Abstract
Aim
The associations of non‐pathogenic variants of APP, PSEN1, and PSEN2 with Alzheimer's disease (AD) remain unclear. This study is aimed at determining the role of these variants in AD.
Methods
Our study recruited 1154 AD patients and 2403 controls. APP, PSEN1, PSEN2, and APOE were sequenced using a targeted panel. Variants were classified into common or rare variants with the minor allele frequencies (MAF) cutoff of 0.01. Common variant (MAF≥0.01)‐based association test was performed by PLINK 1.9, and gene‐based (MAF <0.01) association analysis was conducted using Sequence Kernel Association Test‐Optimal (SKAT‐O test). Additionally, using PLINK 1.9, we performed AD endophenotypes association studies.
Results
A common variant, PSEN2 rs11405, was suggestively associated with AD risk (p = 1.08 × 10−2). The gene‐based association analysis revealed that the APP gene exhibited a significant association with AD (p = 1.43 × 10−2). In the AD endophenotypes association studies, APP rs459543 was nominally correlated with CSF Aβ42 level (p = 7.91 × 10−3).
Conclusion
Our study indicated that non‐pathogenic variants in PSEN2 and APP may be involved in AD pathogenesis in the Chinese population.
Keywords: Alzheimer's disease, APP, PSEN1, PSEN2, the Chinese population
In this study, we systematically explored the relationship between Alzheimer's disease (AD) and non‐pathogenic variants of APP, PSEN1, and PSEN2 in a total of 3557 individuals in a Chinese population. The common variant association test revealed that PSEN2 rs11405 were nominally associated with AD. Gene‐based association analysis indicated that non‐pathogenic variants in APP gene may contribute to the etiology of AD. Our study indicated that non‐pathogenic variants in PSEN2 and APP may be involved in AD pathogenesis in the Chinese population.
1. INTRODUCTION
Alzheimer's disease (AD) is a common progressive neurodegenerative disease characterized by memory decline and cognitive dysfunction. The prevalence of dementia is rising rapidly and imposing a heavy burden on families and societies. 1 AD is a highly heritable disease with its heritability estimated to be as high as 60%–80%. 2 The etiology of AD remains complex. Genetics plays an important role in AD development. Large‐scale genome‐wide association studies (GWAS) have identified 75 susceptibility loci in AD. 3 , 4 , 5 However, the heritability of AD is still missing and remains to be identified. 6 More genetic studies are required to illustrate the pathogenesis of AD.
Three genes, including amyloid precursor protein (APP), presenilin 1 (PSEN1), and presenilin 2 (PSEN2), are the causative genes of AD. The pathogenic variants of these three genes only presented a low proportion of AD patients (<5%). In our cohort, pathogenic/likely pathogenic variants were only identified in 2.2% of AD patients. 7 Additionally, according to the American college of medical genetics and genomics and the association for molecular pathology (ACMG‐AMP) guidelines, 26.5% of variants of APP, PSEN1, and PSEN2 were classified as variants of uncertain significance or benign variants (hereafter referred to as non‐pathogenic variants). 8 A few studies examined the role of non‐pathogenic variants of APP, PSEN1, and PSEN2 in AD pathogenesis. For example, in non‐Hispanic Caucasian individuals, the PSEN1 p. E318G variant conferred susceptibility to AD only in patients carrying APOE ε4 allele. 9
Nevertheless, the association of non‐pathogenic variants with AD received limited attention. Meanwhile, the sample size in some previous studies is limited. 10 Consequently, to determine the role of non‐pathogenic variants of APP, PSEN1, and PSEN2 in AD, we comprehensively analyzed these non‐pathogenic variants between AD patients and controls in a large Chinese population via a targeted sequencing panel.
2. METHODS
2.1. Participants
We recruited 1154 AD patients and 2403 controls from Xiangya Hospital and a community in Changsha. Based on the National Institute on Aging‐Alzheimer's Association criteria for probable AD 11 , the patients were diagnosed with AD by two expert neurologists. Participants with causative mutations for AD, vascular dementia, and frontotemporal dementia (including C9orf72) had been excluded by Sanger sequencing or repeat‐prime PCR (RP‐PCR) analysis. This study was approved by the Ethics Committee of Xiangya Hospital, Central South University, China. Written informed consent was obtained from each participant or guardian.
2.2. Genomic DNA isolation
Genomic DNA was extracted from the peripheral blood leukocytes using phenol‐chloroform extraction and ethanol precipitation. 12 The DNA's quality and quantity were assessed via a NanoDrop spectrophotometer (Thermo Scientific). The DNA sample was diluted to 50–100 ng/μL.
2.3. Targeted gene sequencing
The targeted sequencing panel includes APP, PSEN1, PSEN2, and APOE. All of their exons and flanking regions were sequenced by our panel. The genomic DNA was broken into fragments with 150–200 bp length via Biorupter Pico, followed by end‐repairing, A‐tailing, adaptor ligation, and PCR amplification. Using the Illumina NovaSeq 6000 platform, the fragmented DNA was sequenced. The low‐quality reads fastq data were deleted by FastQC (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/). The sequence reads were mapped to the human reference genome (UCSC hg19/GRCH37) using the BWA software (version 0.7.15, http://bio‐bwa.sourceforge.net). 13 Duplicate sequence reads were removed by Picard (version 2.18.7, http://broadinstitute.github.io/picard/). The quality‐score recalibration, local realignments, and variant calling were performed by the Genome Analysis Toolkit (version 3.2, https://software.broadinstitute.org/gatk/). 14 Variants were annotated using ANNOVAR (https://hpc.nih.gov/apps/ANNOVAR.html). 15 According to minor allele frequencies (MAF), variants were classified into common or rare variants with the MAF cutoff of 0.01. In addition, we predicted the pathogenicity of missense variants by ReVe. 16 In our study, the damaging variants included damaging missense variants (ReVe >0.7) or loss‐of‐function (LoF) variants. LoF variants involved nonsense, frameshift, or splicing variants.
2.4. Statistical analysis
The normality of data was tested using SPSS 26. Using PLINK 1.9, 17 the variants with genotyping rate < 95%, genotype quality (GQ) ≤ 20, and Hardy–Weinberg equilibrium p‐value <1 × 10−6 in controls were excluded in our study. The common variant‐based association analysis was performed by PLINK 1.9. Age, APOE ε4 status (APOE ε4+, APOE ε4‐), and gender were adjusted for each common variant.
Furthermore, gene‐based association tests were performed by aggregating rare variants using the Sequence Kernel Association Test‐Optimal (SKAT‐O test). 18 Rare variants were further divided into three groups: rare damaging variants (MAF <0.01, LoF or ReVe >0.7), rare damaging missense variants (MAF <0.01, ReVe >0.7), and rare missense variants (MAF <0.01, missense). Also, age, gender, and APOE ε4 status were also adjusted in the SKAT‐O test. According to Bonferroni correction, a cutoff p‐value <0.05/n was considered to reach statistical significance (n: the number of variants or genes).
3. RESULTS
3.1. Demographic and clinical information
1154 AD patients and 2403 controls were recruited in our study. The average age of onset of AD patients was 64.44 years old, and the average age of controls was 64.76 years old. No significant age difference was observed between AD patients and controls (p = 0.52). The AD patients' MMSE scores were lower than those of controls (p = 1.31 × 10−12) (Table 1).
TABLE 1.
Demographic and clinical information of AD patients and controls
| AD | Control | p‐value | |
|---|---|---|---|
| Number | 1154 | 2403 | – |
| Age(years), mean ± SD | 64.44 ± 10.85 | 64.76 ± 7.78 | 0.52 a |
| Gender(M/F) | 458/696 | 1152/1251 | 4.37 × 10−6 b |
| MMSE, mean ± SD | 10.32 ± 7.61 | 26.75 ± 2.79 | 1.31 × 10−12 a |
| MoCA, mean ± SD | 8.23 ± 5.78 | ‐ | ‐ |
| CDR, mean ± SD | 1.29 ± 0.70 | ‐ | ‐ |
| ADL, mean ± SD | 33.58 ± 12.53 | ‐ | ‐ |
| NPI, mean ± SD | 17.41 ± 14.98 | ‐ | ‐ |
Abbreviations: AD, Alzheimer's disease; ADL, Activities of daily living; CDR, Clinical Dementia Rating; F, female; M, male; MMSE, Mini‐mental State Examination; MoCA, Montreal Cognitive Assessment; NPI, Neuropsychiatric Inventory; SD, standard deviation.
p‐value was calculated by Mann–Whitney U test (it did not exhibit a normal/Gaussian distribution).
p‐value was calculated by chi‐squared test.
3.2. Common variant association analysis
After quality control, seven common variants were observed in our study, including two APP variants, one PSEN1 variant, and four PSEN2 variants. These common variants were located in exons (42.9%, 3/7), introns (42.9%, 3/7), and 5′‐untranslated region (5’‐UTR; 14.2%, 1/7). The single common variant association test identified that a common variant in PSEN2, rs11405, was nominally linked to AD risk after adjusting for age, gender, and APOE ε4 status (p = 1.08 × 10−2) (Table 2). However, after the Bonferroni correction, this common variant was no longer associated with AD risk (p > 7.14 × 10−3).
TABLE 2.
Common variants between AD patients and controls
| Gene | Position | Rs ID | Region | Variant | Effect allele | MAF | OR (95% CI) | p | Adjusted p | |
|---|---|---|---|---|---|---|---|---|---|---|
| Case | Control | |||||||||
| PSEN2 | 1:227069677 | rs11405 | Exonic | c.69 T > C:p.A23A | T | 0.452 | 0.482 | 0.875(0.801–0.979) | 1.73 × 10−2 | 1.08 × 10−2 |
| PSEN2 | 1:227071525 | rs1046240 | Exonic | c.261C > T:p.H87H | T | 0.400 | 0.375 | 1.096(1.004–1.231) | 4.23 × 10−2 | 8.73 × 10−2 |
| PSEN2 | 1:227077809 | rs75733498 | Exonic | c.861C > T:p.P287P | T | 0.076 | 0.074 | 1.074(0.851–1.241) | 7.76 × 10−1 | 4.73 × 10−1 |
| APP | 21:27543049 | rs459543 | UTR5 | – | G | 0.182 | 0.186 | 0.965(0.853–1.109) | 6.79 × 10−1 | 6.11 × 10−1 |
| PSEN1 | 14:73664853 | rs165932 | Intronic | – | G | 0.368 | 0.376 | 0.973(0.871–1.073) | 5.23 × 10−1 | 6.15 × 10−1 |
| APP | 21:27254092 | rs45513597 | Intronic | – | G | 0.030 | 0.027 | 0.998(0.824–1.490) | 4.99 × 10−1 | 9.89 × 10−1 |
| PSEN2 | 1:227058440 | rs111297484 | Intronic | – | G | 0.000 | 0.038 | NA(NA) | NA | NA |
Abbreviations: AD, Alzheimer's disease; Adjusted P, adjusted by age, gender, and APOE ε4 status; CI, confidence interval; MAF, minor allele frequency; NA, not applicable; OR, odds ratio; Effect allele represents the minor allele.
3.3. Rare variant aggregation testing
We performed gene‐based aggregation testing by combing the rare variants within genes between AD patients and controls. In the rare missense variants group, after adjusting for age, gender, and APOE ε4 status, the APP gene exhibited a significant association with AD (p = 1.43 × 10−2) (Table 3). Specifically, 1.00% of the AD cases and only 0.29% of the controls carried APP missense variants. In the remaining three groups, including rare LoF variants, rare damaging missense variants, and rare damaging variants, none of these genes were associated with AD risk (Table S1–S9).
TABLE 3.
Significant gene between AD patients and controls in the SKAT‐O test
| Classification | Gene | Location | Variant | AD (n) | Control (n) |
|---|---|---|---|---|---|
|
Rare missense variants (MAF <0.01) |
APP | 21:27277356 | c.1943G > A:p.R648Q | 1 | 0 |
| 21:27284152 | c.1810G > A:p.V604M | 0 | 1 | ||
| 21:27284214 | c.1748A > G:p.E583G | 0 | 1 | ||
| 21:27327949 | c.1579C > T:p.R527W | 1 | 1 | ||
| 21:27327979 | c.1549A > C:p.M517L | 1 | 2 | ||
| 21:27328006 | c.1522A > G:p.T508A | 0 | 1 | ||
| 21:27328065 | c.1463G > A:p.R488H | 1 | 0 | ||
| 21:27347391 | c.1450C > T:p.P484S | 1 | 0 | ||
| 21:27372339 | c.1024G > A:p.G342S | 5 | 0 | ||
| 21:27372368 | c.995A > G:p.D332G | 2 | 0 | ||
| 21:27372467 | c.896C > G:p.P299R | 1 | 0 | ||
| 21:27372473 | c.890C > T:p.T297M | 6 | 2 | ||
| 21:27394180 | c.841G > C:p.E281Q | 0 | 1 | ||
| 21:27394215 | c.806C > T:p.T269I | 0 | 1 | ||
| 21:27394275 | c.746A > G:p.E249G | 0 | 1 | ||
| 21:27394327 | c.694G > A:p.E232K | 0 | 1 | ||
| 21:27423503 | c.475A > G:p.S159G | 1 | 0 | ||
| 21:27423508 | c.470C > T:p.T157I | 1 | 0 | ||
| 21:27462283 | c.331 T > C:p.F111L | 0 | 1 | ||
| 21:27484325 | c.196A > G:p.K66E | 1 | 0 | ||
| 21:27484444 | c.77C > G:p.A26G | 1 | 0 | ||
| 21:27542895 | c.44C > T:p.A15V | 0 | 1 | ||
| Allele count/total number of alleles (n/n) | 23/2308 | 14/4806 | |||
| Frequency (%) | 1.00 | 0.29 | |||
| Adjusted P (SKAT‐O) | 1.43 × 10−2 | ||||
Abbreviations: AD, Alzheimer's disease; Adjusted P, adjusted by age, gender, and APOE ε4 status; MAF, minor allele frequency; n, number; SKAT‐O, Sequence Kernel Association Test‐Optimal.
3.4. AD endophenotypes association studies
To illustrate the role of APP, PSEN1, and PSEN2 in AD endophenotypes, we conducted AAO, MMSE, MoCA, CDR, and cerebrospinal fluid (CSF) biomarkers association studies in AD patients. In the AD endophenotypes association studies, seven common variants were left after quality control. After adjusting for gender and APOE ε4 status, none of these variants were linked to the AAO, MMSE, MoCA, and CDR in AD. In our study, a subgroup of 95 AD patients underwent CSF testing. CSF Aβ42, Aβ40, Total tau (T‐tau), and phosphorylated tau (P‐tau) were determined. In the CSF biomarkers association analyses, we identified that APP rs459543 was nominally correlated with CSF Aβ42 levels (p = 7.91 × 10−3) (Table 4). After the Bonferroni correction, rs459543 was no longer associated with CSF Aβ42.
TABLE 4.
CSF Aβ42 association analyses in AD patients
| Gene | Position | Rs ID | Region | Variant | Effect allele | β | Adjusted p |
|---|---|---|---|---|---|---|---|
| APP | 21:27543049 | rs459543 | UTR5 | c.‐111G > C | G | −157.2 | 7.91 × 10−3 |
Abbreviations: AD, Alzheimer's disease; Adjusted P, adjusted by age, gender, and APOE ε4 status; Aβ42, amyloid‐β 1–42; CSF, cerebrospinal fluid; UTR5, 5′ untranslated region.
4. DISCUSSION
In our study, to determine the associations of non‐pathogenic variants of APP, PSEN1, and PSEN2 with AD risk, we comprehensively studied APP, PSEN1, and PSEN2 genes in a large‐scale Chinese population cohort. The common variant association study revealed that PSEN2 rs11405 was nominally associated with AD risk. The gene‐based analysis indicated that the APP gene reached statistical significance between AD patients and controls. AAO and MMSE association studies demonstrated that none of these variants were linked to AD endophenotypes.
In the 1990s, based on the genetic linkage analysis, APP, PSEN1, and PSEN2 were identified as pathogenic genes for AD. 19 , 20 , 21 , 22 All of these genes increased the production of amyloid‐β (Aβ) or elevated the ratio of amyloid‐β1‐42 to amyloid‐β1‐40, which subsequently lead to the dominant hypothesis–amyloid hypothesis in AD 23 . The most common genetic causes of AD are pathogenic variants in PSEN1, followed by APP and PSEN2 24 . Nonetheless, only a few AD patients were caused by variants in these three genes. For example, even in early‐onset Alzheimer's disease patients, less than 5% of them carried the pathogenic variants in APP, PSEN1, and PSEN2 25 . Additionally, some of the variants have no exact clinical significance, and their associations with AD are needed to be investigated. 26
Our study revealed that PSEN2 rs11405 reached nominal significance between AD patients and controls. PSEN2 is located on chromosome 1q42.13. To date, 63 PSEN2 variants have been found and only approximately half of them were considered to cause AD 8 . A previous study showed that rare coding variants in PSEN2 contributed to susceptibility for apparently sporadic late‐onset AD 27 . However, another study found that no variants in PSEN2 exhibited a significant association with AD risk. Thus, the association of variants in PSEN2 with AD remains controversial. In this large‐scale Chinese cohort, we firstly identified that a common variant, PSEN2 rs11405, was nominally correlated with AD risk. Although further studies are needed to replicate this result in AD, our finding indicated that PSEN2 rs11405 may be involved in the pathogenesis of AD. The role of PSEN2 rs11405 is needed to be replicated in other larger populations.
Furthermore, we identified that the rare missense variants in APP were associated with AD risk. Located on chromosome 21q21.3, the APP gene encodes amyloid‐beta precursor protein (APP). APP is a transmembrane protein, and its proteolysis can lead to the production of Aβ protein. 28 It is reported that a rare coding variant, APP p.G322A, increased the risk for late‐onset AD 29 . Nevertheless, two previous studies demonstrated that rare coding variants were not linked to AD risk. 27 , 30 Our study firstly found that rare variants in APP were linked to AD risk using the gene‐based analysis (SKAT‐O test), indicating the significant role of APP in AD pathogenesis.
Additionally, we found that neither common variants in PSEN1 nor rare variants in PSEN1 were associated with AD risk. Although a previous study revealed that PSEN1 p.E318G conferred susceptibility for AD 31 , other studies showed that variants in PSEN1 were not associated with AD risk. 30 , 32 , 33 Thus, the associations of common PSEN1 variants with AD remain controversial. Our result further suggested that variants in PSEN1 may be not correlated with AD risk. Meanwhile, we found that the common variants in APP, PSEN1, and PSEN2 were not correlated with AD endophenotypes, including the age of onset and MMSE, suggesting that these variants were not involved in AD development.
In AD endophenotypes association studies, we identified that APP rs459543 was nominally correlated with CSF Aβ42 levels. In the Dominantly Inherited Alzheimer's Network (DIAN) observational study, PSEN1, PSEN2, or APP pathogenic variants presented markedly differential Pittsburgh‐Compound‐B PET (PiB‐PET) signal but with similar CSF Aβ42 levels. 34 Another study revealed that a PSEN2 haplotype was associated with CSF Aβ42 concentrations. 35 Our study showed that APP rs459543 was suggestively correlated with CSF Aβ42 levels, indicating its potential role in AD biomarker changes. However, only a few AD patients underwent CSF testing and the association disappeared after multiple tests. Therefore, it is necessary to determine whether APP rs459543 modulates CSF Aβ42 concentrations in the future.
Although we systematically investigated the role of non‐pathogenic variants of APP, PSEN1, and PSEN2 in AD, a few limitations exist. First, our sample size is relatively limited, and the results needed to be replicated in larger sample sizes. Second, we analyzed these genes in the Chinese population, and the results also warrant replication in other populations.
Taken together, we analyzed the non‐pathogenic variants in APP, PSEN1, and PSEN2 between AD and controls in a large Chinese cohort. The common variant association test suggested that PSEN2 rs11405 was nominally linked to AD risk. Rare missense variants in APP contributed to the pathogenesis of AD. This study indicated that non‐pathogenic variants in APP and PSEN2 were involved in AD development.
CONFLICT OF INTEREST
The authors declare that there is no conflict of interest associated with the contents of this article.
Supporting information
Table S1–S9
ACKNOWLEDGMENTS
The authors thanked the support of the Bioinformatics Center and National Clinical Research Centre for Geriatric Disorders, Xiangya Hospital, Central South University. This study was supported by the National Key R&D Program of China (No.2020YFC2008500), the National Major Projects in Brain Science and Brain‐like Research (No.2021ZD0201803), the National Natural Science Foundation of China (No.81901171, 81971029, 82071216), Hunan Innovative Province Construction Project (No.2019SK2335), and Hu‐Xiang Youth Project (No. 2021RC3028), the Youth Program of Science Foundation of Xiangya Hospital (No.2018Q017).
Xiao X, Liu H, Zhou L, et al. The associations of APP , PSEN1 , and PSEN2 genes with Alzheimer's disease: A large case–control study in Chinese population. CNS Neurosci Ther. 2023;29:122‐128. doi: 10.1111/cns.13987
Xuewen Xiao and Hui Liu contributed equally to this work.
DATA AVAILABILITY STATEMENT
The data that support the findings of this study are available from the corresponding author upon reasonable request.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Table S1–S9
Data Availability Statement
The data that support the findings of this study are available from the corresponding author upon reasonable request.