APOE2 homozygotes from a 5,000-person neuropathological study. Nat. Commun. 2020, 11, 667. [Google Scholar] [CrossRef] [PubMed]
  12. Rawle, M.J.; Davis, D.; Bendayan, R.; Wong, A.; Kuh, D.; Richards, M. Apolipoprotein-E (Apoe) ε4 and cognitive decline over the adult life course. Transl. Psychiatry 2018, 8, 18. [Google Scholar] [CrossRef] [PubMed]
  13. Reas, E.T.; Laughlin, G.A.; Bergstrom, J.; Kritz-Silverstein, D.; Barrett-Connor, E.; McEvoy, L.K. Effects of APOE on cognitive aging in community-dwelling older adults. Neuropsychology 2019, 33, 406–416. [Google Scholar] [CrossRef] [PubMed]
  14. Riaz, M.; Huq, A.; Ryan, J.; Orchard, S.G.; Tiller, J.; Lockery, J.; Woods, R.L.; Wolfe, R.; Renton, A.E.; Goate, A.M.; et al. Effect of APOE and a polygenic risk score on incident dementia and cognitive decline in a healthy older population. Aging Cell 2021, 20, e13384. [Google Scholar] [CrossRef] [PubMed]
  15. Makkar, S.R.; Lipnicki, D.M.; Crawford, J.D.; Kochan, N.A.; Castro-Costa, E.; Lima-Costa, M.F.; Diniz, B.S.; Brayne, C.; Stephan, B.; Matthews, F.; et al. APOE ε4 and the Influence of Sex, Age, Vascular Risk Factors, and Ethnicity on Cognitive Decline. J. Gerontol. A Biol. Sci. Med. Sci. 2020, 75, 1863–1873. [Google Scholar] [CrossRef] [PubMed]
  16. Montagne, A.; Nation, D.A.; Sagare, A.P.; Barisano, G.; Sweeney, M.D.; Chakhoyan, A.; Pachicano, M.; Joe, E.; Nelson, A.R.; D’Orazio, L.M.; et al. APOE4 leads to blood–brain barrier dysfunction predicting cognitive decline. Nature 2020, 581, 71–76. [Google Scholar] [CrossRef] [PubMed]
  17. Sweigart, B.; Andersen, S.L.; Gurinovich, A.; Cosentino, S.; Schupf, N.; Perls, T.T.; Sebastiani, P. APOE E2/E2 Is Associated with Slower Rate of Cognitive Decline with Age. J. Alzheimer’s Dis. 2021, 83, 853–860. [Google Scholar] [CrossRef] [PubMed]
  18. Fan, K.-H.; Feingold, E.; Rosenthal, S.L.; Demirci, F.Y.; Ganguli, M.; Lopez, O.L.; Kamboh, M.I. Whole-Exome Sequencing Analysis of Alzheimer’s Disease in Non-APOE*4 Carriers. J. Alzheimer’s Dis. 2020, 76, 1553–1565. [Google Scholar] [CrossRef] [PubMed]
  19. Jun, G.; Ibrahim-Verbaas, C.A.; Vronskaya, M.; Lambert, J.C.; Chung, J.; Naj, A.C.; Kunkle, B.W.; Wang, L.-S.; Bis, J.C.; Bellenguez, C.; et al. A novel Alzheimer disease locus located near the gene encoding tau protein. Mol. Psychiatry 2016, 21, 108–117. [Google Scholar] [CrossRef] [PubMed]
  20. Ma, Y.; Jun, G.R.; Zhang, X.; Chung, J.; Naj, A.C.; Chen, Y.; Bellenguez, C.; Hamilton-Nelson, K.; Martin, E.R.; Kunkle, B.W.; et al. Analysis of Whole-Exome Sequencing Data for Alzheimer Disease Stratified by APOE Genotype. JAMA Neurol. 2019, 76, 1099–1108. [Google Scholar] [CrossRef] [PubMed]
  21. Võsa, U.; Claringbould, A.; Westra, H.-J.; Bonder, M.J.; Deelen, P.; Zeng, B.; Kirsten, H.; Saha, A.; Kreuzhuber, R.; Yazar, S.; et al. Large-scale cis- and trans-eQTL analyses identify thousands of genetic loci and polygenic scores that regulate blood gene expression. Nat. Genet. 2021, 53, 1300–1310. [Google Scholar] [CrossRef] [PubMed]
  22. Bellenguez, C.; Küçükali, F.; Jansen, I.E.; Kleineidam, L.; Moreno-Grau, S.; Amin, N.; Naj, A.C.; Martin-Campos, R.; Grenier-Boley, B.; Andrade, V.; et al. New insights into the genetic etiology of Alzheimer’s disease and related dementias. Nat. Genet. 2022, 54, 412–436. [Google Scholar] [CrossRef] [PubMed]
  23. Sherva, R.; Gross, A.; Mukherjee, S.; Koesterer, R.; Amouyel, P.; Bellenguez, C.; Dufouil, C.; Bennett, D.A.; Chibnik, L.; Cruchaga, C.; et al. Genome-wide association study of rate of cognitive decline in Alzheimer’s disease patients identifies novel genes and pathways. Alzheimer’s Dement. 2020, 16, 1134–1145. [Google Scholar] [CrossRef] [PubMed]
  24. Lee, J.J.; Wedow, R.; Okbay, A.; Kong, E.; Maghzian, O.; Zacher, M.; Nguyen-Viet, T.A.; Bowers, P.; Sidorenko, J.; Linnér, R.K.; et al. Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals. Nat. Genet. 2018, 50, 1112–1121. [Google Scholar] [CrossRef] [PubMed]
  25. Giuliani, A.; Bui, T.T.; Helmy, M.; Selvarajoo, K. Identifying toggle genes from transcriptome-wide scatter: A new perspective for biological regulation. Genomics 2022, 114, 215–228. [Google Scholar] [CrossRef] [PubMed]
  26. Poliseno, L. Pseudogenes: Newly discovered players in human cancer. Sci. Signal. 2012, 5, re5. [Google Scholar] [CrossRef] [PubMed]
  27. Liu, Q.; Liu, X.; Zhao, D.; Ruan, X.; Su, R.; Shang, X.; Wang, D.; Yang, C.; Xue, Y. Pseudogene ACTBP2 increases blood–brain barrier permeability by promoting KHDRBS2 transcription through recruitment of KMT2D/WDR5 in Aβ1–42 microenvironment. Cell Death Discov. 2021, 7, 142. [Google Scholar] [CrossRef] [PubMed]
  28. Tu, Y.-K.; Duman, J.G.; Tolias, K.F. The Adhesion-GPCR BAI1 Promotes Excitatory Synaptogenesis by Coordinating Bidirectional Trans-synaptic Signaling. J. Neurosci. 2018, 38, 8388–8406. [Google Scholar] [CrossRef] [PubMed]
  29. Wong, T.-S.; Li, G.; Li, S.; Gao, W.; Chen, G.; Gan, S.; Zhang, M.; Li, H.; Wu, S.; Du, Y. G protein-coupled receptors in neurodegenerative diseases and psychiatric disorders. Signal Transduct. Target. Ther. 2023, 8, 177. [Google Scholar] [CrossRef] [PubMed]
  30. Zhu, D.; Li, C.; Swanson, A.M.; Villalba, R.M.; Guo, J.; Zhang, Z.; Matheny, S.; Murakami, T.; Stephenson, J.R.; Daniel, S.; et al. BAI1 regulates spatial learning and synaptic plasticity in the hippocampus. J. Clin. Investig. 2015, 125, 1497–1508. [Google Scholar] [CrossRef] [PubMed]
  31. Amor, D.J.; Marsh, A.P.L.; Storey, E.; Tankard, R.; Gillies, G.; Delatycki, M.B.; Pope, K.; Bromhead, C.; Leventer, R.J.; Bahlo, M.; et al. Heterozygous mutations in HSD17B4 cause juvenile peroxisomal D-bifunctional protein deficiency. Neurol. Genet. 2016, 2, e114. [Google Scholar] [CrossRef] [PubMed]
  32. Pierce, S.B.; Walsh, T.; Chisholm, K.M.; Lee, M.K.; Thornton, A.M.; Fiumara, A.; Opitz, J.M.; Levy-Lahad, E.; Klevit, R.E.; King, M.-C. Mutations in the DBP-Deficiency Protein HSD17B4 Cause Ovarian Dysgenesis, Hearing Loss, and Ataxia of Perrault Syndrome. Am. J. Hum. Genet. 2010, 87, 282–288. [Google Scholar] [CrossRef] [PubMed]
  33. Matsuda, Y.; Morino, H.; Miyamoto, R.; Kurashige, T.; Kume, K.; Mizuno, N.; Kanaya, Y.; Tada, Y.; Ohsawa, R.; Yokota, K.; et al. Biallelic mutation of HSD17B4 induces middle age-onset spinocerebellar ataxia. Neurol. Genet. 2020, 6, e396. [Google Scholar] [CrossRef] [PubMed]
  34. Jamieson, S.E.; Fakiola, M.; Tang, D.; Scaman, E.; Syn, G.; Francis, R.W.; Coates, H.L.; Anderson, D.; Lassmann, T.; Cordell, H.J.; et al. Common and Rare Genetic Variants That Could Contribute to Severe Otitis Media in an Australian Aboriginal Population. Clin. Infect. Dis. 2021, 73, 1860–1870. [Google Scholar] [CrossRef] [PubMed]
  35. Shojaei, E.; Jafari, Z.; Gholami, M. Effect of Early Intervention on Language Development in Hearing-Impaired Children. Iran. J. Otorhinolaryngol. 2016, 28, 13–21. [Google Scholar] [PubMed]
  36. Jiang, K.; Armstrong, N.M.; Agrawal, Y.; Gross, A.L.; Schrack, J.A.; Lin, F.R.; Ferrucci, L.; Resnick, S.M.; Deal, J.A.; Powell, D.S. Associations of audiometric hearing and speech-in-noise performance with cognitive decline among older adults: The Baltimore Longitudinal Study of Aging (BLSA). Front. Neurol. 2022, 13, 1029851. [Google Scholar] [CrossRef] [PubMed]
  37. Huang, A.R.; Jiang, K.; Lin, F.R.; Deal, J.A.; Reed, N.S. Hearing loss and dementia prevalence in older adults in the US. JAMA 2023, 329, 171–173. [Google Scholar] [CrossRef] [PubMed]
  38. Living, G.; Huntley, J.; Liu, K.Y.; Costaferda, S.G.; Selbæk, G.; Suvarna, A. Dementia prevention, intervention, and care: 2024 report of the Lancet standing Commission. Lancet 2024, 404, 572–628. [Google Scholar] [CrossRef] [PubMed]
  39. Ermis Akyuz, E.; Bell, S.M. The Diverse Role of CUB and Sushi Multiple Domains 1 (CSMD1) in Human Diseases. Genes 2022, 13, 2332. [Google Scholar] [CrossRef] [PubMed]
  40. Koiliari, E.; Roussos, P.; Pasparakis, E.; Lencz, T.; Malhotra, A.; Siever, L.J.; Giakoumaki, S.G.; Bitsios, P. The CSMD1 genome-wide associated schizophrenia risk variant rs10503253 affects general cognitive ability and executive function in healthy males. Schizophr. Res. 2014, 154, 42–47. [Google Scholar] [CrossRef] [PubMed]
  41. Fu, A.K.Y.; Hung, K.-W.; Yuen, M.Y.F.; Zhou, X.; Mak, D.S.Y.; Chan, I.C.W.; Cheung, T.H.; Zhang, B.; Fu, W.-Y.; Liew, F.; et al. IL-33 ameliorates Alzheimer’s disease-like pathology and cognitive decline. Proc. Natl. Acad. Sci. USA 2016, 113, E2705–E2713. [Google Scholar] [CrossRef] [PubMed]
  42. Jiang, Y.; Zhou, X.; Wong, H.Y.; Ouyang, L.; Ip, F.C.F.; Chau, V.M.N.; Lau, S.-F.; Wu, W.; Wong, D.Y.K.; Seo, H.; et al. An IL1RL1 genetic variant lowers soluble ST2 levels and the risk effects of APOE-ε4 in female patients with Alzheimer’s disease. Nat. Aging 2022, 2, 616–634. [Google Scholar] [CrossRef] [PubMed]
  43. Zong, X.; Fan, Q.; Zhang, H.; Yang, Q.; Xie, H.; Chen, Q.; Zhang, R.; Tao, R. Soluble ST2 levels for predicting the presence and severity of metabolic syndrome. Endocr. Connect. 2021, 10, 336–344. [Google Scholar] [CrossRef] [PubMed]
  44. Okbay, A.; Wu, Y.; Wang, N.; Jayashankar, H.; Bennett, M.; Nehzati, S.M.; Sidorenko, J.; Kweon, H.; Goldman, G.; Gjorgjieva, T.; et al. Polygenic prediction of educational attainment within and between families from genome-wide association analyses in 3 million individuals. Nat. Genet. 2022, 54, 437–449. [Google Scholar] [CrossRef] [PubMed]
  45. Homann, J.; Osburg, T.; Ohlei, O.; Dobricic, V.; Deecke, L.; Bos, I.; Vandenberghe, R.; Gabel, S.; Scheltens, P.; Teunissen, C.E.; et al. Genome-Wide Association Study of Alzheimer’s Disease Brain Imaging Biomarkers and Neuropsychological Phenotypes in the European Medical Information Framework for Alzheimer’s Disease Multimodal Biomarker Discovery Dataset. Front. Aging Neurosci. 2022, 14, 840651. [Google Scholar] [CrossRef] [PubMed]
  46. Naqvi, S.; Sleyp, Y.; Hoskens, H.; Indencleef, K.; Spence, J.P.; Bruffaerts, R.; Radwan, A.; Eller, R.J.; Richmond, S.; Shriver, M.D.; et al. Shared heritability of human face and brain shape. Nat. Genet. 2021, 53, 830–839. [Google Scholar] [CrossRef] [PubMed]
  47. Wang, L.; Liang, Z.; Li, G. Rab22 controls NGF signaling and neurite outgrowth in PC12 cells. Mol. Biol. Cell 2011, 22, 3853–3860. [Google Scholar] [CrossRef] [PubMed]
  48. Shen, R.; Zhao, X.; He, L.; Ding, Y.; Xu, W.; Lin, S.; Fang, S.; Yang, W.; Sung, K.; Spencer, B.; et al. Upregulation of RIN3 induces endosomal dysfunction in Alzheimer’s disease. Transl. Neurodegener. 2020, 9, 26. [Google Scholar] [CrossRef] [PubMed]
  49. Schott, J.M.; Crutch, S.J.; Carrasquillo, M.M.; Uphill, J.; Shakespeare, T.J.; Ryan, N.S.; Yong, K.X.; Lehmann, M.; Ertekin-Taner, N.; Graff-Radford, N.R.; et al. Genetic risk factors for the posterior cortical atrophy variant of Alzheimer’s disease. Alzheimer’s Dementia. 2016, 12, 862–871. [Google Scholar] [CrossRef] [PubMed]
  50. Schott, J.M.; Crutch, S.J. Posterior Cortical Atrophy. Contin. Lifelong Learn. Neurol. 2019, 25, 52–75. [Google Scholar] [CrossRef] [PubMed]
  51. Golanska, E.; Sieruta, M.; Gresner, S.M.; Pfeffer, A.; Chodakowska-Zebrowska, M.; Sobow, T.M.; Klich, I.; Mossakowska, M.; Szybinska, A.; Barcikowska, M.; et al. APBB2 genetic polymorphisms are associated with severe cognitive impairment in centenarians. Exp. Gerontol. 2013, 48, 391–394. [Google Scholar] [CrossRef] [PubMed]
  52. Dubé, J.B.; Johansen, C.T.; Robinson, J.F.; Lindsay, J.; Hachinski, V.; Hegele, R.A. Genetic determinants of “cognitive impairment, no dementia”. J. Alzheimer’s Dis. 2013, 33, 831–840. [Google Scholar] [CrossRef] [PubMed]
  53. Ren, D.; Wei, X.; Lin, L.; Yuan, F.; Bi, Y.; Guo, Z.; Liu, L.; Ji, L.; Yang, X.; Han, K.; et al. A novel heterozygous missense variant of the ARID4A gene identified in Han Chinese families with schizophrenia-diagnosed siblings that interferes with DNA-binding activity. Mol. Psychiatry 2022, 27, 2777–2786. [Google Scholar] [CrossRef] [PubMed]
  54. Sim, K.; Chan, W.Y.; Woon, P.S.; Low, H.Q.; Lim, L.; Yang, G.L.; Lee, J.; Chong, S.A.; Sithoh, Y.-Y.; Chan, Y.H.; et al. ARVCF genetic influences on neurocognitive and neuroanatomical intermediate phenotypes in Chinese patients with schizophrenia. J. Clin. Psychiatry 2012, 73, 320–326. [Google Scholar] [CrossRef] [PubMed]
  55. Han, Y.; Yin, H.; Xu, Y.; Zhu, Q.; Luo, J.; Wang, X.; Chen, G. Increased expression of calponin-3 in epileptic patients and experimental rats. Exp. Neurol. 2012, 233, 430–437. [Google Scholar] [CrossRef] [PubMed]
  56. Furuta, N.; Makioka, K.; Fujita, Y.; Ikeda, M.; Takatama, M.; Matsuoka, M.; Okamoto, K. Reduced expression of BTBD10 in anterior horn cells with Golgi fragmentation and pTDP-43-positive inclusions in patients with sporadic amyotrophic lateral sclerosis. Neuropathology 2013, 33, 397–404. [Google Scholar] [CrossRef] [PubMed]
  57. Bello, A.I.; Goswami, R.; Brown, S.L.; Costanzo, K.; Shores, T.; Allan, S.; Odah, R.; Mohan, R.D. Deubiquitinases in Neurodegeneration. Cells 2022, 11, 556. [Google Scholar] [CrossRef] [PubMed]
  58. Lin, T.; Liu, G.A.; Perez, E.; Rainer, R.D.; Febo, M.; Cruz-Almeida, Y.; Ebner, N.C. Systemic Inflammation Mediates Age-Related Cognitive Deficits. Front. Aging Neurosci. 2018, 10, 236. [Google Scholar] [CrossRef] [PubMed]
  59. Zhang, W.; Xiao, D.; Mao, Q.; Xia, H. Role of neuroinflammation in neurodegeneration development. Signal Transduct. Target. Therapy 2023, 8, 267. [Google Scholar] [CrossRef] [PubMed]
  60. Dunn, G.A.; Nigg, J.T.; Sullivan, E.L. Neuroinflammation as a risk factor for attention deficit hyperactivity disorder. Pharmacol. Biochem. Behav. 2019, 182, 22–34. [Google Scholar] [CrossRef] [PubMed]
  61. Fiani, B.; Zhu, L.; Musch, B.L.; Briceno, S.; Andel, R.; Sadeq, N.; Ansari, A.Z. The Neurophysiology of Caffeine as a Central Nervous System Stimulant and the Resultant Effects on Cognitive Function. Cureus 2021, 13, e15032. [Google Scholar] [CrossRef] [PubMed]
  62. Singh, N.; Vrontakis, M.; Parkinson, F.; Chelikani, P. Functional bitter taste receptors are expressed in brain cells. Biochem. Biophys. Res. Commun. 2011, 406, 146–151. [Google Scholar] [CrossRef] [PubMed]
  63. Duarte, A.C.; Costa, A.R.; Gonçalves, I.; Quintela, T.; Preissner, R.; Santos, C.R.A. The druggability of bitter taste receptors for the treatment of neurodegenerative disorders. Biochem. Pharmacol. 2022, 197, 114915. [Google Scholar] [CrossRef] [PubMed]
  64. Tiroch, J.; Sterneder, S.; Di Pizio, A.; Lieder, B.; Hoelz, K.; Holik, A.-K.; Pignitter, M.; Behrens, M.; Somoza, M.; Ley, J.P.; et al. Bitter Sensing TAS2R50 Mediates the trans-Resveratrol-Induced Anti-inflammatory Effect on Interleukin 6 Release in HGF-1 Cells in Culture. J. Agric. Food Chem. 2021, 69, 13339–13349. [Google Scholar] [CrossRef] [PubMed]
  65. Herrmann, M.; Simstich, S.; Fauler, G.; Hofer, E.; Fritz-Petrin, E.; Herrmann, W.; Schmidt, R. The relationship between plasma free fatty acids, cognitive function and structural integrity of the brain in middle-aged healthy humans. Aging 2021, 13, 22078–22091. [Google Scholar] [CrossRef] [PubMed]
  66. Li, Z.; Shue, F.; Zhao, N.; Shinohara, M.; Bu, G. APOE2: Protective mechanism and therapeutic implications for Alzheimer’s disease. Mol. Neurodegener. 2020, 15, 63. [Google Scholar] [CrossRef] [PubMed]
  67. Yu, L.; Boyle, P.A.; Nag, S.; Leurgans, S.; Buchman, A.S.; Wilson, R.S.; Arvanitakis, Z.; Farfel, J.M.; Jager, P.L.D.; Bennett, D.A.; et al. APOE and cerebral amyloid angiopathy in community-dwelling older persons. Neurobiol. Aging 2015, 36, 2946–2953. [Google Scholar] [CrossRef] [PubMed]
  68. Dekosky, S.T.; Fitzpatrick, A.; Ives, D.G.; Saxton, J.; Williamson, J.; Lopez, O.L.; Burke, G.; Fried, L.; Kuller, L.H.; Robbins, J.; et al. The Ginkgo Evaluation of Memory (GEM) study: Design and baseline data of a randomized trial of Ginkgo biloba extract in prevention of dementia. Contemp. Clin. Trials. 2006, 27, 238–253. [Google Scholar] [CrossRef] [PubMed]
  69. Dekosky, S.T.; Williamson, J.D.; Fitzpatrick, A.L.; Kronmal, R.A.; Ives, D.G.; Saxton, J.A.; Lopez, O.L.; Burke, G.; Carlson, M.C.; Fried, L.P.; et al. Ginkgo biloba for prevention of dementia: A randomized controlled trial. JAMA 2008, 300, 2253–2262. [Google Scholar] [CrossRef] [PubMed]
  70. Ganguli, M.; Snitz, B.; Bilt, J.V.; Chang, C.-C.H. How much do depressive symptoms affect cognition at the population level? The monongahela-youghiogheny healthy aging team (MYHAT) study. Int. J. Geriatr. Psychiatry 2009, 24, 1277–1284. [Google Scholar] [CrossRef] [PubMed]
  71. Ganguli, M.; Snitz, B.E.; Lee, C.-W.; Vanderbilt, J.; Saxton, J.A.; Chang, C.-C.H. Age and education effects and norms on a cognitive test battery from a population-based cohort: The Monongahela–Youghiogheny Healthy Aging Team. Aging Ment. Health 2010, 14, 100–107. [Google Scholar] [CrossRef] [PubMed]
  72. Ganguli, M.; Seaberg, E.C.; Ratcliff, G.G.; Belle, S.H.; DeKosky, S.T. Cognitive stability over 2 years in a rural elderly population: The MoVIES project. Neuroepidemiology 1996, 15, 42–50. [Google Scholar] [CrossRef] [PubMed]
  73. Purcell, S.; Neale, B.; Todd-Brown, K.; Thomas, L.; Ferreira, M.A.R.; Bender, D.; Maller, J.; Sklar, P.; Bakker, P.I.W.B.; Daly, M.J.; et al. PLINK: A Tool Set for Whole-Genome Association and Population-Based Linkage Analyses. Am. J. Hum. Genet. 2007, 81, 559–575. [Google Scholar] [CrossRef] [PubMed]
  74. Harper, J.D.; Fan, K.H.; Aslam, M.M.; Snitz, B.E.; DeKosky, S.T.; Lopez, O.L.; Feingold, E.; Kamboh, M.I. Genome-Wide Association Study of Incident Dementia in a Community-Based Sample of Older Subjects. J. Alzheimer’s Dis. 2022, 88, 787–798. [Google Scholar] [CrossRef] [PubMed]
  75. Willer, C.J.; Li, Y.; Abecasis, G.R. METAL: Fast and efficient meta-analysis of genomewide association scans. Bioinformatics 2010, 26, 2190–2191. [Google Scholar] [CrossRef] [PubMed]
  76. Wang, K.; Li, M.; Hakonarson, H. ANNOVAR: Functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 2010, 38, e164. [Google Scholar] [CrossRef] [PubMed]
  77. Watanabe, K.; Taskesen, E.; Van Bochoven, A.; Posthuma, D. Functional mapping and annotation of genetic associations with FUMA. Nat. Commun. 2017, 8, 1826. [Google Scholar] [CrossRef] [PubMed]
  78. Giusti-Rodriguez, P.M.; Sullivan, P.F. Using three-dimensional regulatory chromatin interactions from adult and fetal cortex to interpret genetic results for psychiatric disorders and cognitive traits. bioRxiv 2019. [Google Scholar] [CrossRef]
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Figure 1. Association of rs6559700 with decline in attention in the APOE 3/3 group. (Top) Manhattan plot shows the association of rs6559700 with decline in attention in the APOE 3/3 group. The black dashed line indicates genome-wide significance (p = 5 × 10−8), and the gray dashed line indicates suggestive significance (p = 1 × 10−5). (Bottom) Locus Zoom plot of the association of rs6559700 with decline in attention in the APOE 3/3 (β = −0.288; p = 9.95 × 10−9) group.
Figure 1. Association of rs6559700 with decline in attention in the APOE 3/3 group. (Top) Manhattan plot shows the association of rs6559700 with decline in attention in the APOE 3/3 group. The black dashed line indicates genome-wide significance (p = 5 × 10−8), and the gray dashed line indicates suggestive significance (p = 1 × 10−5). (Bottom) Locus Zoom plot of the association of rs6559700 with decline in attention in the APOE 3/3 (β = −0.288; p = 9.95 × 10−9) group.
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Figure 2. Association of rs116379916 with decline in global cognitive function in the APOE 3/3 group. (Top): Manhattan plot showing the association of rs116379916 with decline in global cognitive function in the APOE 3/3 group. The black dashed line indicates genome-wide significance (p = 5 × 10−8), and the gray dashed line indicates suggestive significance (p = 1 × 10−5). (Bottom): LocusZoom plot of the association of rs116379916 with decline in global cognitive function in APOE 3/3 (β = −0.507; p = 1.44 × 10−9).
Figure 2. Association of rs116379916 with decline in global cognitive function in the APOE 3/3 group. (Top): Manhattan plot showing the association of rs116379916 with decline in global cognitive function in the APOE 3/3 group. The black dashed line indicates genome-wide significance (p = 5 × 10−8), and the gray dashed line indicates suggestive significance (p = 1 × 10−5). (Bottom): LocusZoom plot of the association of rs116379916 with decline in global cognitive function in APOE 3/3 (β = −0.507; p = 1.44 × 10−9).
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Figure 3. Association of rs13187183 with decline in language in the APOE4 group. (Top) Manhattan Plot showing the association of rs13187183 with the decline in language in the APOE4 group. The black dashed line indicates genome-wide significance (p = 5 × 10−8), and the gray dashed line indicates suggestive significance (p = 1 × 10−5). (Bottom) LocusZoom plot of the association of rs13187183 with the decline in language in APOE4 (β = 0.693; p = 3.79 × 10−8).
Figure 3. Association of rs13187183 with decline in language in the APOE4 group. (Top) Manhattan Plot showing the association of rs13187183 with the decline in language in the APOE4 group. The black dashed line indicates genome-wide significance (p = 5 × 10−8), and the gray dashed line indicates suggestive significance (p = 1 × 10−5). (Bottom) LocusZoom plot of the association of rs13187183 with the decline in language in APOE4 (β = 0.693; p = 3.79 × 10−8).
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Table 1. Demographic profile of GEM, MYHAT, and MoVIES stratified by APOE.
Table 1. Demographic profile of GEM, MYHAT, and MoVIES stratified by APOE.
Gingko Evaluation of Memory (GEM)Monongahela-Youghiogheny Healthy
Aging Team
(MYHAT)		Monongahela Valley Independent Elders Survey
(MoVIES)
Total Participants1898745378
Age (Mean (SD))78.13 ± 3.0477.15 ± 7.3277.46 ± 4.23
Female (N, %)840 (44.25) 451 (60.53)254 (67.19)
Education (Years) 14.4 (2.9)13.04 (2.51)11.81 (2.27)
Baseline CDR, (N, %)
01263 (66.54)570 (76.51)378 (100)
0.5635 (33.45)175 (23.48)0 (0)
Last CDR, (N, %)
0939 (49.47)501 (67.24)378 (100)
0.5959 (50.52)220 (29.53)0 (0)
≥10 (0)24 (3.22)0 (0)
APOE2 (N)25810055
Age (Mean (SD))78.45 ± 3.2579.02 ± 7.5977.70 ± 5.16
Female (N, %)118 (45.73)52 (52)46 (95)
Education (Years)14.191311.89
Baseline CDR (N, %)
0162 (62.79) 80 (80)55 (100)
0.596 (37.2) 20 (20)0 (0)
≥1 0 (0)
Last CDR (N, %)
0 128 (49.61)70 (70)55 (100)
0.5130 (50.38)28 (28)0 (0)
≥10 (0)2 (2)0 (0)
APOE 3/3 (N)1237498260
Age (Mean (SD))78.18 ± 3.0677.22 ± 7.3177.49 ± 4.18
Female (%)531 (42.92)305 (61.24)169 (65)
Education (years)14.4013.0211.73
Baseline CDR (N, %)
0840 (67.90)382 (76.7)260 (100)
0.5397 (32.09)116 (23.2)0 (0)
≥10 (0)0 (0)0 (0)
Last CDR (N, %)
0615 (49.71)340 (68.3)260 (100)
0.5622 (50.27)145 (29.1)0 (0)
≥10 (0)13 (2.6)0 (0)
APOE4 (N)36713856
Age (Mean (SD))77.74 ± 2.6575.71 ± 7.0276.96 ± 3.37
Female (N, %)174 (47.70)88 (63.76)34 (60.71)
Education (years)14.4813.2112.17
Baseline CDR (N, %)
0238 (64.9)102 (73.9)56 (100)
0.5129 (35.1)36 (26.1)0 (0)
≥10 (0)0 (0)0 (0)
Last CDR (N, %)
0181 (49.3)84 (60.9)56 (100)
0.5186 (50.7)46 (33.3)0 (0)
≥10 (0)8 (5.8)0 (0)
Note: CDR = Clinical Dementia Rating.
Table 2. List of top and suggestive SNPs with p < 1 × 10−6 associated with cognitive domains and global cognitive function in three APOE groups (APOE2, APOE 3/3 and APOE4). Bold font indicates the genome-wide significant loci.
Table 2. List of top and suggestive SNPs with p < 1 × 10−6 associated with cognitive domains and global cognitive function in three APOE groups (APOE2, APOE 3/3 and APOE4). Bold font indicates the genome-wide significant loci.
APOE
Subgroups		DomainSNPChr:PositionA1/A2Meta-AnalysisLocGene
MAFBetap
APOE4
Group 		Attentionrs623719935:42622849C/A0.04−0.771.87 × 10−7intronicGHR
rs732003613:85848704C/T0.140.424.44 × 10−7intergenicLINC00375, LINC00351
rs622530013:69767057G/A0.070.576.60 × 10−7intergenicFRMD4B, MITF
rs47323637:138990811T/C0.490.288.27 × 10−7UTR3UBN2
Executive
Function		rs2938798:4586377T/G0.320.328.49 × 10−8intronicCSMD1
rs624999718:20125115T/C0.19−0.362.01 × 10−7intronicLZTS1
rs29436741:85105006C/T0.10−0.457.31 × 10−7intergenicLINC01555, SSX2IP
Memoryrs7284555717:60458122C/A0.120.466.60 × 10−7intronicEFCAB3
Language rs13187183 5:117976098 C/T 0.06 0.69 3.79 × 10−8 intergenic LINC02215, DTWD2
rs488864716:73748328C/T0.09−0.489.52 × 10−7intergenicLINC01568, LOC101928035
Globalrs622973164:5376604G/C0.021.211.58 × 10−7intronicSTK32B
rs127465981:240305899T/C0.34−0.309.81 × 10−7intronicFMN2
APOE 3/3
Group 		Attention rs6559700 9:85751913 A/G 0.09 −0.29 9.95 × 10−9 intergenic RASEF, FRMD3
rs7784858116:26919840C/T0.010.717.23 × 10−7intergenicHS3ST4, C16orf82
Executive
Function		rs623202804:131795996T/C0.13−0.235.19 × 10−7intergenicLINC02479, SNHG27
rs11235664313:55089341T/C0.02−0.506.99 × 10−7intergenicMIR1297, MIR5007
Memoryrs386546019:41447928G/A0.210.185.35 × 10−7lncRNA
intronic		CYP2B7P
rs1510908367:38728382C/T0.03−0.465.56 × 10−7intergenicFAM183BP, VPS41
rs42617113:110557648C/A0.35−0.156.04 × 10−7intergenicIRS2, LINC00396
rs1167255604:8374858T/C0.05−0.357.73 × 10−7intronicACOX3
Visuospatial
Function		rs583024052:218000504T/C0.03−0.462.69 × 10−7intergenicLINC01921, DIRC3-AS1
rs625218438:124198306A/G0.03−0.445.17 × 10−7intronicFAM83A
rs13985169010:2600111C/T0.020.569.48 × 10−7intergenicLINC02645, LOC101927824
Global Function rs116379916 6:68140492 T/G 0.03 −0.51 1.44 × 10−9 intergenic RNU7-66P, RNA5SP208
APOE2 GroupAttentionrs771271142:103000000A/T0.040.868.64 × 10−8intronicIL1RL1
rs7270642414:93522585A/G0.050.712.17 × 10−7intronicITPK1
rs114655962:103000000A/C0.100.534.67 × 10−7intronicIL18R1
rs117871538:139697286C/T0.250.368.22 × 10−7intronicCOL22A1
rs45416565:123971932A/C0.340.339.84 × 10−7downstreamZNF608
Executive
Function		rs358711597:30781232T/C0.360.332.97 × 10−7intergenicCRHR2, INMT
rs1333909316:78619484C/T0.060.693.65 × 10−7intronicWWOX
rs1692193612:19924660G/T0.04−0.813.94 × 10−7intergenicAEBP2, LINC02398
Languagers1161918365:113000000T/C0.021.415.66 × 10−8intergenicYTHDC2, KCNN2
Visuospatial
Function		rs1084922312:5384521C/T0.140.512.98 × 10−7intergenicLINC02443, NTF3
rs1231944512:92357858G/A0.02−1.143.30 × 10−7intergenicDCN, LINC01619
Global Functionrs7516874314:44488668C/T0.070.631.65 × 10−7intergenicNONE, FSCB
rs799814913:79077882G/A0.48−0.341.85 × 10−7ncRNA
intronic		OBI1-AS1
rs126416774:182290596G/A0.10−0.524.25 × 10−7intergenicLINC02500, TEMN3-AS1
Table 3. Number of genes mapped for each cognitive trait across domains using positional, eQTL, and chromatin mapping (given in numbers) and genes mapped using all three mapping methods (given by names).
Table 3. Number of genes mapped for each cognitive trait across domains using positional, eQTL, and chromatin mapping (given in numbers) and genes mapped using all three mapping methods (given by names).
DomainAPOE2 CarriersAPOE 3/3APOE4 Carriers
Attention65
ZNF608, ARID4A		118
CNN3, RP4-639F20.1		120
USP36, IRF1
Executive Function82
- *		81
CTB-129O4.1, PDP1		150
LZTS1, KAT2A, RAB5C, ARVCF, DGCR8
Memory74
VANGL1, PTPN14		38
APBB2		153
PPP2R5A, TMEM206, BTBD10
Language78
MSRA, CTB113P19.3		64
ASNSD1, OSGEPL1, OSGEPL1-AS1, ORMDL1, PRR5L		89
PDE6B
Visuospatial
Function		147
ZNF69, ZNF788, ZNF20, RSL24D1P8		41
-		122
PIGCP1, CSTF3, HIPK3, SAMD4A
Global Function88
-		95
-		79
-
* Note: Dash (-) indicates no genes were mapped using all three mapping methods.
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Acharya, V.; Fan, K.-H.; Snitz, B.E.; Ganguli, M.; DeKosky, S.T.; Lopez, O.L.; Feingold, E.; Kamboh, M.I. APOE Genotype-Stratified Meta-Analysis of Cognitive Decline Reveals Novel Loci for Language and Global Cognitive Function in Older Adults. Int. J. Mol. Sci. 2025, 26, 6940. https://doi.org/10.3390/ijms26146940

AMA Style

Acharya V, Fan K-H, Snitz BE, Ganguli M, DeKosky ST, Lopez OL, Feingold E, Kamboh MI. APOE Genotype-Stratified Meta-Analysis of Cognitive Decline Reveals Novel Loci for Language and Global Cognitive Function in Older Adults. International Journal of Molecular Sciences. 2025; 26(14):6940. https://doi.org/10.3390/ijms26146940

Chicago/Turabian Style

Acharya, Vibha, Kang-Hsien Fan, Beth E. Snitz, Mary Ganguli, Steven T. DeKosky, Oscar L. Lopez, Eleanor Feingold, and M. Ilyas Kamboh. 2025. "APOE Genotype-Stratified Meta-Analysis of Cognitive Decline Reveals Novel Loci for Language and Global Cognitive Function in Older Adults" International Journal of Molecular Sciences 26, no. 14: 6940. https://doi.org/10.3390/ijms26146940

APA Style

Acharya, V., Fan, K.-H., Snitz, B. E., Ganguli, M., DeKosky, S. T., Lopez, O. L., Feingold, E., & Kamboh, M. I. (2025). APOE Genotype-Stratified Meta-Analysis of Cognitive Decline Reveals Novel Loci for Language and Global Cognitive Function in Older Adults. International Journal of Molecular Sciences, 26(14), 6940. https://doi.org/10.3390/ijms26146940

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