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Published in final edited form as: Neurobiol Aging. 2013 Jul 17;34(11):2696.e1–2696.e5. doi: 10.1016/j.neurobiolaging.2013.05.028

Alzheimer disease risk genes and the age-at-onset phenotype

Madhav Thambisetty 1,*, Yang An 2, Toshiko Tanaka 3
PMCID: PMC4038407  NIHMSID: NIHMS490767  PMID: 23870418

Abstract

Despite the recent identification of several novel risk genes for Alzheimer’s disease (AD), little is known about their influence on the age-at-onset (AAO) of AD. The AAO is a phenotype with a heritable component distinct from disease risk and may be a useful trait to study in the context of developing interventions for delaying the onset of AD. We studied the influence of ten recently identified AD risk genes as well as APOE in relation to AAO in a large cohort of AD patients (N=2569). We find that the novel AD risk gene, PICALM exerts a small effect on the AAO of AD with earlier disease onset in risk allele carriers. In addition, we confirmed the previously reported association between the APOE ε4 allele and earlier disease onset. None of the other AD risk genes influenced AAO of AD. Our results suggest that besides APOE, other genes associated with AD risk do not exert large effects on the AAO phenotype of AD.

Introduction

Recent large scale genome-wide association studies (GWAS) have identified several novel risk variants for Alzheimer’s disease (AD) (Harold et al., 2009; Hollingworth et al., 2011; Lambert et al., 2009; Naj et al., 2011). However, these single nucleotide polymorphisms (SNPs) occur commonly in the general population and exert small effect sizes, making it unlikely that they will be of clinical utility in predicting disease risk in older individuals (Seshadri et al., 2010). As the primary outcome in conventional GWAS in AD is the identification of variants associated with increased disease risk, the case versus control design in these studies largely ignores heritable variations in several other disease-related phenotypes that may be revealing of pathogenesis and of clinical utility. Examples of such potentially heritable phenotypes include clinical measures such as rates of cognitive decline (Ruiz et al., 2013), as well as those related to disease pathology such as brain atrophy (Furney et al., 2011; Meda et al., 2013; Potkin et al., 2009) amyloid deposition and tau accumulation/phosphorylation (Bekris et al., 2012; Han et al., 2010; Kim et al., 2011) (Cruchaga et al., 2013). Some recent studies have examined whether SNPs associated with AD risk were also related to other phenotypes such as rates of decline in memory performance and cerebrospinal fluid measures of AD pathology (Alexopoulos et al., 2011; Kauwe et al., 2011; Sweet et al., 2012).

Age-at-onset (AAO) of AD is a phenotype that is believed to be mediated by a heritable component distinct from disease risk (Dickson et al., 2008; Holmans et al., 2005). Modeling studies have suggested that upto 40% of variability in AAO of AD may be heritable (Daw et al., 1999; Daw et al., 2000; Li et al., 2002). APOE genotype, the most robust genetic factor associated with risk for AD only explains about 10% of variation in AAO of AD (Slooter et al., 1998) and it has been suggested that there might be several other loci with effect sizes on AAO comparable to that of APOE. In this study, we examined whether AD risk variants identified by recent large scale GWAS also influenced the AAO phenotype in AD patients. We also compared the relative contributions of these novel Alzheimer risk genes to APOE in explaining variation in AAO.

Materials and methods

Subjects

Data used in this analysis were derived from ‘ADC cohorts 1–3’ from the 29 National Institute on Aging (NIA)-funded Alzheimer Disease Centers (ADCs), with data coordinated by the National Alzheimer Coordinating Center (NACC). Access to the data was facilitated by the National Institute on Aging Genetics of Alzheimer’s Disease Data Storage Site (NIAGADS), a national genetics data repository that facilitates access of genotypic data to qualified investigators for the study of the genetics of late-onset Alzheimer's disease. Detailed descriptions of the ADC1, 2 and 3 cohorts are available at: https://www.alz.washington.edu/ and have previously been described in several publications (Beekly et al., 2007; Beekly et al., 2004; Morris et al., 2006; Weintraub et al., 2009).

Genotyping and SNPs of interest

Methodological details on genotyping, data cleaning and quality control in the ADC samples have been described by Naj et al. in their recent publication reporting the identification of several novel AD risk variants in a large GWAS (Naj et al., 2011). Briefly, genotyping in the ADC1 and ADC2 samples was performed on Illumina 660 high-density SNP microarrays and in the ADC3 samples, on the Illumina OmniExpress platform. APOE genotyping was performed using SNPs rs7412 and rs429358. In this analysis, we selected the AD-risk variant SNPs reported in recent large GWAS to examine their effect on AAO of AD. These included SNPs in the following genes: CLU (rs11136000), PICALM (rs3851179), BIN1 (rs744373), CR1 (rs3818361), ABCA7 (rs3764650), MS4A6A (rs610932), MS4A4E (rs670139), EPHA1 (rs11767557), CD33 (rs3865444) and EXOC3L2 (rs597668).

The criteria we adopted for the selection of these specific SNPs in the current report were:

  1. significant association with AD risk in a large index GWAS (Harold et al., 2009; Hollingworth et al., 2011; Lambert et al., 2009; Naj et al., 2011) and,

  2. replication of the reported SNP’s association with AD risk by independent GWAS and/or by meta -analysis of other GWAS data (Carrasquillo et al., 2011; Hu et al., 2011; Jun et al., 2010; Shang et al., 2013).

Where such independent replication for individual SNPs was not available in the case of ABCA7 (rs3764640) and MS4A6A/MS4A4E (rs610932; rs670139), we selected these SNPs based solely on their reported association with AD risk in the index GWAS.

Age at onset of AD

Data on the AAO of AD were collected in the ADC1–3 cohorts in two phases, as described in: https://www.alz.washington.edu/ and in previous publications (Beekly et al., 2007; Beekly et al., 2004; Morris et al., 2006; Weintraub et al., 2009). Phase-1 data were collected from ADC enrollees between 1984 and 2005. Phase-2 data were collected between 2005 to the present. Demographic details of subjects included in the analysis are shown in table-1. This analysis was restricted to Caucasian subjects with a diagnosis of AD.

Table-1.

N Sex AAO (SD)
Range
Total 2569 1182 (46%) male
1387 (54%) female		 72.6 (7.9)
43 – 97
Phase 1 1708 746 (44%) male
962 (56%) female		 72.2 (7.6)
45 – 97
Phase 2 861 436 (51%) male
425 (49%)		 73.3 (8.3)
43–96
Diff p-value P=0.0008 P=0.0021
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Demographic details of subjects included in this analysis.

Statistical analysis

General linear models (GLM) were used with age at onset of AD as the dependent variable, and the number of AD-risk alleles of each gene as the main predictor in separate models. Other covariates included sex and the data collection phase. As the analysis was restricted to SNPs associated with increased risk of AD, our a priori hypothesis was that the number of risk alleles of each gene would be negatively correlated with the AAO of AD i.e. the presence of a greater number of AD risk alleles would be associated with an earlier AAO. We report our results as one-sided p-values for significance and after adjusting for multiple comparisons using the false discovery rate (FDR) method (Benjamini and Hochberg, 1995).

Results

Data on age-at-onset of AD was available in 2569 subjects (table-1). There were significant differences in the sex distribution between phase-1 and phase-2 samples (p=0.0008) with a slightly earlier AAO for males relative to females (1.92±0.31 years; p<.0001). The mean AAO of AD in phase-1 and phase-2 samples were 72.2±7.6 and 73.3±8.3 years respectively (p=0.0021). We therefore entered sex and data collection phase as covariates in our analysis on the effect of AD-risk genes on AAO.

Table-2 shows the AD risk allele frequencies in each of the genes studied. APOE ε4 carriers had a significantly lower AAO of AD than ε4 non-carriers with a decrease in 3.02 years in AAO for each unit increase in the number of ε4 alleles (pFDR-adjusted<.0001). Moreover, APOE genotype explained 6.7% of variance in AAO of AD. Among the recently discovered Alzheimer risk genes, risk allele carriers of the AD variant of PICALM (rs3851179) showed a significantly lower AAO than non-carriers, with a decrease in 0.55 years in AAO of AD for each unit increase in the number of risk alleles (pFDR-adjusted=0.0473). Variation at the PICALM gene explained 0.24% of variance in AAO of AD. None of the SNPs in the other genes tested showed a significant effect on AAO of AD (table-3).

Table-2.

Gene SNP RAF 0 RA 1 RA 2 RA N missing
APOE - 0.41 854 (33.9%) 1272 (50.5%) 394 (15.6%) 49
CLU rs11136000 .62 361 (14.2%) 1217 (47.7%) 974 (38.2%) 17
PICALM rs3851179 .66 287 (11.2%) 1169 (45.5%) 1112 (43.3%) 1
BIN1 rs744373 .33 1144 (44.5%) 1132 (44.1%) 293 (11.4%) 0
CR1 rs3818361 .21 1599 (62.4%) 841 (32.8%) 124 (4.8%) 5
ABCA7 rs3764650 .10 2059 (80.2%) 483 (18.8%) 27 (1.1%) 0
MS4A6A rs610932 .59 414 (16.1%) 1283 (50%) 869 (33.9%) 3
MS4A4E rs670139 .41 638 (33.8%) 944 (50.0%) 305 (16.2%) 682
EPHA1 rs11767557 .83 87 (3.4%) 724 (28.2%) 1758 (68.4%) 0
CD33 rs3865444 .70 227 (8.8%) 1103 (42.9%) 1239 (48.2%) 0
EXOC3L2 rs597668 .20 1225 (64.9%) 585 (31.0%) 77 (4.1%) 682
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Characteristics of the Alzheimer’s disease risk genes analyzed in the study. SNP; single nucleotide polymorphism, RAF; risk allele frequency, RA; risk allele

Table-3.

Gene dose effect
(yrs)		 Std err p-value
(1-side)		 FDR Adjusted
1-sided p		 Variance
Explained in
AAO
APOE −3.02 0.22 <.0001 <.0001 6.70%
CLU −0.26 0.23 0.1225 0.2820 0.068%
PICALM −0.55 0.23 0.0086 0.0473 0.24%
BIN1 −0.26 0.23 0.1282 0.2820 0.049%
CR1 −0.26 0.26 0.1615 0.2961 0.011%
ABCA7 −0.24 0.36 0.2484 0.3416 0.018%
MS4A6A 0.022 0.22 0.5396 0.6596 0.013%
MS4A4E −0.19 0.23 0.2045 0.3214 0.036%
EPHA1 0.36 0.28 0.9028 0.9028 0.064%
CD33 0.25 0.24 0.8573 0.9028 0.044%
EXOC3L2 −0.39 0.28 0.0849 0.2820 0.099%
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General linear models (GLM) were used with age at onset of AD as the dependent variable, and the number of AD-risk alleles of each gene as the main predictor in separate models. Other covariates included sex and the data collection phase. The dose effect is interpreted as the estimated changes in AD age in years for each unit change in risk alleles.

Discussion

Recent large-scale GWAS have dramatically enhanced our knowledge about genetic contributions to risk for AD (Harold et al., 2009; Hollingworth et al., 2011; Lambert et al., 2009; Naj et al., 2011). However, little is known about the influence of these risk variants on the age-at-onset of AD. The AAO phenotype, believed to be influenced by a strong genetic component, distinct from AD risk, may be useful to study in the context of disease-modifying treatments. Even as the recent failures of pivotal phase-III treatment trials in AD have spurred the field to target disease-modifying drugs at subjects in pre-symptomatic phases of the disease (Mullard, 2012), there is also growing recognition of the critical importance of studying strategies aimed at delaying the onset of AD (Brookmeyer et al., 1998; Duffy, 2005). Despite this, the AAO phenotype has received relatively little attention relative to disease risk in GWAS of AD.

In the current analysis, we confirmed the well-established association of APOE ε4 with AAO of AD (Blacker et al., 1997; Slooter et al., 1998) and also observed a small effect of the novel AD risk variant SNP in PICALM with AAO. None of the other AD risk genes were found to be associated with AAO of AD. It is interesting to note that a recent report suggested that the PICALM risk variant SNP rs3851179 also exerts a small effect on the rate of disease progression in AD patients (Ruiz et al., 2013; Sweet et al., 2012). Similar to our results in the current report, a recent study by Jones et al found that AD risk variants in the APOE and PICALM genes lower the age at onset of AD in patients with Down syndrome (Jones et al., 2013). While the precise mechanisms underlying the association between PICALM and AD pathogenesis are unclear, it has been suggested that PICALM, through regulation of clathrin-mediated endocytosis, is capable of influencing both APP processing and Aβ generation (Xiao et al., 2012). Another biological pathway relevant in the possible role of PICALM in AD pathogenesis may be through its influence on iron homeostasis and cell proliferation (Scotland et al., 2012). It must be noted that the AD risk variant PICALM SNP rs3851179 occurs at the 5′ end of PICALM and a recent report suggested that this variant may be in high linkage disequilibrium with rs592297, a known coding synonymous SNP that is part of an exonic splice enhancer in the gene (Schnetz-Boutaud et al., 2012).

Some methodological considerations are important to bear in mind in relation to estimating the AAO of AD. This measure is susceptible to multiple sources of error including variation in informants' abilities to detect changes in cognition as well as in physicians' assessments of symptom onset (Friedman, 1993; Schofield et al., 1995). It is plausible that inaccurate estimates of AAO and variation in methods used to derive the AAO across multiple centers may have decreased the power of our analysis to detect small effects on AAO exerted by the novel AD risk genes. In the context of GWAS in AD, designing studies with AAO as the primary phenotype of interest in large prospectively assessed cohorts of older individuals may prove challenging. However, such studies may be useful in identifying genetic components of a phenotype that may be amenable to interventions aimed at delaying the onset of AD.

In summary, besides confirming the established association of APOE genotype with AAO, we also find that the newly discovered AD risk gene, PICALM, exerts a small effect on AAO of AD. Our findings further strengthen the link between PICALM and AD pathogenesis. In showing that the majority of AD risk variants do not influence AAO of the disease, they also highlight the importance of studying AAO as a primary phenotype, distinct from AD risk, in GWAS of AD.

Acknowledgements

Biological samples and Associated Phenotypic Data used in primary data analyses were stored at Principal Investigators’ institutions, and at the National Cell Repository for Alzheimer’s Disease (NCRAD) at Indiana University funded by NIA. Associated Phenotypic Data used in primary and secondary data analyses were provided by Principal Investigators, the NIA funded Alzheimer’s Disease Centers (ADCs), and the National Alzheimer’s Coordinating Center (NACC) and stored at Principal Investigators’ institutions, NCRAD, and at the National Institute on Aging Alzheimer’s Disease Data Storage Site (NIAGADS) at the University of Pennsylvania, funded by NIA under a cooperative agreement U24 041689. Contributors to the Genetic Analysis Data included Principal Investigators on projects that were individually funded by NIA, other NIH institutes, private U.S. organizations, or foreign governmental or nongovernmental organizations. This research was supported in part by the Intramural Research Program of the NIH, National Institute on Aging. MT is grateful to Dr. Marilyn Miller, Ph.D. Program Director, Genetics of Alzheimer's Disease, Tau, and Hormone Research, Division of Neuroscience, NIA/NIH/DHHS for advice during the early planning stages of this study.

Footnotes

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