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. Author manuscript; available in PMC: 2013 Jun 1.
Published in final edited form as: Mol Psychiatry. 2011 Oct 18;17(12):1340–1346. doi: 10.1038/mp.2011.135

Genome-wide association analysis of age-at-onset in Alzheimer’s disease

M Ilyas Kamboh 1, M Michael Barmada 1, F Yesim Demirci 1, Ryan L Minster 1, Minerva M Carrasquillo 2, V Shane Pankratz 2, Steven G Younkin 2, Andrew J Saykin 3; The Alzheimer’s Disease Neuroimaging Initiative*, Robert A Sweet 4,6, Eleanor Feingold 1, Steven T DeKosky 5, Oscar L Lopez 6
PMCID: PMC3262952  NIHMSID: NIHMS319080  PMID: 22005931

Abstract

The risk of Alzheimer’s disease (AD) is strongly determined by genetic factors and recent genome-wide association studies (GWAS) have identified several genes for the disease risk. In addition to the disease risk, age-at-onset (AAO) of AD has also strong genetic component with an estimated heritability of 42%. Identification of AAO genes may help to understand the biological mechanisms that regulate the onset of the disease. Here we report the first GWAS focused on identifying genes for the AAO of AD. We performed a genome-wide meta analysis on 3 samples comprising a total of 2,222 AD cases. A total of ~2.5 million directly genotyped or imputed SNPs were analyzed in relation to AAO of AD. As expected, the most significant associations were observed in the APOE region on chromosome 19 where several SNPs surpassed the conservative genome-wide significant threshold (P<5E-08). The most significant SNP outside the APOE region was located in the DCHS2 gene on chromosome 4q31.3 (rs1466662; P=4.95E-07). There were 19 additional significant SNPs in this region at P<1E-04 and the DCHS2 gene is expressed in the cerebral cortex and thus is a potential candidate for affecting AAO in AD. These findings need to be confirmed in additional well-powered samples.

Keywords: Genome-wide association study, age-at-onset, Alzheimer’s disease, single-nucleotide polymorphisms, meta analysis

Introduction

Alzheimer’s disease (AD), a devastating neurodegenerative disease, is the most common form of dementia among the elderly. Genetically, AD is a complex and multifactorial disease with the possible involvement of multiple genes. The rare early-onset form of the disease usually follows an autosomal dominant inheritance pattern and to date three genes have been identified: amyloid precursor protein (APP) and presenilin 1 and 2 (PSEN1, PSEN2). The common late-onset form of the disease is much more complex than the early-onset form and until recently the apolipoprotein E (APOE) gene was the only major genetic factor accounting for 20–29% of the risk for late-onset AD.1–2 Recent large genome-wide association studies have identified nine additional genes for late-onset AD, including CR1, BIN1, CLU (a.k.a. APOJ), PICALM, MS4A4/MS4A6E, CD2AP, CD33, EPHA1 and ABCA7.3–7 There is high heritability for AD risk (up to 80%),8 but the total risk attributable to all confirmed loci is about 50%, indicating the presence of additional risk genes for late-onset AD.

In addition to the disease risk, age-at-onset (AAO) of AD is also genetically influenced9–10 with an estimated heritability of about 42%.10 The APOE gene, in addition to affecting the risk of AD, also has a significant impact on AAO of AD and explains about 10% of its variation.1 Several additional loci with effect sizes similar to or even greater than that of APOE have been suggested.9 However, some additional genes implicated in AAO of AD have effect sizes far less than the effect of APOE.11–12 Recently a genome-wide association study (GWAS) implicated a locus on chromosome 14 with AAO of AD,13 but it was not confirmed in a replication study.14 In this study we have used GWAS data from three independent samples in an effort to identify additional AAO loci in AD. The initial association analysis was performed in a new GWAS data derived from the University of Pittsburgh Alzheimer’s Disease Research Center (ADRC) which was subsequently combined with two existing GWAS data from Mayo15 and the Alzheimer’s Disease Neuroimaging Initiative (ADNI)16 for meta analysis.

Materials and Methods

University of Pittsburgh ADRC Sample

Genomic DNA from 1,440 AD cases was genotyped using the Illumina HumanOmni1-Quad as part of the stage 1 discovery sample. All subjects were Caucasian Americans. AD cases (mean age-at-onset [AAO] 72.8 ± 6.5 years; 62.9% women; 23.5% autopsy- confirmed) were derived from the University of Pittsburgh ADRC, all of whom met the National Institute of Neurological and Communication Disorders and Stroke (NINCDS) and Alzheimer’s Disease and Related Disorders Association (ADRDA) criteria for probable or definite AD. The University of Pittsburgh ADRC follows a standard evaluation protocol, including medical history, general medical and neurological examinations, psychiatric interview, neuropsychological testing and MRI scan. All subjects were recruited with informed consent, and the study was approved by the University of Pittsburgh Institutional Review Board.

Mayo Sample

The Mayo AD GWAS comprised 844 AD cases between the ages of 60–80 (mean AAO: 74 ± 4.8 years; 57.2 % women) which were previously genotyped using the Illumina HumanHap300 BeadChip.15 AD diagnosis was established using the NINCDS-ADRDA criteria.

The Alzheimer’s Disease Neuroimaging Initiative (ADNI) Sample

AD GWAS dataset consists of 188 AD cases with a clinical diagnosis of AD at baseline visit that were genotyped using the Illumina 610- Quad BeadChip.16 AD cases were between the ages of 55–90 (mean AAO: 71.9 ± 8.1 years; 44.6% women) and met the NINCDS-ADRDA criteria. Details of the clinical evaluation and sample characterization are described elsewhere.17–18 The ADNI data used in this report were obtained from the ADNI database (adni.loni.ucla.edu). The initial goal of ADNI was to recruit 800 adults, ages 55 to 90, to participate in research on the sensitivity and specificity of neuroimaging and other biomarkers for detecting and monitoring AD pathology in vivo. In ADNI, approximately 200 cognitively normal older individuals and 400 people with amnestic MCI were followed for 3 years and 200 people with mild early stage AD followed for 2 years. For up-to-date information, see www.adni-info.org.

Genotyping and Quality Control of Genotype Data

The University of Pittsburgh ADRC sample of 1,440 AD cases was genotyped using the Illumina Omni1-Quad chip (containing probes for 1,016,423 SNPs and/or copy-number variations) at the Feinstein Institute of Medical Research (Manhasset, NY). Genotypes for two APOE SNPs, rs429358 (E*4) and rs7412 (E*2) were determined either as previously described19 or using TaqMan SNP genotyping assays. Exclusion criteria for individual samples included high genotype failure rate (106 individuals were removed because of a genotype failure rate >2%), and cryptic relatedness (43 individuals were removed because they displayed an average degree of sharing (identity by state, or IBS) > 0.4 with other members of the data set). An additional 101 cases that passed the stringent quality control criteria were excluded because of the uncertainty in AAO. Exclusion criteria for markers included minor allele frequency (189,727 SNPs were removed because of MAF <1%), deviation from Hardy-Weinberg expectations (2,239 SNPs gave a HWE test P-value <= 1E-06), and high genotype failure rates (22,385 SNPs were removed because of genotype failure rates >2%).The final ADRC sample after all exclusions consisted of 1,190 cases genotyped at 803,322 SNPs.

Population stratification

Population stratification testing was done using a multi-dimensional scaling based method using all SNPs, as implemented in PLINK.20 Four components were conservatively determined to be relevant to the determination of population origin based on visual examination of principle component plots.

Imputation

Genotype posterior probabilities were imputed with MACH v.1.0, on all three GWAS data using haplotypes from the HapMap CEU v3 data release as a reference sample. The imputation generated data for >3 million SNPs that were subsequently filtered to exclude SNPs with r2<0.3 and eventually 2,543,888 were included in the final analysis.

Association analyses

SNP analyses were conducted using a linear regression framework implemented in PLINK20, using covariates of sex, and the four principle components of population stratification identified above. A Bonferroni adjusted significance level of P<5E-08 was employed to determine genome-wide significance following meta analysis. Significance values from linear regression analyses were used for ranking purposes only, and so were not adjusted for multiple testing. After performing analyses for each SNP in each study sample individually, a meta analysis was performed to obtain pooled estimates of the effect of these SNPs on AAO of AD across all study groups. Meta-analysis was done using a fixed-effects methodology, as implemented in PLINK. Heterogeneity testing was accomplished using Cochran’s Q statistic, summarized as the I2 statistic (the percentage of total variation across studies that is due to heterogeneity rather than chance).21–22 All analyses were done in R and/or PLINK using methods that correctly employ the imputed posterior probabilities for all genotypes at each SNP.

Results

Initially we performed association analysis of AAO in our new GWAS data on the University of Pittsburgh ADRC sample where a total of 1,440 AD cases were genotyped using the Illumina Human Omni-Quad1. After standard quality control filters for both genotypes and samples and imputing for unobserved genotypes, a total of ~2.5 million SNPs were examined in 1,190 AD cases for association analysis. Association of SNPs with AAO was tested using linear regression of the quantitative phenotype that included sex and the first four principal components as covariates.

Figure 1A shows the quantile-quantile plot for comparison of observed and expected P-values distribution in the ADRC sample that demonstrates no evidence of significant population stratification but strong evidence of genetic associations. Figure 2 shows the genome-wide P-values in the ADRC sample in a Manhattan plot. As expected, the most significant associations were observed in the APOE region on chromosome 19 where five SNPs surpassed the genome-wide significant threshold of P<5E-08 in the ADRC sample. The most significant SNP was rs429358 (E*4) in the APOE gene (P=3.37E-18) followed by rs6857 in TOMM40 (P=4.85E-15) rs4420638 in APOC1 (P=4.14E-14), rs157582 in TOMM40 (P=1.91E-13) and rs2075650 in TOMM40 (P=2.17E-10). The regional association plot including all SNPs in the APOE region is given in Supplementary Figure 1. After removing SNPs in the APOE region, a deviation of P-values from the null distribution remained in the quantile-quantile plot, although within the 95% confidence interval of the expectation (Fig. 1B). Since no other SNPs outside the APOE region were genome-wide significant, we performed a meta analysis by combining the University of Pittsburgh ADRC GWAS data with the Mayo (n=844) and ADNI (n=188) GWAS data. Figure 3 shows the distribution of AAO in the three samples. While the AAO distribution is almost normal in the ADRC and ADNI samples, it is skewed towards older AAO. The quantile-quantile plot with the meta analysis results is shown in Supplementary Figure 2. The meta analysis of 3 samples yielded eleven non-APOE loci with P<1E-05 (Table 1). All SNPs with P<1E-04 in the meta analysis are presented in the Supplementary Table.

Figure 1.

Figure 1

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Quantile-quantile plots showing the observed versus the expected P-values in the University of Pittsburgh ADRC sample including all SNPs (A) and after removing SNPs in the APOE region (B). The red line shows the distribution under the null-hypothesis.

Figure 2.

Figure 2

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Manhattan plots showing the genome-wide P-values in the University of Pittsburgh ADRC sample after adjusting for sex and principle components. Red line indicates genome-wide significant level (P< 5E-08) and blue line indicates suggestive associations (P<1E-04) in the ADRC sample.

Figure 3.

Figure 3

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Distribution of age-at-onset in the University of Pittsburgh ADRC, Mayo and ADNI samples.

Table 1.

Genetic Loci Associated with Age-at-Onset of AD with P<E.05

Chromosome Top SNPa Position BP Total SNPsb Nearest Gene Pittsburgh Sample
Mayo Sample
ADNI Sample
Meta Analysis P Homogeneity Testc Meta APO-E Adjusted P Homogeneity Test
β P β P β P
19 rs4420638 50.11 7 (5) APOE/TOMM40/APOC1 2.20 4.14E-14 0.70 2.47E-02 −0.05 9.83E-01 1.11E-12 84.6 1.32E-05 0
4q31 rs1466662 155.57 20 (4) DCHS2 −0.96 1.19E-03 -1.00 1.37E-04 −0.58 7.59E-01 4.95E-07 0 2.35E-06 0
12q24 rs17429217 115.78 4 (1) HRK/RNFT2 2.44 4.00E-03 2.84 2.48E-04 4.76 5.03E-01 2.57E-06 0 3.18E-06 0
3p14 rs704454 64.90 2 (1) ADAMTS9 0.50 8.32E-02 1.22 4.60E-06 2.94 1.54E-01 2.60E-06 53.4 1.55E-05 53.5
9p24 rs2034764 2.73 11 (4) KCNV2/VLDLR 0.88 1.11E-03 0.76 1.62E-03 2.52 2.36E-01 3.82E-06 0 4.34E-06 0
4p15 rs10517270 32.88 25 (6) - −3.31 2.67E-04 -3.95 1.13E-02 −18.66 4.08E-02 4.13E-06 34.3 5.87E-06 45.9
6p21 rs2104362 33.93 16 (5) LEMD2/MLN/MIR1275 0.84 5.87E-03 0.90 8.36E-04 4.04 6.79E-02 6.91E-06 6.83 5.03E-06 2.5
16q24 rs12933233 85.67 2 (1) - 0.79 8.46E-03 0.91 5.63E-04 2.92 1.35-E-01 7.64E-06 0 1.99E-05 0
18q21 rs1037757 54.90 6 (2) LOC390958/Sec11C −4.56 2.93E-05 -2.26 3.69E-02 -3.45 7.10E-01 8.29E-06 10.7 1.36E-05 0
15q25 rs3743162 83.23 5 (4) ZNF592/ALPK3/SLC28A1 −0.88 5.70E03 −1.03 6.02E-04 −1.84 4.96E-01 8.68E-06 0 9.99E-05 0
2q37 rs753855 231.77 3 (2) PSMD1/HTR2B/ARMC9 0.91 7.00E-04 0.69 4.75 E-03 1.13 5.36E-01 9.82E-06 0 4.09E-06 0
14q31 rs17764668 79.31 1 (0) NRXN3 1.14 3.65E-03 1.15 1.14E-03 2.72 4.37E-01 9.84E-06 0 2.08E-05 0
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a

SNP with lowest P-value in a given gene region after meta analysis.

b

Total genotyped or imputed significant SNPs with P<1E-04 in a given region after meta analysis. The number of genotyped SNPs is given in parentheses.

c

Heterogeneity testing was accomplished using Cochran’s Q statistic, summarized as the I2 statistic.21–22 Values closer to 0 indicate no heterogeneity, whereas larger numbers indicate increasing degrees of hererogeneity between studies.

The association of all top eleven non-APOE significant SNPs was consistent in direction in the three samples as reflected in ß-values. Furthermore, ten of the top eleven non-APOE SNPs were either directly genotyped or had proxy genotyped SNPs with P<1E-04 on Illumina arrays (see Table 1 and Supplementary Table), thus eliminating potential spurious associations due to imputation artifact. The most significant SNP outside the APOE region, rs1466662, was located in the DCHS2 gene on chromosome 4q31.3 at position 155.57 Mb (P= 4.95E-07). There were 19 additional significant SNPs in this region at P<1E-04 a. Figure 4 shows the regional plot for SNPs within 500 kb on either side of the DCHS2 index SNP and the meta P-values for markers which had P<IE-04 in the meta analysis. Chromosome 4 harbors another potential region for AAO at position 32.88 Mb (P=4.13E-06 for top SNP rs10517270) and this region had the most number of significant SNPs (n=25) at P<1E-04 (Supplementary Fig. 3). There is no known gene in this region. The regional plots for the remaining 9 top loci associated with AAO are shown in Supplementary Figures 4-12. In order to remove any variation in AAO due to the established effect of APOE, we also analyzed the data after adjusting for the effect of APOE*4, but found no appreciable difference in P-values for the non-APOE loci (Table 1), indicating that their effects are independent of APOE.

Figure 4.

Figure 4

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Regional association plot on chromosome 4, including the best SNP (rs1466662) for AAO of AD in the DCHS2 gene in the University of Pittsburgh ADRC data (blue dots), and meta analysis (red triangles).

Discussion

The focus of almost all reported GWAS related to AD has been on identifying genes for the disease risk. However, in addition to the disease risk, AAO of AD has also strong genetic component with an estimated heritability of 42%.9–10 Here we report the first GWAS focused on identifying genes for the AAO of AD. We performed a genome-wide meta analysis on three GWAS data sets comprising a total of 2,222 cases. Only the APOE/TOMM40/APOC1 region demonstrated genome-wide significant association with AAO, which is due to the previously well established association of the APOE *4 SNP, as this demonstrated the most significant association in the discovery sample.

The present GWAS for AAO has identified eleven suggestive loci which although do not meet the strict criteria for genome-wide significant, their consistent and directional associations in three independent samples suggest that they are worthy of follow-up in additional studies. This is supported further by the fact that many of these suggestive loci contain genes that are expressed in the brain, and thus they may be relevant to AAO of AD. Our top SNP (rs1466662) is located in the DCHS2 gene on 4q13 that is expressed in cerebral cortex.23 Interestingly, another potential region on chromosome 4 (the fifth top SNP on 4p15), although gene poor, is located in the same broad region recently implicated in differential response to antipsychotic drugs in schizophrenia patients.24 The second top SNP (rs17429217) is located in the HRK (also known as DP5) gene on 12q24, which is a member of the pro-apoptotic-only Bcl2 family and is abundantly expressed in the brain, especially in hippocampus.25 The third top SNP on 3p14 (rs704454) is near the ADAMTS9 gene which is expressed in spinal cord and brain along with other tissues26 and ADAMTS9 is a susceptibility gene for type 2 diabetes.27 The fourth top SNP on 9p24 (rs2034764) is in the KCNV2 gene which is adjacent to the VLDL-receptor (VLDLR) gene that is expressed in the brain and binds to APOE28 and genetic variation in VLDLR has previously been linked to dementia.29 The fifth top SNP (rs2104362) resides in an intergenic region on 6p21 that contains a nearby micro RNA gene MIR1275 of unknown function. The last top SNP (rs17764668) is located in the NRXN3 (neurexin 3) gene on 14q13 at ~80 Mb. NRXN3 is expressed in the brain and previous studies have identified this gene to be associated with addiction and obesity.30,31 Previously Bertram et al.13 reported that a SNP (rs11159647) located at ~84 Mb in this region of 14q13 was associated with AAO of AD. However, this SNP did not show significant association in any of our three samples (P=0.335, 0.075, and 0.301 in the ADRC, Mayo and ADNI samples, respectively; P=0.446 in stage 3 meta analysis). Other members of the neurexin gene family have been associated with AD, including structural neuroimaging phenotypes in ADNI32 where NXPH1 (rs6463843) was related to decreased gray matter density on MRI in individuals with the T/T relative to the G/G genotype. In particular, AD patients homozygous for the T allele showed differential vulnerability to right hippocampal atrophy as indicated by a SNP × diagnosis interaction.

The potential significance of the other 4 regions in the present study, not discussed above, is not clear as the 16q24 SNP is not located in a known gene and the known neighboring genes on both sides of this SNP are ~400 kb away. Although the other three regions on 18q21, 15q25 and 2q37 have known genes, their role in AD is not clear.

In conclusion, this study has confirmed the established association of the APOE locus with AAO of AD by revealing several genome-wide significant SNPs in this region. Although we did not identify any non-APOE SNPs meeting a conservative threshold of genome-wide significance, we have identified 11 suggestive loci, many of them harboring potential biological candidate genes that warrant follow-up in additional samples.

Supplementary Material

supplementary Table and Figure

Acknowledgments

This study was supported by the National Institute on Aging grants AG030653 (MIK), AG005133 (ADRC), AG027224 (RAS) and AG18023 (SGY). We thank Drs. Peter Gregersen and Annette Lee of the Feinstein Institute of Medical Research where the genotyping of the discovery sample was performed. We acknowledge Drs. Neill R. Graff-Radford, Dennis W. Dickson and Ronald C. Petersen for their key contribution in collecting the Mayo replication sample. One of the replication data sets was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904; RC2 AG036535). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: Abbott, AstraZeneca AB, Bayer Schering Pharma AG, Bristol-Myers Squibb, Eisai Global Clinical Development, Elan Corporation, Genentech, GE Healthcare, GlaxoSmithKline, Innogenetics, Johnson and Johnson, Eli Lilly and Co., Medpace, Inc., Merck and Co., Inc., Novartis AG, Pfizer Inc, F. Hoffman-La Roche, Schering-Plough, Synarc, Inc., as well as non-profit partners the Alzheimer's Association and Alzheimer's Drug Discovery Foundation, with participation from the U.S. Food and Drug Administration. Private sector contributions to ADNI are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer's Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of California, Los Angeles. This research was also supported by NIH grants P30 AG010129, K01 AG030514, and the Dana Foundation. Some sample collection was supported by the NIA National Cell Repository for Alzheimer’s Disease (NCRAD; U24 AG021886) and additional support for data analysis was provided by R01 AG19771 and P30 AG10133. We thank the following people for their contributions to the ADNI genotyping project: Tatiana Foroud, Kelley Faber, Li Shen, Steven G. Potkin, Matthew J. Huentelman, David W. Craig, Sungeun Kim, Kwangsik Nho and Bryan M. DeChairo.

Footnotes

Conflict of Interest

The authors declare no conflict of interest.

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Supplementary Materials

supplementary Table and Figure
NIHMS319080-supplement-supplementary_Table_and_Figure.pdf (1.4MB, pdf)

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