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Published in final edited form as: J Alzheimers Dis. 2015;45(2):651–658. doi: 10.3233/JAD-142442

Genetic determinants of survival in patients with Alzheimer’s disease

Xingbin Wang 1,2, Oscar Lopez 3,4, Robert A Sweet 3,4,5,7, James T Becker 3,4, Steven T DeKosky 6, Mahmud M Barmada 1, Eleanor Feingold 1,2, F Yesim Demirci 1, M Ilyas Kamboh 1,4,5
PMCID: PMC4486643  NIHMSID: NIHMS657115  PMID: 25649651

Abstract

There is a strong genetic basis for late-onset of Alzheimer's disease (LOAD) and thus far >20 genes/loci have been identified that affect the risk of LOAD. In addition to disease risk, genetic variation at these loci may also affect components of the natural history of AD, such as survival in AD. In this study, we first examined the role of known LOAD genes with survival time in 983 AD patients. We then performed genome-wide single-nucleotide polymorphism (SNP) and gene-based association analyses to identify novel loci that may influence survival of AD. Survival analysis was conducted using Cox proportional hazards regression under an additive genetics model. We found multiple nominally significant associations (P<0.01) either within or adjacent to known LOAD genes. Genome-wide SNP analysis identified multiple suggestive novel loci and two of them were also significant in gene-based analysis (CCDC85C and NARS2) that survived after controlling for false-discovery rate (FDR) at 0.05. In summary, we have identified two novel genes for survival in AD that need to be replicated in independent samples. Our findings highlight the importance of focusing on AD-related phenotypes that may help to identify additional genes relevant to AD.

Keywords: LOAD, GWAS, Survival, Gene-Based Analysis

Introduction

Alzheimer’s disease (AD), especially the late-onset form (LOAD), is a complex multifactorial neurodegenerative disease and the leading cause of dementia among the elderly [1]. Currently, there are approximately 5 million AD cases in the United States, and about 81.1 million cases worldwide [2]. Due to its long clinical course, AD is a major public health problem. Genetic susceptibility at multiple genes and interactions among them and/or environmental factors likely influence the risk of AD, which has a strong genetic basis with heritability estimates up to 80% [3]. Previous studies have shown that the mean survival in patients with AD ranges from 4 to 9 years [4–18]. While demographic, clinical and environmental factors associated with survival in AD patients have been implicated [19–21], the role of genetic factors affecting survival in AD has not been explored extensively. The effect of APOE*4 allele of APOE on survival in AD has been explored in previous studies [22–26]; however, results have not been conclusive. To date, genome-wide association studies (GWAS) have identified >20 additional susceptibility loci, including BIN1, INPP5D, MEF2C, CD2AP,HLA-DRB1/ HLA-DRB5,TREM2,EPHA1,NME8, ZCWPW1, CLU, PTK2B, CELF1, MS4A6A/MS4A4E, PICALM, SORL1, FERMT2, SLC24A4/ RIN3, DSG2, ABCA7, CD33,TRIP4,TP53INP1, IGHV1-67 and CASS4 [27–34]. In addition to AD risk, genetic variations at these loci may also affect the survival in AD. We have tested this hypothesis by examining the role of known LOAD genes in AD survival. In addition, studies focusing on AD-related phenotypes, like survival in AD, may help to identify additional AD-relevant genes [35] Thus, we have also examined a genome-wide data set to determine if genomic regions contain novel loci associated with AD survival. We have performed genome-wide single-nucleotide polymorphism (SNP) and gene-based association analyses and have identified two novel loci for AD survival.

Materials and Methods

Subjects

The AD patients were recruited from Alzheimer’s Research Program (ARP; 1983–1988) and the Alzheimer’s Disease Research Center (ADRC) at the University of Pittsburgh (1988 to present). A total of 1,886 Probable AD patients were examined between April 1983 and December 2005; details of the cohort are described elsewhere [36]. All subjects received an extensive neuropsychiatric evaluation including medical history and physical examination, neurological history and examination, semi-structured psychiatric interview, neuroimaging, and neuropsychological assessment. Among these patients, 983 had available follow-up data and they are included in this study. Table 1 shows the demographic and clinical characteristics of these 983 patients. The overall survival time was calculated from the study entry.

Table 1.

Demographic and Clinical Characteristics of 983 AD Patients

Age at onset (mean ± SD) 72.2 ± 6.6
Age at entry (mean ± SD) 77.3± 6.2
Education (mean SD) 12.6 ± 3.0
MMSE (mean SD) 18.1 ± 5.5
Gender (male/female) 346/637
Psychosis (Yes/No) 426/567
Diabetes Mellitus (Yes/No) 89/894
Heart disease (Yes/No) 200/783
Medication (Yes/No) 629/354
Depression (Yes/No) 182/801
Hypertension (Yes/No) 477/506
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Genotyping and quality control of genotype data

Samples were genotyped using the Illumina Omni1-Quad chip as described previously [37, 38]. SNPs with call rate <98% and minor allele frequency (MAF<1%), and failing to adhere to the Hardy-Weinberg equilibrium (HWE) test (P<1E-06) were removed. Genotypes for two APOE SNPs, rs429358 (E*4) and rs7412 (E*2) were determined either as previously described [39] or using TaqMan SNP genotyping assays. For GWAS, a total of 803,323 QC-passed SNPs were selected for analysis.

Statistical analysis

The proportional hazard model was used to examine the risks associated with time to death. The survival analysis was conducted using Cox proportional hazards regression under an additive genetics model with adjustments for baseline MMSE score, gender, psychosis (the presence or absence of psychotic symptoms; psychosis was included as a time-dependent covariate), education level and the first four principle components. Single SNP association analyses were performed first in candidate genes and then on the genome-wide data. We also performed the versatile gene-based association (VEGA) analysis [40], which incorporates information from a full set of SNPs within a gene region and accounts for linkage disequilibrium (LD) between SNPs and it may provide more power than single SNP analysis to detect novel associations[33]. Survival analyses were done in R. We applied Benjamini-Hochberg procedure to control false-discovery rate (FDR) for multiple testing correction[41] and used a FDR threshold at 0.05.

Results

Demographic and Clinical Risk factors of AD Survival

The base-line MMSE scores were strongly associated with time to death with hazard ratios (HR) of 0.95 (95% CI: 0.93 to 0.96, P=1.09E-10, Table 2), indicating that AD patients with a higher MMSE score at baseline would have a better survival than patients with a lower baseline MMSE score. Females had a longer survival than males (HR=0.72, 95% CI: 0.60 to 0.86, P=2.76E-04, Table 2). AD patients who had diabetes (HR=1.41, 95% CI: 1.04 to 1.91; P=2.73E-02) and experienced more psychotic symptoms (psychosis) (HR=1.37, 95% CI: 1.14 to 1.65, P=7.63E-04) had a shorter survival than patients without diabetes and psychosis. Age, education level, blood pressure, heart disease, and depression did not relate to survival (Table 2).

Table 2.

Results of the Proportional Hazard Model Examining Risks Associated with AD survival

Hazard Ratio (95% CI) z P
MMSE 0.95 (0.93, 0.96) −6.45414 1.09E-10
Gender (female/male) 0.72 (0.60, 0.86) −3.63687 2.76E-04
Psychosis (yes/no) 1.37 (1.14, 1.65) 3.365772 7.63E-04
Diabetes Mellitus (yes/no) 1.41 (1.04, 1.91) 2.207311 2.73E-02
Heart disease (yes/no) 1.18 (0.96, 1.46) 1.577429 1.15E-01
Medication (yes/no) 0.88 (0.74, 1.05) −1.39636 1.63E-01
Depression (yes/no) 1.11 (0.88, 1.40) 0.873664 3.82E-01
Hypertension (yes/no) 1.06 (0.89, 1.27) 0.67541 4.99E-01
Education (years) 1.01 (0.98, 1.04) 0.534399 5.93E-01
Age (years) 1.00 (0.98, 1.01) 0.028572 9.77E-01
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*

Age: patients’ age at entry; MMSE: the mean Mini-Mental state examination scores; Education: the years of getting education; Medication: taking any cholinesterase inhibitor (AChEI) treatment or not; psychosis: the presence or absence of psychotic symptom; Z: the log-rank test scores; P: P-values associated with testing the effect of the variables

Single SNP association analysis in known LOAD genes

We first examined the associations of AD survival with genetic variations in known 27 LOAD genes and the results are presented in Table 3. SNPs in 7 genes (BIN1, INPP5D, HLA-DRB1, RIN3, TRIP4, APOE and CASS4) were associated with AD survival at P<0.01. While SNPs in 4 genes were associated with shorter survival (BIN1/rs12476995, HR=1.25, P=4.75E-04; INPP5D/rs3792117, HR=1.55, P=4.53E-04 RIN3/rs7160605, HR=1.35, P=4.05E-03, and TRIP4/rs1163552, HR=1.46, P=6.61E-03), SNPs in 3 genes were associated with longer survival (HLA-DRB1/rs9392025, HR=0.83, P=2.76E-03, APOE/rs429358, HR=0.83, P=5.02E-03 and CASS4/rs2426622, HR=0.83; P=4.61E-03). However, none of these associations were significant after controlling for FDR at 0.05.

Table 3.

Results of Association Analysis Between LOAD Genes and AD Survival

CHR Gene Start Stop Total SNPs Lead SNP BP Allele MAF HR P SNPs
(P<0.05)
2 BIN1 127522076 127581334 34 rs12476995 127512490 G 0.20 1.25 4.75E-04 31
2 INPP5D 233633279 233781288 60 rs3792117 233780076 A 0.03 1.55 4.53E-04 8
5 MEF2C 88051921 88214780 29 rs11951031 88174487 A 0.14 0.72 2.82E-02 2
6 CD2AP 47553483 47702955 23 rs3818866 47657588 C 0.29 1.09 2.30E-01 0
6 HLA-DRB1 32654524 32665540 19 rs9392025 3924750 A 0.05 0.83 2.76E-03 7
6 HLA-DRB5 32593131 32605984 2 SNP6-32605940 32605940 G 0.18 0.86 1.32E-01 0
6 TREM2 41234223 41238900 5 rs12202176 41246027 A 0.23 1.19 4.84E-01 0
7 EPHA1 142798326 142816107 21 SNP7-142790182 142790182 A 0.18 0.72 8.94E-02 0
7 NME8 37854723 37906527 59 rs17171184 37867527 G 0.26 0.84 2.57E-02 7
7 ZCWPW1 99836430 99864238 12 rs5015756 99851393 A 0.24 1.06 3.38E-01 0
8 CLU 27510367 27528244 15 rs12680584 27502941 A 0.20 0.88 1.03E-01 0
8 PTK2B 27224915 27372820 99 rs7833348 27227403 A 0.05 0.66 2.50E-02 1
8 TP53INP1 96007375 96030791 14 rs524678 96033180 A 0.06 1.24 8.20E-02 0
11 CELF1 47444064 47531368 10 rs4752845 47496273 A 0.07 0.87 5.37E-02 0
11 MS4A4E 59725302 59767151 10 rs668134 59756902 G 0.31 0.84 0.179737 0
11 MS4A6A 59695655 59707250 6 rs610932 59695883 A 0.05 0.92 1.99E-01 0
11 PICALM 85346132 85457756 28 rs10501605 85421021 G 0.06 0.89 1.12E-01 0
11 SORL1 120828170 121009681 61 rs1784933 120994626 A 0.24 1.34 3.67E-02 1
14 FERMT2 52393742 52487460 27 rs11157934 52435566 A 0.12 1.06 3.48E-01 0
14 RIN3 92049877 92225087 75 rs7160605 92063089 C 0.10 1.35 4.05E-03 3
17 SLC24A4 7125777 7132091 12 rs222842 7132815 G 0.26 1.09 2.01E-01 0
15 TRIP4 62467055 62534555 11 rs11635527 62462366 G 0.05 1.46 6.61E-03 6
18 DSG2 27332024 27382812 30 rs7239805 27322066 A 0.05 0.90 1.07E-01 0
19 ABCA7 991101 1016570 32 rs2242436 1016945 A 0.18 0.84 4.41E-02 1
19 APOE 50100878 50104490 20 rs429358 50103781 A 0.14 0.83 5.02E-03 6
19 CD33 56420146 56435086 10 rs3865444 56419774 A 0.05 1.09 2.06E-01 0
20 CASS4 54420770 54467243 28 rs2426622 54410913 G 0.19 0.83 4.61E-03 1
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*

CHR: chromosome; Gene: candidate gene associated with AD; Start: the coordinate of the beginning of gene; Stop: the coordinate of the end of the gene; Total SNPs: the total number of the SNPs in the gene region; Lead SNP: most significant SNP associated with time to death in the gene region; Minor Allele: the minor allele; MAF: the frequency of minor allele; SNPs (P<0.05): total number of SNPs associated with time to death with P < 5E-02 in the gene region; HR: Hazard ratio; P: p-values associated with testing the effect of lead SNP.

Genome-wide single SNP association analysis

Next we examined our genome-wide data in order to identify potential novel loci for survival in AD patients. Quantile-quantile plot of the observed and expected P values is shown in Supplementary Figure 1, and the Manhattan plot showing association signals is presented in Supplementary Figure 2. In the genome-wide SNP analysis, the genetic inflation factors with and without principal components as covariates, were 1.021 and 1.023, respectively, which indicates that population stratification did not inflate the significance of the test statistics in our data. The top SNP, rs2243170 (P=6.62E-07), was located in the intronic region of IL19 on chromosome 1. There were 4 additional SNPs with P<0.05 in this region (Table 4). Supplementary Figure 3 shows the Linkage disequilibrium (LD) structure of IL19. The other top SNPs were, NCKAP5/rs7588354 (P=1.37E-06) on chromosome 2, CCDC85C/rs2400749 (P=2.25E-06) on chromosome 14, NARS2/rs4474465 (P=3.41E-06) on chromosome 11, PKNOX2/rs11601321 (P=8.03E-06) on chromosome 11, SDR9C7/rs840163 (P=2.42E-06) on chromosome 12, and ALDH4A1/rs6695033 (P=3.245E-06) on chromosome 1(Table 4). The regional association plots containing SNPs within 500kb on either side of the top SNP in the top loci are shown in Supplementary Figures 4–10.

Table 4.

Novel top loci in Genome-Wide Single SNP and Gene-Based analyses Associated with AD Survival (P<1.00E-05).

Single locus analysis Gene-based analysis
CHR Gene Start Stop Total SNPs Lead SNP BP Minor
Allele		 MAF HR P SNPs
(P<0.05)		 Test P FDR
1 IL19 205038837 205082948 24 rs2243170 205076533 A 0.10 1.65 6.62E-07 5 88.4654 3.76E-03 0.29
2 NCKAP5 133145841 134042501 282 rs7588354 134049231 A 0.21 0.66 1.37E-06 31 387.7963 7.46E-02 0.73
14 CCDC85C 99047355 99140480 35 rs2400749 99106771 G 0.46 0.72 2.25E-06 15 220.2165 4.00E-06 0.03
12 SDR9C7 55603204 55614456 15 rs840163 55606978 G 0.26 1.40 2.42E-06 10 95.3635 1.38E-03 0.14
11 NARS2 77824890 77963367 26 rs4474465 77882028 G 0.20 1.44 3.41E-06 22 421.4499 4.00E-06 0.03
1 ALDH4A1 19070512 19101659 41 rs6695033 19074506 G 0.03 2.71 3.24E-06 3 71.0355 9.30E-02 0.76
11 PKNOX2 124539768 124808495 125 rs11601321 124698806 G 0.07 1.63 8.03E-06 9 177.1226 1.12E-01 0.77
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Genome-wide gene-based association analysis

Supplementary Figure 11 presents the Manhattan plot of the gene-based P-values. We identified two genes which remained significant after controlling for FDR at 0.05: CCDC85C (P=4.00E-06, FDR=0.03) and NARS2 (P=4.00E-06, FDR=0.03). Interestingly, these two genes are also suggestive loci in genome-wide SNP analysis (see Table 4). Supplementary Figures 12–13 show the LD structure of these two genes. Gene-based analysis also identified ANLN as a suggestive gene (P=8.20E-05, FDR=0.13), but this was not among the top hits in genome-wide SNP analysis (ANLN/rs2392436, P=1.76E-04).

Discussion

In this study we have used a recently described GWAS data of LOAD to examine: 1) if variants in known LOAD genes are associated with survival in AD patients, 2) if additional novel variants in the genome also affect AD survival, irrespective if they are genome-wide significant or not, for hypothesis generation, and 3) if gene-wide analysis provides better power than genome-wide SNP analysis to detect new genes for AD survival. In our primary analysis, we observed 7 significant associations at P<0.01 with survival in AD in known LOAD genes (BIN1, INPP5D, HLA-DRB, RIN3, TRIP4, CASS4 and APOE). These data suggest that at least some of the known LOAD genes may be relevant to affecting survival in AD patients; however, none of these associations remained significant after controlling for FDR at 0.05. Previously, the effect of APOE*4 on survival of AD has been explored in several studies, however, the results were conflicting. Male APOE*4 carriers were found to have a shorter survival than non-carriers but not in females[22], while others found longer survival among APOE*4 carriers [23, 42]. However, in other studies APOE*4 was not found to be related to survival of AD [25, 26]. We found that APOE*4 was significantly associated with longer survival (P=5.02E-03, HR=0.83) in our sample after adjusting for sex, baseline MMSE score, education level and psychosis. A previous study has suggested that the processes increasing the risk of AD may differ from those that determine its clinical course [43]. Thus, although the presence of APOE*4 increases the risk of AD, it may have a different effect on survival among AD patients. This seems to be a plausible interpretation of our findings in which APOE*4 was associated with longer survival among AD patients.

In our genome-wide SNP analysis, we identified multiple novel suggestive loci associated with AD survival, including the top hit at P=6.62E-07 (IL19 on chromosome 1) and 6 loci at P<1E-05 (CCDC85C on chromosome 14 NARS2 on chromosome 11, NCKAP5 on chromosome 2, PKNOX2 on chromosome 11, SDR9C7 on chromosome 12, and ALDH4A1 on chromosome 1). Although these loci are not genome-wide significant and wait confirmation in future studies, we believe they provide insight for future studies as many of them may affect survival through their known associations with AD and other diseases. Especially the top hit, IL19 is a cytokine that belongs to the IL10 cytokine subfamily, which may modulate AD progression[44]. A systemic meta-analysis has suggested an association between IL110 polymorphisms and AD [45, 46]. Furthermore, significant associations between AD and IL10 polymorphism have been reported in Chinese and Italian populations [47, 48]. Polymorphisms in the IL10 gene cluster have also been found to be involved with major depressive disorder [49].

Interestingly, in the genome-wide gene-based analysis, two of these suggestive genes (CCDC85C and NARS2) were gene-wide significance at FDR <0.05. This indicates that gene-based analysis provides additional useful information not captured in the genome-wide SNP analysis, as has also been recently shown in another study [33]. The two genes are biologically relevant to AD. The CCDC85C (coiled-coil domain containing 85C) gene is highly expressed in different brain regions and may play important role in cortical development, especially in the 7 maintenance of radial glia [50]. NARS2 is located immediately adjacent to the GAB2 gene, which has been previously reported to be associated with AD [51]. The NARS2 and GAB2 genes occur in a strong LD block that spans the full lengths of both transcripts.

Our study has few limitations. First, inferences from our results must be interpreted with caution because the actual cause of death was not investigated. Second, the procedure of disease death is very complex and many unknown demographic and clinical variables not included in this study could have confounded our results. Although our gene-based analysis has identified two novel genes that remained significant after controlling for FDR at <0.05, further studies in large and independent samples are needed to replicate these findings.

In summary, our results indicate that genetic variation in some genes associated with LOAD risk may also affect survival of AD. Our study also implicates two novel loci as possible genetic regions associated with overall survival among AD patients. Additional large independent studies are needed to confirm our findings and to further establish the genetic basis of survival in AD.

Supplementary Material

Supplementary data

Acknowledgments

This study was supported by the National Institutes of Health grants AG030653, AG041718, AG005133 and AG027224. The content is solely the responsibility of the authors and does not necessarily represent the official views of the Department of Veterans Affairs, the National Institutes of Health, or the United States Government.

Footnotes

Conflict of Interest

The authors declare no conflict of interest.

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