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Original Contributions |

Association and Expression Analyses With Single-Nucleotide Polymorphisms in TOMM40 in Alzheimer Disease FREE

Carlos Cruchaga, PhD; Petra Nowotny, PhD; John S. K. Kauwe, PhD; Perry G. Ridge, MS; Kevin Mayo, BA; Sarah Bertelsen, MS; Anthony Hinrichs, PhD; Anne M. Fagan, PhD; David M. Holtzman, MD; John C. Morris, MD; Alison M. Goate, PhD; for the Alzheimer's Disease Neuroimaging Initiative
[+] Author Affiliations

Author Affiliations: Departments of Psychiatry (Drs Cruchaga, Nowotny, Hinrichs, and Goate; Mr Mayo; and Ms Bertelsen), Neurology (Drs Fagan, Holtzman, Morris, and Goate), Pathology and Immunology (Dr Morris), Genetics (Dr Goate), and Developmental Biology (Dr Holtzman), Knight Alzheimer's Disease Research Center (Drs Cruchaga, Fagan, Holzman, Morris, and Goate), and Hope Center for Neurological Disorders (Drs Cruchaga, Fagan, Holtzman, Morris, and Goate), Washington University School of Medicine, St Louis, Missouri; Department of Biology, Brigham Young University, Provo, Utah (Dr Kauwe and Mr Ridge); and ARUP Institute for Clinical and Experimental Pathology, Salt Lake City, Utah (Mr Ridge).


Arch Neurol. 2011;68(8):1013-1019. doi:10.1001/archneurol.2011.155.
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Published online

Background Apolipoprotein E (APOE) is the most statistically significant genetic risk factor for late-onset Alzheimer disease (LOAD). The linkage disequilibrium pattern around the APOE gene has made it difficult to determine whether all the association signal is derived from APOE or whether there is an independent signal from a nearby gene.

Objective To attempt to replicate a recently reported association of APOE 3–TOMM40 haplotypes with risk and age at onset.

Design We used standard techniques to genotype several polymorphisms in the APOE–TOMM40 region in a large case-control series, in a series with cerebrospinal fluid biomarker data, and in brain tissue.

Setting Alzheimer's Disease Research Center.

Participants Research volunteers who were cognitively normal or had Alzheimer disease.

Main Outcome Measures Disease status and age at onset.

Results We did not replicate the previously reported association of the polyT polymorphism (rs10524523) with risk and age at onset. We found a significant association between rs10524523 and risk of LOAD in APOE 33 homozygotes but in the opposite direction as the previously reported association (the very long allele was underrepresented in cases vs controls in this study (P = .004]). We found no association between rs10524523 and cerebrospinal fluid tau or β-amyloid 42 levels or TOMM40 or APOE gene expression.

Conclusions Although we did not replicate the earlier association between the APOE 3–TOMM40 haplotypes and age at onset, we observed that the polyT polymorphism is associated with risk of LOAD in APOE 33 homozygotes in a large case-control series but in the opposite direction as in the previous study.

Figures in this Article

The most statistically significant signals in genome-wide association studies for late-onset Alzheimer disease (LOAD) are detected with single-nucleotide polymorphisms (SNPs) in the region encoding apolipoprotein E (APOE) (OMIM 107741)and TOMM40 (translocase of outer mitochondrial membrane 40 homologue [OMIM 608061]).110 For several reasons, it is difficult to determine whether the signals in this area are due solely to the APOE genotype. First, the SNPs that code for the APOE ε2, ε3, and ε4 isoforms are not included in the most popular genome-wide SNP chips. Second, extensive linkage disequilibrium in this region of the genome makes it difficult to definitively determine the genetic variant(s) that drives the association. Third, several SNPs up to 50 kilobase from APOE exhibit significant associations with LOAD.

Previous studies1113 have explored this issue using case-control data sets, endophenotypes, and genome-wide pathway analyses. Roses et al12 used DNA sequencing and an evolutionary network approach to demonstrate that a polyT polymorphism in intron 6 of TOMM40 (rs10524523) is associated with age at onset (AAO) in APOE 33 and 34 individuals. Roses et al found that the very long allele of rs10524523 is associated with increased risk and lower AAO of LOAD.

In this study, we attempted to replicate the findings from Roses et al.12 We analyzed a large case-control sample to test whether APOE 3–TOMM40 haplotypes or TOMM40 alleles exhibit an APOE- independent effect on risk of disease, AAO of LOAD, cerebrospinal fluid (CSF) biomarker levels, and expression of TOMM40/APOE in the brain.

PARTICIPANTS

Risk of disease and AAO analyses were performed in 1594 LOAD cases (474 APOE 33 homozygotes) and 1190 cognitively normal controls (701 APOE 33 homozygotes) matched for age, sex, and ethnicity. These samples were obtained from the Knight Alzheimer Disease Research Center at Washington University (WU-ADRC) (759 cases and 345 controls) and from the National Institute on Aging (NIA) LOAD Family Study (835 cases and 845 controls). Each case received a diagnosis of dementia of the Alzheimer type using criteria equivalent to those of the National Institute of Neurological Disorders and Stroke–Alzheimer's Disease and Related Disorders Association for probable AD.14,15 Individuals with a Clinical Dementia Rating Scale (CDR) score of 0.5 who did not meet the clinical criteria for probable AD were not included in the analyses. Controls received the same assessment as cases but were cognitively normal. All the individuals were of European descent, and written consent was obtained from all the participants.

Expression studies were conducted using complementary DNA (cDNA) obtained from the parietal lobes of 82 AD cases and 39 cognitively normal individuals (CDR score = 0) obtained through the WU-ADRC Neuropathology Core. Association with CSF tau, tau phosphorylated at threonine 181, β-amyloid (Aβ) 42, and Aβ40 levels was tested in an independent series of 474 samples from the WU-ADRC and 259 samples from the Alzheimers Disease Neuroimaging Initiative (ADNI) (Table 1). Cerebrospinal fluid was collected and biomarker measurements were obtained as described previously.1618 A summary of the demographics of all the participants is given in Table 1.

GENOTYPING

rs7412 and rs429358 (which define the APOE ε2, ε3, and ε4 isoforms), rs1160985, and rs4420638 (TOMM40) were genotyped using KASPar (KBioscience, Herts, United Kingdom) and TaqMan (Applied Biosystems, Foster City, California) genotyping technologies. The APOE genotype for the NIA-LOAD and ADNI series was provided by the NIA-LOAD or ADNI. The polyT repeat in intron 6 of TOMM40 (rs10524523) was genotyped using fluorescence-based fragment size analysis (Supplemental Figure 1; http://neuroscienceresearch.wustl.edu/pages/cruchaga2011.aspx).19 A detailed explanation of the fluorescence-based fragment size genotyping, quality control steps, allele frequency, and linkage disequilibrium between the studied polymorphisms can be found at http://neuroscienceresearch.wustl.edu/Pages/cruchaga2011.aspx. The linkage disequilibrium between the genotyped variants can be found in Supplemental Table 1

GENOTYPE CALLS

The polyT repeat (rs10524523) genotypes were placed into categories modeled after those reported by Roses et al12: short (246-267 base pair [bp]), long (268-279 bp), and very long (280-289 bp) (Supplemental Figure 2; http://neuroscienceresearch.wustl.edu/Pages/cruchaga2011.aspx). The base pair numbers do not correspond to those provided by Roses et al because the present numbers refer to the total length of the polymerase chain reaction product, not the number of polyT repeats (see http://neuroscienceresearch.wustl.edu/Pages/cruchaga2011.aspx for quality control and call comparison between the present study and previous studies).

GENE EXPRESSION

Quantification of gene expression was performed by real-time polymerase chain reaction20 as explained previously.21 We also used the GEO data set GSE15222 for replication (see http://neuroscienceresearch.wustl.edu/Pages/cruchaga2011.aspx for a detailed explanation).

PHYLOGENETIC ANALYSES

Because the polyT repeat is reported as the key variant to define TOMM40 clades A and B,12 we used this marker and APOE isoform information to perform analyses based on phylogenetic groups as described by Roses et al.12 Haplotype phase was estimated using PHASE software.22 The phylogeny, which represents the evolutionary relatedness of the haploytpes, was estimated using neighbor-joining with 10 000 bootstrapping replicates in the CLC DNA workbench (CLC bio, Aarhus, Denmark) (Supplemental Figure 3; http://neuroscienceresearch.wustl.edu/Pages/cruchaga2011.aspx). We tested for differences in mean AAO between APOE 3–TOMM40 clade A and B haplotypes using a t test. Association of APOE 3–TOMM40 clade A and B haplotypes with case-control status was performed using a Fisher exact test.

STATISTICAL ANALYSES

Additional association tests between the polyT repeat and disease status, AAO, TOMM40 and APOE brain expression, and CSF tau and Aβ42 levels were performed using UNPHASED v3.1.4 and SAS, version 9.2 (SAS Institute Inc, Cary, North Carolina). Several analyses were restricted to APOE 33 homozygotes, thus removing uncertainty in haplotype phasing as a possible confounding factor. (See http://neuroscienceresearch.wustl.edu/Pages/cruchaga2011.aspx for a detailed description of the statistical analyses.)

Multiple Test Correction

We tested 4 SNPs for association with 2 phenotypes. A conservative threshold for multiple test correction would be to set the significance at P < .006, which would be the Bonferroni correction for 4 × 2 tests. The SNPs that were associated with risk of disease or AAO were tested for association with CSF biomarker levels and gene expression to investigate potential pathogenic mechanisms. In this case, no multiple test correction was applied because only 1 or 2 SNPs with specific hypotheses were tested for association.

ADNI Material and Methods

Data used in the preparation of this article were obtained from the ADNI database (http://www.loni.ucla.edu/ADNI). The ADNI was launched in 2003 by the NIA, the National Institute of Biomedical Imaging and Bioengineering, the Food and Drug Administration, private pharmaceutical companies, and nonprofit organizations. The primary goal of the ADNI has been to test whether serial magnetic resonance imaging, positron emission tomography, other biological markers, and clinical and neuropsychological assessments can be combined to measure the progression of mild cognitive impairment and early AD. The principal investigator of this initiative is Michael W. Weiner, MD. The ADNI is the result of efforts of many coinvestigators from a broad range of academic institutions and private corporations, and participants have been recruited from more than 50 sites across the United States and Canada. The initial goal of the ADNI was to recruit 800 adults to participate in the research. For up-to-date information, see http://www.adni-info.org.

The main aim of this study was to attempt to replicate the association of the APOE 3TOMM40 polyT repeat (rs10524523) haplotype groups with AAO and risk reported by Roses et al.12 Frequencies of APOE 3 alleles and APOE genotypes in TOMM40 clades A and B in the phased haplotype data were consistent with those reported (Supplemental Figure 2.12 We also included rs4420638 and rs1160985 (TOMM40), which have been reported to be associated with risk of disease or AAO independent of APOE genotype.23

ASSOCIATION WITH AAO

When using sex, but not APOE genotype, as a covariate, we found a significant association between the polyT repeat (rs10524523) and AAO in the WU-ADRC + NIA-LOAD case-control series (P = 1.03 × 10−19). To discern whether this association was driven by the polyT repeat or by APOE genotype, we performed 2 additional analyses: 1 including APOE genotype as a covariate in the model and 2 analyzing the polyT association in the APOE 33 stratum. When APOE genotype was included as a covariate, the P value dropped to .11, indicating that the association with AAO was driven by the APOE polymorphisms (Table 2). In the present analyses, the very long allele carriers had a higher, but not statistically significantly different, AAO than did the short allele carriers.

Table Graphic Jump LocationTable 2. MAF and P Values for Association With Risk and AAO in the Entire Series and in the APOE 33 Substratum

When the analyses were restricted to individuals with an APOE 33 genotype, the polyT repeat showed no association with AAO in the WU-ADRC + NIA-LOAD case-control series (P = .19) (Table 2). The same result was found when controls were included as censored data in the Kaplan-Meier analyses: in the APOE 33 stratum, the very long allele carriers had a higher, but not statistically significantly different, AAO than did the short allele carriers (Figure). We also found the same pattern in APOE 34 carriers: carriers of the long and very long alleles had a slightly higher, but not statistically significantly different, AAO than did carriers of the short alleles (Supplemental Figure 4; http://neuroscienceresearch.wustl.edu/Pages/cruchaga2011.aspx). Haplotype analyses showed similar results. We found a trend toward association between APOE 3–TOMM40 haplotypes and AAO (P = .057). Individuals with APOE 3–TOMM40 clade A haplotypes had a mean AAO of 73.31years vs 72.93 years for APOE 3–TOMM40 clade B. Thus, in this much larger study (total cases = 1594, total controls = 1190; total APOE 33 cases = 474, total APOE 33 controls = 701) than the original study (N = 34), we found a trend toward association but in the opposite direction than previously reported. In the APOE 33 stratum, rs4420638 showed the most significant association with AAO (P = .01), but this association did not pass multiple test correction (Table 2).

Place holder to copy figure label and caption
Graphic Jump Location

Figure. rs10524523 is not associated with age at onset of late-onset Alzheimer disease (LOAD) in apolipoprotein E (APOE) 33 carriers. A, Age at onset was analyzed for association with rs10524523 in 282 APOE 33 LOAD cases from the Alzheimer Disease Research Center at Washington University (WU-ADRC) series using the Kaplan-Meier method and was tested for significant differences using the log-rank test. B, Age at onset was analyzed for association with rs10524523 in 282 LOAD cases and 213 controls from the WU-ADRC series with an APOE 33 genotype by the Kaplan-Meier method and was tested for significant differences using the log-rank test. In both analyses, the short-short (S-S), short–very long (S-VL), and very long–very long (VL-VL) genotypes are highlighted because they were the most frequent in these strata. No significant differences in the survival curves were found (P > .05). L-L indicates long-long; L-VL, long–very long; and S-L, short-long.

ASSOCIATION WITH RISK OF DISEASE

We also analyzed whether the TOMM40 polyT repeat (rs10524523) was associated with risk of LOAD. We found an allelic association when sex and age, but not APOE genotype, were included as covariates (P = 4.14 × 10−88). The polyT repeat showed a trend toward association with risk of LOAD in the WU-ADRC + NIA-LOAD case-control series when APOE genotype was included in the model (P = .08) (Table 2). When we restricted this analysis to individuals with an APOE 33 genotype and used age and sex as covariates, there was a significant association with risk (P = .004) (Table 2). The frequency of the very long allele of the polyT repeat (rs10524523) was significantly lower in cases compared with controls in the WU-ADRC + NIA-LOAD series (0.41 vs 0.48, P = .004; odds ratio (OR) = 0.78, 95% confidence interval (CI) = 0.65-0.95) (Table 2). In this case, the association passed the multiple test correction threshold (α = .006). No other studied SNP showed a significant association with risk.

EVALUATION OF POSSIBLE MECHANISMS OF DISEASE RISK: ASSOCIATION WITH CSF BIOMARKER LEVELS AND GENE EXPRESSION

To determine a possible mechanism underlying the observed disease risk associated with the polyT repeat (rs10524523), we examined several endophenotypes, including CSF Aβ and tau levels and APOE and TOMM40 gene expression in the brain. A very strong association was observed between rs10524523 and CSF Aβ42 levels when CDR score, age, and sex, but not APOE genotype, were included as covariates (P = 4.50 × 10−8 for the WU-ADRC CSF series and P = 2.42 × 10−12 for the WU-ADRC + ADNI CSF series). However, this association was driven by APOE genotype because inclusion of APOE genotype as a covariate in the model eliminated the association between rs10524523 and CSF Aβ42 levels (P = .49 for the WU-ADRC CSF series and P = .40 for the WU-ADRC + ADNI CSF series) (Supplemental Table 2), suggesting that the rs10524523 association reflects linkage disequilibrium with the APOE genotype. We also did not detect an association between rs10524523 and CSF Aβ42, tau, or tau phosphorylated at threonine 181 levels in the entire series and in the APOE 33 stratum. For the WU-ADRC CSF samples, we also had CSF Aβ40 but found no association between the polyT repeat and this phenotype in the entire series or in the APOE 33 stratum (Supplemental Table 2).

Last, we tested whether these polymorphisms are associated with variability in APOE or TOMM40 messenger RNA (mRNA) expression in the human parietal cortex. There was a marginal correlation between the cDNA levels of APOE and TOMM40, with P < .001 and a Pearson correlation coefficient of −0.33. Because the brain samples are derived from cognitively normal (CDR score = 0) and demented (CDR score > 0.5) individuals, we first tested whether there was an association between mRNA levels and CDR score. We found no association between APOE cDNA levels and CDR score (P = .63; age, sex, and postmortem interval as covariates). We found a significant association between TOMM40 cDNA levels and CDR score (P = 3.55 × 10−3; age, sex, APOE genotype, and postmortem interval as covariates) in the WU-ADRC neuropathology series (82 AD cases and 39 cognitively normal individuals). However, we did not replicate this finding in the GEO data set GSE15222. In this data set, the TOMM40 cDNA levels in cases (n = 176) and controls (n = 188) are not significantly different (P = .17).

We found no association between any studied SNP and either TOMM40 or APOE cDNA levels (Supplemental Table 3; http://neuroscienceresearch.wustl.edu/Pages/cruchaga2011.aspx). We also did not detect an association between APOE or TOMM40 cDNA expression and APOE genotype (P = .45 and P = .63, respectively). The association between TOMM40 cDNA levels and CDR score led us to stratify the samples by CDR scores for further analyses, but we did not detect association between any SNP in either cases or controls (Supplemental Table 3). We also analyzed the APOE 33 stratum alone but found no associations (data not shown).

It is unclear whether all the association with risk of LOAD found in the APOE–TOMM40 gene region in the genome-wide association studies110 is driven by APOE genotype. Identification of new polymorphisms/genes that modify risk of LOAD could provide a better understanding of the pathways involved in LOAD and identify new drug targets for AD treatment. In this study, we attempted to replicate the recent study by Roses et al12 that reported an association of APOE 3–TOMM40 polyT polymorphism (rs10524523) haplotypes with AAO and risk of AD. We also performed extensive analyses in individuals with APOE 33 and analyzed several endophenotypes for LOAD to investigate different potential effects of the TOMM40 polymorphisms. We did not find a significant association between the polyT polymorphism (rs10524523) and AAO despite the fact that this large series of 2784 individuals (1175 APOE 33) provides high statistical power. Indeed, we found that in APOE 33 and 34 individuals, the longer alleles of the polyT polymorphism are associated with later onset and a protective effect in the opposite direction as the association in the original study.12 We also studied 2 SNPs in TOMM40 that have been suggested to modify risk of AD or AAO23 but found no significant association when APOE genotype was included as a covariate or when the APOE 33 stratum was analyzed alone.

We found that the frequency of the very long alleles of the polyT polymorphism is significantly lower in cases than in controls in 2 independent series (7% and 5% lower for the WU-ADRC and NIA-LOAD case-control series, respectively). Only when we combined the 2 independent case-control series did we find a significant association after multiple test correction in the APOE 33 stratum. In the joint analyses, allele frequency for the very long rs10524523 allele was 0.41 in cases and 0.48 in controls (P = .004; OR = 0.78, 95% CI = 0.65-0.95), which is opposite that reported by Roses et al.12 Although the magnitude of the observed effect is stronger than that observed for the genome-wide association study significant SNP in CLU, rs11136000 (0.36 vs 0.40; OR = 0.88, 95% CI = 0.86-0.91), a 4-fold larger sample size would be required to detect a genome-wide significant association for rs10524523 in APOE 33 homozygous individuals (ie, 6376 unselected cases and 4760 unselected controls or 1896 APOE 33 cases and 2804 APOE 33 controls).

These results suggest that the TOMM40 polyT repeat may be associated with risk of disease. TOMM40 is in close proximity to APOE, but it is unknown whether the polyT repeat affects risk of AD through an APOE-dependent mechanism or a totally independent mechanism. We used CSF biomarker phenotypes to test whether the polyT repeat increases risk of LOAD through an Aβ42-(similar to APOE21,2426) or a tau-dependent mechanism, but these results suggest that the polyT repeat may affect risk of AD through another mechanism. We also found no association of the polyT repeat with APOE or TOMM40 mRNA expression in the parietal cortex. We did not find evidence of any obvious potential mechanism that could explain the association with risk, but the power of these analyses are limited, and studies in larger series should be performed to identify the potential disease mechanism.

In conclusion, these data do not support the findings reported by Roses et al12 because we observed no association between APOE 3–TOMM40 clade A and B haplotypes or the polyT repeat (rs10524523) and AAO. We observed an association between the polyT repeat and risk of disease but in the opposite direction as that reported previously.12 It is unclear whether these results represent a type 1 error, but they highlight the importance of using large series, particularly when evaluating APOE subgroups, which require sample stratification–reducing power. Confirmation that rs10524523 is independently associated with AD risk will require a much larger sample. This could potentially be accomplished by imputation of rs1160985, a SNP that is in high linkage disequilibrium (r2 = 0.93) with the polyT variant in the APOE 33 carriers, in the large genome-wide association study data sets.9,10,27

TOMM40 codes for a mitochondrial protein, suggesting that mitochondrial integrity and energy metabolism could play an important role in LOAD. Mitochondrial morphologic features are altered in AD brains, and several studies have reported deficiencies in energy-related enzymes. The hypothesis that mitochondria may play an important role in LOAD is also supported by the fact that we did not find an association between the TOMM40 polymorphism and CSF Aβ42 and tau levels. However, more genetic and molecular studies are necessary to determine whether the reported genetic association with rs10524523 in TOMM40 is real.

Correspondence: Alison M. Goate, PhD, Samuel & Mae S. Ludwig Professor of Genetics in Psychiatry, Department of Psychiatry, B8134 Washington University School of Medicine, 660 S Euclid Ave, St Louis, MO 63110 (goatea@psychiatry.wustl.edu).

Accepted for Publication: April 4, 2011.

Author Contributions: All authors had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Study concept and design: Cruchaga, Kauwe, Holtzman, and Goate. Acquisition of data: Nowotny, Mayo, Fagan, and Morris. Analysis and interpretation of data: Cruchaga, Kauwe, Ridge, Bertelsen, Hinrichs, Fagan, Holtzman, and Goate. Drafting of the manuscript: Cruchaga, Nowotny, Kauwe, Holtzman, and Goate. Critical revision of the manuscript for important intellectual content: Cruchaga, Nowotny, Kauwe, Ridge, Mayo, Bertelsen, Hinrichs, Fagan, Holtzman, Morris, and Goate. Statistical analysis: Cruchaga, Kauwe, Bertelsen, and Hinrichs. Obtained funding: Holtzman, Morris, and Goate. Administrative, technical, and material support: Nowotny, Ridge, Mayo, Bertelsen, Fagan, Holtzman, Morris, and Goate. Study supervision: Holtzman, Morris, and Goate.

Financial Disclosure: Dr Morris has served as a consultant to or has received speaking honoraria from AstraZeneca AB, Bristol-Myers Squibb, Eisai Inc, Elan/Janssen Alzheimer Immunotherapy Program, Genentech Inc, Eli Lilly & Co, Merck & Co Inc, Novartis International AG, Otsuka Pharmaceuticals, Pfizer/Wyeth, and Schering-Plough. Dr Goate has served as a consultant to AstraZeneca; has provided expert testimony to Howrey & Associates; has speaking honoraria for AstraZeneca AB and Pfizer; and has received royalties from Taconic.

Funding/Support: This work was supported by grants AG16208, P01AG03991, P50AG05681, P01AG026276, AG23185, and AG05136 from the National Institutes of Health; the Barnes-Jewish Hospital Foundation; the Ford Foundation; and a fellowship from “Fundación Alfonso Martin Escudero” (Dr Cruchaga). Samples from the National Cell Repository for Alzheimer's Disease, which receives government support under a cooperative agreement grant (U24 AG21886) awarded by the NIA, were used in this study. Samples from the NIA-LOAD (National Institute on Aging Genetics Initiative for Late-Onset Alzheimer’s Disease) family study were collected under a cooperative agreement awarded by the NIA (U24 AG026395). Data collection and sharing for this project was funded by the ADNI (National Institutes of Health grant U01 AG024904). The ADNI is funded by the NIA, the National Institute of Biomedical Imaging and Bioengineering, and generous contributions from Abbott Laboratories, AstraZeneca AB, Bayer Schering Pharma AG, Bristol-Myers Squibb, Eisai Global Clinical Development, Elan Corp, Genentech, GE Healthcare, GlaxoSmithKline, Innogenetics, Johnson & Johnson, Eli Lilly & Co, Medpace Inc, Merck & Co Inc, Novartis International AG, Pfizer Inc, F. Hoffman-La Roche, Schering-Plough, and Synarc Inc and from the nonprofit partners the Alzheimer's Association and the Alzheimer's Drug Discovery Foundation, with participation from the US Food and Drug Administration. Private sector contributions to the ADNI are facilitated by the Foundation for the National Institutes of Health (http://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. The ADNI data are disseminated by the Laboratory for NeuroImaging at the University of California, Los Angeles. This research was also supported by grants P30 AG010129 and K01 AG030514 from the National Institutes of Health and by the Dana Foundation.

Role of the Sponsor: The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.

A complete list of ADNI investigators can be found at http://adni.loni.ucla.edu/wp-content/uploads/how_to_apply/ADNI_Authorship_List.pdf.

Disclaimer: Data used in the preparation of this article were obtained from the ADNI database (http://www.loni.ucla.edu/ADNI). As such, the investigators in the ADNI contributed to the design and implementation of the ADNI and provided data but did not participate in analysis or writing of this report.

Previous Presentation: This study is presented in part at the International Conference on Alzheimer Disease; July 16-21, 2011; Paris, France.

Online-Only Materials: The supplemental figures and table can be found at http://neuroscienceresearch.wustl.edu/Pages/cruchaga2011.aspx.

Additional Contributions: We thank the contributors, including the AD centers, who collected the samples used in this study; the patients and their families, whose help and participation made this work possible; the Clinical Core of the Knight ADRC for clinical and cognitive assessments of the participants; the Genetics Core of the Knight ADRC for APOE genotypes; and the Biomarker Core of the Adult Children Study for the CSF collection and assays.

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PubMed   |  Link to Article
Berg L, McKeel DW Jr, Miller JP,  et al.  Clinicopathologic studies in cognitively healthy aging and Alzheimer's disease: relation of histologic markers to dementia severity, age, sex, and apolipoprotein E genotype.  Arch Neurol. 1998;55(3):326-335
PubMed   |  Link to Article
McKhann G, Drachman D, Folstein M, Katzman R, Price D, Stadlan EM. Clinical diagnosis of Alzheimer's disease: report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer's Disease.  Neurology. 1984;34(7):939-944
PubMed   |  Link to Article
Fagan AM, Mintun MA, Mach RH,  et al.  Inverse relation between in vivo amyloid imaging load and cerebrospinal fluid Aβ42 in humans.  Ann Neurol. 2006;59(3):512-519
PubMed   |  Link to Article
Fagan AM, Roe CM, Xiong C, Mintun MA, Morris JC, Holtzman DM. Cerebrospinal fluid tau/β-amyloid(42) ratio as a prediction of cognitive decline in nondemented older adults.  Arch Neurol. 2007;64(3):343-349
PubMed   |  Link to Article
Shaw LM, Vanderstichele H, Knapik-Czajka M,  et al; Alzheimer's Disease Neuroimaging Initiative.  Cerebrospinal fluid biomarker signature in Alzheimer's disease neuroimaging initiative subjects.  Ann Neurol. 2009;65(4):403-413
PubMed   |  Link to Article
Imle P. Fluorescence-based fragment size analysis.  Methods Mol Biol. 2005;311:139-146
PubMed
Myers AJ, Gibbs JR, Webster JA,  et al.  A survey of genetic human cortical gene expression.  Nat Genet. 2007;39(12):1494-1499
PubMed   |  Link to Article
Cruchaga C, Kauwe JS, Mayo K,  et al; Alzheimer's Disease Neuroimaging Initiative.  SNPs associated with cerebrospinal fluid phospho-tau levels influence rate of decline in Alzheimer's disease.  PLoS Genet. 2010;6(9):pii: e1001101
PubMed   |  Link to Article
Stephens M, Donnelly P. A comparison of bayesian methods for haplotype reconstruction from population genotype data.  Am J Hum Genet. 2003;73(5):1162-1169
PubMed   |  Link to Article
Huentelman MJ, Corneveaux J, Myers A,  et al.  Genome-wide association study for Alzheimer's disease risk in a large cohort of clinically characterized and neuropathologically verified subjects. In: ICAD-2010. Honolulu, Hawaii: Alzheimer's Association; 2010:16
Kauwe JS, Cruchaga C, Mayo K,  et al.  Variation in MAPT is associated with cerebrospinal fluid tau levels in the presence of amyloid-β deposition.  Proc Natl Acad Sci U S A. 2008;105(23):8050-8054
PubMed   |  Link to Article
Kauwe JS, Jacquart S, Chakraverty S,  et al.  Extreme cerebrospinal fluid amyloid β levels identify family with late-onset Alzheimer's disease presenilin 1 mutation.  Ann Neurol. 2007;61(5):446-453
PubMed   |  Link to Article
Kauwe JS, Cruchaga C, Bertelsen S,  et al; Alzheimer's Disease Neuroimaging Initiative.  Validating predicted biological effects of Alzheimer's disease associated SNPs using CSF biomarker levels.  J Alzheimers Dis. 2010;21(3):833-842
PubMed
Jun G, Naj AC, Beecham GW,  et al; Alzheimer's Disease Genetics Consortium.  Meta-analysis confirms CR1, CLU, and PICALM as Alzheimer disease risk loci and reveals interactions with APOE genotypes.  Arch Neurol. 2010;67(12):1473-1484
PubMed   |  Link to Article

Figures

Place holder to copy figure label and caption
Graphic Jump Location

Figure. rs10524523 is not associated with age at onset of late-onset Alzheimer disease (LOAD) in apolipoprotein E (APOE) 33 carriers. A, Age at onset was analyzed for association with rs10524523 in 282 APOE 33 LOAD cases from the Alzheimer Disease Research Center at Washington University (WU-ADRC) series using the Kaplan-Meier method and was tested for significant differences using the log-rank test. B, Age at onset was analyzed for association with rs10524523 in 282 LOAD cases and 213 controls from the WU-ADRC series with an APOE 33 genotype by the Kaplan-Meier method and was tested for significant differences using the log-rank test. In both analyses, the short-short (S-S), short–very long (S-VL), and very long–very long (VL-VL) genotypes are highlighted because they were the most frequent in these strata. No significant differences in the survival curves were found (P > .05). L-L indicates long-long; L-VL, long–very long; and S-L, short-long.

Tables

Table Graphic Jump LocationTable 2. MAF and P Values for Association With Risk and AAO in the Entire Series and in the APOE 33 Substratum

References

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PubMed   |  Link to Article
Coon KD, Myers AJ, Craig DW,  et al.  A high-density whole-genome association study reveals that APOE is the major susceptibility gene for sporadic late-onset Alzheimer's disease.  J Clin Psychiatry. 2007;68(4):613-618
PubMed   |  Link to Article
Reiman EM, Webster JA, Myers AJ,  et al.  GAB2 alleles modify Alzheimer's risk in APOE ε4 carriers.  Neuron. 2007;54(5):713-720
PubMed   |  Link to Article
Bertram L, Lange C, Mullin K,  et al.  Genome-wide association analysis reveals putative Alzheimer's disease susceptibility loci in addition to APOE.  Am J Hum Genet. 2008;83(5):623-632
PubMed   |  Link to Article
Li H, Wetten S, Li L,  et al.  Candidate single-nucleotide polymorphisms from a genomewide association study of Alzheimer disease.  Arch Neurol. 2008;65(1):45-53
PubMed   |  Link to Article
Carrasquillo MM, Zou F, Pankratz VS,  et al.  Genetic variation in PCDH11X is associated with susceptibility to late-onset Alzheimer's disease.  Nat Genet. 2009;41(2):192-198
PubMed   |  Link to Article
Züchner S, Wendland JR, Ashley-Koch AE,  et al.  Multiple rare SAPAP3 missense variants in trichotillomania and OCD.  Mol Psychiatry. 2009;14(1):6-9
PubMed   |  Link to Article
Beecham GW, Martin ER, Li YJ,  et al.  Genome-wide association study implicates a chromosome 12 risk locus for late-onset Alzheimer disease.  Am J Hum Genet. 2009;84(1):35-43
PubMed   |  Link to Article
Harold D, Abraham R, Hollingworth P,  et al.  Genome-wide association study identifies variants at CLU and PICALM associated with Alzheimer's disease.  Nat Genet. 2009;41(10):1088-1093
PubMed   |  Link to Article
Lambert JC, Heath S, Even G,  et al; European Alzheimer's Disease Initiative Investigators.  Genome-wide association study identifies variants at CLU and CR1 associated with Alzheimer's disease.  Nat Genet. 2009;41(10):1094-1099
PubMed   |  Link to Article
Yu CE, Seltman H, Peskind ER,  et al.  Comprehensive analysis of APOE and selected proximate markers for late-onset Alzheimer's disease: patterns of linkage disequilibrium and disease/marker association.  Genomics. 2007;89(6):655-665
PubMed   |  Link to Article
Roses AD, Lutz MW, Amrine-Madsen H,  et al.  A TOMM40 variable-length polymorphism predicts the age of late-onset Alzheimer's disease.  Pharmacogenomics J. 2010;10(5):375-384
PubMed   |  Link to Article
Hong MG, Alexeyenko A, Lambert JC, Amouyel P, Prince JA. Genome-wide pathway analysis implicates intracellular transmembrane protein transport in Alzheimer disease.  J Hum Genet. 2010;55(10):707-709
PubMed   |  Link to Article
Berg L, McKeel DW Jr, Miller JP,  et al.  Clinicopathologic studies in cognitively healthy aging and Alzheimer's disease: relation of histologic markers to dementia severity, age, sex, and apolipoprotein E genotype.  Arch Neurol. 1998;55(3):326-335
PubMed   |  Link to Article
McKhann G, Drachman D, Folstein M, Katzman R, Price D, Stadlan EM. Clinical diagnosis of Alzheimer's disease: report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer's Disease.  Neurology. 1984;34(7):939-944
PubMed   |  Link to Article
Fagan AM, Mintun MA, Mach RH,  et al.  Inverse relation between in vivo amyloid imaging load and cerebrospinal fluid Aβ42 in humans.  Ann Neurol. 2006;59(3):512-519
PubMed   |  Link to Article
Fagan AM, Roe CM, Xiong C, Mintun MA, Morris JC, Holtzman DM. Cerebrospinal fluid tau/β-amyloid(42) ratio as a prediction of cognitive decline in nondemented older adults.  Arch Neurol. 2007;64(3):343-349
PubMed   |  Link to Article
Shaw LM, Vanderstichele H, Knapik-Czajka M,  et al; Alzheimer's Disease Neuroimaging Initiative.  Cerebrospinal fluid biomarker signature in Alzheimer's disease neuroimaging initiative subjects.  Ann Neurol. 2009;65(4):403-413
PubMed   |  Link to Article
Imle P. Fluorescence-based fragment size analysis.  Methods Mol Biol. 2005;311:139-146
PubMed
Myers AJ, Gibbs JR, Webster JA,  et al.  A survey of genetic human cortical gene expression.  Nat Genet. 2007;39(12):1494-1499
PubMed   |  Link to Article
Cruchaga C, Kauwe JS, Mayo K,  et al; Alzheimer's Disease Neuroimaging Initiative.  SNPs associated with cerebrospinal fluid phospho-tau levels influence rate of decline in Alzheimer's disease.  PLoS Genet. 2010;6(9):pii: e1001101
PubMed   |  Link to Article
Stephens M, Donnelly P. A comparison of bayesian methods for haplotype reconstruction from population genotype data.  Am J Hum Genet. 2003;73(5):1162-1169
PubMed   |  Link to Article
Huentelman MJ, Corneveaux J, Myers A,  et al.  Genome-wide association study for Alzheimer's disease risk in a large cohort of clinically characterized and neuropathologically verified subjects. In: ICAD-2010. Honolulu, Hawaii: Alzheimer's Association; 2010:16
Kauwe JS, Cruchaga C, Mayo K,  et al.  Variation in MAPT is associated with cerebrospinal fluid tau levels in the presence of amyloid-β deposition.  Proc Natl Acad Sci U S A. 2008;105(23):8050-8054
PubMed   |  Link to Article
Kauwe JS, Jacquart S, Chakraverty S,  et al.  Extreme cerebrospinal fluid amyloid β levels identify family with late-onset Alzheimer's disease presenilin 1 mutation.  Ann Neurol. 2007;61(5):446-453
PubMed   |  Link to Article
Kauwe JS, Cruchaga C, Bertelsen S,  et al; Alzheimer's Disease Neuroimaging Initiative.  Validating predicted biological effects of Alzheimer's disease associated SNPs using CSF biomarker levels.  J Alzheimers Dis. 2010;21(3):833-842
PubMed
Jun G, Naj AC, Beecham GW,  et al; Alzheimer's Disease Genetics Consortium.  Meta-analysis confirms CR1, CLU, and PICALM as Alzheimer disease risk loci and reveals interactions with APOE genotypes.  Arch Neurol. 2010;67(12):1473-1484
PubMed   |  Link to Article

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