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. Author manuscript; available in PMC: 2021 Mar 3.
Published in final edited form as: J Alzheimers Dis. 2020;77(4):1469–1482. doi: 10.3233/JAD-200019

Examination of the Effect of Rare Variants in TREM2, ABI3, and PLCG2 in LOAD Through Multiple Phenotypes

Claudia Olive a,b, Laura Ibanez a,b, Fabiana H Geraldo Farias a,b, Fengxian Wang a,b, John P Budde a,b, Joanne B Norton a,b,c, Jen Gentsch a,b,c, John C Morris b,c, Zeran Li a,b, Umber Dube a,b, Jorge Del-Aguila a,b, Kristy Bergmann a,b, Joseph Bradley a,b, Bruno A Benitez a,b, Oscar Harari a,b, Anne Fagan d, Beau Ances d, Carlos Cruchaga a,b,1, Maria Victoria Fernandez a,b,1,*
PMCID: PMC7927150  NIHMSID: NIHMS1671753  PMID: 32894242

Abstract

Background:

Rare variants in PLCG2 (p.P522R), ABI3 (p.S209F), and TREM2 (p.R47H, p.R62H) have been associated with late onset Alzheimer’s disease (LOAD) risk in Caucasians. After the initial report, several studies have found positive results in cohorts of different ethnic background and with different phenotype.

Objective:

In this study, we aim to evaluate the association of rare coding variants in PLCG2, ABI3, and TREM2 with LOAD risk and their effect at different time points of the disease.

Methods:

We used a European American cohort to assess the association of the variants prior onset (using CSF Aβ42, tau, and pTau levels, and amyloid imaging as endophenotypes) and after onset (measured as rate of memory decline).

Results:

We confirm the association with LOAD risk of TREM2 p.R47H, p.R62H and ABI3 p.S209F variants, and the protective effect of PLCG2 p.P522R. In addition, ABI3 and TREM2 gene-sets showed significant association with LOAD risk. TREM2 p.R47H and PLCG2 p.P522R variants were also statistically associated with increase of amyloid imaging and AD progression, respectively. We did not observe any association of ABI3 p.S209F with any of the other AD endophenotypes.

Conclusion:

The results of this study highlight the importance of including biomarkers and alternative phenotypes to better understand the role of novel candidate genes with the disease.

Keywords: ABI3, endophenotypes, late onset Alzheimer’s disease, PLCG2, progression, TREM2

INTRODUCTION

Alzheimer’s disease (AD) is the most common cause of dementia in the elderly and there is not a known effective method to treat or prevent it. About 95% of all AD cases are considered sporadic, of which approximately 90% have a late-onset (LOAD) of the disease [1]. People affected with AD start presenting symptoms at the age of 65–70, 15 to 20 years after the etiology of this disease starts [2]. Apolipoprotein E (APOE) ε4 allele was the first locus discovered to be significantly associated with LOAD risk [3, 4]. Up until today, genome-wide association studies (GWAs) have identified 29 independent risk loci for AD, most of them tagged by common variants (minor allele frequency (MAF) >5%) with very small risk effects [4–9]. Only ~31% of the heritability of this trait can be explained by the known common variants identified by GWAs [10]. Thus, this high missing heritability is currently being searched within rare variants that have moderate to high effect on disease risk [10–13]. Back in 2013, rare variants in TREM2 (p.R47H, p.R62H among others) were associated with LOAD [14, 15] not only in European Americans but also in African Americans [16]. Recently, Sims et al. reported the genome-wide association with LOAD of four rare coding variants: two risk variants in TREM2 (p.R62H, OR = 1.67; p.R47H, OR = 2.46), a protective variant in PLCG2 (p.P522R, OR = 0.68), and a risk variant in ABI3 (p.S209F, OR = 1.43) [17].

Recent studies have replicated the association of those variants in different ethnicities. Dalmasso et al. reported the significant association with AD of TREM2 p.R47H (p = 0.02, OR = 2.29) and PLCG2 p.P522R (p = 0.05, OR = 0.60) in 905 Argentinian individuals with European ancestry [18]. Similarly, Lancaster et al. reported the association of TREM2 p.R62H, PLCG2 p.P522R, and ABI3 p.S209F with increased AD risk in early life of 766 young healthy European Americans [19]. Their results suggest that the association of TREM2 p.R62H variant and PLCG2 p.P522R variant with volumetric reductions in basal ganglia may be linked with LOAD susceptibility [19]. PLCG2 p.P522R variant has also been associated with reduced AD risk and increased likelihood to reach the age of 90 years [20]. Other groups have replicated the association with AD of p.R47H and p.P522R variants in an Argentinian cohort [18], and p.P522R and p.S209F variants in African American subjects [16, 21]. Due to the heterogeneity of populations, replication of GWAs findings in independent cohorts with the same ancestry as the original finding is necessary to provide evidence for the impact that novel genetic components have in the same population for a complex trait like AD.

AD-endophenotypes can help to provide a biological interpretation of the genes identified in genetic studies. Levels of tau, phosphorylated tau (pTau), and amyloid-β42 (Aβ42) biomarkers in cerebrospinal fluid (CSF) change before clinical symptoms of AD can be observed [2]. CSF Aβ42 and higher CSF tau and pTau, decrease with progression of the disease whereas CSF levels of tau and pTau, are associated with AD status. Amyloid imaging allows the measurement of in vivo Aβ42 deposition in the brain, is one of the most reliable current biomarkers for AD at this moment, and increases as the disease progresses [22, 23]. Both measures complement each other and can be used as intermediate phenotypes that can link genetic variation caused by these variants, to disease-predisposing factors [2, 24, 25]. Finally, rate of dementia progression over time provides longitudinal data also useful to complete the understanding of the association of a genotype with AD [8].

Our study aims to replicate, in an independent European American cohort, the association with AD of the TREM2 variants (p.R62H, p.R47H [14, 15]) as well as the two novel genes/variants reported by Sims et al. (PLCG2 p.P522R and ABI3 p.S209F). In addition, here we aim to expand the knowledge on the effects these variants have on AD prior and after diagnosis. We study the effects of the variants on endophenotypes measurable prior to disease (amyloid imaging and CSF levels of tau, pTau, and Aβ42 [2]) and rate memory decline after disease onset.

MATERIALS AND METHODS

Ethics statement

The Institutional Review Boards of all participating institutions approved the study and research was carried out in accordance with the approved protocols. Written informed consent was obtained from participants or their family members. The IRB approval number for this study is 201104178.

Cohorts descriptions

For this study, we aimed to generate a large dataset of European American unrelated individuals combining data from the Knight-Alzheimer’s Disease Research Center (Knight ADRC) [26], the National Institute on Aging Genetics Initiative for Late-Onset Alzheimer’s Disease (NIA-LOAD) [27], and the Alzheimer Disease Sequencing Project (ADSP - pht003392.v7.p4) (Table 1). A description of the Knight-ADRC and the NIA-LOAD datasets has been previously reported [26, 27]. The ADSP data is available to qualified researchers through the database of Genotypes and Phenotypes (https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/dataset.cgi?studyid=phs000572.v8.p4&pht=3392).

Table 1.

Summary statistics of the samples included in each analysis

Phenotype N %CA %Fe Age (±SD) %APOE ε4 Cohort Dataset
Aβ42 558 33.51 68.33 75.98 (6.47) 40.04 K GWAs
Tau 560 33.63 51.34 76.06 (6.56) 40.22 K GWAs
pTau 553 32.73 51.18 76.85 (7.65) 40.15 K GWAs
Amyloid imaging 420 20.95 53.57 76.25 (6.52) 37.38 K GWAs-WES
AD progression 446 100.00 57.17 75.85 (7.02) 62.50 K GWAs-WES
AD risk single variant 12,372 55.95 54.84 80.79 (8.32) 30.84 K-N-A GWAs-WES
AD risk gene based 10,226 55.87 54.46 81.64 (8.27) 28.56 K-N-A WES
Total dataset 12,372 55.95 54.84 80.79 (8.32) 30.84 K-N-A GWAs-WES
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N, number of samples included; %CA, percentage of cases; %Fe, percentage of females; AGE, average age at onset for cases and average age at last assessment for controls; K, Knight-ADRC; N, NIA-LOAD; A, ADSP; SD, standard error.

We used GWAs and whole exome sequencing (WES) data from the previous studies as well as CSF (Aβ42, tau, and pTau), amyloid imaging and AD progression data, to perform the analyses in this study (Supplementary Table 1). Neuropathology autopsy information and clinical diagnosis using Clinical Dementia Rating (CDR) [28] were used to define the phenotype status of the subjects included in our analyses. People with dementia caused by neurological diseases other than AD or younger than 65 years old were not included in the analyses.

Genetic data

The generation and quality control (QC) of the GWAs and WES data was performed as previously described [29, 30]. Principal Component Analysis (PCA) was performed using HapMap reference panel to select European ancestry subjects. We used Identity-by-Descendent (IBD) estimations to identify and remove duplicated samples across cohorts and to evaluate relatedness. Unrelated samples (Pihat <0.2) and one sample from every pair of related individuals were included in the analyses, prioritizing the selection of older controls and younger cases. From the NIA-LOAD dataset there was GWAs and WES data available for 928 and 279 unrelated European American samples, respectively. From the Knight-ADRC dataset, there was GWAs and WES data available for 1,218 and 416 unrelated European American samples, respectively. We also included 9,621 unrelated European American samples with WES data from the ADSP study (Supplementary Table 1). There were 90 overlapping samples between the NIA-LOAD and the ADSP dataset; we examined each pair and kept the sample with better genotyping rate. Our final dataset included 7,000 cases (5,791 with WES data and 1,209 with GWAs data) and 5,462 controls (4,525 with WES data and 937 with GWAs data; Supplementary Table 1).

We restricted our analyses to all variants with a 98% genotyping rate, in either the GWAs or WES datasets, for the TREM2 (chr.6 : 41,126,244–41,130, 924), PLCG2 (chr.16 : 81,772,702–81,991,899), and ABI3 (chr.17 : 47,287,589–47300587) genes (chromosomic ranges refer to the GRCh37 reference genome). The genotyping rate for the previously reported variants (TREM2 p.R47H and p.R62H, PLCG2 p.P522R and ABI3 p.S209F) within the GWAs dataset was higher than 98%. In the WES dataset TREM2 and PLCG2 variants had a genotyping rate higher than 99% but ABI3 p.S209F had a 96% genotyping rate, so we had to force its incorporation in the dataset (Supplementary Table 2).

Statistical analyses

AD risk analyses: Single variant

Single variant analysis was conducted to test the association of PLCG2 p.P522R, ABI3 p.S209F, and TREM2 p.R62H and p.R47H with AD risk. Genotypic information for these variants was obtained by combining all GWAs and WES data [30] from the previously mentioned projects. The final analysis was done with 12,462 subjects from the ADSP, Knight ADRC, and NIA-LOAD cohorts (Supplementary Table 1). Given the high and similar genotyping rate in both datasets, we decided to run joint analysis for TREM2 and PLCG2 variants using the logistic regression model from PLINKv1.9 [23] and correcting by the two first principal components (PCs) and sex. The analysis for ABI3 variant also included whether the sample came from the GWAs or the WES data as covariate given the different genotyping rate for variants within this gene (Supplementary Table 2). Given that there is certain sample overlap between Sims et al. [17] and the cohorts used in this analysis, we also performed the single variant analysis association after removing the overlapping samples. Bonferroni correction was applied.

Cerebrospinal fluid

CSF levels of tau, pTau, and Aβ42 were measured for individuals in the Knight-ADRC cohort. CSF Aβ42 thresholds have been previously determined only for this cohort. Individuals with CSF Aβ42 values ≥500 pg/mL were excluded as individuals below these thresholds have been shown to have fibrillary Aβ42 deposits in brain [31, 32] whereas those with higher levels are more likely to not have AD. CSF data normalization was done as previously described [24, 33].

We used the linear regression model in PLINKv1.9 [23] to assess the association of the SNPs of interest with the following AD endophenotypes, employed as the dependent variable assuming an additive model: CSF levels of tau (N = 559), pTau (N = 553), and Aβ42 (N = 558) (Table 1). The two first PCs, sex and age at CSF collection were used as covariates for these analyses. Bonferroni correction was applied.

Imaging

Molecular imaging with positron emission tomography (PET) was used to detect Aβ42 deposition during life. Detailed methods are described in previous reports for Knight-ADRC [34]. The analysis of these data was done using only one value of Aβ42 scan for each individual, thus we only used the baseline visit for the analysis of individuals with more than one Aβ42 scan. After this selection, we included a total of 420 individuals in this analysis (Table 1) that was performed using the linear regression model in PLINKv1.9 [23] and corrected by the two first PCs and sex. Bonferroni correction was applied.

Memory decline

Clinical Dementia Rating Scale Sum of Boxes (CDR-SB) was used to determine rate of memory decline; CDR-SB is a sum of the scores from six tests (boxes) that measure different AD dementia symptoms to provide a score in staging dementia severity from 0 (cognitively normal) to 18 (the most severe dementia). We included only samples with three or more clinical assessments and 1.5 years of follow-up and we removed samples with a constant 0 or 18 value of CDR-SB over time [8]. Finally, 446 samples from Knight-ADRC were included in this analysis (Table 1). The association of the SNPs of interest with AD progression was tested by longitudinal regression using the nlme package in R [35]. Change of CDR-SB was set as the independent variable with the following covariates:

CDR Sum of Boxes = SNP ∗ time interval

+ CDR baseline ∗ time interval

+ SNP + time interval

+ CDR baseline

+ Sex + Age baseline

+ Education + PC1 + PC2

Gene based

We performed gene-based analysis for the following AD phenotypes: AD risk, CSF Aβ42, CSF tau, CSF pTau, and amyloid imaging.

For the AD risk phenotype, we restricted our gene-based analysis to those samples with WES data (10,316 individuals from Knight-ADRC, NIA-LOAD, and ADSP). We used SnpEff [36] to predict the functional consequence of the observed polymorphisms in TREM2, ABI3, and PLCG2 and we restricted our analysis to predicted nonsynonymous, missense and loss-of-function (LOF) variants [37] with MAF<0.01 and we kept for analysis those variants with a genotyping rate over 90% across both datasets. The final dataset included 211 nonsynonymous variants, 39 variants in TREM2, 136 in PLCG2, and 36 in ABI3 (Supplementary Table 3). We performed the gene-based analysis at three levels; one that included all non-synonymous variants, one that included all non-synonymous variants minus the top leading variant, and one in which only common variants across all cohorts were included. We also performed the gene-based analysis stratified per each one of the major cohorts to highlight the contribution of each one of these cohorts to the final value reported (Supplementary Table 4).

For the AD endophenotypes (CSF Aβ42, tau, pTau, and amyloid imaging), we used the variants from the previous subset of rare non-synonymous variants that were present in the GWAs and WES samples that had data for these endophenotypes. That included three variants for TREM2, five variants for PLCG2 and one variant for ABI3.

To perform gene-based analysis, we used the SKAT-O algorithm in the R-package Sequence Kernel Association Test (SKAT) which combines collapsing and variance component tests into one Statistical method [38]. We used the SKAT null model and adjust the analysis for the two first PCs and sex (for the AD Risk analysis) and we also corrected for age at what the CSF sample, or the PET Imaging was taken (for the endophenotype analysis). Bonferroni correction was applied.

RESULTS

We performed single variant analysis to assess the association with AD risk of the four variants of interest (PLCG2 p.P522R, ABI3 p.S209F, and TREM2 p.R62H and p.R47H) followed by a gene-based analysis to test the burden of rare non-synonymous variants in each gene. In addition, we conducted AD progression and quantitative trait analyses to evaluate the association of the variants with other characteristics of the disease. Demographic summary statistics for the samples included in each analysis and the datasets used for each analysis are shown in Table 1.

After merging GWAs and WES data from NIA-LOAD, Knight ADRC, and ADSP, genotyping data for 12,372 individuals for the variants of interest passed QC. We confirmed the previously reported associations and in the same direction for all the SNPs of interest [14, 15, 17]; both variants in TREM2 (p.R62H, p = 1.73 × 10−03, OR = 1.49; p.R47H, p = 2.09 × 10−11, OR = 4.38) and the variant in ABI3 (p.S209F, p = 1.03 × 10−03, OR = 1.59) were associated with increased AD risk, while the variant in PLCG2 (p.P522R, p = 2.16 × 10−03, OR=0.63) confirmed its protective effect in our cohort (Table 2). In order to evaluate whether the Knight-ADRC and NIA-LOAD datasets from WASHU are contributing to the already reported effect for these variants by the ADSP consortia [41], we performed the previous analysis stratified by major cohort (Supplementary Tables 4–6).

Table 2.

Single variant analysis for TREM2, PLCG2, and ABI3 variants of interest using individuals with GWAs or WES data from ADSP, Knight-ADRC, and NIA-LOAD cohorts

Locus (variant) OR 95% CI P MAF cases MAF controls N
TREM2 (p.R62H) 1.49 1.16, 1.93 1.73 × 10−3 0.013 0.008 12,332
TREM2 (p.R47H) 4.38 2.84, 6.78 2.09 × 10−11 0.010 0.002 12,337
PLCG2 (p.P522R) 0.63 0.47, 0.85 2.16 × 10−3 0.006 0.009 12,360
ABI3 (p.S209F)* 1.59 1.20, 2.11 1.03 × 10−3 0.011 0.007 11,887
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OR, odds ratio; CI, confidence interval;p,p-value; MAF, minor allele frequency; N, number of samples included in the analysis. Significant p-values after Bonferroni correction in bold.

*

The association analysis for this variant was run using WES/GWAs as covariate, in addition to SEX, PC1, PC2 and to account for the difference in genotyping rate between the GWAs data and the WES data.

Some of the samples in this study were also included in the analysis performed by Sims et al. [17]. After excluding overlapping samples (final sample size = 5,972), TREM2 (p.R47H) and PLCG2 (p.P522R) remained nominally significant with similar OR to the analysis including all samples (Supplementary Table 7).

For the gene-based analysis, only rare nonsynonymous variants were included. 39 variants in TREM2 (including p.R62H and p.R47H), 36 variants in ABI3 (including p.S209F), and 136 variants in PLCG2 (including p.P522R) across 10,316 individuals from Knight-ADRC, NIA-LOAD, and ADSP cohorts went into analysis (Supplementary Table 3). We found a significant association with AD risk for TREM2 (p = 9.37 × 10−14) and ABI3 (p = 0.001). TREM2 remained significant (p = 1.75 × 10−03) after removing p.R47H and p.R62H from analysis. We even still observed a nominal association in the TREM2 gene-based analyses event after the p.R47H, p.R62H, p.D87N, and p.H157T were removed (Supplementary Table 8), indicating that additional variants in TREM2 contribute to AD risk. ABI3 and PLCG2 were not significant after removing the p.S209F and the p.P522R variant respectively. The overall gene direction for ABI3 (OR = 1.21) followed that of the key variants, even when these were removed from the analysis (Table 3), suggesting that additional risk variants in these genes confer risk for AD. Finally, since some variants were private for each of the cohorts included, we performed a gene-based analysis considering only the common variants across datasets, to confirm whether the association was driven by any particular cohort. All three genes resulted significant when all cohorts were analyzed together (Table 3); a closer analysis revealed that significance for TREM2 was driven by the ADSP and Knight-ADRC cohorts, PLCG2 was significant for the NIA-LOAD cohort, and ABI3 was significant for the ADSP cohort.

Table 3.

Gene based analysis for TREM2, PLCG2, and ABI3 including rare (MAF < 1%) nonsynonymous variants, using 10,316 individuals with WES data from ADSP, Knight-ADRC, and NIA-LOAD cohorts

Locus OR 95% CI P N
TREM2 1.99 1.52, 2.62 9.37 × 10−14 39
TREM2* 1.89 1.12, 3.20 2.59 × 10−05 38
TREM2** 1.69 1.50, 2.05 3.91 × 10−12 3
PLCG2 0.91 0.73, 1.12 0.064 136
PLCG2* 1.00 0.79, 1.28 0.683 135
PLCG2** 0.51 0.41,0.54 0.049 6
ABI3 1.44 1.07, 1.95 6.64 × 10−4 36
ABI3* 1.21 0.76, 1.90 0.127 35
ABI3** 2.49 2.07, 3.21 5.02 × 10−5 2
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OR, odds ratio; CI, confidence interval; p, p-value; N, number of variants included.

*

gene-based analyses using the same variants but excluding variants of interest.

**

gene-based analyses using only the common variants across all cohorts Significant p-values after Bonferroni correction in bold.

After confirming that our study has power to identify significant association with AD risk, we analyzed if any of the variants and genes were associated with other AD-related phenotypes. TREM2 p.R47H showed a trend toward association with CSF levels of Aβ42 (p = 0.041, β = −0.68) and a significant association (after Bonferroni correction) for amyloid imaging (p = 0.008; Table 4). TREM2 was also associated at gene-level with the levels of amyloid imaging (p = 0.012, Supplementary Table 9). No significant association with this phenotype was observed for PLCG2 p.P522R, TREM2 p.R62H, and ABI3 p.S209F. None of the variants analyzed showed significant association with CSF levels of tau and pTau (Table 4). The PLCG2 p.P522R variant showed a trend towards association with change in CDR-SB (p = 0.028) equivalent to a slower progression of AD dementia (Table 5). TREM2 p.R62H, pR27H and ABI3 p.S209F variants were not significant in this analysis.

Table 4.

Quantitative trait analyses for TREM2, PLCG2, and ABI3 variants of interest using CSF levels of Aβ42, tau, pTau, and amyloid imaging, using individuals with GWAs or WES data from Knight-ADRC and NIA-LOAD cohorts

Phenotype Locus (variant) β 95% CI P N
Aβ42 TREM2 (p.R62H) −0.19 −1.32, 0.95 0.745 556
TREM2 (p.R47H) −0.68 −1.34, 0.03 0.041 557
PLCG2 (p.P522R) 0.19 −0.47, 0.85 0.576 558
ABI3 (p.S209F) 0.08 −0.49, 0.66 0.773 556
tau TREM2 (p.R62H) 0.23 −0.80, 1.26 0.666 557
TREM2 (p.R47H) 0.12 −0.51, 0.75 0.710 558
PLCG2 (p.P522R) −0.05 −0.65, 0.55 0.860 559
ABI3 (p.S209F) 0.01 −0.51, 0.53 0.973 557
pTau TREM2 (p.R62H) 0.21 −0.86, 1.29 0.695 551
TREM2 (p.R47H) 0.23 −0.47, 0.93 0.526 552
PLCG2 (p.P522R) −0.02 −0.64, 0.61 0.957 553
ABI3 (p.S209F) 0.05 −0.49, 0.60 0.854 551
Amyloid imaging TREM2 (p.R62H) 0.01 −0.13, 0.16 0.889 420
TREM2 (p.R47H) 0.17 0.05, 0.30 0.008 420
PLCG2 (p.P522R) −0.01 −0.09, 0.08 0.904 420
ABI3 (p.S209F) 0.06 −0.02, 0.15 0.143 418
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β, beta; CI, confidence interval; p, p-value; N, number of samples included. Significant p-values after Bonferroni correction in bold.

Table 5.

AD progression analysis for TREM2, PLCG2, and ABI3 variants of interest, using individuals with GWAs or WES data from Knight-ADRC and NIA-LOAD cohorts

Phenotype Locus (variant) Beta SE P MAF cases N
Progression TREM2 (p.R62H) 0.20 0.76 0.789 0.005 440
TREM2 (p.R47H) 0.07 0.31 0.798 0.014 441
PLCG2 (p.P522R) −1.25 0.58 0.028 0.007 445
ABI3 (p.S209F) −0.24 0.57 0.677 0.006 438
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SE, standard error; p, p-value; MAF, minor allele frequency; N, number of samples included. Significant p-values after Bonferroni correction in bold.

DISCUSSION

GWAs have been instrumental in the field of AD to identify novel loci associated with the disease. However, the functional genetic variants are mainly unknown as so is their real effect on the pathology of the disease.

In this study, we analyzed the association of TREM2 p.R62H, TREM2 p.R47H, PLCG2 p.P522R, and ABI3 p.S209F variants previously associated with AD using GWAs and WES data of 12,462 individuals. Using single variant analysis, we confirmed the association with LOAD status and the direction of effect previously reported for the four variants of interest (Table 2). Additionally, we observed that other nonsynonymous rare variants in TREM2 contribute to AD risk (gene-based after removing p.R62H and p.R47H variants, p = 1.75 × 10–03), reinforcing the important role of rare variants in TREM2 towards LOAD susceptibility [16, 40]. Particularly, we observe that variant p.D87N (6 : 41129133:C:T, rs142232675) is nominally significantly associated with AD risk (Supplementary Table 4). Even though this variant has been found in AD cases before [14, 41], this is the first study in which a significant association with AD risk is found even in the Sims et al. study [17]. The TREM2 variant p.H157Y (6 : 41127543:G:A, rs2234255) also showed a trend to association with AD risk (Supplementary Table 4) in our study. This variant has been previously reported to be significantly associated with AD in the ADSP cohort [41], in a Chinese cohort and in a meta-analysis of Caucasian, Japanese, and African American cohorts [42].

TREM2 p.R47H has been associated with AD risk in multiple studies [14, 16, 18, 43–52]. This variant increases risk for AD almost three-fold and is the greatest genetic risk factor identified for AD after the APOE ε4 allele [14, 15, 53]. TREM2 p.R47H has been previously associated with increasing levels of tau and pTau in CSF, without affecting Aβ42 [43, 49]. In this cohort the TREM2 p.R47H showed the same direction of association but not significant in our quantitative trait analysis for tau and pTau; however, we observe a significant association with decreasing levels of Aβ42 in CSF (p = 0.041, β = −0.68). In addition, we observe a significant association for p.R47H with increased amyloid imaging (p = 0.008, β = 0.07). This is the first time, to our knowledge, that TREM2 is associated with AD risk, Aβ42 levels in CSF and amyloid imaging at the same time using the same cohort. TREM2 p.R62H variant has been previously associated with a faster AD progression (p = 0.027, β = 0.31) in 1,499 samples from ADNI and Knight-ADRC datasets [8], but it does not show association with progression in our 446 samples from Knight-ADRC, probably due to the smaller size of our cohort.

In this study we replicate the association of PLCG2 p.P522R with AD risk after removing the overlapping samples with Sims et al., which support the role of this variant and gene in AD and reinforces the importance of investigating the role of this variant in AD. In addition, we also observed that this variant has a “protective” effect on memory decline (p = 0.028, β = −1.25) which relates to previous observations that this variant is enriched in a healthy centenarians cohort [20]. PLCG2 p.P522R has been seen to modify the generation of the PLCγ2 enzyme36,3 [20, 54], opening the venues for novel pharmacological targets that may attenuate the progression of this disease.

This study faces some limitations. 48% of the samples used in this study for the AD risk analysis have been previously published (e.g., Sims et al. [17] and Kunkle et al. [7]). However, the main goal of this study is not to replicate the association of those rare variants with AD but to determine if those variants also influence other AD phenotypes such as CSF and imaging biomarkers. Another limitation is that we only have WES data for half of the samples with endophenotype information available at the Knight-ADRC. WES data is being generated for the entire Knight-ADRC cohort; hence, future studies should be able to better evaluate the role of rare variants on AD endophenotypes.

In conclusion, we confirmed the association of TREM2 p.R47H, not only with AD risk but also with amyloid imaging. Also, other rare variants in this gene participate in disease pathology which deserves further examination. Finally, we confirm the protective effect of PLCG2 p.P522R variant, which could exert its role by slowing the rate of memory decline.

Supplementary Material

supplementary material

ACKNOWLEDGMENTS

We thank all participants and their families for their commitment and dedication to helping advance research into the early detection and causation of AD. This work was supported by access to the equipment made possible by the Hope Center for Neurological Disorders, and the Departments of Neurology and Psychiatry at Washington University School of Medicine. The authors gratefully acknowledge the study subjects for their participation and the many researchers whose efforts contributed to the assembly and characterization of these datasets.

This work was supported by grants from the NIH to C. Cruchaga (R01AG044546, P01AG003991, RF1AG053303, RF1AG058501, U01AG058922) and to M.V.F (1K99AG061281–01). C. Cruchaga was also supported by the Alzheimer’s Association (NIRG-11–200110, BAND-14–338165, AARG-16–441560, and BFG-15–362540). The recruitment and clinical characterization of research participants at Washington University was supported by NIH P50 AG05681, P01 AG03991, and P01 AG026276.

The Alzheimer’s Disease Sequencing Project (ADSP) is comprised of two Alzheimer’s Disease (AD) genetics consortia and three National Human Genome Research Institute (NHGRI) funded Large Scale Sequencing and Analysis Centers (LSAC). The two AD genetics consortia are the Alzheimer’s Disease Genetics Consortium (ADGC) funded by NIA (U01 AG032984), and the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) funded by NIA (R01 AG0 33193), the National Heart, Lung, and Blood Institute (NHLBI), other National Institute of Health (NIH) institutes and other foreign governmental and nongovernmental organizations. The Discovery Phase analysis of sequence data is supported through UF1AG047133 (to Drs. Schellenberg, Farrer, Pericak-Vance, Mayeux, and Haines); U01AG049505 to Dr. Seshadri; U01AG049506 to Dr. Boerwinkle; U01AG049507 to Dr. Wijsman; and U01AG049508 to Dr. Goate and the Discovery Extension Phase analysis is supported through U01AG052411 to Dr. Goate, U01AG052410 to Dr. Pericak-Vance and U01 AG052409 to Drs. Seshadri and Fornage. Data generation and harmonization in the Follow-up Phases is supported by U54AG052427 (to Drs. Schellenberg and Wang).

The ADGC cohorts include: Adult Changes in Thought (ACT), the Alzheimer’s Disease Centers (ADC), the Chicago Health and Aging Project (CHAP), the Memory and Aging Project (MAP), Mayo Clinic (MAYO), Mayo Parkinson’s Disease controls, University of Miami, the Multi-Institutional Research in Alzheimer’s Genetic Epidemiology Study (MIRAGE), the National Cell Repository for Alzheimer’s Disease (NCRAD), the National Institute on Aging Late Onset Alzheimer’s Disease Family Study (NIA-LOAD), the Religious Orders Study (ROS), the Texas Alzheimer’s Research and Care Consortium (TARC), Vanderbilt University/Case Western Reserve University (VAN/CWRU), the Washington Heights-Inwood Columbia Aging Project (WHICAP) and the Washington University Sequencing Project (WUSP), the Columbia University Hispanic- Estudio Familiar de Influencia Genetica de Alzheimer (EFIGA), the University of Toronto (UT), and Genetic Differences (GD).

The CHARGE cohorts are supported in part by National Heart, Lung, and Blood Institute (NHLBI) infrastructure grant HL105756 (Psaty), RC2HL102419 (Boerwinkle) and the neurology working group is supported by the National Institute on Aging (NIA) R01 grant AG033193. The CHARGE cohorts participating in the ADSP include the following: Austrian Stroke Prevention Study (ASPS), ASPS-Family study, and the Prospective Dementia Registry-Austria (ASPS/PRODEM-Aus), the Atherosclerosis Risk in Communities (ARIC) Study, the Cardiovascular Health Study (CHS), the Erasmus Rucphen Family Study (ERF), the Framingham Heart Study (FHS), and the Rotterdam Study (RS). ASPS is funded by the Austrian Science Fond (FWF) grant number P20545-P05 and P13180 and the Medical University of Graz. The ASPS-Fam is funded by the Austrian Science Fund (FWF) project I904),the EU Joint Programme - Neurodegenerative Disease Research (JPND) in frame of the BRIDGET project (Austria, Ministry of Science) and the Medical University of Graz and the Steiermärkische Krankenanstalten Gesellschaft. PRODEM-Austria is supported by the Austrian Research Promotion agency (FFG) (Project No. 827462) and by the Austrian National Bank (Anniversary Fund, project 15435. ARIC research is carried out as a collaborative study supported by NHLBI contracts (HHSN268201100005C, HHSN268201100006C, HHSN268201100007C, HHSN268201100008C, HHSN268201100009C, HHSN268201100010C, HHSN268201100011C, and HHS N268201100012C). Neurocognitive data in ARIC is collected by U01 2U01HL096812, 2U01HL096814, 2U01HL096899, 2U01HL096902, 2U01HL096917 from the NIH (NHLBI, NINDS, NIA and NIDCD), and with previous brain MRI examinations funded by R01-HL70825 from the NHLBI. CHS research was supported by contracts HHSN268201200036C, HHSN268200800007C, N01HC55222, N01HC85079, N01HC85080, N01HC85081, N01HC85082, N01HC85083, N01HC85086, and grants U01HL080295 and U01HL130114 from the NHLBI with additional contribution from the National Institute of Neurological Disorders and Stroke (NINDS). Additional support was provided by R01AG023629, R01AG15928, and R01AG20098 from the NIA. FHS research is supported by NHLBI contracts N01-HC-25195 and HHSN268201500001I. This study was also supported by additional grants from the NIA (R01s AG054076, AG049607 and AG033040 and NINDS (R01 NS017950). The ERF study as a part of EUROSPAN (European Special Populations Research Network) was supported by European Commission FP6 STRP grant number 018947 (LSHG-CT-2006–01947) and also received funding from the European Community’s Seventh Framework Programme (FP7/2007–2013)/grant agreement HEALTH-F4–2007-201413 by the European Commission under the programme “Quality of Life and Management of the Living Resources” of 5th Framework Programme (no. QLG2-CT-2002–01254). High-throughput analysis of the ERF data was supported by a joint grant from the Netherlands Organization for Scientific Research and the Russian Foundation for Basic Research (NWO-RFBR 047.017.043). The Rotterdam Study is funded by Erasmus Medical Center and Erasmus University, Rotterdam, the Netherlands Organization for Health Research and Development (ZonMw), the Research Institute for Diseases in the Elderly (RIDE), the Ministry of Education, Culture and Science, the Ministry for Health, Welfare and Sports, the European Commission (DG XII), and the municipality of Rotterdam. Genetic data sets are also supported by the Netherlands Organization of Scientific Research NWO Investments (175.010.2005.011, 911–03-012), the Genetic Laboratory of the Department of Internal Medicine, Erasmus MC, the Research Institute for Diseases in the Elderly (014–93-015;RIDE2), and the Netherlands Genomics Initiative (NGI)/Netherlands Organization for Scientific Research (NWO) Netherlands Consortium for Healthy Aging (NCHA), project 050–060-810. All studies are grateful to their participants, faculty and staff. The content of these manuscripts is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or the U.S. Department of Health and Human Services.

The four LSACs are: the Human Genome Sequencing Center at the Baylor College of Medicine (U54HG003273), the Broad Institute Genome Center (U54HG003067), The American Genome Center at the Uniformed Services University of the Health Sciences (U01AG057659), and the Washington University Genome Institute (U54HG003079).

Biological samples and associated phenotypic data used in primary data analyses were stored at Study Investigators institutions, and at the National Cell Repository for Alzheimer’s Disease (NCRAD, U24AG021886) at Indiana University funded by NIA. Associated Phenotypic Data used in primary and secondary data analyses were provided by Study Investigators, the NIA funded Alzheimer’s Disease Centers (ADCs), and the National Alzheimer’s Coordinating Center (NACC, U01AG016976) and the National Institute on Aging Genetics of Alzheimer’s Disease Data Storage Site (NIAGADS, U24AG041689) at the University of Pennsylvania, funded by NIA, and at the Database for Genotypes and Phenotypes (dbGaP) funded by NIH. This research was supported in part by the Intramural Research Program of the National Institutes of health, National Library of Medicine. Contributors to the Genetic Analysis Data included Study Investigators on projects that were individually funded by NIA, and other NIH institutes, and by private U.S. organizations, or foreign governmental or nongovernmental organizations.

The funders of the study had no role in the collection, analysis, or interpretation of data; in the writing of the report; or in the decision to submit the paper for publication.

Footnotes

Authors’ disclosures available online (https://www.j-alz.com/manuscript-disclosures/20-0019r3).

SUPPLEMENTARY MATERIAL

The supplementary material is available in the electronic version of this article: https://dx.doi.org/10.3233/JAD-200019.

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