International Journal of Medical Genetics

Volume 2014 (2014), Article ID 689832, 6 pages

http://dx.doi.org/10.1155/2014/689832

Research Article

Genetic Variants in the SORL1 Gene Are Associated with Age at Onset of Alzheimer Disease: A Survival Analysis

Ke-Sheng Wang,¹ Xuefeng Liu,¹ Liang Wang,¹ David Briones,² and Chun Xu²

¹Department of Biostatistics and Epidemiology, College of Public Health, East Tennessee State University, P.O. Box 70259, Lamb Hall, Johnson City, TN 37614-1700, USA

²Departments of Psychiatry and Neurology and the Center of Excellence in Neuroscience, Paul L. Foster School of Medicine, Texas Tech University Health Sciences Center, El Paso, TX 79905, USA

Received 17 January 2014; Revised 27 July 2014; Accepted 31 July 2014; Published 12 August 2014

Academic Editor: Jezabel V. López

Copyright © 2014 Ke-Sheng Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

Few studies focused on the association of SORL1 with the age at onset (AAO) of Alzheimer disease (AD). This study investigated the association of 43 SNPs in SORL1 with the AAO of AD by using the Kaplan-Meier survival analysis and the Cox proportional hazards model in SAS version 9.2 and linear regression model in PLINK software (791 AD patients and 782 controls). Both logrank test and Cox regression model showed that five SNPs (rs1784934, rs676759, rs560573, rs593769, and rs11218313) were associated with the AAO of AD in the male sample, while one SNP (rs17125558) was associated with the AAO of AD in the female sample (). SNP rs560573, previously associated with the risk of late-onset AD, showed the most association with the AAO in the male sample ( for logrank test and in the Cox model). The mean AAO was approximately 2.5 years earlier in individuals who were homozygous for the minor allele compared with those who had at least one major allele. Linear regression model showed that rs2282649 and rs726601 were associated with AAO in the whole sample ( and 0.0367, resp.). These findings provide evidence of several genetic variants in SORL1 influencing the AAO of AD.

1. Introduction

Alzheimer disease (AD) is the most common form of dementia. Most often, AD is diagnosed in people over 65 years of age, although the less-prevalent early-onset Alzheimer’s can occur much earlier [1]. In the United States, the prevalence of AD in 2000 was estimated to be about 1.6% in the 65–74 age group, with the rate increasing to 19% in the 75–84 age group and to 42% in the greater than 84 age group [2]. The World Health Organization estimated that, in 2005, 0.379% of people worldwide had dementia, and the prevalence would increase to 0.441% in 2015 and to 0.556% in 2030 [3]. Another study estimated that, in 2006, 0.40% of the world population (about 26.6 million) was afflicted by AD, which is predicted to affect 1 in 85 people globally by 2050 [4].

In addition to the disease risk, age at onset (AAO) of AD is also genetically influenced with an estimated heritability of about 42% [5, 6]. Genetic effects account for 57%–78% of the variance of AAO [7], while twin studies suggest that the heritability of AD exceeds 60% [8]. AD has a strong genetic predisposition (60–80% of the attributable risk) [9].

The sortilin-related receptor (SORL1) gene is located at 11q23.2-q24.2 and is expressed as a 10.5 kb transcript in brain, spinal cord, and testis [10]. Rogaeva et al. [11] reported that inherited variants of the SORL1 neuronal sortilin-related receptor were associated with late-onset AD. Lee et al. [12] reported associations between various SNPs and haplotypes in the SORL1 gene and AD among a total of 296 AD patients comprising 3 cohorts of African American, Caribbean Hispanic, and non-Hispanic white individuals. Their results suggested extensive allelic heterogeneity in SORL1, with specific SNPs associated with specific groups. Cellini et al. [13] concluded that their findings confirmed the association between SORL1 and late-onset AD. Reitz et al. [14] also reported that their findings provided confirmatory evidence of the association of multiple SORL1 variants with AD risk. However, few studies have focused on the association of SORL1 with the AAO of AD [12, 15]. This study explored the association of SORL1 with the AAO of AD in a Canadian sample by using the Kaplan-Meier survival analysis, the Cox proportional hazards model, and linear regression model.

2. Materials and Methods

2.1. Subjects

Seven hundred and ninety-one patients with AD and 782 controls with complete genotype and phenotype information in a Canadian sample were selected from the Multi-Site Collaborative Study for Genotype-Phenotype Associations in Alzheimer’s Disease and the Longitudinal Follow-Up of Genotype-Phenotype Associations in Alzheimer’s Disease and the Neuroimaging Component of Genotype-Phenotype Associations in Alzheimer’s Disease (dbGaP Study Accession: phs000219.v1.p1). The details about these subjects were described elsewhere [16, 17]. Genotyping was conducted using the Affymetrix 500 K Set comprising the Mapping250 K_Nsp and Mapping250 K_Sty Arrays (total 262,264 SNPs). We investigated the genetic associations of 43 single-nucleotide polymorphisms (SNPs) within the SORL1 gene with the AAO of AD.

2.2. Statistical Methods

Hardy-Weinberg equilibrium was tested for all SNPs using the controls by using Haploview software [18]. Then, minor allele frequency (MAF) was determined for each SNP. The assessment of the association between genotypes of each SNP and AAO was initially performed using the logrank test (a nonparametric test) in Kaplan-Meier (KM) survival analysis. The KM survival curves were used to plot the survival function. When a significant value was obtained in the KM testing, the Cox proportional hazards model (a semiparametric model) was used to confirm the findings of KM analysis. Hazard ratio (HR) and 95% CI were calculated with Cox proportional hazards analysis of SNPs. Descriptive statistics for AAO and survival analyses were conducted with SAS statistical software, version 9.2 (SAS Institute, Cary, NC, USA).

To test for association with AAO as a quantitative trait, linear regression analysis was performed by PLINK 1.07 [19] to obtain the regression coefficient () and Wald test asymptotic value. For statistical significance, empirical values were generated by 100,000 permutation tests using Max (T) permutation procedure implemented in PLINK. This procedure allows calculation of the pointwise estimates of an individual SNP’s significance (empirical pointwise values). Pairwise linkage disequilibrium (LD) statistics () were assessed for the controls using Haploview [18]. We identified haplotype blocks based on solid spine of LD using Haploview [18]. Then we chose several SNPs within blocks including the associated SNPs for further analyses. Haplotype analysis based on a slide-window was performed using PLINK. To perform gene-based analysis, we constructed SNP sets based on haplotpye blocks; then we perform set-based tests using PLINK.

3. Results

3.1. Subjects Characteristics

The demographic characteristics of the subjects in the study are detailed in Table 1. The mean AAO for the cases was 72.3 years (71.8 years for the males and 72.6 years for the females, resp.). The mean age at entry into study was 77.6 years for cases (77.5 years for the males and 78.2 years for the females, resp.) and 73.4 years for the controls.

Table 1: Descriptive characteristics of cases and controls.

Variable
Total patients
Male patients
Female patients
Control

Sample size
791
334
457
782

Mean AAO (years ± SD)
72.3 (±8.5)
71.8 (±8.3)
72.6 (±8.6)
NA

Median AAO (years)
73
73
73
NA

Range of age at onset (years)
40–97
44–90
40–97
NA

Mean age at entry (years ± SD)
77.6 (±8.6)
77.5 (±8.4)
78.2 (±8.7)
73.4 (±7.9)

Median age at entry (years)
79
79
79
79

Range of age at entry (years)
43–100
49–94
43–100
48–94

3.2. Survival Analyses

Of the 43 SNPs, none was associated with the AAO of AD in the whole sample using the logrank test. Interestingly, five SNPs were associated with the AAO of AD in the male sample, while one SNP was associated with the AAO of AD in the female sample (). The results of the top 6 associated SNPs based on the logrank test are summarized in Table 2. All 6 SNPs were in Hardy-Weinberg equilibrium in the controls. The most statistically significant associated SNPs were rs560573 () and rs593760 () in the male sample.

Table 2: Top 6 SNPs associated with the age at onset of Alzheimer’s disease using the logrank test.

SNP
Position
A1a
MAFb
HWEc
Logrank-alld
Logrank-malee
Logrank-femalef

rs1784934
121467284
C
0.36
0.35
0.288
0.040
0.96

rs676759
121488556
G
0.40
0.49
0.154
0.008
0.933

rs560573
121490175
T
0.39
0.59
0.176
0.0077
0.836

rs593769
121493294
C
0.39
0.84
0.152
0.0077
0.933

rs11218313
121512162
G
0.09
0.38
0.185
0.0317
0.89

rs17125558
121003094
T
0.03
0.71
0.0715
0.758
0.039

aMinor allele; bminor allele frequency; cHardy-Weinberg equilibrium value; d value was based on logrank test for the whole sample; e value was based on the logrank test for the males; f value was based on the logrank test for the females.

Cox proportional models further confirmed the associations of these 6 SNPs (Table 3). SNPs rs560573 and rs593769 showed the most association with AAO in the Cox model with HR = 1.47 (95% CI = 1.09–1.97). The mean AAO was approximately 2.5 years earlier for individuals (TT for rs560573 and CC for rs593769) who were homozygous for the minor allele compared with those (AA or AT for rs560573 and CT + TT for rs593769) who had at least one major allele. Figure 1 shows the survival function for different genotypes of SNP rs560573. The median of AAO was 71 years for individuals with TT genotype and 73 years for individuals with AA or AT genotypes.

Table 3: Top 6 SNPs associated with the age at onset of Alzheimer’s disease using the Cox proportional hazards model.

SNP
Male
Mean
HRa
95% CIb

Female
Mean
HR
95% CI

rs1784934

    AA
42
70.6
1.39
1.00–1.93
0.049
59
72.9
1.01
0.76–1.32
0.962

    AG + GG
292
72.0
1.00
398
73.5
1.00

rs676759

    GG
53
69.8
1.46
1.09–1.97
0.0121
65
73.4
0.99
0.76–1.29
0.937

    AA + AG
281
72.2

392
72.5
1.00

rs560573

    TT
53
69.7
1.47
1.09–1.97
0.0117
63
73.5
0.97
0.74–1.27
0.845

    AA + AT
281
72.2
1.00
394
72.4
1.00

rs593769

    CC
53
69.7
1.47
1.09–1.97
0.0117
65
73.4
0.99
0.76–1.29
0.937

    CT + TT
281
72.2
1.00
392
72.5
1.00

rs11218313

    AG + GG
45
74.2
0.72
0.53–0.99
0.0459
80
72.6
0.98
0.77–1.26
0.897

    AA
289
71.4
1.00
377
72.6
1.00

rs17125558

    CT + TT
14
71.4
1.08
0.63–1.85
0.772
25
70.1
1.50
1.00–2.25
0.0494

    CC
320
71.8
1.00
432
72.7
1.00

aHR refers to hazard ratio; bCI refers to confidence interval.

Figure 1: Survival function by genotype of rs560573 0 = TT Genotype, 1 = AA + AT Genotypes.

3.3. Linear Regression Analysis

The results of linear regression model are summarized in Table 4. Single-marker analysis showed that rs2282649 and rs726601 were associated with AAO in the whole sample ( and 0.0367, resp.) and rs11218313 was associated with AAO in the male sample (). For the rs11218313 (), the mean AAO in male was approximately 2.8 years earlier for the minor allele G compared to major allele. All 3 SNPs had empirical pointwise values < 0.05 using a permutation procedure (Table 4).

Table 4: Association results for 8 SNPs and the age at onset of Alzheimer’s disease using the linear regression model.

SNP
Position
A1a
-allb
-allc
EMP1d
-malee
-malef
EMP-maleg
-femaleh
-femalei
EMP-femalej

rs1784934
121467284
C
0.08
0.865
0.865
−0.71
0.296
0.295
0.65
0.261
0.258

rs676759
121488556
G
0.14
0.749
0.752
−0.78
0.240
0.242
0.85
0.144
0.147

rs560573
121490175
T
0.13
0.764
0.767
−0.77
0.238
0.24
0.85
0.148
0.15

rs593769
121493294
C
0.12
0.784
0.788
−0.79
0.223
0.225
0.83
0.152
0.157

rs11218313
121512162
G
0.96
0.225
0.225
2.82
0.0326
0.0318
−0.09
0.93
0.935

rs2282649
121608249
T
0.97
0.0394
0.0374
0.67
0.359
0.365
1.15
0.0636
0.0637

rs726601
121610698
T
0.96
0.0382
0.0367
0.77
0.287
0.296
1.06
0.083
0.0844

rs17125558
121003094
T
−1.78
0.648
0.653
−0.46
0.881
0.883
−2.61
0.67
0.657

aMinor allele; b refers to the regression coefficient of the linear regression for all patients; c value refers to the Wald test asymptotic value for all patients; dempirical value for all patients generated by 100,000 permutation tests using Max (T) permutation procedure; e refers to the regression coefficient of the linear regression for male patients; f value refers to the Wald test asymptotic value for male patients; gempirical value for male patients generated by 100,000 permutation tests using Max (T) permutation procedure; h refers to the regression coefficient of the linear regression for female patients; i value refers to the Wald test asymptotic value for female patients; jempirical value for female patients generated by 100,000 permutation tests using Max (T) permutation procedure.

Using Haploview software, we identified 4 haplotype blocks for 43 SNPs. Haplotype analyses for the whole sample (Table 5) showed that the T-T haplotype from rs2282648 and rs2282649 was associated with AAO (), and the T-T haplotype from rs2282649 and rs726601 () was associated with AAO (). Furthermore, haplotype analyses for the male sample (Table 6) showed that the C-G haplotype from rs668387 and rs12218313 (D ′ = 1) was associated with AAO (), and the C-G haplotype from rs1218313 and rs626885 (D ′ = 0.91) was associated with AAO ().

Table 5: Haplotype analysis for the whole sample.

Haplotype
Frequencya
b
valuec

rs2282648
rs2282649

T
T
0.30
0.97
0.0394

T
C
0.02
−0.48
0.744

C
C
0.68
−0.89
0.0554

rs2282649
rs726601

T
T
0.29
1.03
0.0294

C
T
0.02
−0.30
0.842

C
C
0.69
−0.91
0.0502

aHaplotype frequency in the sample.

b refers to the regression coefficient of the linear regression.

c value for the haplotype.

Table 6: Haplotype analysis for the male sample.

Haplotype
Frequencya
b
valuec

rs668387
rs11218313

C
G
0.08
2.82
0.0326

T
A
0.45
−0.28
0.66

C
A
0.47
−0.35
0.51

rs11218313
rs626885

A
C
0.43
−0.09
0.878

C
G
0.07
2.82
0.0326

A
G
0.5
−0.56
0.38

aHaplotype frequency in the sample.

b refers to the regression coefficient of the linear regression.

c value for the haplotype.

Based on the 4 haplotype blocks, we built 4 set SNPs. We performed set-based tests using PLINK for the whole sample and also for the male sample. We did not find any significant associations for the set-based analyses (data not shown).

4. Discussion

In the present study we explored the association of 43 SNPs in the SORL1 gene with the AAO of AD by using the Kaplan-Meier survival analysis, the Cox proportional hazard model, and the linear regression model. Using the Kaplan-Meier survival analysis and Cox proportional hazard we found that five SNPs were significantly associated with the AAO of AD in the male sample and one SNP was associated with the AAO of AD in the female sample (). Linear regression model showed that two SNPs (rs2282649 and rs726601) were associated with AAO in the whole sample. SNP rs11218313 was associated with AAO in the male sample using logrank test and Cox model was also associated with AAO in the male sample in the linear model. Haplotype analyses in the linear model further supported the associations of single-marker analyses.

It has been reported that SORL1 gene is consistently downregulated approximately 2-fold in lymphoblasts from patients with AD, and the structure and function of SORL1 as a mosaic ApoE receptor strongly suggest that it plays a role in AD [20]. Sager et al. [21] also found a correlation between decreased SORL1 expression and impaired cognitive function among 34 individuals, including 10 patients with AD, 15 with mild cognitive impairment, and 9 controls, suggesting that decreased SORL1 expression reflects cognitive performance and may predispose individuals with mild cognitive impairment to the development of AD.

Several previous studies have reported that SORL1 gene is associated with the risk of AD [11, 12, 14]; however, few studies have focused on the association of SORL1 with the AAO of AD. Lee et al. [12] examined the associations of 7 SNPs in SORL1 gene with the AAO of AD and found that 2 SNPs (rs536360 and rs556349) were associated with the AAO of AD. Another study [15] investigated the association of 4 SORL1 genetic variants (rs2070045, SORL1-18ex26, rs3824968, and rs1010159) with the AAO of AD and found that SORL1-18ex26 was associated with an earlier AAO of AD in a German population. In the present study, of the 43 SNPs, none was associated with the AAO of AD in the whole sample using the logrank test; however, two SNPs were associated with AAO in the whole sample in the linear model. Furthermore, we found that five SNPs (rs1784934, rs676759, rs560573, rs593769, and rs11218313) were statistically significantly associated with the AAO of AD in the male sample, and one SNP (rs17125558) was associated with the AAO of AD in the female sample () using survival analyses. Several studies examined rs560573 with the risk of AD [11, 12, 14, 22, 23] and one study found that rs560573 was associated with the risk of AD [23]. Previous studies did not find the associations of other SNPs (rs676759, rs593769, rs11218313, and rs17125558) with the risk of AD [22].

There are a number of strengths in this study. First, our sample size was relatively large for this type of study. Second, we used the logrank test to examine the association with the AAO and further confirmed the results using the Cox proportional model. Furthermore, we conducted linear regression analyses. In addition, we detected gender differences. We also realized some limitations in this study. First, our current findings might be spurious or subject to type I error. Second, these findings need to be replicated in additional samples.

5. Conclusions

Our results demonstrate that genetic variants in SORL1 gene are associated with the AAO of AD. These findings may serve as a resource for replication in other populations. Future functional study of this gene may help to better characterize the genetic architecture of the AAO of AD.

Conflict of Interests

The authors declare that there is no conflict of interests regarding the publication of this paper.

Acknowledgments

We acknowledge the NIH GWAS Data Repository, the Contributing Investigator(s) who contributed the phenotype data and DNA samples from his/her original study, and the primary funding organization that supported the contributing study “Multi-Site Collaborative Study for Genotype-Phenotype Associations in Alzheimer’s Disease and Longitudinal Follow-Up of Genotype-Phenotype Associations in Alzheimer’s Disease and Neuroimaging Component of Genotype-Phenotype Associations in Alzheimer’s Disease.” The genotypic and associated phenotypic data used in the study, “Multi-Site Collaborative Study for Genotype-Phenotype Associations in Alzheimer’s Disease (GenADA)” were provided by the GlaxoSmithKline, R&D Limited. The datasets used for analyses described in this paper were obtained from dbGaP at http://www.ncbi.nlm.nih.gov/gap through dbGaP Accession no. phs000219.v1.p1.

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