Polygenic effects on the risk of Alzheimer’s disease in the Japanese population

Masataka Kikuchi; Akinori Miyashita; Norikazu Hara; Kensaku Kasuga; Yuko Saito; Shigeo Murayama; Akiyoshi Kakita; Hiroyasu Akatsu; Kouichi Ozaki; Shumpei Niida; Ryozo Kuwano; Takeshi Iwatsubo; Akihiro Nakaya; Takeshi Ikeuchi; the Alzheimer’s Disease Neuroimaging Initiative; the Japanese Alzheimer’s Disease Neuroimaging Initiative

doi:10.1186/s13195-024-01414-x

. 2024 Feb 27;16:45. doi: 10.1186/s13195-024-01414-x

Polygenic effects on the risk of Alzheimer’s disease in the Japanese population

Masataka Kikuchi ^1,^2,^✉, Akinori Miyashita ³, Norikazu Hara ³, Kensaku Kasuga ³, Yuko Saito ⁴, Shigeo Murayama ^4,⁵, Akiyoshi Kakita ⁶, Hiroyasu Akatsu ⁷, Kouichi Ozaki ^8,⁹, Shumpei Niida ¹⁰, Ryozo Kuwano ¹¹, Takeshi Iwatsubo ¹², Akihiro Nakaya ¹, Takeshi Ikeuchi ^3,^✉; the Alzheimer’s Disease Neuroimaging Initiative; the Japanese Alzheimer’s Disease Neuroimaging Initiative

PMCID: PMC10898021 PMID: 38414085

Abstract

Background

Polygenic effects have been proposed to account for some disease phenotypes; these effects are calculated as a polygenic risk score (PRS). This score is correlated with Alzheimer’s disease (AD)-related phenotypes, such as biomarker abnormalities and brain atrophy, and is associated with conversion from mild cognitive impairment (MCI) to AD. However, the AD PRS has been examined mainly in Europeans, and owing to differences in genetic structure and lifestyle, it is unclear whether the same relationships between the PRS and AD-related phenotypes exist in non-European populations. In this study, we calculated and evaluated the AD PRS in Japanese individuals using genome-wide association study (GWAS) statistics from Europeans.

Methods

In this study, we calculated the AD PRS in 504 Japanese participants (145 cognitively unimpaired (CU) participants, 220 participants with late mild cognitive impairment (MCI), and 139 patients with mild AD dementia) enrolled in the Japanese Alzheimer’s Disease Neuroimaging Initiative (J-ADNI) project. In order to evaluate the clinical value of this score, we (1) determined the polygenic effects on AD in the J-ADNI and validated it using two independent cohorts (a Japanese neuropathology (NP) cohort (n = 565) and the North American ADNI (NA-ADNI) cohort (n = 617)), (2) examined the AD-related phenotypes associated with the PRS, and (3) tested whether the PRS helps predict the conversion of MCI to AD.

Results

The PRS using 131 SNPs had an effect independent of APOE. The PRS differentiated between CU participants and AD patients with an area under the curve (AUC) of 0.755 when combined with the APOE variants. Similar AUC was obtained when PRS calculated by the NP and NA-ADNI cohorts was applied. In MCI patients, the PRS was associated with cerebrospinal fluid phosphorylated-tau levels (β estimate = 0.235, p value = 0.026). MCI with a high PRS showed a significantly increased conversion to AD in APOE ε4 noncarriers with a hazard rate of 2.22. In addition, we also developed a PRS model adjusted for LD and observed similar results.

Conclusions

We showed that the AD PRS is useful in the Japanese population, whose genetic structure is different from that of the European population. These findings suggest that the polygenicity of AD is partially common across ethnic differences.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13195-024-01414-x.

Keywords: Polygenic risk score, Alzheimer’s disease, Mild cognitive impairment

Background

Alzheimer’s disease (AD) is a neurodegenerative disease caused by environmental and genetic factors [1, 2]. Environmental factors, which are acquired and modifiable, associated with AD include smoking status, alcohol consumption, diet, and physical activity [3]. On the other hand, the heritability of AD is approximately 70%, and genetic factors are inborn and nonmodifiable [4, 5]. However, knowing one’s genetic risk early in life can motivate one to improve modifiable factors. Indeed, sharing genetic test results with carriers of genetic risk for disease may promote behavioural changes rather than increase psychological distress [6, 7]. Thus, knowledge of the individual genetic risk of AD is expected to contribute to delaying the onset of AD and early therapeutic intervention.

The largest genetic risk factor for AD is the ε4 allele of the apolipoprotein E (APOE) gene, but APOE ε4 explains only approximately 10% of AD cases based on heritability [4, 5]. In addition, even when other AD-associated genetic variants found in previous genome-wide association studies (GWAS) are also considered, they do not explain all the genetic variance in AD patients [8], suggesting the existence of additional unknown AD-related genetic variants. To clarify this “missing heritability”, polygenic effects that aggregate the small effects of many alleles have been proposed to underlie AD.

Polygenic risk score (PRS) is a measure to quantify the combined effect of genetic variants on an individual’s risk for disease. The combination of the APOE ε4 allele dose and PRS has been shown to improve disease prediction accuracy in the European population [9]. Moreover, the PRS is associated with AD-related phenotypes, such as brain volumes [10–12], brain amyloid-beta (Aβ) burden [11, 12], and plasma phosphorylated tau [13], and has been reported to be useful in predicting conversion from mild cognitive impairment (MCI) to AD [14, 15].

However, the clinical application of the PRS must be approached with caution. One of several concerns is that the effects of the PRS are not consistent across different ancestries [16, 17]. This is because genetic structures, such as linkage disequilibrium (LD) blocks, are different across populations and because the GWAS summary statistics used as a weight for each single-nucleotide polymorphism (SNP) to calculate the PRS are based primarily on people of European ancestry. Taking a PRS calculation method based on GWAS summary statistics from European individuals and applying it to non-European individuals compromises prediction accuracy since the genetic risk of that population may not be reflected properly [18]. Therefore, for future clinical application of the AD PRS, it is necessary to evaluate the utility of this score in populations of different ancestry. In addition, harmonization of protocols such as inclusion and exclusion criteria is critical for rigorous comparisons between different cohorts.

Therefore, in this study, we calculated the AD PRS in 504 Japanese participants (145 cognitively unimpaired participants, 220 participants with late MCI, and 139 patients with mild AD dementia) enrolled in the Japanese Alzheimer’s Disease Neuroimaging Initiative (J-ADNI) project and evaluated its effectiveness in the North American ADNI (NA-ADNI) cohort including North American 1070 participants. The J-ADNI study used a harmonized protocol to the NA-ADNI study. The previous comparative study of AD dementia between the US and Japan in the ADNI projects reported that MCI in the Japanese population shows similar progression profile as MCI in North America in terms of cognitive function [19]. We moreover validated the AD PRS using independent genomic data from 565 Japanese individuals with a neuropathological diagnosis by autopsy. Furthermore, we also examined the AD endophenotypes in association with PRS and tested whether the PRS is useful for predicting conversion from MCI to AD.

Materials and methods

Japanese participants from the J-ADNI cohort

Data used in the preparation of this article were obtained from the J-ADNI database deposited in the National Bioscience Database Center Human Database, Japan (Research ID: hum0043.v1, 2016) [19]. This database enrolled cognitively unimpaired (CU) participants, participants with late MCI, and patients with mild AD dementia (ADD) using criteria consistent with those of the North American ADNI (NA-ADNI) [20]. The J-ADNI was launched in 2007 as a public–private partnership led by Principal Investigator Takeshi Iwatsubo, MD. The J-ADNI was aimed to test whether serial magnetic resonance imaging (MRI), positron emission tomography (PET), other biological markers, and clinical and neuropsychological assessment can be combined to measure the progression of late MCI and mild ADD in the Japanese population. The J-ADNI did not recruit participants with early MCI. The ethics committees of the University of Tokyo, Osaka University and Niigata University approved the study.

A total of 715 volunteer participants between the ages of 60 and 84 years were diagnosed with late MCI or mild ADD or were CU and considered for inclusion in the J-ADNI. Of the 715 participants assessed for study eligibility, 537 met the criteria and were enrolled. Of these 537 participants, 508 (CU, 147; MCI, 221; ADD, 140) underwent genotyping analysis. Participants were evaluated every 6 or 12 months over a period of 36 months for CU and MCI participants and over a period of 24 months for participants with ADD, as in the NA-ADNI. As detailed below, the J-ADNI collected various imaging, clinical and neuropsychological data from these participants in addition to the genomic data. These data were obtained from the database described above.

Japanese neuropathological cohort

An independent neuropathological (NP) cohort composed of 577 brain donors was used for PRS validation [21]. Of these donors, 365 control donors had little pathological findings associated with AD and 212 case donors had those consistent with AD. All ADD patients were neuropathologically diagnosed by senile plaque and neurofibrillary tangle. No neuropathological features of other neurodegenerative disorders such as dementia with Lewy body disease, frontotemporal lobal degeneration, and Parkinson’s disease, were observed. Control individuals did not show the typical neuropathological hallmarks of AD. As no clinical diagnosis is provided in this cohort, the term case or control is used in this study. As shown below, 565 brain donors (358 controls and 207 cases) passed QC. The demographic data of all the participants from the NP cohort are shown in Table S1.

Genotyping, quality control, and imputation

Whole blood samples from 508 participants in the J-ADNI cohort and post-mortem frontal cortices from 577 donors in the NP cohort were genotyped using the Infinium Asian Screening Array (Illumina), containing 657,490 SNPs. APOE genotypes in each participant were determined by haplotypes derived from rs7412 and rs429358, which were genotyped using TaqMan Assays (Applied Biosystems). We excluded SNPs that (i) had duplicated genomic positions, (ii) had low call rates (< 5%), (iii) deviated from Hardy–Weinberg equilibrium compared to controls (p < 1 × 10⁻⁵), or (iv) had low minor allele frequency (< 0.01). For QC purposes, we excluded participants who (i) had sex inconsistencies, (ii) had autosomal heterozygosity deviation (|F_het|≥ 0.2), (iii) had < 99% of their genotypes called, or (iv) were in the same family according to pi-hat (> 0.2). Furthermore, we used principal component analysis to remove outliers based on the 1000 Genomes Project samples (Phase3 v5) [22]. Finally, 451,713 autosomal SNPs and the samples, including 504 participants from the J-ADNI cohort and 565 brain donors from the NP cohort passed the QC procedures.

Next, we performed phasing with Eagle v2.4.1 [23] and imputation with Minimac4 [24] using the whole-genome sequencing data of 3541 participants obtained from the BioBank Japan Project [25] and the 1000 Genomes Project [22] as reference genome data. After repeating the above QC procedure for the imputed SNP markers, we excluded SNPs with poor imputation quality (r² ≤ 0.3). Finally, we obtained 7,633,670 SNPs and the samples, including the 504 participants from the J-ADNI (CU, 145; MCI, 220; and ADD, 139) and 565 brain donors from the NP cohort (control, 358; case, 207).

The NA-ADNI genetic data

The independent cohort data used in this study were obtained from the NA-ADNI [26]. The NA-ADNI was launched in 2003 as a public–private partnership led by Principal Investigator Michael W. Weiner, MD. The NA-ADNI was aimed to test whether serial MRI and PET data and the analysis of other biological markers and clinical and neuropsychological assessments can be combined to characterize the progression of MCI and early ADD.

SNP data from the NA-ADNI project were available for 1674 participants across ADNI 1 and ADNI GO/2. Genotyping was conducted using three different platforms: Human610-Quad, HumanOmniExpress and Omni 2.5 M (Illumina) [27]. The SNP data were imputed using the TOPMeD imputation server after identical marker QC and sample QC as was used for the J-ADNI was performed. The SNP data analysed on each of the three platforms were imputed separately. After repeating the QC for the imputed SNP markers, we excluded SNPs with poor imputation quality (r² ≤ 0.3). If a participant was genotyped on more than one genotyping array, the dataset with the fewest missing values was selected.

According to the following procedures, we selected participants with predicted central European ancestry and self-reported white non-Hispanic ethnicity. For predicted ancestry, we used SNPweights software to infer genetic ancestry from genotyped SNPs [28]. The reference panel comprised European, West African, East Asian and Native American ancestral populations. Participants with predicted central European ancestry of 80% or more were retained. We obtained self-reported ethnicity information from the NA-ADNI database. The clinical diagnosis at the final visit was used to categorize the data. Furthermore, four participants who had significant memory concerns but no cognitive impairment were excluded. Finally, 1482 participants (CU, 377; MCI, 481; and ADD, 624) remained.

Of the 1482 participants, 412 participants were participants in the Alzheimer’s Disease Genetics Consortium (ADGC) and were included in the meta-analysis of AD GWAS used as SNP weights in the PRS calculation described below. We analysed a set of 1070 participants (CU, 257; MCI, 453; and ADD, 360), excluding the 412 participants to avoid overfitting. The demographic data of all the participants from the NA-ADNI cohort are shown in Table S2.

Calculation of the PRS and prediction accuracy

The PRS was calculated for each individual and is expressed as the following weighted sum:

{PRS}_{i} = \sum_{j = 1}^{M} β_{j} x_{i, j} / M,

where PRS_i is the PRS for individual i; M is the total number of SNPs used in the calculation; β_j is the weight of SNP_j, defined according to the effect size calculated by an independent GWAS; and x_i,j is the number of minor alleles of SNP_j that individual i has, thus has a value of 0, 1, or 2. In other words, the more minor alleles that are strongly associated with the disease, the higher the PRS.

SNPs included in the PRS were determined by the clumping and thresholding (C + T) method, the most common and supported method in AD studies [29, 30]. We used PRSice software implementing the C + T method to calculate the PRS [31]. The clumping method preferentially retains markers most strongly associated with disease from correlated markers in the same LD block. The thresholding method removes variants with GWAS p values greater than the selected p value threshold (p_T) (p > p_T). To determine the optimal p_T, we tested p_T values of 5 × 10⁻⁸, 1 × 10⁻⁶, 1 × 10⁻⁵, 1 × 10⁻⁴, 1 × 10⁻³, 1 × 10⁻², 0.05, 0.5, and 1.0. SNPs were weighted by their effect sizes (beta coefficient) from the AD GWAS in the European population [32].

The ability of the PRS to accurately classify CU participants and ADD patients was estimated in terms of (1) Nagelkerke’s R², the proportion of the variance explained by the regression model and (2) the area under the receiver operator characteristic curve (AUC). To calculate Nagelkerke’s R², we constructed a logistic regression model, including the PRS and the first two components from the multidimensional scaling (MDS) analysis (full model), and compared it to a model with only the first two MDS components (null model). We assessed the difference in Nagelkerke’s R² between the full and null models (R² = R²_Full − R²_Null) and used the p_T corresponding to the highest value of Nagelkerke’s R². The Nagelkerke’s R² was calculated by PRSice software using default parameters [31]. To avoid potential overfitting due to differences in LD between the European and Japanese populations, we used the LD score (R²) of the EUR population of 1000 Genomes in the LDpop Tool [33] to exclude SNPs suspected of LD using the criterion of R² > 0.5. In this analysis, when adjacent SNPs had R² > 0.5, one SNP with a lower GWAS p-value was selected to calculate PRS and the other was excluded. When more than one SNP was observed between two SNPs with R² > 0.5, all of them may be in the same LD block, and the SNP showing the lowest GWAS p-value was selected from this LD block.

The AUC was calculated based on the prediction results of the logistic regression model using the J-ADNI cohort as a test cohort. We also performed fivefold cross validation (CV) to evaluate a predictive performance in a test cohort. We estimated the 95% credible intervals by using the ci.auc function from the R package “pROC”. DeLong’s test was conducted to assess potential significant differences between curves using the roc.test function from the R package “pROC”.

CSF biomarkers

In the J-ADNI cohort, cerebrospinal fluid (CSF) samples were assayed for Aβ(1–42), total tau (tTau), and phosphorylated tau (pTau) by using a multiplex xMAP Luminex platform (Luminex Corp, Austin, TX) with an Innogenetics (INNO-BIA AlzBio3; Ghent, Belgium) immunoassay kit-based reagent [34]. Of the 504 participants who underwent genotyping, 192 participants (CU, 52; MCI, 85; ADD, 55) also underwent CSF biomarker measurements at baseline.

Structural MRI and PET imaging

All participants in the J-ADNI cohort underwent a structural MRI scan at a signal strength of 1.5 Tesla using a three-dimensional magnetization-prepared rapid-acquisition gradient-echo sequence according to a standardized protocol [35]. Cross-sectional and longitudinal processing streams in FreeSurfer, version 5.3, were used to estimate the atrophic changes in specific regions; we also evaluated the cortical thickness extracted in the longitudinal analysis. Of the 504 participants who underwent genotyping, the entorhinal cortex and hippocampus of 443 participants (CU, 133; MCI, 196; ADD, 114) was assessed by the FreeSurfer longitudinal stream. Each cortical thickness value was adjusted by the total intracranial volume.

Of the 504 participants, 315 and 162 individuals underwent a positron emission tomography (PET) scan using ¹⁸F-2-fluoro-2-deoxy-D-glucose (FDG) and ¹¹C-Pittsburgh compound B (PiB), respectively. The PET scanning protocol was standardized to minimize the inter-site and inter-scanner variability [36]. All PET images went through the J-ADNI PET QC process [36]. The FDG PET images were classified into seven categories based on the criteria of Silverman et al. [37]. We analysed only PET images of 110 participants classified as having a normal pattern (N1 pattern) and 161 participants classified as having an AD pattern (P1 pattern). For PiB PET, the visual interpretation of four cortical areas on each side (frontal lobe, lateral temporal lobe, lateral parietal lobe, and precuneus/posterior cingulate gyrus) was evaluated by classifying PiB uptake in each cortical region as positive, equivocal, or negative. Cases with one or more positive cortical areas were defined as amyloid scan positive, and those with negative results in all four cortical regions were defined as amyloid scan negative. Other cases were considered equivocal. We analysed 65 negative and 87 positive amyloid scans, excluding 10 participants who were judged to be equivocal.

Neuropsychological tests

All participants in the J-ADNI cohort underwent the following neuropsychological tests: Mini–Mental State Examination (MMSE), Functional Assessment Questionnaire (FAQ), Clinical Dementia Rating Scale Sum of Boxes (CDR-SB), and AD Assessment Scale–Cognitive Subscale (ADAS-Cog).

Statistical analyses

Gene functional enrichment analysis of the closest genes around SNPs included in the PRS was performed using the Metascape database (http://metascape.org/) [38].

For the association analyses between the PRS and endophenotypes, we compared slopes with zero by linear regression model analyses. The covariates included age at baseline examination, sex, years of education, the first two principal components (PCs), and doses of APOE ε4 and ε2 alleles. P values were adjusted by false discovery rate (FDR) to avoid type I error.

Cox proportional hazards models using months of follow-up as a time scale were used to analyse the effects of PRSs on incident AD, presented as hazard ratios (HRs) and 95% confidence intervals (CIs) derived from a model with the following covariates: age at baseline examination, sex, years of education, the first two PCs, and dose of APOE ε4 and ε2 alleles. We analysed 208 MCI participants over a follow-up period of ≥ 12 months. Nonconverters were censored at the end of follow-up. Log-rank test was performed to examine the difference in conversion to AD between two PRS groups. This test was performed using only the PRS without covariates because the covariates other than PRS could affect the differences between the groups. Cox proportional hazard model analyses and log-rank tests were performed using the coxph and survdiff functions from the R package “survival”, respectively.

Results

The PRS successfully distinguish ADD patients and CU individuals in the J-ADNI cohort

After quality control of the genotyping data, the J-ADNI cohort included the 504 participants. The group with ADD had a higher mean age (p value < 0.001), a lower mean length of education (p value < 0.001), and a higher frequency of APOE ɛ4 carriers (p value < 0.001) than the CU group, whereas no differences were found in sex (p value = 0.429) or the frequency of APOE ɛ2 carriers (p value = 0.292) (Table 1).

Table 1.

Summary of the J-ADNI participants

	CU	MCI	ADD	p value
N	145	220	139	-
Age in years, mean ± SE	67.8 ± 0.472	72.8 ± 0.397	73.8 ± 0.563	< 2.00 × 10^−16a
Sex (M:F)	72:73	106:114	59:80	0.429^b
Years of education, mean ± SE	13.8 ± 0.230	13.0 ± 0.189	12.5 ± 0.266	8.69 × 10^{−4 a}
APOEε4 alleles (0:1:2)	109:34:2	106:97:17	55:62:22	2.31 × 10^{−10 c}
APOEε2 alleles (0:1:2)	134:11:0	211:9:0	133:6:0	0.323 ^c

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Abbreviations: CU Cognitively unimpaired, MCI Mild cognitive impairment, ADD Alzheimer’s disease dementia, APOE Apolipoprotein E, SE Standard error, ANOVA analysis of variance

^aOne-way ANOVA

^bChi-squared test

^cFisher’s exact test

We investigated whether the PRSs that were calculated using the statistics from the AD GWAS in the European population [32] are useful for discriminating between patients with ADD and CU individuals in the Japanese population. We calculated PRSs for 145 CU participants and 139 patients with ADD from the J-ADNI cohort. Our model using 173 SNPs showed the highest predictive power at p_T < 1 × 10⁻⁵ and had a Nagelkerke’s R² of 0.167 (left side of Table 2), indicating that it explained more than 15% of the variance between the CU and ADD groups.

Table 2.

Nagelkerke's R2 at differenct p value thresholds

	PRS (All SNPs)			PRS.noAPOE (except APOE region)
p_T	Nagelkerke's R²	p value	#SNPs	Nagelkerke's R²	p value	#SNPs
p < 5 × 10^–8	0.148	5.92 × 10^–8	81	0.081	4.57 × 10^–5	44
p < 1 × 10^–6	0.143	9.91 × 10^–8	107	0.067	2.07 × 10^–4	70
p < 1 × 10^–5	0.167	1.40 × 10^–8	173	0.085	3.18 × 10^–5	131
p < 1 × 10^–4	0.091	1.53 × 10^–5	410	0.029	0.013	364
p < 1 × 10^–3	0.034	6.85 × 10^–3	1,696	0.014	0.077	1,642
p < 1 × 10^–2	0.004	0.324	10,101	0.007	0.213	10,041
p < 0.05	0.000	0.929	33,065	0.000	0.979	32,999
p < 0.5	0.005	0.288	121,721	0.004	0.332	121,647
p < 1.0	0.006	0.262	158,289	0.005	0.303	158,208

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The highest accuracy was highlighted in bold

P value was calculated by Wald test

Given the known predictive power of SNPs in the APOE region for AD, we next removed this region from our PRS calculation to evaluate the predictive power of other loci. To exclude the effect of APOE, we excluded ± 500 kb around APOE (Figure S1). This PRS, referred to as the PRS.noAPOE, was used in subsequent analyses. The predictive power of the PRS.noAPOE was the highest for p_T < 1 × 10⁻⁵, with a Nagelkerke’s R² of 0.085 (right side of Table 2). To remove the effect of APOE regions completely, we also validated PRS.nochr19 excluding SNPs located on chromosome 19. The predictive power of the PRS.nochr19 was the highest for p_T < 1 × 10⁻⁵, with a Nagelkerke’s R² of 0.082 (Table S3). To further avoid potential overfitting due to differences in LD between the European and Japanese populations, we excluded 18 SNPs with suspected LD in the European population from PRS.noAPOE (see “Methods”). We referred to this PRS adjusted for LD as the PRS.adjLD. A Nagelkerke’s R² of the PRS.adjLD was 0.075 (p value = 9.31 × 10⁻⁵). We analysed the PRS.noAPOE and PRS.adjLD in this study. The normalized values of the PRS.noAPOE and PRS.adjLD of the ADD patients were significantly higher than those of the CU and MCI participants (p value < 0.05, Tukey’s honestly significant difference (HSD) test; Fig. 1), while there were no significant difference between the CU and MCI participants (p value = 0.180 in PRS.noAPOE, p value = 0.296 in PRS.adjLD, Tukey’s HSD test; Fig. 1). These results suggest that the PRS contribute to distinguish between ADD patients and CU individuals in J-ADNI cohort even when the APOE region is excluded.

Fig. 1 — The PRS.noAPOE in the ADD group was significantly higher than those in the CU and MCI groups. The PRS.noAPOEs (A) or PRS.adjLD (B) in each group were represented by violin plots (CU, n = 145; MCI, n = 220; ADD, n = 139). Each violin plot includes the kernel probability density of the data at different values and the box plots with the median value and the interquartile range. Tukey’s HSD test was used to perform multiple comparisons of PRSs among each group. We normalized the PRS distribution to have a mean of 0 and an SD of 1. CN = cognitively normal; MCI = mild cognitive impairment; ADD = Alzheimer’s disease dementia

The PRS in combination with the APOE alleles improves predictive power

Next, we examined whether the PRSs and the characteristics of the participants independently influence the predictive power in J-ADNI cohort. The PRS.noAPOE and PRS.adjLD were not correlated with sex, years of education, age at baseline examination, or the dose of the APOE ε4 or ε2 allele, even when participants were stratified into CU, MCI, and ADD groups (p value > 0.05; Figures S2 and S3). These results suggest that these factors contribute independently to the discrimination of AD and that combinations of these factors improve discrimination accuracy. We constructed models including only the PRS.noAPOE or PRS.adjLD and doses of APOE ε4 and ε2 alleles. These models showed predictive performance of AUC = 0.755 in the model including PRS.noAPOE (95% CI = 0.695–0.807) and AUC = 0.748 in the model including PRS.adjLD (95% CI = 0.687–0.800) (Table 3). The predictive performance of a monogenic model of only APOE alleles without the PRS.noAPOE was AUC = 0.696 (95% CI = 0.640–0.751) (Table 3). The addition of polygenic effects significantly improved the predictive accuracy of the monogenic model using only APOE (p value = 9.36 × 10⁻⁴ in the PRS.noAPOE model, p value = 2.59 × 10⁻³ in the PRS.adjLD model, DeLong test). Additionally, the PRS model incorporating APOE alleles independently (PRS.noAPOE + APOE doses) has higher accuracy than the PRS model that includes SNPs in the APOE region (PRS.incAPOE) (AUC = 0.706; 95% CI = 0.643–0.764; p value = 0.049, DeLong test). Therefore, we constructed a predictive model including the PRS.noAPOE, sex, years of education, age at baseline examination, and doses of APOE ε4 and ε2 alleles. This model showed discriminative performance of AUC = 0.855 in distinguishing between the ADD patients and CU individuals in the J-ADNI cohort (95% CI = 0.808–0.898) (Table 3). This tendency was conserved even when LD effects were adjusted (AUC = 0.853; 95% CI = 0.806–0.897). These predictive performances showed the similar tendencies when evaluated by fivefold CV (Table S4). Taken together, these results showed that the PRS based on European GWAS statistics was useful in discriminating between patients with ADD and CU participants in the Japanese population. Furthermore, the PRS had an effect independent of APOE alleles, and their combination improved predictive accuracy.

Table 3.

Predictive accuracy of each model

	Training cohort		Validation cohort
	J-ADNI		NP		NA-ADNI
Model	AUC	95% CI	AUC	95% CI	AUC	95% CI
PRS and APOE alleles
APOE ε4	0.693	0.638–0.747	0.691	0.654–0.730	0.701	0.665–0.735
APOE ε4 + ε2	0.696	0.640–0.751	0.698	0.659–0.737	0.712	0.675–0.750
PRS.noAPOE	0.640	0.576–0.704	0.550	0.500–0.599	0.602	0.559–0.649
PRS.adjLD	0.639	0.574–0.704	0.541	0.493–0.589	0.594	0.552–0.640
PRS.incAPOE	0.706	0.643–0.764	0.628	0.590–0.625	0.679	0.639–0.720
PRS.noAPOE + APOE ε4 + ε2	0.755	0.695–0.807	0.731	0.686–0.773	0.730	0.692–0.767
PRS.adjLD + APOE ε4 + ε2	0.748	0.687–0.800	0.728	0.680–0.771	0.731	0.693–0.769
PRS and all covariates
Age + Sex + (Education) + APOE ε4	0.837	0.788–0.883	0.725	0.681–0.770	0.710	0.670–0.750
Age + Sex + (Education) + APOE ε4 + ε2	0.838	0.789–0.883	0.723	0.679–0.768	0.706	0.665–0.746
Age + Sex + (Education) + APOE ε4 + ε2 + PRS.noAPOE	0.855	0.808–0.898	0.737	0.693–0.780	0.722	0.683–0.761
Age + Sex + (Education) + APOE ε4 + ε2 + PRS.adjLD	0.853	0.806–0.897	0.733	0.690–0.777	0.718	0.678–0.757

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Abbreviations: Age, age at examination; Education, years of education. Years of education were not provided in the NP cohort

The effect of our PRS model is replicated in the independent cohorts

To examine the predictive accuracy of PRS.noAPOE and PRS.adjLD in independent cohorts, we calculated the PRS values for 565 brain donors in the NP cohort (control, 358; case, 207) and 617 participants (CU, 257; ADD, 360) in the NA-ADNI using our PRS models. We note that the samples from the NP cohort received a definitive diagnosis based on the typical neuropathological hallmarks of AD using autopsy brains. The logistic regression model constructed in the J-ADNI cohort was applied to each cohort to assess discrimination accuracy. The predictive performance of PRS.noAPOE for the NP cohort was lower than that for the J-ADNI cohort (AUC = 0.550 (95% CI = 0.500–0.599) in the PRS.noAPOE; AUC = 0.541 (95% CI = 0.493–0.589) in the PRS.adjLD), but when APOE alleles were added, the predictive performance was replicated (AUC = 0.731 (95% CI = 0.686–0.773) in the PRS.noAPOE model; AUC = 0.728 (95% CI = 0.680–0.771) in the PRS.adjLD model) (Table 3).

We also analysed the NA-ADNI cohort to verify the transferability of PRS.noAPOE in different ancestries. In the NA-ADNI cohort, the imputed genotyping data included 130 of the 131 SNPs used in the PRS.noAPOE. The PRS.adjLD model used all 113 SNPs. A similar analysis in the NA-ADNI cohort also showed that the predictive performance of PRS.noAPOE or PRS.adjLD in combination with APOE alleles were similar to that of the NP cohort (AUC = 0.730 (95% CI = 0.692–0.767) in the PRS.noAPOE model; AUC = 0.731 (95% CI = 0.693–0.769) in the PRS.adjLD model). These analyses showed the reproducibility of our PRS model in independent cohorts.

ADD in the J-ADNI shows the polygenicity related to immune pathway

In order to examine the polygenicity of our PRS, we compared a model including only the PRS.noAPOE with a single-variable model for each of the 131 SNPs comprising the PRS.noAPOE. The single models with individual SNPs showed AUCs of 0.499 to 0.605 (median AUC = 0.515), while the model including only the PRS.noAPOE showed an AUC of 0.640 (95% CI = 0.576–0.704) (Table 3 and S5), suggesting that the PRS.noAPOE reflects a polygenic effect. Here, SNPs with AUCs of less than 0.5 indicate protection rather than risk in our data.

We examined the genes closest to 131 SNPs included in the PRS.noAPOE. We found the 96 closest genes located within ± 100 kb around the SNPs (Table S6). These genes were associated with leukocyte-mediated immunity (FDR = 3.78 × 10⁻⁵), haematopoietic cell lineage (FDR = 4.45 × 10⁻⁵), the amyloid precursor protein (APP) catabolic process (FDR = 5.16 × 10⁻⁵), regulation of transferase activity (FDR = 3.57 × 10⁻⁴), and glial cell proliferation (FDR = 5.60 × 10⁻³) (Table S7). The 89 closest genes in the PRS.adjLD also contained basically similar pathways (Tables S6 and S8). Overall, we found that the integrated scores of multiple SNPs around genes mainly associated with immune pathways may explain the Japanese AD traits.

The PRS associates with AD-related phenotypes

To examine whether our PRS associates with clinical characteristics, we next investigated the correlation between the PRS.noAPOE or PRS.adjLD and AD-related phenotypes, namely CSF biomarker data and FDG and PiB PET brain imaging data. We performed linear regression model analyses based on three models controlling for seven covariates: age at baseline examination, sex, years of education, the first two PCs, and the doses of APOE ε4 and ε2 alleles. Model 1 controlled only age at baseline examination, sex, years of education, and the first two PCs. Models 2 and 3 took into the dose of APOE ε4 allele in addition to Model 1. Model 3 also added the dose of APOE ε2 allele as a full model.

The CSF tTau/Aβ42 and pTau/Aβ42 ratios were significantly associated with the PRS.noAPOE and PRS.adjLD values. These associations were basically maintained in all models (FDR < 0.05, Wald test; Table 4a and Fig. 2) and reflected the influences of tTau and pTau levels but not Aβ42 levels (Table S9).

Table 4.

Associations between PRS and AD-related phenotypes

	Model 1 (Age, Sex, Education, PC1, PC2)		Model 2 (Age, Sex, Education, PC1, PC2, APOEε4)		Model 3 (Age, Sex, Education, PC1, PC2, APOEε4, APOEε2)
	Beta (noAPOE, adjLD)	p value (noAPOE, adjLD)	Beta (noAPOE, adjLD)	p value (noAPOE, adjLD)	Beta (noAPOE, adjLD)	p value (noAPOE, adjLD)
4a CSF biomarker
	All subjects
tTau/Aβ42	0.167, 0.159	0.017^a, 0.017^a	0.149, 0.146	0.058, 0.053	0.163, 0.156	0.040^a, 0.039^a
pTau/Aβ42	0.200, 0.168	0.004^b, 0.011^b	0.188, 0.154	0.013^a, 0.033^a	0.189, 0.155	0.013^a, 0.032^a
	CU subjects
tTau/Aβ42	-0.082, 0.204	0.804, 0.522	-0.050, 0.265	0.888, 0.435	-0.131, 0.194	0.717, 0.579
pTau/Aβ42	-0.143, -0.010	0.488, 0.960	-0.134, 0.014	0.545, 0.948	-0.178, -0.029	0.430, 0.895
	MCI subjects
tTau/Aβ42	0.292, 0.277	0.025^a, 0.032^a	0.294, 0.290	0.043^a, 0.043^a	0.295, 0.292	0.044^a, 0.044^a
pTau/Aβ42	0.363, 0.318	0.004^b, 0.011^a	0.396, 0.355	0.006^a, 0.013^a	0.396, 0.357	0.007^a, 0.014^a
	ADD patients
tTau/Aβ42	-0.054, -0.042	0.665, 0.703	-0.053, -0.043	0.682, 0.703	0.026, 0.006	0.854, 0.959
pTau/Aβ42	0.066, 0.018	0.577, 0.866	0.070, 0.019	0.565, 0.862	0.084, 0.028	0.485, 0.796
4b Brain volume
	All subjects
Entorhinal	-0.014, -0.024	0.776, 0.622	-0.008, -0.018	0.869, 0.714	-0.010, -0.019	0.836, 0.699
Hippocampus	-0.109, -0.115	0.042, 0.033	-0.101, -0.106	0.087, 0.073	-0.101, -0.106	0.088, 0.073
	CU subjects
Entorhinal	0.032, 0.024	0.512, 0.626	0.033, 0.024	0.509, 0.626	0.036, 0.033	0.495, 0.529
Hippocampus	0.041, -0.010	0.758, 0.943	0.041, -0.010	0.760, 0.942	0.043, -0.016	0.754, 0.908
	MCI subjects
Entorhinal	-0.355, -0.357	0.150, 0.156	-0.335, -0.344	0.180, 0.176	-0.332, -0.341	0.184, 0.180
Hippocampus	-0.064, -0.082	0.505, 0.407	-0.041, -0.071	0.694, 0.505	-0.043, -0.073	0.684, 0.497
	ADD patients
Entorhinal	0.108, -0.078	0.745, 0.811	0.084, -0.089	0.801, 0.788	0.112, -0.070	0.739, 0.832
Hippocampus	-0.043, -0.055	0.755, 0.688	-0.080, -0.072	0.574, 0.609	-0.069, -0.064	0.633, 0.652
4c PET imaging
	All subjects
FDG (positive)	0.180, 0.193	0.189, 0.149	0.162, 0.174	0.260, 0.218	0.162, 0.172	0.262, 0.224
PiB (positive)	0.386, 0.359	0.024^a, 0.030	0.446, 0.417	0.025^a, 0.030	0.442, 0.412	0.027, 0.033
	CU subjects
FDG (positive)	-0.119, -0.070	0.729, 0.839	-0.109, -0.064	0.753, 0.852	-0.109, -0.061	0.755, 0.860
PiB (positive)	0.037, 0.068	0.923, 0.858	0.072, 0.221	0.874, 0.620	0.059, 0.194	0.899, 0.671
	MCI subjects
FDG (positive)	0.020, 0.053	0.942, 0.845	0.038, 0.077	0.892, 0.787	0.040, 0.076	0.888, 0.791
PiB (positive)	0.201, 0.175	0.489, 0.552	0.299, 0.320	0.432, 0.407	0.303, 0.327	0.433, 0.405
	ADD patients
FDG (positive)	-0.395, -0.232	0.357, 0.542	-0.390, -0.232	0.366, 0.545	-0.352, -0.195	0.417, 0.611
PiB (positive)	-0.716, -0.777	0.313, 0.196	-0.747, -0.935	0.314, 0.133	-0.716, -0.915	0.338, 0.146
4d Neuropsychological test
	All subjects
ADAS	0.171, 0.127	0.001^b, 0.008^b	0.163, 0.117	0.002^b, 0.019^a	0.164, 0.116	0.002^b, 0.019^a
CDRSB	0.188, 0.167	6.29 × 10^−5b, 0.001^b	0.182, 0.160	1.78 × 10^−4b, 0.003^b	0.182, 0.160	1.84 × 10^−4b, 0.003^b
FAQ	0.166, 0.168	3.93 × 10^−4b, 3.58 × 10^−4b	0.159, 0.161	0.001^b, 0.001^b	0.159, 0.161	0.001^b, 0.001^b
MMSE	-0.160, -0.149	0.001^b, 0.002^b	-0.152, -0.141	0.002^b, 0.005^b	-0.152, -0.141	0.002^b, 0.005^b
	CU subjects
ADAS	-0.118, 0.045	0.592, 0.837	-0.119, 0.045	0.591, 0.837	-0.111, 0.057	0.617, 0.795
CDRSB	-1.178, -0.835	0.152, 0.305	-1.208, -0.883	0.152, 0.290	-1.217, -0.896	0.150, 0.284
FAQ	0.291, 0.359	0.473, 0.368	0.294, 0.359	0.470, 0.369	0.287, 0.349	0.483, 0.385
MMSE	-0.153, -0.228	0.454, 0.258	-0.153, -0.229	0.459, 0.258	-0.140, -0.212	0.503, 0.303
	MCI subjects
ADAS	0.194, 0.193	0.098, 0.107	0.176, 0.185	0.147, 0.136	0.182, 0.192	0.133, 0.123
CDRSB	0.146, 0.162	0.289, 0.251	0.144, 0.160	0.297, 0.256	0.149, 0.166	0.280, 0.241
FAQ	0.119, 0.145	0.239, 0.161	0.121, 0.146	0.233, 0.159	0.118, 0.143	0.246, 0.168
MMSE	-0.065, -0.062	0.604, 0.626	-0.055, -0.056	0.661, 0.664	-0.045, -0.046	0.720, 0.722
	ADD patients
ADAS	-0.048, -0.026	0.719, 0.844	-0.057, -0.029	0.671, 0.824	-0.057, -0.029	0.672, 0.826
CDRSB	0.102, 0.063	0.290, 0.501	0.109, 0.066	0.260, 0.485	0.097, 0.060	0.318, 0.528
FAQ	0.078, 0.056	0.327, 0.472	0.093, 0.062	0.248, 0.430	0.088, 0.060	0.275, 0.450
MMSE	0.036, 0.045	0.794, 0.734	0.031, 0.044	0.823, 0.746	0.033, 0.045	0.810, 0.740

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β estimates and p value were calculated by a linear regression model. P value was corrected in each subject group. Statistically significance was highlighted in bold

^aFDR < 0.05

^bFDR < 0.01

Fig. 2 — The PRS.noAPOE and PRS.adjLD correlated with CSF Tau/Aβ42 ratios in the MCI. CSF tTau/Aβ42 (A, C) and pTau/Aβ42 (B, D) ratios by decile of PRS are shown in each diagnostic group. The participants were divided into ten groups based on the PRS.noAPOE, ranging from the lowest group (1st decile) to the highest group (10th decile). CN = cognitively normal; MCI = mild cognitive impairment; ADD = Alzheimer’s disease dementia

To investigate the PRS effects to brain atrophy, we first tested the associations between the PRS and the volumes of the entorhinal cortex and hippocampus. Hippocampal volume showed a significant association with the PRSs in Model 1 that did not include APOE alleles, but this association did not remain significance after FDR correction (p value = 0.042 in the PRS.noAPOE, p value = 0.033 in the PRS.adjLD, Wald test; Table 4b). We investigated whether the PRSs contribute to the discrimination between the normal pattern (N1 pattern) and the AD pattern (P1 pattern) in FDG PET imaging and between negative and positive amyloid scans in PiB PET imaging. As a result, the PRSs were associated only with PiB PET imaging (p value = 0.024 in the PRS.noAPOE, p value = 0.030 in the PRS.adjLD, Wald test; Table 4c).

We also investigated the correlations between the PRSs and cognitive functions. The neuropsychological tests, including the ADAS-Cog, CDR-SB, FAQ, and MMSE, were significantly associated in all models (FDR < 0.01, Wald test; Table 4d).

We next stratified the participants into the CU, MCI and ADD groups and examined the association between the PRS.noAPOE or PRS.adjLD and each phenotype. Significant positive correlations between the PRSs and CSF tTau/Aβ and between the PRSs and pTau/Aβ42 ratios were observed in only the MCI participants (FDR < 0.05, Wald test; Table 4a; Fig. 2). In contrast, these ratios remained stable or reached a plateau relative to the PRSs in the CU and ADD participants (Fig. 2), suggesting that the polygenic burden beyond APOE explains some of the heterogeneity in MCI, especially in terms of tau-related biomarker.

APOE ε4 non-carriers with high PRS are at high risk of AD conversion

Finally, we examined difference in conversion to AD in the participants with MCI stratified by PRS. We divided MCI participants into three groups based on the PRS.noAPOE or PRS.adjLD distribution of all participants. We compared the conversion to AD of MCI participants in the 1st tertile, referred to as the low-PRS group, and of MCI participants in the 3rd tertile, noted as the high-PRS group. We performed Cox proportional hazard model analysis controlling seven covariates: age at baseline examination, sex, years of education, the first two PCs, and the doses of APOE ε4 and ε2 alleles. We did not find significantly different conversion patterns between the high- and low-PRS groups (p value = 0.202 in the PRS.noAPOE, p value = 0.236 in the PRS.adjLD, log-rank test; Table 5a and Fig. 3).

Table 5.

Polygenic risk of conversion of MCI to AD

5a PRS.noAPOE
	All MCI subjects			MCI subjects without APOE ε4			MCI subjects with APOE ε4
	HR	95% CI	p value	HR	95% CI	p value	HR	95% CI	p value
PRS (High group)	1.301	0.847–1.998	0.230	2.216	1.058–4.643	0.035	0.985	0.770–1.259	0.902
Age	1.070	0.846–1.353	0.574	1.069	0.725–1.578	0.736	0.961	0.687–1.344	0.816
Sex (Male)	0.858	0.532–1.383	0.529	0.589	0.257–1.353	0.212	0.983	0.539–1.793	0.956
Education year	0.791	0.605–1.035	0.087	0.745	0.452–1.229	0.249	0.856	0.610–1.202	0.37
PC1	0.939	0.769–1.146	0.534	0.769	0.541–1.093	0.143	1.034	0.795–1.343	0.806
PC2	0.954	0.753–1.209	0.697	1.028	0.713–1.481	0.884	0.900	0.647–1.251	0.529
APOE ε4 alleles	1.604	1.153–2.230	0.005	NA	NA	NA	1.108	0.516–2.382	0.793
APOE ε2 alleles	1.447	0.440–4.755	0.543	1.665	0.492–5.635	0.412	NA	NA	NA
Log-rank test (high vs low PRS)			0.202			0.031			0.292
5b PRS.adjLD
	All MCI subjects			MCI subjects without APOE ε4			MCI subjects with APOE ε4
	HR	95% CI	p value	HR	95% CI	p value	HR	95% CI	p value
PRS (High group)	1.161	0.752–1.792	0.500	1.547	0.727–3.296	0.258	1.030	0.812–1.305	0.809
Age	1.137	0.902–1.433	0.278	1.251	0.819–1.912	0.301	1.011	0.742–1.379	0.943
Sex (Male)	0.736	0.445–1.219	0.234	0.571	0.234–1.394	0.219	0.808	0.425–1.538	0.516
Education year	0.927	0.714–1.205	0.573	0.873	0.531–1.435	0.592	0.962	0.689–1.344	0.821
PC1	0.909	0.738–1.119	0.368	0.682	0.460–1.011	0.057	1.042	0.801–1.355	0.758
PC2	0.927	0.733–1.173	0.529	1.006	0.697–1.452	0.975	0.910	0.663–1.249	0.558
APOE ε4 alleles	1.560	1.102–2.209	0.012	NA	NA	NA	1.201	0.543–2.658	0.651
APOE ε2 alleles	0.708	0.169–2.965	0.636	0.709	0.163–3.085	0.647	NA	NA	NA
Log-rank test (high vs low PRS)			0.236			0.174			0.650

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Abbreviations: HR Hazard ratio, 95% CI 95% confidence interval, APOE apolipoprotein E

Cox proportional hazard model: Conversion/follow-up = PRS (Low = 0, High = 1) + Age + Sex (Female = 0, Male = 1) + Education year + PC1 + PC2 + APOE ε4 alleles + APOE ε2 alleles

HR, 95% CI, and p-value were calculated by a Cox proportional hazard model controlling age at examination, sex, education years, the first two PCs, and the doses of APOE ε4 and ε2 alleles. Statistically significant was highlighted in bold

Fig. 3 — The high-PRS group was more likely to convert to AD than the low-PRS group in the APOE ε4 non-carrier individuals with MCI. Kaplan–Meier survival curves for conversion rates of MCI to AD in the low-PRS group (1st tertile) and the high-PRS group (3rd tertile). The shaded area represents the 95% confidence interval

When we examined the contribution of each variable, we found that the dose of the APOE ε4 allele significantly affected the conversion to AD (HR = 1.604, 95% CI = 1.153–2.230, and p value = 0.005 in the PRS.noAPOE; HR = 1.560, 95% CI = 1.102–2.209, and p value = 0.012 in the PRS.adjLD, Wald test; Table 5a), suggesting that this difference in conversion between the two PRS groups was influenced by the APOE ε4 allele dose. Therefore, we stratified MCI participants into those with and without APOE ε4. In that analysis, we found that in the PRS.noAPOE, among MCI participants without APOE ε4, the high-PRS group showed a significantly higher conversion to AD than the low-PRS group (p value = 0.031, log-rank test; Table 5a and Fig. 3A). Moreover, the PRS.noAPOE significantly contributed to the difference in AD conversion between the two groups (HR = 2.216; 95% CI = 1.058–4.643; p value = 0.035, Wald test; Table 5a). We also found no difference in AD conversion among MCI participants with APOE ε4 (p value = 0.292, log-rank test; Table 5a and Fig. 3A). In the PRS.adjLD, no significance was observed (Table 5b and Fig. 3B). These results suggested that polygenic effects increase the risk of AD conversion, particularly in MCI subjects without APOE ε4.

On the other hand, in APOE ε4 carriers, a single factor, namely, APOE ε4, may explain much of the AD conversion risk. As expected, there was no significant difference between the APOE ε4 noncarrier group with high-PRS and the APOE ε4 carrier group (p value = 0.595 in the PRS.noAPOE, p value = 0.345 in the PRS.adjLD, log-rank test; Figure S4). Although age differences between the groups compared in the above analysis could have affected the results, there were no differences in age at baseline examination between the low- and high-PRS groups or between the converted and nonconverted participants (p value > 0.05, Wilcoxon rank-sum test; Figure S5). These results suggest that the PRS contributes to the conversion to AD in participants without APOE ε4.

Discussion

In this study, we evaluated the utility of the PRS for AD in a Japanese cohort. The results showed that the PRS had an effect independent of APOE and showed relatively high predictive accuracy when combined with APOE ε4. In addition, this effect was replicated in the cohort with a neuropathological diagnosis and the protocol-harmonized independent NA-ADNI cohort. The PRS was significantly associated with CSF tau levels in MCI participants, and MCI with a high PRS was associated with an elevated risk of AD conversion in APOE ε4 noncarriers.

Despite the difference in genetic structure between the European and Japanese populations [39], the PRS developed in this study, PRS.noAPOE, showed meaningful predictive accuracy. We also developed PRS.adjLD, which avoids overfitting due to differences between European and Japanese LD blocks, and showed that PRS.adjLD had similar accuracy. Such predictive accuracy may be achieved because all participants were diagnosed according to unified inclusion and exclusion criteria and harmonized standardized diagnostic criteria using the same neuropsychological tests (MMSE, CDR-SB, and Wechsler Memory Scale Logical Memory II). The optimal p value threshold for the PRS excluding the APOE region was also similar to that reported in previous studies, p_T < 1 × 10⁻⁵ [5, 10, 40]. Moreover, while dozens of SNPs were incorporated into these previous PRSs, 131 or 113 SNPs were included to calculate the PRS in our study. This difference in the number of SNPs is likely due to differences in genetic structure such as LD blocks. Hence, even if there are ancestral differences, adding a few dozen SNPs may preserve accuracy.

We also examined potential overfitting due to differences in LD between European and Japanese populations, which may cause a small reduction in predictive accuracy. On the other hand, it is possible that SNPs in the same LD in Japanese are independent (i.e. linkage equilibrium) in European population. In this case, underfitting may occur and the actual predictive accuracy may be underestimated. To solve this issue, a larger AD GWAS data derived from Japanese population will be needed, and this warrants further investigation.

There is no consensus on the number of SNPs that should be included in the AD PRS. According to a systematic review of PRS studies in AD, PRSs of AD can be organized into two groups: PRSs containing relatively large numbers of SNPs, ranging from 4431 to 359,500, and PRSs containing relatively small numbers, ranging from 5 to 31 [41]. The latter group is referred to as the oligogenic effect, in contrast to the polygenic effect [42]. From this perspective, our PRS apparently represents an oligogenic effect. Notably, a relatively small number of SNPs has the advantage of providing an inexpensive gene panel. In addition, a PRS composed of many SNPs may be sensitive to geographic differences in genetic structure, whereas a PRS composed of a few dozen SNPs is robust to population bias [43, 44]. However, we should note that our PRS may reflect ancestral differences due to the use of European GWAS statistics. In the future, more robust polygenic effects could be verified by using GWAS statistics for large groups of East Asians, including Japanese individuals.

In our study, the genes contributing to the PRS.noAPOE or PRS.adjLD were associated with APP degradation, immunity, and glial cell proliferation. Genetic variants found in a recent AD GWAS were associated with the APP catabolic process and tau protein binding [45]. In addition, many of the genes affected by their genetic variants are expressed in microglia [45]. An analysis of cognitively healthy centenarians in addition to ADD patients and healthy controls revealed that the PRS associated with the immune system was lower in the centenarian group independent of APOE ε4, indicating that immune system function is involved in AD resistance [46]. Therefore, our results suggest that common factors related to AD may be shared in the vulnerability of clearance mechanisms and neuroimmune surveillance in the brain among different population.

In our study, the PRS.noAPOE and PRS.adjLD showed significant correlations with CSF tTau/Aβ42 and pTau/Aβ42 ratios only in individuals with MCI. Tau but not Aβ42 strongly influenced this result even controlling APOE effect. CU and AD are relatively homogeneous in terms of AD-related biomarker changes. However, MCI is a heterogeneous condition, in which CSF biomarkers are highly variable with dynamic changes. Because of this variation in CSF biomarkers, significant correlations with PRS were observed in MCI group. Interestingly, NA-ADNI studies have shown that the PRS is associated beyond APOE with CSF tau but not CSF Aβ42 [44, 47]. From the above, independent studies in different ancestry groups have confirmed that polygenic effects are associated with tau-related biomarkers, especially in individuals with MCI.

Although our results are noteworthy, we must approach the clinical application of our PRS with caution at this stage because the predictive accuracy of our PRS alone is not very high. Similar to currently available PRSs, few biomarkers can perfectly distinguish disease or not; most markers bear some uncertainty. AD and MCI are explained not only by genetic aspects such as PRS, but also by anatomic aspects such as MRI and PET imaging and biological aspects such as CSF biomarkers [48], suggesting that combining multiple biomarkers could compensate for each other’s weaknesses in predictive performance. PRS will allow individuals’ disease risk to be assessed at a relatively early stage, leading to future lifestyle modification and disease prevention.

There were several limitations to this study. First, the CU participants included in the J-ADNI were relatively young. We acknowledge that these CU participants include potential patients who will develop AD in the future. Considering the average age of onset of AD and the allele frequency of APOE ε4 in the Japanese population, future work should ideally include CU participants that are over 70 years old [49]. Second, because the number of participants available for the study was small, there was limited power to identify relationships between the PRS and some phenotypes. Larger studies are needed to validate the results of this study. Therefore, combining samples from multiple East Asian cohorts, including cohorts from Japan, is necessary for analysis.

Conclusion

This study demonstrated that the AD PRS showed a relatively high performance in the Japanese population, despite differences in genetic structure from the European population. Furthermore, this PRS was replicated in the independent Japanese and European cohorts. The AD PRS correlated with phenotypes such as CSF tau levels in MCI. The AD PRS predicted the development of AD in MCI participants without APOE ε4. The application of the PRS will allow us to know an individuals’ disease risk at a relatively early life stage, which may lead to future lifestyle modification and disease prevention.

Supplementary Information

13195_2024_1414_MOESM1_ESM.pdf^{(1.7MB, pdf)}

Additional file 1: Figure S1. The excluded region around the APOE gene. We removed the APOE region, consisting of ±500 kb, from around the top-hit SNP rs1160985 (chr19:45403412) in our data. Each data point indicates GWAS p values from Jansen et al. [32] used as SNP weights in the PRS calculation. Figure S2. Associations between the PRS and covariates. Age at baseline examination and years of education were examined by Spearman correlation. Sex and doses of APOE ε4 and ε2 alleles were analysed by t tests or ANOVAs. CN = cognitively normal; MCI = mild cognitive impairment; ADD = Alzheimer's disease dementia. Figure S3. Associations between the PRS.adjLD and covariates. Age at examination and years of education were examined by Spearman correlations. Sex and dose of APOE ε4 and ε2 alleles were analysed by t tests or ANOVAs. Figure S4. Comparison of AD conversion between the APOE ε4 carriers and the APOE ε4 noncarriers with high PRS. Kaplan–Meier survival curves for conversion rates of MCI to AD in the APOE ε4 carriers and the APOE ε4 noncarriers with high PRS values. p-values were calculated by log-rank test. Figure S5. Age differences between the low- and high-PRS groups and between the nonconverters and converters. Baseline ages were compared between groups using the Wilcoxon rank-sum test. Each violin plot includes the kernel probability density of the data at different values and the box plots with the median value and the interquartile range.

Additional file 2. ^{(38.8KB, xlsx)}

Acknowledgements

We thank all the participants and staff of the J-ADNI and NA-ADNI, and the donors and facility staff for providing autopsy brains. The J-ADNI was supported by the following funding sources: the Translational Research Promotion Project from the New Energy and Industrial Technology Development Organization of Japan; Research on Dementia, Health Labor Sciences Research Grant; the Life Science Database Integration Project of Japan Science and Technology Agency; the Research Association of Biotechnology (Astellas Pharma Inc., Bristol-Myers Squibb, Daiichi-Sankyo, Eisai, Eli Lilly and Company, Merck-Banyu, Mitsubishi Tanabe Pharma, Pfizer Inc., Shionogi & Co., Ltd., Sumitomo Dainippon, and Takeda Pharmaceutical Company), Japan; and a grant from an anonymous foundation. The reference genome data used for this research were originally obtained by participants in the Tailor-made Medical Treatment Program (BioBank Japan: BBJ), led by Prof. Michiaki Kubo; these data are available at the website of the NBDC Human Database/the Japan Science and Technology Agency (JST).

Consortia

The Alzheimer’s Disease Neuroimaging Initiative (ADNI)

Michael W. Weiner¹³, Sara S. Mason¹³, Colleen S. Albers¹³, David Knopman¹³, Kris Johnson¹³, Paul Aisen¹⁴, Ronald Petersen¹⁵, Clifford R. Jack¹⁶, William Jagust¹⁷, John Q. Trojanowki¹⁸, Arthur W. Toga¹⁹, Lon S. Schneider¹⁹, Sonia Pawluczyk¹⁹, Mauricio Beccera¹⁹, Liberty Teodoro¹⁹, Bryan M. Spann¹⁹, Laurel Beckett²⁰, Robert C. Green²¹, John Morris²², Leslie M. Shaw²², Beau Ances²², John C. Morris²², Maria Carroll²², Mary L. Creech²², Erin Franklin²², Mark A. Mintun²², Stacy Schneider²², Angela Oliver²², Jeffrey Kaye²³, Joseph Quinn²³, Lisa Silbert²³, Betty Lind²³, Raina Carter²³, Sara Dolen²³, James Brewer²⁴, Helen Vanderswag²⁴, Adam Fleisher^24,63, Judith L. Heidebrink²⁵, Joanne L. Lord²⁵, Rachelle S. Doody²⁶, Javier Villanueva-Meyer²⁶, Munir Chowdhury²⁶, Susan Rountree²⁶, Mimi Dang²⁶, Yaakov Stern²⁷, Lawrence S. Honig²⁷, Karen L. Bell²⁷, Daniel Marson²⁸, Randall Griffith²⁸, David Clark²⁸, David Geldmacher²⁸, John Brockington²⁸, Erik Roberson²⁸, Marissa Natelson Love²⁸, Hillel Grossman²⁹, Effie Mitsis²⁹, Raj C. Shah³⁰, Leyla deToledo-Morrell³⁰, Ranjan Duara³¹, Daniel Varon³¹, Maria T. Greig³¹, Peggy Roberts³¹, Marilyn Albert³², Chiadi Onyike³², Daniel D'Agostino³², Stephanie Kielb³², James E. Galvin³³, Brittany Cerbone³³, Christina A. Michel³³, Dana M. Pogorelec³³, Henry Rusinek³³, Mony J. de Leon³³, Lidia Glodzik³³, Susan De Santi³³, P. Murali Doraiswamy³⁴, Jeffrey R. Petrella³⁴, Salvador Borges-Neto³⁴, Terence Z. Wong³⁴, Edward Coleman³⁴, Charles D. Smith³⁵, Greg Jicha³⁵, Peter Hardy³⁵, Partha Sinha³⁵, Elizabeth Oates³⁵, Gary Conrad³⁵, Anton P. Porsteinsson³⁶, Bonnie S. Goldstein³⁶, Kim Martin³⁶, Kelly M. Makino³⁶, M. Saleem Ismail³⁶, Connie Brand³⁶, Ruth A. Mulnard³⁷, Gaby Thai³⁷, Catherine Mc-Adams-Ortiz³⁷, Kyle Womack³⁸, Dana Mathews³⁸, Mary Quiceno³⁸, Allan I. Levey³⁹, James J. Lah³⁹, Janet S. Cellar³⁹, Jeffrey M. Burns⁴⁰, Russell H. Swerdlow⁴⁰, William M. Brooks⁴⁰, Liana Apostolova⁴¹, Martin R. Farlow⁴¹, Ann Marie Hake⁴¹, Brandy R. Matthews⁴¹, Jared R. Brosch⁴¹, Scott Herring⁴¹, Cynthia Hunt⁴¹, Kathleen Tingus⁴², Ellen Woo⁴², Daniel H. S. Silverman⁴², Po H. Lu⁴², George Bartzokis⁴², Neill R. Graff-Radford⁴³, Francine Parfitt⁴³, Tracy Kendall⁴³, Heather Johnson⁴³, Christopher H. van Dyck⁴⁴, Richard E. Carson⁴⁴, Martha G. MacAvoy⁴⁴, Pradeep Varma⁴⁴, Howard Chertkow⁴⁵, Howard Bergman⁴⁵, Chris Hosein⁴⁵, Sandra Black⁴⁶, Bojana Stefanovic⁴⁶, Curtis Caldwell⁴⁶, Ging-Yuek Robin Hsiung⁴⁷, Howard Feldman⁴⁷, Benita Mudge⁴⁷, Michele Assaly⁴⁷, Elizabeth Finger⁴⁸, Stephen Pasternack⁴⁸, Irina Rachisky⁴⁸, Dick Trost⁴⁸, Andrew Kertesz⁴⁸, Charles Bernick⁴⁹, Donna Munic⁴⁹, Marek Marsel Mesulam⁵⁰, Kristine Lipowski⁵⁰, Sandra Weintraub⁵⁰, Borna Bonakdarpour⁵⁰, Diana Kerwin⁵⁰, Chuang-Kuo Wu⁵⁰, Nancy Johnson⁵⁰, Carl Sadowsky⁵¹, Teresa Villena⁵¹, Raymond Scott Turner⁵², Kathleen Johnson⁵², Brigid Reynolds⁵², Reisa A. Sperling⁵³, Keith A. Johnson⁵³, Gad Marshall⁵³, Jerome Yesavage⁵⁴, Joy L. Taylor⁵⁴, Barton Lane⁵⁴, Allyson Rosen⁵⁴, Jared Tinklenberg⁵⁴, Marwan N. Sabbagh⁵⁵, Christine M. Belden⁵⁵, Sandra A. Jacobson⁵⁵, Sherye A. Sirrel⁵⁵, Neil Kowall⁵⁶, Ronald Killiany⁵⁶, Andrew E. Budson⁵⁶, Alexander Norbash⁵⁶, Patricia Lynn Johnson⁵⁶, Thomas O. Obisesan⁵⁷, Saba Wolday⁵⁷, Joanne Allard⁵⁷, Alan Lerner⁵⁸, Paula Ogrocki⁵⁸, Curtis Tatsuoka⁵⁸, Parianne Fatica⁵⁸, Evan Fletcher⁵⁹, Pauline Maillard⁵⁹, John Olichney⁵⁹, Charles DeCarli⁵⁹, Owen Carmichael⁵⁹, Smita Kittur⁶⁰, Michael Borrie⁶¹, T.-Y. Lee⁶¹, Rob Bartha⁶¹, Sterling Johnson⁶², Sanjay Asthana⁶², Cynthia M. Carlsson⁶², Steven G. Potkin³⁷, Adrian Preda³⁷, Dana Nguyen³⁷, Pierre Tariot⁶³, Anna Burke⁶³, Nadira Trncic⁶³, Stephanie Reeder⁶³, Vernice Bates⁶⁴, Horacio Capote⁶⁴, Michelle Rainka⁶⁴, Douglas W. Scharre⁶⁵, Maria Kataki⁶⁵, Anahita Adeli⁶⁵, Earl A. Zimmerman⁶⁶, Dzintra Celmins⁶⁶, Alice D. Brown⁶⁶, Godfrey D. Pearlson⁶⁷, Karen Blank⁶⁷, Karen Anderson⁶⁷, Laura A. Flashman⁶⁸, Marc Seltzer⁶⁸, Mary L. Hynes⁶⁸, Robert B. Santulli⁶⁸, Kaycee M. Sink⁶⁹, Leslie Gordineer⁶⁹, Jeff D. Williamson⁶⁹, Pradeep Garg⁶⁹, Franklin Watkins⁶⁹, Brian R. Ott⁷⁰, Henry Querfurth⁷⁰, Geoffrey Tremont⁷⁰, Stephen Salloway⁷¹, Paul Malloy⁷¹, Stephen Correia⁷¹, Howard J. Rosen⁷², Bruce L. Miller⁷², David Perry⁷², Jacobo Mintzer⁷³, Kenneth Spicer⁷³, David Bachman⁷³, Nunzio Pomara⁷⁴, Raymundo Hernando⁷⁴, Antero Sarrael⁷⁴, Norman Relkin⁷⁵, Gloria Chaing⁷⁵, Michael Lin⁷⁵, Lisa Ravdin⁷⁵, Amanda Smith⁷⁶, Balebail Ashok Raj⁷⁶, Kristin Fargher.⁷⁶

¹³Magnetic Resonance Unit at the VA Medical Center and Radiology, Medicine, Psychiatry and Neurology, University of California, San Francisco, CA, USA. ¹⁴UC San Diego School of Medicine, University of California, La Jolla, CA, USA. ¹⁵Neurology, Mayo Clinic, Rochester, MN, USA. ¹⁶Radiology, Mayo Clinic, Rochester, MN, USA. ¹⁷University of California, Berkeley, Berkeley, CA, USA. ¹⁸University of Pennsylvania, Philadelphia, PA, USA. ¹⁹University of Southern California, Los Angeles, CA, USA. ²⁰University of California, Davis, Davis, CA, USA. ²¹Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA. ²²Washington University, St. Louis, MO, USA. ²³Oregon Health and Science University, Portland, OR, USA. ²⁴University of California, San Diego, San Diego, CA, USA. ²⁵University of Michigan, Ann Arbor, MI, USA. ²⁶Baylor College of Medicine, Houston, TX, USA. ²⁷Columbia University Medical Center, New York, NY, USA. ²⁸University of Alabama, Birmingham, AL, USA. ²⁹Mount Sinai School of Medicine, New York, NY, USA. ³⁰Rush University Medical Center, Chicago, IL, USA. ³¹Wien Center, Miami Beach, FL, USA. ³²Johns Hopkins University, Baltimore, MD, USA. ³³New York University, New York, NY, USA. ³⁴Duke University Medical Center, Durham, NC, USA. ³⁵University of Kentucky, Lexington, KY, USA. ³⁶University of Rochester Medical Center, Rochester, NY, USA. ³⁷University of California, Irvine, Irvine, CA, USA. ³⁸University of Texas Southwestern Medical School, Dallas, TX, USA. ³⁹Emory University, Atlanta, GA, USA. ⁴⁰University of Kansas Medical Center, Kansas City, KS, USA. ⁴¹Indiana University, Bloomington, IN, USA. ⁴²Universityof California, Los Angeles, Los Angeles, CA, USA. ⁴³Mayo Clinic, Jacksonville, FL, USA. ⁴⁴Yale University School of Medicine, New Haven, CT, USA. ⁴⁵McGill University and Jewish General Hospital, Montreal, Quebec, Canada. ⁴⁶Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada. ⁴⁷UBC Clinic for Alzheimer Disease and Related Disorders, Vancouver, British Columbia, Canada. ⁴⁸Cognitive Neurology-St. Joseph's, London, Ontario, Canada. ⁴⁹Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, USA. ⁵⁰Northwestern University, Evanston, IL, USA. ⁵¹Premiere Research Institute and Palm Beach Neurology, West Palm Beach, FL, USA. ⁵²Georgetown University Medical Center, Washington, DC, USA. ⁵³Brigham and Women's Hospital, Boston, MA, USA. ⁵⁴Stanford University, Stanford, CA, USA. ⁵⁵Banner Sun Health Research Institute, Sun City, AZ, USA. ⁵⁶Boston University, Boston, MA, USA. ⁵⁷Howard University, Boston, DC, USA. ⁵⁸Case Western Reserve University, Cleveland, OH, USA. ⁵⁹UC Davis School of Medicine, Sacramento, CA, USA. ⁶⁰Neurological Care of CNY, Syracuse, NY, USA. ⁶¹Parkwood Hospital, Philadelphia, PA, USA. ⁶²University of Wisconsin, Madison, WI, USA. ⁶³Banner Alzheimer's Institute, Phoenix, AZ, USA. ⁶⁴DENT Neurologic Institute, New York, NY, USA. ⁶⁵Ohio State University, Columbus, OH, USA. ⁶⁶Albany Medical College, Albany, NY, USA. ⁶⁷Hartford Hospital, Olin Neuropsychiatry Research Center, Hartford, CT, USA. ⁶⁸Dartmouth-Hitchcock Medical Center, Lebanon, NH, USA. ⁶⁹Wake Forest University Health Sciences, Winston-Salem, NC, USA. ⁷⁰Rhode Island Hospital, Providence, RI, USA. ⁷¹Butler Hospital, Providence, RI, USA. ⁷²University of California, San Francisco, San Francisco, CA, USA. ⁷³Medical University of South Carolina, Charleston, SC, USA. ⁷⁴Nathan Kline Institute, Orangeburg, NY, USA. ⁷⁵Cornell University, Ithaca, NY, USA. ⁷⁶USF Health Byrd Alzheimer's Institute, University of South Florida, Tampa, FL, USA.

The Japanese Alzheimer’s Disease Neuroimaging Initiative (J-ADNI)

Takeshi Iwatsubo¹², Takashi Asada^77,119, Hiroyuki Arai^78,105, Morihiro Sugishita⁷⁹, Hiroshi Matsuda^80,129, Noriko Sato^80,148, Hajime Sato⁸⁰, Kengo Ito⁸¹, Teruhiko Kachi⁸¹, Kenji Toba⁸¹, Michio Senda⁸², Kenji Ishii^83,141, Ryozo Kuwano¹¹, Takeshi Ikeuchi³, Shun Shimohama⁸⁴, Masaki Saitoh⁸⁴, Rika Yamauchi⁸⁴, Takashi Hayashi⁸⁴, Chiyoko Takanami⁸⁴, Seiju Kobayashi⁸⁵, Norihito Nakano⁸⁶, Junichiro Kanazawa⁸⁷, Takeshi Ando⁸⁸, Masato Hareyama⁸⁹, Masamitsu Hatakenaka⁹⁰, Eriko Tsukamoto⁹¹, Shinji Ochi⁹², Mikio Shoji⁹³, Etsuro Matsubara⁹³, Takeshi Kawarabayashi⁹³, Yasuhito Wakasaya⁹³, Takashi Nakata⁹³, Naoko Nakahata⁹³, Shuichi Ono⁹⁴, Yoshihiro Takai⁹⁴, Satoshi Takahashi⁹⁵, Hisashi Yonezawa⁹⁵, Junko Takahashi⁹⁵, Masako Kudoh⁹⁵, Kuniko Ueno⁹⁵, Hiromi Sakashita⁹⁵, Kuniko Watanabe⁹⁵, Makoto Sasaki⁹⁶, Yutaka Matsumura⁹⁷, Yohsuke Hirata⁹⁷, Tsuyoshi Metoki⁹⁷, Susumu Hayakawa⁹⁷, Yuichi Sato^97,100, Masayuki Takeda⁹⁷, Koichiro Sera⁹⁷, Kazunori Terasaki⁹⁷, Toshiaki Sasaki⁹⁸, Yoshihiro Saitoh⁹⁹, Shoko Goto⁹⁹, Ken Nagata¹⁰⁰, Tetsuya Maeda¹⁰⁰, Yasushi Kondoh¹⁰⁰, Takashi Yamazaki¹⁰⁰, Daiki Takano¹⁰⁰, Mio Miyata¹⁰⁰, Hiromi Komatsu¹⁰⁰, Mayumi Watanabe¹⁰⁰, Tomomi Sinoda¹⁰⁰, Rena Muraoka¹⁰⁰, Kayoko Kikuchi¹⁰¹, Hitomi Ito¹⁰², Aki Sato¹⁰², Toshibumi Kinoshita¹⁰³, Hideyo Toyoshima¹⁰³, Kaoru Sato¹⁰³, Shigeki Sugawara¹⁰³, Isao Ito¹⁰⁴, Fumiko Kumagai¹⁰⁴, Katsutoshi Furukawa¹⁰⁵, Masaaki Waragai¹⁰⁵, Naoki Tomita¹⁰⁵, Mari Ootsuki¹⁰⁵, Katsumi Sugawara¹⁰⁵, Satomi Sugawara¹⁰⁵, Nobuyuki Okamura¹⁰⁶, Shunji Mugikura¹⁰⁷, Atsushi Umetsu¹⁰⁷, Takanori Murata¹⁰⁷, Tatsuo Nagasaka¹⁰⁷, Yukitsuka Kudo¹⁰⁸, Manabu Tashiro¹⁰⁹, Shoichi Watanuki¹⁰⁹, Masatoyo Nishizawa¹¹⁰, Takayoshi Tokutake¹¹⁰, Saeri Ishikawa¹¹¹, Emiko Kishida¹¹¹, Nozomi Sato¹¹¹, Mieko Hagiwara¹¹², Kumi Yamanaka¹¹², Takeyuki Watanabe¹¹², Taeko Takasugi¹¹², Shoichi Inagawa¹¹³, Kenichi Naito¹¹³, Masanori Awaji¹¹³, Tsutomu Kanazawa¹¹³, Kouiti Okamoto¹¹⁴, Masaki Ikeda¹¹⁴, Yuiti Tasiro¹¹⁴, Syunn Nagamine¹¹⁴, Sathiko Kurose¹¹⁴, Tsuneo Yamazaki¹¹⁵, Shiori Katsuyama¹¹⁵, Sayuri Fukushima¹¹⁶, Etsuko Koya¹¹⁶, Makoto Amanuma¹¹⁷, Kouiti Ujita¹¹⁷, Kazuhiro Kishi¹¹⁷, Kazuhisa Tuda¹¹⁷, Noboru Oriuti¹¹⁸, Katsuyoshi Mizukami¹¹⁹, Tetsuaki Arai¹¹⁹, Etsuko Nakajima¹¹⁹, Katsumi Miyamoto¹²⁰, Tomoya Kobayashi¹²⁰, Saori Itoya¹²⁰, Jun Ookubo¹²⁰, Toshiya Akatsu¹²⁰, Yoshiko Anzai¹²⁰, Junya Ikegaki¹²⁰, Yuuichi Katou¹²⁰, Kaori Kimura¹²⁰, Hajime Saitou¹²⁰, Kazuya Shinoda¹²⁰, Satoka Someya¹²⁰, Hiroko Taguchi¹²⁰, Kazuya Tashiro¹²⁰, Masaya Tanaka¹²⁰, Tatsuya Nemoto¹²⁰, Ryou Wakabayashi¹²⁰, Daisuke Watanabe¹²⁰, Kousaku Saotome¹²¹, Ryou Kuchii¹²¹, Harumasa Takano¹²², Tetsuya Suhara¹²², Hitoshi Shinoto¹²², Hitoshi Shimada^122,126, Makoto Higuchi¹²², Takaaki Mori¹²², Hiroshi Ito¹²³, Takayuki Obata¹²³, Yoshiko Fukushima¹²⁴, Kazuko Suzuki¹²⁴, Izumi Izumida¹²⁴, Katsuyuki Tanimoto¹²⁵, Takahiro Shiraishi¹²⁵, Hitoshi Shinotoh¹²⁶, Junko Shiba¹²⁶, Hiroaki Yano¹²⁶, Miki Satake¹²⁶, Aimi Nakui¹²⁶, Yae Ebihara¹²⁶, Tomomi Hasegawa¹²⁶, Yasumasa Yoshiyama¹²⁷, Mami Kato¹²⁷, Yuki Ogata¹²⁷, Hiroyuki Fujikawa¹²⁷, Nobuo Araki¹²⁸, Yoshihiko Nakazato¹²⁸, Takahiro Sasaki¹²⁸, Tomokazu Shimadu¹²⁸, Kimiko Yoshimaru¹²⁸, Etsuko Imabayashi¹²⁹, Asako Yasuda¹²⁹, Keiko Ozawa¹²⁹, Etuko Yamamoto¹³⁰, Natsumi Nakamata¹³⁰, Noriko Miyauchi¹³⁰, Rieko Hashimoto¹³¹, Taishi Unezawa¹³¹, Takafumi Ichikawa¹³¹, Hiroki Hayashi¹³¹, Masakazu Yamagishi¹³¹, Tunemichi Mihara¹³¹, Masaya Hirano¹³¹, Shinichi Watanabe¹³¹, Junichiro Fukuhara¹³², Hajime Matsudo¹³², Nobuyuki Saito¹³³, Atsushi Iwata¹³⁴, Hisatomo Kowa¹³⁴, Toshihiro Hayashi¹³⁴, Ryoko Ihara¹³⁴, Toji Miyagawa¹³⁴, Mizuho Yoshida¹³⁴, Yuri Koide¹³⁴, Eriko Samura¹³⁴, Kurumi Fujii¹³⁴, Kaori Watanabe¹³⁵, Nagae Orihara¹³⁵, Toshimitsu Momose¹³⁶, Miwako Takahashi¹³⁶, Takuya Arai¹³⁶, Yoshiki Kojima¹³⁶, Akira Kunimatsu¹³⁷, Harushi Mori¹³⁷, Masami Goto¹³⁸, Takeo Sarashina¹³⁸, Syuichi Uzuki¹³⁸, Seiji Katou¹³⁸, Yoshiharu Sekine¹³⁸, Yukihiro Takauchi¹³⁸, Chiine Kagami¹³⁹, Kazutomi Kanemaru¹⁴⁰, Yasushi Nishina¹⁴⁰, Maria Sakaibara¹⁴⁰, Yumiko Okazaki¹⁴⁰, Rieko Okada¹⁴⁰, Maki Obata^140,146, Shigeo Murayama^4,5, Masaki Takao^141,258, Yuko Iwata^142,147,157, Mizuho Minami^142,157, Yasuko Hanabusa^142,147, Hanae Shingyouji^142,147, Kyoko Tottori¹⁴², Aya Tokumaru¹⁴³, Makoto Ichinose¹⁴³, Kazuya Kume¹⁴³, Syunsuke Kahashi¹⁴³, Kunimasa Arima¹⁴⁴, Shin Tanaka¹⁴⁴, Yuko Nagahusa¹⁴⁴, Masuhiro Sakata¹⁴⁴, Mitsutoshi Okazaki¹⁴⁴, Yuko Saito⁴, Maki Yamada¹⁴⁴, Tadashi Tukamoto¹⁴⁵, Tiine Kodama¹⁴⁶, Tomoko Takeuchi¹⁴⁶, Keiichiro Ozawa¹⁴⁶, Yoshiko Kawaji¹⁴⁷, Kyouko Tottori^147,157, Yasuhiro Nakata¹⁴⁸, Satoshi Sawada¹⁴⁸, Makoto Mimatsu¹⁴⁸, Daisuke Nakkamura¹⁴⁸, Takeshi Tamaru¹⁴⁸, Shunichirou Horiuchi¹⁴⁸, Heii Arai¹⁴⁹, Tsuneyoshi Ota¹⁴⁹, Aiko Kodaka¹⁴⁹, Yuko Tagata¹⁴⁹, Tomoko Nakada¹⁴⁹, Eizo Iseki¹⁵⁰, Kiyoshi Sato¹⁵⁰, Hiroshige Fujishiro¹⁵¹, Norio Murayama¹⁵², Masaru Suzuki¹⁵³, Satoshi Kimura¹⁵³, Masanobu Takahashi¹⁵³, Haruo Hanyu¹⁵⁴, Hirofumi Sakurai¹⁵⁴, Takahiko Umahara¹⁵⁴, Hidekazu Kanetaka¹⁵⁴, Kaori Arashino¹⁵⁴, Mikako Murakami¹⁵⁴, Ai Kito¹⁵⁴, Seiko Miyagi¹⁵⁵, Kaori Doi¹⁵⁵, Kazuyoshi Sasaki¹⁵⁵, Mineo Yamazaki¹⁵⁶, Akiko Ishiwata¹⁵⁶, Yasushi Arai¹⁵⁶, Akane Nogami¹⁵⁶, Sumiko Fukuda¹⁵⁶, Koichi Kozaki¹⁵⁸, Yukiko Yamada¹⁵⁸, Sayaka Kimura¹⁵⁸, Ayako Machida¹⁵⁸, Kuninori Kobayashi¹⁵⁹, Hidehiro Mizusawa¹⁶⁰, Nobuo Sanjo¹⁶⁰, Mutsufusa Watanabe¹⁶⁰, Takuya Ohkubo¹⁶⁰, Hiromi Utashiro¹⁶⁰, Yukiko Matsumoto¹⁶⁰, Kumiko Hagiya¹⁶⁰, Yoshiko Miyama¹⁶⁰, Hitoshi Shibuya¹⁶⁰, Isamu Ohashi¹⁶⁰, Akira Toriihara¹⁶⁰, Takako Shinozaki¹⁶¹, Haruko Hiraki¹⁶¹, Shinichi Ohtani¹⁶², Toshifumi Matsui¹⁶³, Tomomi Toyama¹⁶³, Hideki Sakurai¹⁶³, Kumiko Sugiura¹⁶³, Yu Hayasaka¹⁶⁴, Hirofumi Taguchi¹⁶⁵, Shizuo Hatashita¹⁶⁶, Akari Imuta¹⁶⁷, Akiko Matsudo¹⁶⁷, Daichi Wakebe¹⁶⁸, Hideki Hayakawa¹⁶⁸, Mitsuhiro Ono¹⁶⁸, Takayoshi Ohara¹⁶⁸, Yukihiko Washimi¹⁶⁹, Yutaka Arahata¹⁶⁹, Akinori Takeda¹⁶⁹, Akiko Yamaoka¹⁶⁹, Masashi Tsujimoto¹⁶⁹, Takiko Kawai¹⁶⁹, Ai Honda¹⁶⁹, Yoko Konagaya¹⁷⁰, Hideyuki Hattori¹⁷¹, Kenji Yoshiyama^171,212, Rina Miura¹⁷¹, Takashi Sakurai^172,228, Miura Hisayuki¹⁷³, Hidetoshi Endou¹⁷⁴, Syousuke Satake¹⁷⁵, Young Jae Hong¹⁷⁵, Katsunari Iwai¹⁷⁶, Masaki Suenaga¹⁷⁶, Sumiko Morita¹⁷⁶, Kengo Itou¹⁷⁷, Takashi Kato¹⁷⁷, Ken Fujiwara¹⁷⁷, Rikio Katou¹⁷⁸, Mariko Koyama¹⁷⁸, Naohiko Fukaya¹⁷⁸, Akira Tsuji¹⁷⁸, Hitomi Shimizu¹⁷⁸, Hiroyuki Fujisawa¹⁷⁸, Tomoko Nakazawa¹⁷⁸, Satoshi Koyama¹⁷⁸, Takanori Sakata¹⁷⁸, Masahito Yamada¹⁷⁹, Mitsuhiro Yoshita¹⁷⁹, Miharu Samuraki¹⁷⁹, Kenjiro Ono¹⁷⁹, Moeko Shinohara¹⁷⁹, Yuki Soshi¹⁷⁹, Kozue Niwa¹⁷⁹, Chiaki Doumoto¹⁷⁹, Mariko Hata¹⁸⁰, Miyuki Matsushita¹⁸⁰, Mai Tsukiyama¹⁸⁰, Nozomi Takeda¹⁸¹, Sachiko Yonezawa¹⁸¹, Ichiro Matsunari¹⁸¹, Osamu Matsui¹⁸², Fumiaki Ueda¹⁸², Yasuji Ryu¹⁸², Masanobu Sakamoto¹⁸³, Yasuomi Ouchi^183,184, Yumiko Fujita¹⁸³, Madoka Chita¹⁸⁵, Rika Majima¹⁸⁶, Hiromi Tsubota¹⁸⁷, Umeo Shirasawa¹⁸⁸, Masashi Sugimori¹⁸⁸, Wataru Ariya¹⁸⁸, Yuuzou Hagiwara¹⁸⁸, Yasuo Tanizaki¹⁸⁸, Hidenao Fukuyama¹⁸⁹, Shizuko Tanaka-Urayama¹⁸⁹, Shin-Ichi Urayama¹⁸⁹, Ryosuke Takahashi¹⁹⁰, Kengo Uemura¹⁹⁰, Hajime Takechi¹⁹¹, Chihiro Namiki¹⁹², Takeshi Kihara¹⁹³, Hiroshi Yamauchi¹⁹⁴, Emiko Maeda¹⁹⁵, Natsu Saito¹⁹⁶, Shiho Satomi¹⁹⁷, Konomi Kabata¹⁹⁸, Tomohisa Okada¹⁹⁹, Koichi Ishizu²⁰⁰, Shigeto Kawase²⁰¹, Satoshi Fukumoto²⁰¹, Masanori Nakagawa²⁰², Masaki Kondo²⁰², Fumitoshi Niwa²⁰², Toshiki Mizuno²⁰², Yoko Oishi²⁰², Mariko Yamazaki²⁰², Daisuke Yamaguchi²⁰², Takahiko Tokuda²⁰³, Kyoko Ito²⁰⁴, Yoku Asano²⁰⁴, Chizuru Hamaguchi²⁰⁴, Kei Yamada²⁰⁵, Chio Okuyama²⁰⁵, Kentaro Akazawa²⁰⁵, Shigenori Matsushima²⁰⁵, Takamasa Matsuo²⁰⁶, Toshiaki Nakagawa²⁰⁶, Takeshi Nii²⁰⁶, Takuji Nishida²⁰⁶, Kuniaki Kiuchi²⁰⁷, Masami Fukusumi²⁰⁸, Hideyuki Watanabe²⁰⁹, Toshiaki Taoka²¹⁰, Akihiro Nogi²¹¹, Masatoshi Takeda²¹², Toshihisa Tanaka²¹², Hiroaki Kazui²¹², Takashi Kudo²¹², Masayasu Okochi²¹², Takashi Morihara²¹², Shinji Tagami²¹², Masahiko Takaya²¹², Tamiki Wada²¹², Mikiko Yokokoji²¹², Hiromichi Sugiyama²¹², Daisuke Yamamoto²¹², Keiko Nomura²¹², Mutsumi Tomioka²¹², Naoyuki Sato²¹³, Noriyuki Hayashi²¹⁴, Shuko Takeda²¹⁵, Eiichi Uchida²¹⁶, Yoshiyuki Ikeda²¹⁶, Mineto Murakami²¹⁶, Takami Miki²¹⁷, Hiroyuki Shimada²¹⁷, Suzuka Ataka²¹⁷, Akitoshi Takeda²¹⁷, Yuki Iwamoto²¹⁷, Motokatsu Kanemoto²¹⁸, Jun Takeuchi²¹⁹, Rie Azuma²²⁰, Naomi Tagawa²²¹, Junko Masao²²¹, Yuka Matsumoto²²¹, Yuko Kikukawa²²¹, Hisako Fujii²²¹, Junko Matsumura²²¹, Susumu Shiomi²²², Joji Kawabe²²², Yoshihiro Shimonishi²²³, Mitsuji Higashida²²³, Tomohiro Sahara²²³, Takashi Yamanaga²²³, Yukio Miki²²⁴, Shinichi Sakamoto²²⁴, Hiroyuki Tsushima²²⁵, Kiyoshi Maeda²²⁶, Yasuji Yamamoto²²⁶, Kazuo Sakai²²⁶, Haruhiko Oda²²⁶, Yoshihiko Tahara²²⁶, Toshio Kawamata²²⁷, Taichi Akisaki²²⁸, Mizuho Adachi²²⁹, Masako Kuranaga²²⁹, Sachi Takegawa²²⁹, Seishi Terada²³⁰, Yuki Kishimoto²³⁰, Naoya Takeda²³⁰, Nao Imai²³⁰, Mayumi Yabe²³⁰, Reiko Wada²³⁰, Takeshi Ishihara²³¹, Hajime Honda²³², Osamu Yokota²³², Kentaro Ida²³³, Daigo Anami²³⁴, Seiji Inoue²³⁴, Toshi Matsushita²³⁴, Shinsuke Hiramatsu²³⁵, Hiromi Tonbara²³⁶, Reiko Yamamoto²³⁶, Kenji Nakashima²³⁷, Kenji Wada-Isoe²³⁷, Saori Yamasaki²³⁷, Eijiro Yamashita²³⁸, Yu Nakamura²³⁹, Ichiro Ishikawa²³⁹, Sonoko Danjo²³⁹, Tomomi Shinohara²³⁹, Yuka Kashimoto²³⁹, Miyuki Ueno²⁴⁰, Yoshihiro Nishiyama²⁴¹, Yuka Yamamoto²⁴¹, Narihide Kimura²⁴¹, Kazuo Ogawa²⁴², Yasuhiro Sasakawa²⁴², Takashi Ishimori²⁴², Yukito Maeda²⁴², Tatsuo Yamada²⁴³, Shinji Ouma²⁴³, Aika Fukuhara-Kaneumi²⁴³, Nami Sakamoto²⁴⁴, Rie Nagao²⁴⁴, Kengo Yoshimitsu²⁴⁵, Yasuo Kuwabara²⁴⁵, Ryuji Nakamuta²⁴⁶, Minoru Tanaka²⁴⁶, Manabu Ikeda²⁴⁷, Yuusuke Yatabe²⁴⁷, Mamoru Hashimoto²⁴⁸, Keiichirou Kaneda²⁴⁸, Kazuki Honda²⁴⁸, Naoko Ichimi²⁴⁸, Mariko Morinaga²⁴⁸, Miyako Noda²⁴⁸, Fumi Akatuka²⁴⁹, Mika Kitajima²⁵⁰, Toshinori Hirai²⁵¹, Shinya Shiraishi²⁵¹, Naoji Amano²⁵², Shinsuke Washizuka²⁵², Tetsuya Hagiwara²⁵², Yatsuka Okada²⁵², Tomomi Ogihara²⁵², Toru Takahashi²⁵³, Shin Inuzuka²⁵³, Nobuhiro Sugiyama²⁵³, Takehiko Yasaki²⁵³, Minori Kitayama²⁵³, Tomonori Owa²⁵³, Akiko Ryokawa²⁵³, Rie Takeuchi²⁵⁴, Satoe Goto²⁵⁴, Keiko Yamauchi²⁵⁴, Mie Ito²⁵⁴, Tomoki Kaneko²⁵⁵, Hitoshi Ueda²⁵⁶, Shuichi Ikeda²⁵⁷, Ban Mihara²⁵⁸, Hirofumi Kubo²⁵⁸, Akiko Takano²⁵⁸, Gou Yasui²⁵⁸, Masami Akuzawa²⁵⁸, Kaori Yamaguchi²⁵⁸, Toshinari Odawara²⁵⁹, Naomi Oota²⁵⁹, Megumi Shimamura²⁶⁰, Mikiko Sugiyama²⁶⁰, Atsushi Watanabe²⁶¹, Shigeo Takebayashi²⁶², Yoshigazu Hayakawa²⁶², Mitsuhiro Idegawa²⁶², Noriko Toya²⁶², Kazunari Ishii.²⁶³

⁷⁷Tsukuba University, Tsukuba, Japan. ⁷⁸Tohoku University, Sendai, Japan. ⁷⁹Institute of Brain and Blood Vessels, Isesaki, Japan. ⁸⁰National Center of Neurology and Psychiatry, Kodaira, Japan. ⁸¹National Center of Geriatrics and Gerontology, Tokyo, Japan. ⁸²Institute of Biomedical Research and Innovation, Kobe, Japan. ⁸³Tokyo Metropolitan Geriatric Hospital and Institute of Gerontology, Tokyo, Japan. ⁸⁴Department of Neurology, Sapporo Medical University, Sapporo, Japan. ⁸⁵Department of Neuropsychiatry, Sapporo Medical University, Sapporo, Japan. ⁸⁶Department of Clinical Psychology, School of Psychological Science, Health Sciences University of Hokkaido, Sapporo, Japan. ⁸⁷School of Psychological Science, Health Sciences University of Hokkaido, Sapporo, Japan. ⁸⁸Graduate School of Psychological Science, Health Sciences University of Hokkaido, Sapporo, Japan. ⁸⁹Department of Radiology, Sapporo Medical University, Sapporo, Japan. ⁹⁰Division of Diagnostic Radiology, Sapporo Medical University, Sapporo, Japan. ⁹¹Social Medical Corporation Teishinkai Central CI Clinic, Sapporo, Japan. ⁹²Radiology Department, Social Medical Corporation Teishinkai Central CI Clinic, Sapporo, Japan. ⁹³Department of Neurology, Hirosaki University Graduate School of Medicine, Hirosaki, Japan. ⁹⁴Department of Radiology, Hirosaki University Graduate School of Medicine, Hirosaki, Japan. ⁹⁵Division of Neurology and Gerontology, Department of Internal Medicine, Iwate Medical University, Morioka, Japan. ⁹⁶Division of Ultrahigh Field MRI, Institute for Biomedical Sciences, Iwate Medical University, Morioka, Japan. ⁹⁷Center for Radiological Sciences, Iwate Medical University, Morioka, Japan. ⁹⁸Cyclotron Research Center, Iwate Medical University, Morioka, Japan. ⁹⁹Nishina Memorial Cyclotron Center, Japan Radioisotope Association, Takizawa, Japan. ¹⁰⁰Departmetnt of Neurology, Research Institute for Brain and Blood Vessels-Akita, Akita, Japan. ¹⁰¹Pharmacy, Research Institute for Brain and Blood Vessels-Akita, Akita, Japan. ¹⁰²Medical Support Department, Research Institute for Brain and Blood Vessels-Akita, Akita, Japan. ¹⁰³Department of Radiology, Research Institute for Brain and Blood Vessels-Akita, Akita, Japan. ¹⁰⁴Clinical Laboratory, Research Institute for Brain and Blood Vessels-Akita, Akita, Japan. ¹⁰⁵Geriatrics and Gerontology, Tohoku University Hospital, Sendai, Japan. ¹⁰⁶Pharamacology, Tohoku University Hospital, Sendai, Japan. ¹⁰⁷RadiologyTohoku University Hospital, Sendai, Japan. ¹⁰⁸Neuroimaging, Tohoku University Hospital, Sendai, Japan. ¹⁰⁹Nuclear Medicine, Tohoku University Hospital, Sendai, Japan. ¹¹⁰Department of Neurology, Brain Research Institute, Niigata University, Niigata, Japan. ¹¹¹Department of Rehabilitation, Niigata University Medical and Dental Hospital, Niigata, Japan. ¹¹²Clinical Research Center, Niigata University Medical and Dental Hospital, Niigata, Japan. ¹¹³Department of Radiology, Niigata University Medical and Dental Hospital, Niigata, Japan. ¹¹⁴Department of Neurology, Gunma University Hospital, Maebashi, Japan. ¹¹⁵Department of Health of Rehabilitation, Gunma University Hospital, Maebashi, Japan. ¹¹⁶Clinical Trial Unit, Gunma University Hospital, Maebashi, Japan. ¹¹⁷Department of Radiology, Gunma University Hospital, Maebashi, Japan. ¹¹⁸Department of Nuclear Medicine Image, Gunma University Hospital, Maebashi, Japan. ¹¹⁹Department of Psychiatry Division of Clinical Medicine Faculty of Medicine, University of Tsukuba, Tsukuba, Japan. ¹²⁰Department of Radiological Technology, Tsukuba Medical Center Hospital, Tsukuba, Japan. ¹²¹Former Department of Radiological Technology, Tsukuba Medical Center Hospital, Tsukuba, Japan. ¹²²Molecular Imaging Center Molecular Neuroimaging Program, National Institute of Radiological Sciences, Chiba, Japan. ¹²³Molecular Imaging Center Biophysics Program, National Institute of Radiological Sciences, Chiba, Japan. ¹²⁴Molecular Imaging Center Planning and Promotion Unit Clinical Research Support Section, National Institute of Radiological Sciences, Chiba, Japan. ¹²⁵Hospital Research Center for Charged Particle Therapy Radiological Technology Section, National Institute of Radiological Sciences, Chiba, Japan. ¹²⁶Asahi Hospital for Neurological Diseases and Rehabilitation, Matsudo, Japan. ¹²⁷National Hospital Organization Chiba East National Hospital, Chiba, Japan. ¹²⁸Department of Neurology, Stroke Care Unit, Saitama Medical University Hospital, Moroyama, Japan. ¹²⁹Department of Nuclear Medicine, Saitama Medical University Hospital, Moroyama, Japan. ¹³⁰Department of Rehabilitation, Saitama Medical University Hospital, Moroyama, Japan. ¹³¹Central Radiology Division, Saitama Medical University Hospital, Moroyama, Japan. ¹³²Pharmacy Division, Saitama Medical University Hospital, Moroyama, Japan. ¹³³SHI Accelerator Service Ltd. (SAS), Tokyo, Japan. ¹³⁴Department of Neurology, The University of Tokyo Hospital, Tokyo, Japan. ¹³⁵Clinical Research Center, The University of Tokyo Hospital, Tokyo, Japan. ¹³⁶Department of Nuclear Medicine, The University of Tokyo Hospital, Tokyo, Japan. ¹³⁷Department of Diagnostic Radiology, The University of Tokyo Hospital, Tokyo, Japan. ¹³⁸Radiological Center, The University of Tokyo Hospital, Tokyo, Japan. ¹³⁹Neuroscience, Faculty of Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan. ¹⁴⁰Department of Neurology, Tokyo Metropolitan Geriatric Hospital, Tokyo, Japan. ¹⁴¹Tokyo Metropolitan Institute of Gerontology, Tokyo, Japan. ¹⁴²Clinical Support Corporation, Tokyo Metropolitan Geriatric Hospital and Institute of Gerontology, Tokyo, Japan. ¹⁴³Department of Radiology, Tokyo Metropolitan Geriatric Hospital, Tokyo, Japan. ¹⁴⁴Department of Psychiatry, National Center Hospital National Center of Neurology and Psychiatry, Kodaira, Japan. ¹⁴⁵Department of Neurology, National Center Hospital National Center of Neurology and Psychiatry, Kodaira, Japan. ¹⁴⁶Administration Office of J-ADNI, National Center Hospital National Center of Neurology and Psychiatry, Kodaira, Japan. ¹⁴⁷Clinical Support Corporation, National Center Hospital National Center of Neurology and Psychiatry, Kodaira, Japan. ¹⁴⁸Department of Radiology, National Center Hospital National Center of Neurology and Psychiatry, Tokyo, Japan. ¹⁴⁹Juntendo University Hospital, Mental Clinic, Tokyo, Japan. ¹⁵⁰PET/CT Dementia Research Center, Juntendo Tokyo Koto Geriatric Medical Center, Tokyo, Japan. ¹⁵¹PET/CT Dementia Research Center, Department of Neuro-Psychiatry, Juntendo Tokyo Koto Geriatric Medical Center, Tokyo, Japan. ¹⁵²Department of Neuro-Psychiatry, Juntendo Tokyo Koto Geriatric Medical Center, Tokyo, Japan. ¹⁵³Radiology, Juntendo Tokyo Koto Geriatric Medical Center, Tokyo, Japan. ¹⁵⁴Department of Geriatric Medicine, Tokyo Medical University, Tokyo, Japan. ¹⁵⁵Tokyo Medical University, Tokyo, Japan. ¹⁵⁶Division of Neurology, Department of Internal Medicine, Nippon Medical School, Tokyo, Japan. ¹⁵⁷Clinical Support Corporation, Nippon Medical School Hospital, Japan. ¹⁵⁸Geriatric Medicine, Kyorin University, Mitaka, Japan. ¹⁵⁹Radiation Medicine, Kyorin University, Mitaka, Japan. ¹⁶⁰Tokyo Medical and Dental University Graduate school of Medicine and Dental Sciences, Tokyo, Japan. ¹⁶¹Clinical Research Center, Tokyo Medical and Dental University Hospital Faculty of Medicine, Tokyo, Japan. ¹⁶²Department of Radiology, Tokyo Medical and Dental University Hospital Faculty of Medicine, Tokyo, Japan. ¹⁶³Clinical Center for Neurodegenerative, National Hospital Organization Kurihama Medical, Addiction Center, Yokosuka, Japan. ¹⁶⁴Department of Psychiatry, Kitasato University School of Medicine, Sagamihara, Japan. ¹⁶⁵Department of Radiology, National Hospital Organization Kurihama Medical, Addiction Center, Yokosuka, Japan. ¹⁶⁶Neurosurgery, Shonan-atsugi Hospital, Atsugi, Japan. ¹⁶⁷Clinical Research Center, Shonan-atsugi Hospital, Atsugi, Japan. ¹⁶⁸Radiology, Shonan-atsugi Hospital, Atsugi, Japan. ¹⁶⁹Department of Cognitive Disorders, National Center of Geriatrics and Gerontology, Obu, Japan. ¹⁷⁰Dementia Care Research and Training Center, National Center of Geriatrics and Gerontology, Obu, Japan. ¹⁷¹Department of Psychiatry, National Center of Geriatrics and Gerontology, Obu, Japan. ¹⁷²Center for Comprehensive Care and Research on Memory Disorders, National Center of Geriatrics and Gerontology, Obu, Japan. ¹⁷³Department of Home Medical Care Support, National Center of Geriatrics and Gerontology, Obu, Japan. ¹⁷⁴Department of Comprehensive Geriatric Medicine, National Center of Geriatrics and Gerontology, Obu, Japan. ¹⁷⁵Department of Geriatrics, National Center of Geriatrics and Gerontology, Obu, Japan. ¹⁷⁶Department of Neurology, National Center of Geriatrics and Gerontology, Obu, Japan. ¹⁷⁷Department of Clinical and Experimental Neuroimaging, National Center of Geriatrics and Gerontology, Obu, Japan. ¹⁷⁸Department of Radiology, National Center of Geriatrics and Gerontology, Obu, Japan. ¹⁷⁹Department of Neurology and Neurobiology of Aging, Kanazawa University Graduate School of Medical Science, Kanazawa, Japan. ¹⁸⁰Center for Clinical Research Management, Kanazawa University Hospital, Kanazawa, Japan. ¹⁸¹The Medical and Pharmacological Research Center Foundation, Kanazawa, Japan. ¹⁸²Department of Radiology, Kanazawa University Graduate School of Medical Science, Kanazawa, Japan. ¹⁸³Department of Neurology, Hamamatsu Medical Center, Hamamatsu, Japan. ¹⁸⁴Hamamatsu University School of Medicine, Hamamatsu, Japan. ¹⁸⁵Hamamatsu Medical Center, Hamamatsu, Japan. ¹⁸⁶Department of Psychiatry, Hamamatsu Medical Center, Hamamatsu, Japan. ¹⁸⁷Department of Clinical Research Management, Hamamatsu Medical Center, Hamamatsu, Japan. ¹⁸⁸Department of Radiological Technology, Hamamatsu Medical Center, Hamamatsu, Japan. ¹⁸⁹Human Brain Research Center, Kyoto University Graduate School of Medicine, Kyoto, Japan. ¹⁹⁰Department of Neurology, Kyoto University Graduate School of Medicine, Kyoto, Japan. ¹⁹¹Department of Geriatric Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan. ¹⁹²Eli Lilly Japan K.K Medical Science, Kobe, Japan. ¹⁹³Rakuwakai Misasagi Hospital, Kyoto, Japan. ¹⁹⁴Shiga Medical Center Research Institute, Moriyama, Japan. ¹⁹⁵Takemura Clinic, Kyoto, Japan. ¹⁹⁶Tonami General Hospital, Tonami, Japan. ¹⁹⁷University Hospital, Kyoto Prefectural University of Medicine, Kyoto, Japan. ¹⁹⁸Department of Diagnostic Pathology, Kyoto University Hospital, Kyoto, Japan. ¹⁹⁹Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan. ²⁰⁰Department of Human Health Sciences, Kyoto University Graduate School of Medicine, Kyoto, Japan. ²⁰¹Department of Radiology and Nuclear Medicine Service, Kyoto University Hospital, Kyoto, Japan. ²⁰²Department of Neurology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan. ²⁰³Department of Molecular Pathobiology of Brain Diseases (Department of Neurology), Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan. ²⁰⁴Tokyo Yakuriken Co., Ltd, Tokyo, Japan. ²⁰⁵Department of Radiology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan. ²⁰⁶Kyoto Prefectural University of Medicine Hospital, Kyoto, Japan. ²⁰⁷Department of Psychiatry, Nara Medical University, Kashihara, Japan. ²⁰⁸Faculty of Psychology, Doshisha University, Kyotanabe, Japan. ²⁰⁹Heartland Shigisan Hospital, Sango, Japan. ²¹⁰Department of Radiology, Nara Medical University, Kashihara, Japan. ²¹¹Central Radiology, Nara Medical University, Kashihara, Japan. ²¹²Psychiatry, Department of Integrated Medicine, Division of Internal Medicine, Osaka University Graduate School of Medicine, Suita, Japan. ²¹³Department of Clinical Gene Therapy, Department of Geriatric Medicine, Osaka University Graduate School of Medicine, Suita, Japan. ²¹⁴Department of Complementary and Alternative Medicine, Osaka University Graduate School of Medicine, Suita, Japan. ²¹⁵Japan Society for the Promotion of Science, Tokyo, Japan. ²¹⁶Uchida Clinic/Department of radiology, Osaka University Graduate School of Medicine, Ibaraki/Suita, Japan. ²¹⁷Department of Geriatrics and Neurology, Osaka City University Graduate School of Medicine, Osaka, Japan. ²¹⁸Osaka Municipal Kohsaiin Hospital, Suita, Japan. ²¹⁹Kishiwada City Hospital, Kishiwada, Japan. ²²⁰Department of Psychiatry, Kosaka Hospital, Higashiosaka, Japan. ²²¹Osaka City University Hospital Center for Drug and Food Clinical Evaluation, Osaka, Japan. ²²²Department of Nuclear Medicine, Osaka City University Graduate School of Medicine, Osaka, Japan. ²²³Department of Radiology, Osaka City University Hospital, Osaka, Japan. ²²⁴Department of Radiology, Osaka City University Graduate School of Medicine, Osaka, Japan. ²²⁵Department of Radiological Science, Ibaraki Prefectural University of Health Sciences, Ami, Japan. ²²⁶Department of Psychiatry, Kobe University Graduate School of Medicine, Kobe, Japan. ²²⁷Kobe University Graduate School of Health Sciences, Kobe, Japan. ²²⁸Department of General Internal Medicine, Kobe University Graduate School of Medicine, Kobe, Japan. ²²⁹Derartment of Psychiatry and Neurology, Kobe University Hospital, Kobe, Japan. ²³⁰Department of Psychiatry, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical, Okayama, Japan. ²³¹Department of Psychiatry, Kawasaki Hospital, Kurashiki, Japan. ²³²Department of Psychiatry, Okayama University Hospital, Okayama, Japan. ²³³Department of Imaging Diagnosis, Okayama Diagnostic Imaging Center, Okayama, Okayama, Japan. ²³⁴Department of Imaging Technology, Okayama Diagnostic Imaging Center, Okayama, Okayama, Japan. ²³⁵Department of Administration, Okayama Diagnostic Imaging Center, Okayama, Japan. ²³⁶Okayama University Hospital, Okayama, Japan. ²³⁷Division of Neurology, Department of Brain and Neurosciences, Faculty of Medicine, Tottori University, Yonago, Japan. ²³⁸Division of Clinical Radiology, Tottori University Hospital, Yonago, Japan. ²³⁹Department of Neuropsychiatry Kagawa University School of Medicine, Kagawa, Japan. ²⁴⁰Kagawa University Hospital Cancer center, Kagawa, Japan. ²⁴¹Department of Radiology, Faculty of Medicine, Kagawa University, Kagawa, Japan. ²⁴²Department of Clinical Radiology, Kagawa University Hospital, Kagawa, Japan. ²⁴³Department of Neurology, Fukuoka University, Fukuoka, Japan. ²⁴⁴Neues Corporation, Tokyo, Japan. ²⁴⁵Department of Radiology, Fukuoka University, Fukuoka, Japan. ²⁴⁶Radiology Center, Fukuoka University Hospital, Fukuoka, Japan. ²⁴⁷Department of Psychiatry and Neuropathobiology, Faculty of Life Science, Kumamoto University, Kumamoto, Japan. ²⁴⁸Department of Neuropsychiatry, Kumamoto University Hospital, Kumamoto, Japan. ²⁴⁹A Center of Support for Child Development, Kumamoto University Hospital, Kumamoto, Japan. ²⁵⁰Department of Medical Imaging, Kumamoto University, Kumamoto, Japan. ²⁵¹Department of Diagnostic Imaging, Faculty of Life Science, Kumamoto University, Kumamoto, Japan. ²⁵²Department of Psychiatry, Shinshu University School of Medicine, Matsumoto, Japan. ²⁵³Department of Psychiatry, Shinshu University Hospital, Matsumoto, Japan. ²⁵⁴Clinical Trial Research Center, Shinshu University Hospital, Matsumoto, Japan. ²⁵⁵Department of Radiology, Shinshu University School of Medicine, Matsumoto, Japan. ²⁵⁶Radiology Division, Shinshu University Hospital, Matsumoto, Japan. ²⁵⁷Department of Medicine (Neurology and Rheumatology), Shinshu University School of Medicine, Matsumoto, Japan. ²⁵⁸Mihara Memorial Hospital, Isesaki, Japan. ²⁵⁹Psychiatric Center, Yokohama City University Medical Center, Yokohama, Japan. ²⁶⁰Neurology, Yokohama City University Medical Center, Yokohama, Japan. ²⁶¹Yokohama City University Medical Center, Yokohama, Japan. ²⁶²Radiology, Yokohama City University Medical Center, Yokohama, Japan. ²⁶³Kindai University, Sayama, Japan.

Abbreviations

PRS: Polygenic risk score
AD: Alzheimer’s disease
J-ADNI: Japanese Alzheimer’s Disease Neuroimaging Initiative
APOE: Apolipoprotein E
Aβ: Amyloid-beta
MCI: Mild cognitive impairment
GWAS: Genome-wide association study
MRI: Magnetic resonance imaging
CU: Cognitively unimpaired
ADD: Alzheimer’s disease dementia
NA-ADNI: North American Alzheimer’s Disease Neuroimaging Initiative
C + T: Clumping and thresholding
AUC: Area under the receiver operator characteristic curve
MDS: Multidimensional scaling
CSF: Cerebrospinal fluid
tTau: Total tau
pTau: Phosphorylated tau
PET: Positron emission tomography
FDG: ¹⁸F-2-fluoro-2-deoxy-D-glucose
PiB: ¹¹C-Pittsburgh compound B
MMSE: Mini–Mental State Examination
FAQ: Functional Assessment Questionnaire
CDR: Clinical Dementia Rating
CDR-SB: CDR–Sum of Boxes
ADAS-Cog: AD Assessment Scale–Cognitive Subscale
FDR: False discovery rate
HR: Hazard ratio
CI: Confidence interval

Authors’ contributions

MK: Study design, analysis and interpretation of data, and manuscript draft. AM and NH: Genotyping analysis, interpretation of data, and manuscript revision. YS, SM, AK, HA: Provision of autopsy brains and manuscript revision. KK, KO, SN, RK, TIwatsubo, and AN: Interpretation of data and manuscript revision. TIkeuchi: Study design, interpretation of data, and manuscript draft. All authors read and approved the final manuscript.

Funding

This work was supported by a Grant-in-Aid for Scientific Research (grant numbers 20K15778 to MK, 21K07271 and 21H03537 to AM, and 22H04923 to YS) from the Ministry of Education, Culture, Sports, Science and Technology (MEXT), by grants from the Japan Agency for Medical Research and Development (AMED) (grant numbers JP21dk0207045 and JP23dk0207060 to MK, AM, KO, SN, and TI, JP23wm0525019 to MK and TI, and JP21wm0425019 to YS), by Grants-in Aid from the Research Committee of CNS Degenerative Diseases, Research on Policy Planning and Evaluation for Rare and Intractable Diseases, Health, Labour and Welfare Sciences Research Grants, the Ministry of Health, Labour and Welfare, Japan (grant number 20FC1049 to YS). The funders had no role in the study design, data collection, decision to publish, or preparation of the manuscript.

Availability of data and materials

All the J-ADNI data except for the genome data and the reference genome data were obtained from the NBDC Human Database/the Japan Science and Technology Agency (JST) (https://humandbs.biosciencedbc.jp/en/hum0043-v1), (https://humandbs.biosciencedbc.jp/en/hum0014-latest#JGAS000114rp). GWAS statistics were obtained from the Center for Neurogenomics and Cognitive Research (https://ctg.cncr.nl/software/summary_statistics). The J-ADNI genome data are available on request.

The data used in the preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (adni.loni.usc.edu). Thus, the investigators within the ADNI contributed to the design and implementation of the ADNI and/or provided data but did not participate in the analysis or the writing of this report. A complete listing of ADNI investigators can be found at http://adni.loni.usc.edu/wp-content/uploads/how_to_ apply/ADNI_Acknowledgement_List.pdf.

Data used in preparation of this article were obtained from the Japanese Alzheimer’s Disease Neuroimaging Initiative (J-ADNI) database within the National Bioscience Database Center Human Database, Japan (Research ID: hum0043.v1, 2016). Thus, the investigators within J-ADNI contributed to the design and implementation of J-ADNI and/or provided data but did not participate in the analysis or the writing of this report. A complete listing of J-ADNI investigators can be found at https://humandbs.biosciencedbc.jp/en/hum0043-j-adni-authors.

Declarations

Ethics approval and consent to participate

This study was approved by the ethics committees of the University of Tokyo, Osaka University and Niigata University. For both ADNI and J-ADNI, ethics approval was obtained from the review boards of the participating institutions. Informed consent was obtained from every participant prior to enrolment.

Consent for publication

Consent for publication has been granted by J-ADNI administrators.

Competing interests

The authors declare no competing interests.

Footnotes

The original version of this article was revised: the authors corrected the error in Figure 2 (Figures 2C and D are the same figures as Figures 2A and B in the original publication).

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Change history

7/10/2024

A Correction to this paper has been published: 10.1186/s13195-024-01514-8

Contributor Information

Masataka Kikuchi, Email: kikuchi@edu.k.u-tokyo.ac.jp.

Takeshi Ikeuchi, Email: ikeuchi@bri.niigata-u.ac.jp.

the Alzheimer’s Disease Neuroimaging Initiative:

Michael W. Weiner, Sara S. Mason, Colleen S. Albers, David Knopman, Kris Johnson, Paul Aisen, Ronald Petersen, Clifford R. Jack, William Jagust, John Q. Trojanowki, Arthur W. Toga, Lon S. Schneider, Sonia Pawluczyk, Mauricio Beccera, Liberty Teodoro, Bryan M. Spann, Laurel Beckett, Robert C. Green, John Morris, Leslie M. Shaw, Beau Ances, John C. Morris, Maria Carroll, Mary L. Creech, Erin Franklin, Mark A. Mintun, Stacy Schneider, Angela Oliver, Jeffrey Kaye, Joseph Quinn, Lisa Silbert, Betty Lind, Raina Carter, Sara Dolen, James Brewer, Helen Vanderswag, Adam Fleisher, Judith L. Heidebrink, Joanne L. Lord, Rachelle S. Doody, Javier Villanueva-Meyer, Munir Chowdhury, Susan Rountree, Mimi Dang, Yaakov Stern, Lawrence S. Honig, Karen L. Bell, Daniel Marson, Randall Griffith, David Clark, David Geldmacher, John Brockington, Erik Roberson, Marissa Natelson Love, Hillel Grossman, Effie Mitsis, Raj C. Shah, Leyla deToledo-Morrell, Ranjan Duara, Daniel Varon, Maria T. Greig, Peggy Roberts, Marilyn Albert, Chiadi Onyike, Daniel D’Agostino, Stephanie Kielb, James E. Galvin, Brittany Cerbone, Christina A. Michel, Dana M. Pogorelec, Henry Rusinek, Mony J. de Leon, Lidia Glodzik, Susan De Santi, P. Murali Doraiswamy, Jeffrey R. Petrella, Salvador Borges-Neto, Terence Z. Wong, Edward Coleman, Charles D. Smith, Greg Jicha, Peter Hardy, Partha Sinha, Elizabeth Oates, Gary Conrad, Anton P. Porsteinsson, Bonnie S. Goldstein, Kim Martin, Kelly M. Makino, M. Saleem Ismail, Connie Brand, Ruth A. Mulnard, Gaby Thai, Catherine Mc-Adams-Ortiz, Kyle Womack, Dana Mathews, Mary Quiceno, Allan I. Levey, James J. Lah, Janet S. Cellar, Jeffrey M. Burns, Russell H. Swerdlow, William M. Brooks, Liana Apostolova, Martin R. Farlow, Ann Marie Hake, Brandy R. Matthews, Jared R. Brosch, Scott Herring, Cynthia Hunt, Kathleen Tingus, Ellen Woo, Daniel H. S. Silverman, Po H. Lu, George Bartzokis, Neill R. Graff-Radford, Francine Parfitt, Tracy Kendall, Heather Johnson, Christopher H. van Dyck, Richard E. Carson, Martha G. MacAvoy, Pradeep Varma, Howard Chertkow, Howard Bergman, Chris Hosein, Sandra Black, Bojana Stefanovic, Curtis Caldwell, Ging-Yuek Robin Hsiung, Howard Feldman, Benita Mudge, Michele Assaly, Elizabeth Finger, Stephen Pasternack, Irina Rachisky, Dick Trost, Andrew Kertesz, Charles Bernick, Donna Munic, Marek Marsel Mesulam, Kristine Lipowski, Sandra Weintraub, Borna Bonakdarpour, Diana Kerwin, Chuang-Kuo Wu, Nancy Johnson, Carl Sadowsky, Teresa Villena, Raymond Scott Turner, Kathleen Johnson, Brigid Reynolds, Reisa A. Sperling, Keith A. Johnson, Gad Marshall, Jerome Yesavage, Joy L. Taylor, Barton Lane, Allyson Rosen, Jared Tinklenberg, Marwan N. Sabbagh, Christine M. Belden, Sandra A. Jacobson, Sherye A. Sirrel, Neil Kowall, Ronald Killiany, Andrew E. Budson, Alexander Norbash, Patricia Lynn Johnson, Thomas O. Obisesan, Saba Wolday, Joanne Allard, Alan Lerner, Paula Ogrocki, Curtis Tatsuoka, Parianne Fatica, Evan Fletcher, Pauline Maillard, John Olichney, Charles DeCarli, Owen Carmichael, Smita Kittur, Michael Borrie, T.-Y. Lee, Rob Bartha, Sterling Johnson, Sanjay Asthana, Cynthia M. Carlsson, Steven G. Potkin, Adrian Preda, Dana Nguyen, Pierre Tariot, Anna Burke, Nadira Trncic, Stephanie Reeder, Vernice Bates, Horacio Capote, Michelle Rainka, Douglas W. Scharre, Maria Kataki, Anahita Adeli, Earl A. Zimmerman, Dzintra Celmins, Alice D. Brown, Godfrey D. Pearlson, Karen Blank, Karen Anderson, Laura A. Flashman, Marc Seltzer, Mary L. Hynes, Robert B. Santulli, Kaycee M. Sink, Leslie Gordineer, Jeff D. Williamson, Pradeep Garg, Franklin Watkins, Brian R. Ott, Henry Querfurth, Geoffrey Tremont, Stephen Salloway, Paul Malloy, Stephen Correia, Howard J. Rosen, Bruce L. Miller, David Perry, Jacobo Mintzer, Kenneth Spicer, David Bachman, Nunzio Pomara, Raymundo Hernando, Antero Sarrael, Norman Relkin, Gloria Chaing, Michael Lin, Lisa Ravdin, Amanda Smith, Balebail Ashok Raj, and Kristin Fargher

the Japanese Alzheimer’s Disease Neuroimaging Initiative:

Takashi Asada, Hiroyuki Arai, Morihiro Sugishita, Hiroshi Matsuda, Noriko Sato, Hajime Sato, Kengo Ito, Teruhiko Kachi, Kenji Toba, Michio Senda, Kenji Ishii, Shun Shimohama, Masaki Saitoh, Rika Yamauchi, Takashi Hayashi, Chiyoko Takanami, Seiju Kobayashi, Norihito Nakano, Junichiro Kanazawa, Takeshi Ando, Masato Hareyama, Masamitsu Hatakenaka, Eriko Tsukamoto, Shinji Ochi, Mikio Shoji, Etsuro Matsubara, Takeshi Kawarabayashi, Yasuhito Wakasaya, Takashi Nakata, Naoko Nakahata, Shuichi Ono, Yoshihiro Takai, Satoshi Takahashi, Hisashi Yonezawa, Junko Takahashi, Masako Kudoh, Kuniko Ueno, Hiromi Sakashita, Kuniko Watanabe, Makoto Sasaki, Yutaka Matsumura, Yohsuke Hirata, Tsuyoshi Metoki, Susumu Hayakawa, Yuichi Sato, Masayuki Takeda, Koichiro Sera, Kazunori Terasaki, Toshiaki Sasaki, Yoshihiro Saitoh, Shoko Goto, Ken Nagata, Tetsuya Maeda, Yasushi Kondoh, Takashi Yamazaki, Daiki Takano, Mio Miyata, Hiromi Komatsu, Mayumi Watanabe, Tomomi Sinoda, Rena Muraoka, Kayoko Kikuchi, Hitomi Ito, Aki Sato, Toshibumi Kinoshita, Hideyo Toyoshima, Kaoru Sato, Shigeki Sugawara, Isao Ito, Fumiko Kumagai, Katsutoshi Furukawa, Masaaki Waragai, Naoki Tomita, Mari Ootsuki, Katsumi Sugawara, Satomi Sugawara, Nobuyuki Okamura, Shunji Mugikura, Atsushi Umetsu, Takanori Murata, Tatsuo Nagasaka, Yukitsuka Kudo, Manabu Tashiro, Shoichi Watanuki, Masatoyo Nishizawa, Takayoshi Tokutake, Saeri Ishikawa, Emiko Kishida, Nozomi Sato, Mieko Hagiwara, Kumi Yamanaka, Takeyuki Watanabe, Taeko Takasugi, Shoichi Inagawa, Kenichi Naito, Masanori Awaji, Tsutomu Kanazawa, Kouiti Okamoto, Masaki Ikeda, Yuiti Tasiro, Syunn Nagamine, Sathiko Kurose, Tsuneo Yamazaki, Shiori Katsuyama, Sayuri Fukushima, Etsuko Koya, Makoto Amanuma, Kouiti Ujita, Kazuhiro Kishi, Kazuhisa Tuda, Noboru Oriuti, Katsuyoshi Mizukami, Tetsuaki Arai, Etsuko Nakajima, Katsumi Miyamoto, Tomoya Kobayashi, Saori Itoya, Jun Ookubo, Toshiya Akatsu, Yoshiko Anzai, Junya Ikegaki, Yuuichi Katou, Kaori Kimura, Hajime Saitou, Kazuya Shinoda, Satoka Someya, Hiroko Taguchi, Kazuya Tashiro, Masaya Tanaka, Tatsuya Nemoto, Ryou Wakabayashi, Daisuke Watanabe, Kousaku Saotome, Ryou Kuchii, Harumasa Takano, Tetsuya Suhara, Hitoshi Shinoto, Hitoshi Shimada, Makoto Higuchi, Takaaki Mori, Hiroshi Ito, Takayuki Obata, Yoshiko Fukushima, Kazuko Suzuki, Izumi Izumida, Katsuyuki Tanimoto, Takahiro Shiraishi, Hitoshi Shinotoh, Junko Shiba, Hiroaki Yano, Miki Satake, Aimi Nakui, Yae Ebihara, Tomomi Hasegawa, Yasumasa Yoshiyama, Mami Kato, Yuki Ogata, Hiroyuki Fujikawa, Nobuo Araki, Yoshihiko Nakazato, Takahiro Sasaki, Tomokazu Shimadu, Kimiko Yoshimaru, Etsuko Imabayashi, Asako Yasuda, Keiko Ozawa, Etuko Yamamoto, Natsumi Nakamata, Noriko Miyauchi, Rieko Hashimoto, Taishi Unezawa, Takafumi Ichikawa, Hiroki Hayashi, Masakazu Yamagishi, Tunemichi Mihara, Masaya Hirano, Shinichi Watanabe, Junichiro Fukuhara, Hajime Matsudo, Nobuyuki Saito, Atsushi Iwata, Hisatomo Kowa, Toshihiro Hayashi, Ryoko Ihara, Toji Miyagawa, Mizuho Yoshida, Yuri Koide, Eriko Samura, Kurumi Fujii, Kaori Watanabe, Nagae Orihara, Toshimitsu Momose, Miwako Takahashi, Takuya Arai, Yoshiki Kojima, Akira Kunimatsu, Harushi Mori, Masami Goto, Takeo Sarashina, Syuichi Uzuki, Seiji Katou, Yoshiharu Sekine, Yukihiro Takauchi, Chiine Kagami, Kazutomi Kanemaru, Yasushi Nishina, Maria Sakaibara, Yumiko Okazaki, Rieko Okada, Maki Obata, Masaki Takao, Yuko Iwata, Mizuho Minami, Yasuko Hanabusa, Hanae Shingyouji, Kyoko Tottori, Aya Tokumaru, Makoto Ichinose, Kazuya Kume, Syunsuke Kahashi, Kunimasa Arima, Shin Tanaka, Yuko Nagahusa, Masuhiro Sakata, Mitsutoshi Okazaki, Maki Yamada, Tadashi Tukamoto, Tiine Kodama, Tomoko Takeuchi, Keiichiro Ozawa, Yoshiko Kawaji, Kyouko Tottori, Yasuhiro Nakata, Satoshi Sawada, Makoto Mimatsu, Daisuke Nakkamura, Takeshi Tamaru, Shunichirou Horiuchi, Heii Arai, Tsuneyoshi Ota, Aiko Kodaka, Yuko Tagata, Tomoko Nakada, Eizo Iseki, Kiyoshi Sato, Hiroshige Fujishiro, Norio Murayama, Masaru Suzuki, Satoshi Kimura, Masanobu Takahashi, Haruo Hanyu, Hirofumi Sakurai, Takahiko Umahara, Hidekazu Kanetaka, Kaori Arashino, Mikako Murakami, Ai Kito, Seiko Miyagi, Kaori Doi, Kazuyoshi Sasaki, Mineo Yamazaki, Akiko Ishiwata, Yasushi Arai, Akane Nogami, Sumiko Fukuda, Koichi Kozaki, Yukiko Yamada, Sayaka Kimura, Ayako Machida, Kuninori Kobayashi, Hidehiro Mizusawa, Nobuo Sanjo, Mutsufusa Watanabe, Takuya Ohkubo, Hiromi Utashiro, Yukiko Matsumoto, Kumiko Hagiya, Yoshiko Miyama, Hitoshi Shibuya, Isamu Ohashi, Akira Toriihara, Takako Shinozaki, Haruko Hiraki, Shinichi Ohtani, Toshifumi Matsui, Tomomi Toyama, Hideki Sakurai, Kumiko Sugiura, Yu Hayasaka, Hirofumi Taguchi, Shizuo Hatashita, Akari Imuta, Akiko Matsudo, Daichi Wakebe, Hideki Hayakawa, Mitsuhiro Ono, Takayoshi Ohara, Yukihiko Washimi, Yutaka Arahata, Akinori Takeda, Akiko Yamaoka, Masashi Tsujimoto, Takiko Kawai, Ai Honda, Yoko Konagaya, Hideyuki Hattori, Kenji Yoshiyama, Rina Miura, Takashi Sakurai, Miura Hisayuki, Hidetoshi Endou, Syousuke Satake, Young Jae Hong, Katsunari Iwai, Masaki Suenaga, Sumiko Morita, Kengo Itou, Takashi Kato, Ken Fujiwara, Rikio Katou, Mariko Koyama, Naohiko Fukaya, Akira Tsuji, Hitomi Shimizu, Hiroyuki Fujisawa, Tomoko Nakazawa, Satoshi Koyama, Takanori Sakata, Masahito Yamada, Mitsuhiro Yoshita, Miharu Samuraki, Kenjiro Ono, Moeko Shinohara, Yuki Soshi, Kozue Niwa, Chiaki Doumoto, Mariko Hata, Miyuki Matsushita, Mai Tsukiyama, Nozomi Takeda, Sachiko Yonezawa, Ichiro Matsunari, Osamu Matsui, Fumiaki Ueda, Yasuji Ryu, Masanobu Sakamoto, Yasuomi Ouchi, Yumiko Fujita, Madoka Chita, Rika Majima, Hiromi Tsubota, Umeo Shirasawa, Masashi Sugimori, Wataru Ariya, Yuuzou Hagiwara, Yasuo Tanizaki, Hidenao Fukuyama, Shizuko Tanaka-Urayama, Shin-Ichi Urayama, Ryosuke Takahashi, Kengo Uemura, Hajime Takechi, Chihiro Namiki, Takeshi Kihara, Hiroshi Yamauchi, Emiko Maeda, Natsu Saito, Shiho Satomi, Konomi Kabata, Tomohisa Okada, Koichi Ishizu, Shigeto Kawase, Satoshi Fukumoto, Masanori Nakagawa, Masaki Kondo, Fumitoshi Niwa, Toshiki Mizuno, Yoko Oishi, Mariko Yamazaki, Daisuke Yamaguchi, Takahiko Tokuda, Kyoko Ito, Yoku Asano, Chizuru Hamaguchi, Kei Yamada, Chio Okuyama, Kentaro Akazawa, Shigenori Matsushima, Takamasa Matsuo, Toshiaki Nakagawa, Takeshi Nii, Takuji Nishida, Kuniaki Kiuchi, Masami Fukusumi, Hideyuki Watanabe, Toshiaki Taoka, Akihiro Nogi, Masatoshi Takeda, Toshihisa Tanaka, Hiroaki Kazui, Takashi Kudo, Masayasu Okochi, Takashi Morihara, Shinji Tagami, Masahiko Takaya, Tamiki Wada, Mikiko Yokokoji, Hiromichi Sugiyama, Daisuke Yamamoto, Keiko Nomura, Mutsumi Tomioka, Naoyuki Sato, Noriyuki Hayashi, Shuko Takeda, Eiichi Uchida, Yoshiyuki Ikeda, Mineto Murakami, Takami Miki, Hiroyuki Shimada, Suzuka Ataka, Akitoshi Takeda, Yuki Iwamoto, Motokatsu Kanemoto, Jun Takeuchi, Rie Azuma, Naomi Tagawa, Junko Masao, Yuka Matsumoto, Yuko Kikukawa, Hisako Fujii, Junko Matsumura, Susumu Shiomi, Joji Kawabe, Yoshihiro Shimonishi, Mitsuji Higashida, Tomohiro Sahara, Takashi Yamanaga, Yukio Miki, Shinichi Sakamoto, Hiroyuki Tsushima, Kiyoshi Maeda, Yasuji Yamamoto, Kazuo Sakai, Haruhiko Oda, Yoshihiko Tahara, Toshio Kawamata, Taichi Akisaki, Mizuho Adachi, Masako Kuranaga, Sachi Takegawa, Seishi Terada, Yuki Kishimoto, Naoya Takeda, Nao Imai, Mayumi Yabe, Reiko Wada, Takeshi Ishihara, Hajime Honda, Osamu Yokota, Kentaro Ida, Daigo Anami, Seiji Inoue, Toshi Matsushita, Shinsuke Hiramatsu, Hiromi Tonbara, Reiko Yamamoto, Kenji Nakashima, Kenji Wada-Isoe, Saori Yamasaki, Eijiro Yamashita, Yu Nakamura, Ichiro Ishikawa, Sonoko Danjo, Tomomi Shinohara, Yuka Kashimoto, Miyuki Ueno, Yoshihiro Nishiyama, Yuka Yamamoto, Narihide Kimura, Kazuo Ogawa, Yasuhiro Sasakawa, Takashi Ishimori, Yukito Maeda, Tatsuo Yamada, Shinji Ouma, Aika Fukuhara-Kaneumi, Nami Sakamoto, Rie Nagao, Kengo Yoshimitsu, Yasuo Kuwabara, Ryuji Nakamuta, Minoru Tanaka, Manabu Ikeda, Yuusuke Yatabe, Mamoru Hashimoto, Keiichirou Kaneda, Kazuki Honda, Naoko Ichimi, Mariko Morinaga, Miyako Noda, Fumi Akatuka, Mika Kitajima, Toshinori Hirai, Shinya Shiraishi, Naoji Amano, Shinsuke Washizuka, Tetsuya Hagiwara, Yatsuka Okada, Tomomi Ogihara, Toru Takahashi, Shin Inuzuka, Nobuhiro Sugiyama, Takehiko Yasaki, Minori Kitayama, Tomonori Owa, Akiko Ryokawa, Rie Takeuchi, Satoe Goto, Keiko Yamauchi, Mie Ito, Tomoki Kaneko, Hitoshi Ueda, Shuichi Ikeda, Ban Mihara, Hirofumi Kubo, Akiko Takano, Gou Yasui, Masami Akuzawa, Kaori Yamaguchi, Toshinari Odawara, Naomi Oota, Megumi Shimamura, Mikiko Sugiyama, Atsushi Watanabe, Shigeo Takebayashi, Yoshigazu Hayakawa, Mitsuhiro Idegawa, Noriko Toya, and Kazunari Ishii

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

13195_2024_1414_MOESM1_ESM.pdf^{(1.7MB, pdf)}

Additional file 2. ^{(38.8KB, xlsx)}

Data Availability Statement

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PERMALINK

Polygenic effects on the risk of Alzheimer’s disease in the Japanese population

Masataka Kikuchi

Akinori Miyashita

Norikazu Hara

Kensaku Kasuga

Yuko Saito

Shigeo Murayama

Akiyoshi Kakita

Hiroyasu Akatsu

Kouichi Ozaki

Shumpei Niida

Ryozo Kuwano

Takeshi Iwatsubo

Akihiro Nakaya

Takeshi Ikeuchi

Abstract

Background

Methods

Results

Conclusions

Supplementary Information

Background

Materials and methods

Japanese participants from the J-ADNI cohort

Japanese neuropathological cohort

Genotyping, quality control, and imputation

The NA-ADNI genetic data

Calculation of the PRS and prediction accuracy

CSF biomarkers

Structural MRI and PET imaging

Neuropsychological tests

Statistical analyses

Results

The PRS successfully distinguish ADD patients and CU individuals in the J-ADNI cohort

Table 1.

Table 2.

Fig. 1.

The PRS in combination with the APOE alleles improves predictive power

Table 3.

The effect of our PRS model is replicated in the independent cohorts

ADD in the J-ADNI shows the polygenicity related to immune pathway

The PRS associates with AD-related phenotypes

Table 4.

Fig. 2.

APOE ε4 non-carriers with high PRS are at high risk of AD conversion

Table 5.

Fig. 3.

Discussion

Conclusion

Supplementary Information

Acknowledgements

Abbreviations

Authors’ contributions

Funding

Availability of data and materials

Declarations

Ethics approval and consent to participate

Consent for publication

Competing interests

Footnotes

Contributor Information

References

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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