Advertisement
Research Article

Analysis of Copy Number Variation in Alzheimer’s Disease in a Cohort of Clinically Characterized and Neuropathologically Verified Individuals

  • Shanker Swaminathan,

    Affiliations: Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, Indiana, United States of America, Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, United States of America

    X
  • Matthew J. Huentelman,

    Affiliations: Neurogenomics Division, The Translational Genomics Research Institute (TGen), Phoenix, Arizona, United States of America, The Arizona Alzheimer's Consortium, Phoenix, Arizona, United States of America

    X
  • Jason J. Corneveaux,

    Affiliations: Neurogenomics Division, The Translational Genomics Research Institute (TGen), Phoenix, Arizona, United States of America, The Arizona Alzheimer's Consortium, Phoenix, Arizona, United States of America

    X
  • Amanda J. Myers,

    Affiliations: Departments of Psychiatry and Behavioral Sciences, and Human Genetics and Genomics, University of Miami, Miller School of Medicine, Miami, Florida, United States of America, Johnnie B. Byrd Sr. Alzheimer's Center and Research Institute, Tampa, Florida, United States of America

    X
  • Kelley M. Faber,

    Affiliation: Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, United States of America

    X
  • Tatiana Foroud,

    Affiliations: Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, Indiana, United States of America, Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, United States of America, Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, Indiana, United States of America

    X
  • Richard Mayeux,

    Affiliation: The Gertrude H. Sergievsky Center, The Taub Institute for Research on Alzheimer's Disease and the Aging Brain, and the Department of Neurology, Columbia University College of Physicians and Surgeons, New York, New York, United States of America

    X
  • Li Shen,

    Affiliations: Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, Indiana, United States of America, Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, Indiana, United States of America

    X
  • Sungeun Kim,

    Affiliations: Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, Indiana, United States of America, Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, Indiana, United States of America

    X
  • Mari Turk,

    Affiliations: Neurogenomics Division, The Translational Genomics Research Institute (TGen), Phoenix, Arizona, United States of America, The Arizona Alzheimer's Consortium, Phoenix, Arizona, United States of America

    X
  • John Hardy,

    Affiliation: Department of Molecular Neuroscience and Reta Lila Research Laboratories, University College London Institute of Neurology, London, United Kingdom

    X
  • Eric M. Reiman,

    Affiliations: Neurogenomics Division, The Translational Genomics Research Institute (TGen), Phoenix, Arizona, United States of America, The Arizona Alzheimer's Consortium, Phoenix, Arizona, United States of America, Banner Alzheimer’s Institute, Phoenix, Arizona, United States of America

    X
  • Andrew J. Saykin, for the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the NIA-LOAD/NCRAD Family Study Group mail

    asaykin@iupui.edu

    Affiliations: Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, Indiana, United States of America, Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, United States of America, Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, Indiana, United States of America

    X
  • Published: December 05, 2012
  • DOI: 10.1371/journal.pone.0050640

Abstract

Copy number variations (CNVs) are genomic regions that have added (duplications) or deleted (deletions) genetic material. They may overlap genes affecting their function and have been shown to be associated with disease. We previously investigated the role of CNVs in late-onset Alzheimer's disease (AD) and mild cognitive impairment using Alzheimer’s Disease Neuroimaging Initiative (ADNI) and National Institute of Aging-Late Onset AD/National Cell Repository for AD (NIA-LOAD/NCRAD) Family Study participants, and identified a number of genes overlapped by CNV calls. To confirm the findings and identify other potential candidate regions, we analyzed array data from a unique cohort of 1617 Caucasian participants (1022 AD cases and 595 controls) who were clinically characterized and whose diagnosis was neuropathologically verified. All DNA samples were extracted from brain tissue. CNV calls were generated and subjected to quality control (QC). 728 cases and 438 controls who passed all QC measures were included in case/control association analyses including candidate gene and genome-wide approaches. Rates of deletions and duplications did not significantly differ between cases and controls. Case-control association identified a number of previously reported regions (CHRFAM7A, RELN and DOPEY2) as well as a new gene (HLA-DRA). Meta-analysis of CHRFAM7A indicated a significant association of the gene with AD and/or MCI risk (P = 0.006, odds ratio = 3.986 (95% confidence interval 1.490–10.667)). A novel APP gene duplication was observed in one case sample. Further investigation of the identified genes in independent and larger samples is warranted.

Introduction

Alzheimer’s disease (AD) is the most common form of dementia characterized by loss of memory and other cognitive abilities, severe enough to disrupt daily life activities. An estimated 5.4 million Americans have AD, the sixth leading cause of death across all ages in the United States [1]. No treatments at present can slow or halt its progression. Amnestic mild cognitive impairment (MCI) is a clinical condition in which a person has memory problems not normal for his/her age, but not severe enough to interfere significantly with daily functioning. Approximately 14–18% of individuals aged 70 years and older have MCI, and every year 10–15% of these individuals will likely progress to dementia, particularly AD [2].

Genetic factors play a key role in AD development accounting for approximately 58–79% of the phenotypic variation [3]. Mutations in APP, PSEN1 and PSEN2 primarily cause early-onset AD (age at onset<60 or 65 years) [4]. The leading genetic risk factor for the more common late-onset AD (age at onset>60 or 65 years) is the APOE ε4 allele. Large case-control genome-wide association studies (GWASs) have identified and replicated other AD risk loci including: CLU, CR1, PICALM, BIN1, EXOC3L2, MTHFD1L, MS4A4A/MS4A6E, CD2AP, CD33, ABCA7 and CUGBP2 [5][12]. However it is estimated that the APOE ε4 allele accounts for approximately 20% and the non-APOE loci cumulatively account for as much as 35% of the AD risk [10], [13]. A recent study observed only a marginal joint effect of known loci on memory independent from APOE [14]. The combined loci provided minimal improvement of prediction of AD beyond age, sex and APOE. Thus the loci do not explain all the genetic variation associated with AD, and other forms of genetic variation such as copy number variations (CNVs) may play a role.

CNVs are deoxyribonucleic acid (DNA) regions (one kilobase (kb) to several megabases (Mb) in size) that have differences in copy number. These can result in the addition (copy number gains or duplications) or loss (copy number losses or deletions) of genetic material. CNVs often encompass a single gene or multiple genes and may affect their function [15]. The role of CNVs in late-onset AD has been investigated in prior studies [16][19]. Previously, we analyzed the role of CNVs in AD and MCI using data from participants in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) study [20] and the National Institute of Aging-Late Onset AD/National Cell Repository for AD (NIA-LOAD/NCRAD) Family Study [21]. For both studies, DNA extracted either from peripheral blood or brain tissue were used. Case/control association analyses including candidate gene and genome-wide approaches were performed to determine genes overlapped by CNVs only in cases (AD and/or MCI) but not controls. A number of genes were identified in the two studies including ATXN1, CHRFAM7A, CSMD1, DOPEY2, ERBB4, GSTT1, HLA-DPB1, HNRNPCL1, IMMP2L, NRXN1, RELN and SLC35F2.

The aim of the present report is to analyze the role of CNVs in AD using data from a unique cohort of clinically characterized and neuropathologically defined cases (AD) and controls (TGen cohort) [22]. All DNA samples were extracted from brain tissue. Case/control association analyses similar to the two previous studies were performed to determine the CNV burden in cases relative to controls and genes overlapped by CNVs detected in cases but not controls. Here we report analyses identifying a number of previously reported as well as new CNV regions.

Materials and Methods

Samples

The TGen cohort included samples extracted from brain tissue of 1617 Caucasian individuals (1022 AD cases and 595 controls). Recruitment information for the participants has been previously described [22]. Briefly, the United States cohort was obtained from 21 National Institute on Aging-supported Alzheimer's Disease Center brain banks and from the Miami Brain Bank [23], [24]. Cohorts from other brain banks in the United States, United Kingdom, and the Netherlands were obtained similar to the original United States cohort. Genome-wide genotyping for all samples was performed using the Affymetrix Genome-Wide Human SNP 6.0 Array (Santa Clara, California, United States of America) as previously described [22]. APOE genotyping was done using Crook et al.'s method [25] or using a fluorescence-based allele-specific polymerase chain reaction (PCR), also called PCR Amplification of Specific Alleles, on array tape [26] by PreventionGenetics (Marshfield, Wisconsin, United States of America).

The ADNI data used in the preparation of the present report were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (http://adni.loni.ucla.edu/). ADNI's primary goal is to test whether imaging markers, genetic markers, other biological markers, and clinical and neuropsychological assessments can be combined to measure progression of MCI and early AD. More information on ADNI can be found on http://www.adni-info.org/. The Illumina Human610-Quad BeadChip (San Diego, California, United States of America) was used to perform genome-wide genotyping of the ADNI sample as previously described [20], [27]. The APOE polymorphisms (rs429358 and rs7412) were genotyped separately.

The NIA-LOAD/NCRAD Family Study data used in the present report were obtained from the "NIA-Late Onset Alzheimer's Disease and National Cell Repository for Alzheimer's Disease Family Study: Genome-Wide Association Study for Susceptibility Loci" dataset (dbGaP Study Accession: phs000168.v1.p1, Project #2026) on the database of Genotypes and Phenotypes (http://www.ncbi.nlm.nih.gov/projects/gap​/cgi-bin/study.cgi?study_id=phs000168.v1​.p1) website. Recruitment information for NIA-LOAD Family Study and NCRAD participants has been previously described [12]. Genome-wide genotyping for all samples was performed using the Illumina Human610-Quad BeadChip at the Center for Inherited Disease Research (Baltimore, Maryland, United States of America). The APOE polymorphisms (rs429358 and rs7412) were genotyped at PreventionGenetics.

Ethics Statement

De-identification of samples in the TGen cohort was done before receipt, and the study met human studies institutional review board and the Health Insurance Portability and Accountability Act of 1996 regulations. The present work is declared not human-subjects research and is institutional review board exempt under regulation 45 CFR 46. The ADNI study was approved by institutional review boards of all participating institutions and written informed consent was obtained from all participants or authorized representatives. All individuals in the NIA-LOAD/NCRAD Family Study were recruited after providing informed consent and with approval by the relevant institutional review boards. The study was conducted according to the principles in the Declaration of Helsinki.

Generation of CNV Calls and Quality Control

CNV calls were generated for the 1617 TGen samples using PennCNV (2011Jun16 version; http://www.openbioinformatics.org/penncn​v/), a Hidden Markov model based program [28]. The PennCNV-Affy protocol (http://www.openbioinformatics.org/penncn​v/penncnv_tutorial_affy_gw6.html) for the Affymetrix Genome-Wide Human SNP 6.0 Array was first performed to transform raw CEL files into a signal intensity file containing the Log R Ratio (LRR) and B Allele Frequency (BAF) values used by PennCNV to generate CNV calls. The Hidden Markov model “affygw6.hmm”, population frequency of B allele “affygw6.hg18.pfb” and gcmodel “affygw6.hg18.gcmodel” files were used. Extensive quality control (QC) was performed on all samples. A genomic wave adjustment procedure using PennCNV’s gcmodel file was applied as samples that have below optimal genomic wave QC values can be considered unreliable [29]. Frequency distribution plots of the number of CNV calls, LRR standard deviation (SD), BAF Drift and Waviness Factor (WF) were made. A sample was excluded if at least one of the above measures for the sample was greater than 90th percentile of the frequency distribution, i.e. the sample had >56 CNV calls, LRR SD>0.38, BAF Drift>0.01 or WF>0.02.

Due to complications of hemizygosity in males and X-chromosome inactivation in females, analyses were restricted to autosomes. To ensure we were including only high-confidence CNVs in the analysis, CNVs for which the difference of the log likelihood of the most likely copy number state and less likely copy number state was <10, CNVs called based on data <10 SNPs, and CNVs that had >50% overlap with centromeric, telomeric, and immunoglobulin regions as defined in Need et al. [30] were excluded. CNV calls were not filtered for size because both large and small variants could be of potential significance. A case sample observed to have a very large (~8.4 Mb) deletion on chromosome 19, and a control sample observed to have a very large (~22.4 Mb) duplication on chromosome 1, were excluded from the analyses as they may be possible outliers. The ~8.4 Mb deletion on chromosome 19 encompassed both sides of the centromere, but did not overlap any RefSeq or UCSC Genes according to the UCSC Genome Browser [31] (http://genome.ucsc.edu/). 1166 samples (728 cases, 438 controls) with 31045 CNV calls remained after all QC measures and were entered into case/control association analyses.

Case/control Association Analyses

Case/control analyses using permutation-based tests of association in the TGen study were performed similar to the ADNI [20] and NIA-LOAD/NCRAD Family [21] studies. PLINK v1.07 [32] (http://pngu.mgh.harvard.edu/~purcell/pli​nk/) was used to investigate CNV call differences between cases (AD) and controls. Two approaches were used: a candidate gene approach including 317 AD genes identified from the AlzGene database (Updated 5 January 2011) (http://www.alzgene.org/) as having a positive association with AD in at least one study, and a genome-wide approach using 17938 genes from PLINK’s gene list (hg18 coordinates). The AlzGene database is a publicly available online resource that provides a comprehensive and regularly updated catalog of genetic case/control and family association studies in AD [33]. In both approaches, CNV segments either partially or completely overlapping genes were analyzed. The analyses included both deletions and duplications. 50000 null permutations were performed to generate one-sided empirical P values testing genes overlapped by CNV calls in more cases than controls. The analyses focused on genes overlapped by CNV calls in cases, but not in controls, to identify genes that may play a role in AD susceptibility. 317 genes were considered in the candidate gene approach and 17938 genes were considered in the genome-wide approach. Genes that achieved P<0.05 (one-sided) were considered significant.

Meta-analysis

We performed a meta-analysis for the CHRFAM7A gene using results from the ADNI, NIA-LOAD/NCRAD Family and TGen studies to determine differences in frequency of CNV calls overlapping the gene between cases (AD and/or MCI) and controls. A fixed-effects model was run and a summary odds ratio (OR) was calculated using the Mantel Haenszel method. MetaAnalyst Beta 3.13 [34] (http://tuftscaes.org/meta_analyst/index.​html) and Comprehensive Meta-Analysis Version 2 [35] were used for the meta-analysis and generation of the forest plot (Figure 1). The UCSC Genome Browser [31] (http://genome.ucsc.edu/) (March 2006 (NCBI36/hg18) assembly) was used to create representative plots of the CNV calls (Figures 2 and 3). The Genome Browser track for the Affymetrix Genomewide 6.0 array was obtained from the PennCNV website (http://www.openbioinformatics.org/penncn​v/penncnv_download.html).

thumbnail

Figure 1. Forest plot of the CHRFAM7A gene.

The plot represents the meta-analysis of the CHRFAM7A gene using results from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) study, the National Institute of Aging-Late Onset AD/National Cell Repository for AD (NIA-LOAD/NCRAD) Family Study and the TGen study. The odds ratio (OR) and 95% confidence interval (CI) for the odds ratio for each study are represented by black squares and horizontal lines. The summary odds ratio is depicted as a black diamond.

doi:10.1371/journal.pone.0050640.g001
thumbnail

Figure 2. Duplications overlapping the candidate genes APP and DOPEY2 in samples of the TGen cohort.

Representative UCSC Genome Browser (March 2006 (NCBI36/hg18) assembly) plots of duplications overlapping the candidate genes: (A) APP and (B) DOPEY2, in samples of the TGen cohort. The chromosomal location of the gene and probes on the Affymetrix Genomewide 6.0 array are shown. The region with the duplication for each sample relative to the gene is represented by a blue rectangle.

doi:10.1371/journal.pone.0050640.g002

Results

Sample Demographics and CNV Call Characteristics

The sample demographics and CNV call characteristics of the 728 cases and 438 controls who passed all QC measures are shown in Tables 1 and 2. Significant (P<0.05; two-sided) differences in gender, absence or presence of the APOE ε4 allele, age at death, Braak stage and the Consortium to Establish a Registry for Alzheimer's Disease (CERAD) score were observed between cases and controls. 31045 CNV calls (24188 deletions and 6857 duplications) were observed with an average of 45 SNPs per CNV call and an average CNV call length of 64.76 kb. A higher CNV call rate and a lower average CNV call size were observed in deletions compared to duplications. Rates of deletions and duplications did not significantly differ between cases and controls. There were no significant differences in the rates of deletions and rates of duplications when males and females were analyzed separately (data not shown). A large proportion of deletions and duplications were found in the 0.1–0.5 Mb size range (Table 3).

thumbnail

Table 1. Sample demographics in the TGen cohort.

doi:10.1371/journal.pone.0050640.t001
thumbnail

Table 2. Characteristics of copy number variation calls from samples in the TGen cohort.

doi:10.1371/journal.pone.0050640.t002
thumbnail

Table 3. TGen samples grouped by copy number variation call size.

doi:10.1371/journal.pone.0050640.t003

Case/control Association Analyses

The candidate gene approach identified 32 of the 317 genes tested (10.09%) and the genome-wide approach identified 939 of the 17938 genes tested (5.23%) to be overlapped by CNV calls only in cases (AD) but not controls in the TGen study. A significant (P = 0.0003; Fisher’s exact test; two-sided) enrichment of the candidate genes relative to the genome was observed.

Candidate gene approach.

We identified 32 candidate genes in the TGen study overlapped by CNV calls from at least one case (AD) but no controls (Table 4). Representative plots of two genes (APP and DOPEY2) are shown in Figure 2. The HLA-DRA gene was overlapped by deletions in nine cases (uncorrected P = 0.0140; one-sided). This gene was also found to be overlapped by deletions in two controls in the ADNI study. Two genes (RELN overlapped by deletions in two cases and DOPEY2 overlapped by duplications in four cases) identified in this study were also reported from only cases (AD and/or MCI) in the ADNI and NIA-LOAD/NCRAD Family studies. One AD sample (APOE ε2/ε3 genotype, age at death = 67) had a novel APP gene duplication supported by 443 sequential SNP and CNV probes. The CHRFAM7A gene reported in the ADNI and NIA-LOAD/NCRAD Family studies was overlapped by deletions in 10 cases and two controls, and duplications in 12 cases and one control (corrected P = 0.0198; one-sided) in this study (Figure 3).

thumbnail

Figure 3. Deletions and duplications overlapping the CHRFAM7A gene in samples of the TGen cohort.

Representative UCSC Genome Browser (March 2006 (NCBI36/hg18) assembly) plots of deletions and duplications overlapping the CHRFAM7A gene in samples of the TGen cohort. The chromosomal location of the gene and probes on the Affymetrix Genomewide 6.0 array are shown. The region with the deletion for each sample relative to the gene is highlighted by a red rectangle and the region with the duplication for each sample relative to the gene is highlighted by a blue rectangle.

doi:10.1371/journal.pone.0050640.g003
thumbnail

Table 4. Genes overlapped by copy number variation calls from at least one case and no control samples in the TGen cohort using the candidate gene approach.

doi:10.1371/journal.pone.0050640.t004
Genome-wide approach.

We also identified 939 genes across the genome overlapped by CNV calls only in cases (AD) but not controls in the TGen study. Genes overlapped by CNV calls from at least four cases but not controls in the TGen study are shown in Table 5. The HLA-DRA gene overlapped by deletions in nine cases in the TGen study and identified in the candidate gene approach was also found from this approach (uncorrected P = 0.0144; one-sided). The CHRFAM7A gene reported in the ADNI and NIA-LOAD/NCRAD Family Studies and mentioned in the candidate gene approach had an uncorrected P = 0.0046; one-sided. Deletions in 10 cases and two controls, and duplications in 12 cases and one control, overlapped the CHRFAM7A gene in the TGen study (Figure 3). Other genes reported in the ADNI and NIA-LOAD/NCRAD Family studies were overlapped by CNV calls in the TGen study although they did not reach significance at uncorrected P<0.05. These include CSMD1 (deletions in 65 cases and 32 controls, duplications in one case and one control), ERBB4 (deletions in 71 cases and 35 controls, duplication in one control), HNRNPCL1 (deletions in 19 cases and eight controls, duplications in one case and two controls), IMMP2L (deletions in six cases and five controls, duplication in one control), NRXN1 (deletions in two cases and three controls), and SLC35F2 (duplications in two cases and one control). Deletions in three cases and three controls, and duplications in two cases and three controls in the TGen study, overlapped the CHRNA7 gene identified in a previous study [16]. No CNV calls were found in the TGen study overlapping other previously reported regions including CR1 [17], 14q11.2 [18] and 15q11.2 [19].

thumbnail

Table 5. Genes overlapped by copy number variation calls in at least four case samples and no control samples in the TGen cohort using the genome-wide approach.

doi:10.1371/journal.pone.0050640.t005

Meta-analysis

A meta-analysis for the CHRFAM7A gene was performed using findings from the ADNI, NIA-LOAD/NCRAD Family and TGen studies (Table 6 and Figure 1). Overall, the gene was overlapped by CNV calls in 38 of 1797 (2.115%) cases (AD and/or MCI) and four of 752 (0.532%) controls. A significant association was observed for the gene with AD and/or MCI risk (P = 0.006, summary OR = 3.986 (95% confidence interval 1.490–10.667)).

thumbnail

Table 6. Meta-analysis of the CHRFAM7A gene.

doi:10.1371/journal.pone.0050640.t006

Discussion

The present report represents an initial CNV analysis in the TGen cohort, a unique cohort of clinically characterized and neuropathologically verified individuals. After extensive QC, case (AD)/control association analyses using candidate gene and genome-wide approaches were performed. Genes enriched in cases relative to controls were determined, suggesting possible involvement of these genes in AD susceptibility.

Rates of deletions and duplications did not significantly differ between cases and controls. This is different from findings in the ADNI and NIA-LOAD/NCRAD Family studies and could be due to different participant selection criteria, random sampling variation, different QC criteria, and that the NIA-LOAD/NCRAD Family Study and TGen study analyses included only AD and control samples, whereas the ADNI study analyses included MCI samples in addition to AD and control samples.

The candidate gene approach revealed a number of interesting genes (Table 4 and Figure 2). The HLA-DRA (major histocompatibility complex, class II, DR alpha) gene on chromosome 6 is a human leukocyte antigen (HLA) class II alpha chain paralogue which plays an important role in the immune system by presenting peptides derived from extracellular proteins. Variants in HLA-DRA have been associated with Parkinson disease [36], [37] and multiple sclerosis [38], [39], but not with AD. Other HLA alleles however have been investigated for a possible role in AD [40][43].

The CHRFAM7A (CHRNA7 (cholinergic receptor, nicotinic, alpha 7, exons 5–10) and FAM7A (family with sequence similarity 7A, exons A–E) fusion) gene (Figure 3) is located on chromosome 15. It is formed as a hybrid of a partially duplicated CHRNA7 gene and the FAM7A gene [44], [45]. It is highly polymorphic and individuals with and without this gene have been identified. A 2-bp deletion polymorphism at position 497–498 in exon 6 of this gene has been observed to be significantly over-represented in participants with AD, dementia with Lewy bodies and Pick’s disease compared to controls [46].

Although CHRFAM7A is transcribed, its translation and possible function of the resulting protein is uncertain. The gene is expressed in the hippocampus, a brain region known to be first affected in AD. Recently, it has been suggested to possibly modulate α7 subunit receptor-mediated synaptic transmission and cholinergic anti-inflammatory response [47]. It may also be a dominant negative modulator of CHRNA7 function and important for receptor regulation in humans [48]. Duplications overlapping the CHRNA7 gene and an approximately 300 kb region upstream of the gene were identified in a previous study investigating the role of CNVs in AD [16]. A meta-analysis using the CHRFAM7A gene results from the ADNI, NIA-LOAD/NCRAD Family and TGen studies indicated a significant association of the gene with AD and/or MCI risk at an OR of 3.986. This association is striking relative to known AD markers and is similar to the OR for one copy of the APOE ε4 allele. However, unlike the APOE ε4 allele which is a change in a single base within the gene, CNV calls overlapping the CHRFAM7A gene involves more than one base and often a large region with less than perfect definition of boundaries given available methodology. In addition, in a CNV analysis, a gene may be overlapped by deletions or duplications which may have different effects on the protein. Nonetheless, the strength of CNV signals overlapping the CHRFAM7A gene in the present three samples warrants further investigation and replication in additional independent samples.

Two genes (RELN and DOPEY2) in the three studies were found to be overlapped by CNV calls from cases (AD and/or MCI) but not controls. The RELN (reelin) gene on chromosome 7 encodes the glycoprotein reelin, which activates a signaling pathway required for proper positioning of neurons within laminated nervous system parenchyma. Gene variants have been associated with AD [49] and the protein has been observed to have increased expression in pyramidal neurons of the hippocampus in AD individuals and in cognitively intact controls with AD-associated pathology [50]. The DOPEY2 (dopey family member 2, also known as C21orf5) gene located on chromosome 21 in the Down syndrome critical region is a potential Down syndrome candidate gene [51], [52]. Overexpression of the gene may be associated with the neurological phenotypes and mental retardation observed in Down syndrome patients. Although the DOPEY2 gene was identified in the candidate gene approach in the TGen study, CNV calls overlapping this gene also completely overlapped the CBR3 (carbonyl reductase 3) and CBR3-AS1 (CBR3 antisense RNA 1). The CBR3 protein, which catalyzes reduction of carbonyl compounds to their corresponding alcohols, has been found to be differentially expressed in the cerebellum of patients with atypical cases of frontotemporal lobar degeneration with fused in sarcoma-positive inclusions compared to controls [53]. Although DOPEY2 is a known candidate, it is possible that CBR3 and CBR3-AS1 could be pathologic given their involvement in other neurodegenerative diseases. One AD sample (APOE ε2/ε3 genotype, age at death = 67) was identified with an APP duplication. The patient started suffering from forgetfulness at approximately 59 years and was admitted to a nursing home at approximately age 63. The patient had no reported family history of dementia. APP duplications have been associated with early-onset [54][57] but not late-onset AD, and it is possible that this patient may have had an early-onset sporadic form of AD (age at onset<60 or 65 years). Replication in independent samples and laboratory validation can help confirm the role of these genes in AD susceptibility.

The genome-wide approach revealed the candidate genes: HLA-DRA and CHRFAM7A as well as identified genes reported in the two previous studies. The CSMD1, HNRNPCL1, IMMP2L and SLC35F2 genes have not been previously associated with AD. The NRXN1 gene has been associated with autism [58], schizophrenia [59], and has been shown to have reduced expression with increasing AD severity [60]. The ERBB4 gene may play a possible role in the progression of AD pathology [61][63].

It is important to note the limitations of the present report. Although the same software (PennCNV) was used in the three studies, different QC criteria were used for the selection of samples. The ADNI and NIA-LOAD/NCRAD Family study samples were genotyped on the Illumina Human610-Quad BeadChip and used similar QC criteria. The TGen study samples were genotyped on the Affymetrix Genome-Wide Human SNP 6.0 Array and thus we used a slightly different QC criteria. To our knowledge, there does not appear to be consensus on a well defined set of QC criteria for inclusion of the most appropriate samples in CNV analyses. The QC criterion applied in the present study may have been too stringent, leading to samples possibly having informative CNV data being excluded. A direct comparison of CNV calls from the three studies would be difficult as two different genotyping platforms were used. Probes from the two platforms may not correspond with each other with respect to their location. Replication in additional independent data sets and future molecular studies will help confirm the findings.

In sum, we have conducted an initial CNV analysis in samples from a cohort of clinically characterized and neuropathologically verified individuals. Rates of deletions and duplications did not significantly differ between cases and controls. Gene-based association analysis identified a number of genes including those reported in the ADNI and NIA-LOAD/NCRAD Family studies (CHRFAM7A, RELN and DOPEY2) as well as a new gene (HLA-DRA). Meta-analysis from the three studies revealed a significant association for CHRFAM7A with AD and/or MCI risk. Replication in independent samples will be necessary to confirm these findings. Targeted analyses of the identified regions will help determine the biological role of these variants. Overall, there appears to be some consistency of CNVs across AD cohorts and this variation holds promise for revealing novel risk factors and disease mechanisms.

Acknowledgments

Many data and biomaterials for the TGen cohort were collected from several National Institute on Aging (NIA) and National Alzheimer’s Coordinating Center (NACC) funded sites. We thank Amanda J. Myers (University of Miami, Department of Psychiatry) and John A. Hardy (Reta Lila Weston Institute, University College London) for collecting and preparing the series. Marcelle Morrison-Bogorad, Tony Phelps, and Walter Kukull are thanked for helping to co-ordinate this collection. The directors, pathologist and technicians involved include: National Institute on Aging: Ruth Seemann; John Hopkins Alzheimer's Disease Research Center: Juan C. Troncoso, Olga Pletnikova; University of California, Los Angeles: Harry Vinters, Justine Pomakian; The Kathleen Price Bryan Brain Bank, Duke University Medical Center: Christine Hulette, John F. Ervin; Stanford University: Dikran Horoupian, Ahmad Salehi; New York Brain Bank, Taub Institute, Columbia University (NYBB): Jean Paul Vonsattel, Katerina Mancevska; Massachusetts Alzheimer’s Disease Research Center: E. Tessa Hedley-Whyte, M.P. Frosch, Karlotta Fitch; University of Michigan: Roger Albin, Lisa Bain, Eszter Gombosi; University of Kentucky: William Markesbery, Sonya Anderson; Mayo Clinic, Jacksonville: Dennis W. Dickson, Natalie Thomas; University Southern California: Caroll A. Miller, Jenny Tang, Dimitri Diaz; Washington University, St Louis Alzheimer’s Disease Research Center: Dan McKeel, John C. Morris, Eugene Johnson, Jr., Virginia Buckles, Deborah Carter; University of Washington, Seattle: Thomas Montine, Aimee Schantz; University of Pennsylvania School of Medicine, Alzheimer’s Disease Research Center: John Q Trojanowski, Virginia M Lee, Vivianna Van Deerlin, Terry Schuck; Boston University Alzheimer’s Disease Research Center: Ann C. McKee, Carol Kubilus; Sun Health Research Institute Brain Donation Program of Sun City, Arizona: Joseph Rogers, Thomas G. Beach, Lucia I. Sue; Emory University: Bruce H. Wainer, Marla Gearing; University of Texas, Southwestern Medical School: Charles L. White, III, Roger Rosenberg, Marilyn Howell, Joan Reisch; University of California, Davis: William Ellis, Mary Ann Jarvis; Rush University Medical Center, Rush Alzheimer's Disease Center: David A. Bennett, Julie A. Schneider, Karen Skish, Wayne T. Longman; University of Miami/NPF Brain Endowment Bank: Deborah C. Mash, Margaret J Basile, Mitsuko Tanaka; Oregon Health & Science University: Randy Wotljer. Additional tissues include samples from the following sites: Newcastle Brain Tissue Resource: C.M. Morris, Ian G McKeith, Robert H Perry; MRC London Brain Bank for Neurodegenerative Diseases: Simon Lovestone, Safa Al-Sarraj, Claire Troakes; South West Dementia Brain Bank: Seth Love, Patrick Kehoe, Laura Palmer; The Netherlands Brain Bank: Inge Huitinga, Marleen Rademaker, Michiel Kooreman; Institut de Neuropatologia, Servei Anatomia Patologica, Universitat de Barcelona: Isidre Ferrer Abizanda, Susana Casas Boluda.

Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.ucla.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.ucla.edu/wp-content/upl​oads/how_to_apply/ADNI_Acknowledgement_L​ist.pdf. We also thank the following people: genotyping at The Translational Genomics Research Institute (TGen): Matthew Huentelman and David Craig, and sample processing, storage and distribution at the National Cell Repository for Alzheimer’s Disease (NCRAD): Kelley Faber and Colleen Mitchell.

For the National Institute of Aging-Late Onset AD/National Cell Repository for AD (NIA-LOAD/NCRAD) Family Study, we thank the NIH Genome-Wide Association Studies Data Repository for providing the “Genetic Consortium for Late Onset Alzheimer’s Disease” dataset (dbGaP Study Accession: phs000168.v1.p1, Project #2026). Investigators who contributed to the NIA-LOAD/NCRAD Family Study can be found on the dbGaP website (http://www.ncbi.nlm.nih.gov/projects/gap​/cgi-bin/study.cgi?study_id=phs000168.v1​.p1). We would like to thank Susan LaRusse Eckert, Stephan Dean and Jennifer Williamson-Catania at Columbia University, Michele Goodman at Indiana University, Creighton H. Phelps, Marcelle Morrison-Bogorod, Marilyn Miller, and Stephen Snyder, at the NIA, for their guidance and help in coordinating the project.

The authors would like to thank contributors who collected samples as well as patients and their families, whose help and participation made this work possible. We would also like to thank John West for his help.

Author Contributions

Conceived and designed the experiments: SS MJH LS AS. Performed the experiments: SS MJH JJC AJM KMF TF RM LS MT JH EMR AS. Analyzed the data: SS MJH LS SK AS. Contributed reagents/materials/analysis tools: MJH JJC AJM KMF AS. Wrote the paper: SS MJH JJC AJM KMF TF RM LS SK MT JH EMR AS.

References

  1. 1. Alzheimer’s Association, Thies W, Bleiler L (2011) 2011 Alzheimer’s disease facts and figures. Alzheimers Dement 7: 208–244. doi: 10.1016/j.jalz.2011.02.004
  2. 2. Petersen RC, Roberts RO, Knopman DS, Boeve BF, Geda YE, et al. (2009) Mild cognitive impairment: ten years later. Arch Neurol 66: 1447–1455. doi: 10.1001/archneurol.2009.266
  3. 3. Gatz M, Reynolds CA, Fratiglioni L, Johansson B, Mortimer JA, et al. (2006) Role of genes and environments for explaining Alzheimer disease. Arch Gen Psychiatry 63: 168–174. doi: 10.1001/archpsyc.63.2.168
  4. 4. Bekris LM, Yu CE, Bird TD, Tsuang DW (2010) Genetics of Alzheimer disease. J Geriatr Psychiatry Neurol 23: 213–227. doi: 10.1177/0891988710383571
  5. 5. Harold D, Abraham R, Hollingworth P, Sims R, Gerrish A, et al. (2009) Genome-wide association study identifies variants at CLU and PICALM associated with Alzheimer’s disease. Nat Genet 41: 1088–1093. doi: 10.1038/ng.440
  6. 6. Lambert JC, Heath S, Even G, Campion D, Sleegers K, et al. (2009) Genome-wide association study identifies variants at CLU and CR1 associated with Alzheimer’s disease. Nat Genet 41: 1094–1099. doi: 10.1038/ng.439
  7. 7. Jun G, Naj AC, Beecham GW, Wang LS, Buros J, et al. (2010) Meta-analysis confirms CR1, CLU, and PICALM as alzheimer disease risk loci and reveals interactions with APOE genotypes. Arch Neurol 67: 1473–1484. doi: 10.1001/archneurol.2010.201
  8. 8. Seshadri S, Fitzpatrick AL, Ikram MA, DeStefano AL, Gudnason V, et al. (2010) Genome-wide analysis of genetic loci associated with Alzheimer disease. JAMA 303: 1832–1840. doi: 10.1001/jama.2010.574
  9. 9. Naj AC, Beecham GW, Martin ER, Gallins PJ, Powell EH, et al. (2010) Dementia revealed: novel chromosome 6 locus for late-onset Alzheimer disease provides genetic evidence for folate-pathway abnormalities. PLoS Genet 6: e1001130. doi: 10.1371/journal.pgen.1001130
  10. 10. Naj AC, Jun G, Beecham GW, Wang LS, Vardarajan BN, et al. (2011) Common variants at MS4A4/MS4A6E, CD2AP, CD33 and EPHA1 are associated with late-onset Alzheimer’s disease. Nat Genet 43: 436–441. doi: 10.1038/ng.801
  11. 11. Hollingworth P, Harold D, Sims R, Gerrish A, Lambert JC, et al. (2011) Common variants at ABCA7, MS4A6A/MS4A4E, EPHA1, CD33 and CD2AP are associated with Alzheimer’s disease. Nat Genet 43: 429–435. doi: 10.1038/ng.803
  12. 12. Wijsman EM, Pankratz ND, Choi Y, Rothstein JH, Faber KM, et al. (2011) Genome-wide association of familial late-onset Alzheimer’s disease replicates BIN1 and CLU and nominates CUGBP2 in interaction with APOE. PLoS Genet 7: e1001308. doi: 10.1371/journal.pgen.1001308
  13. 13. Slooter AJ, Cruts M, Kalmijn S, Hofman A, Breteler MM, et al. (1998) Risk estimates of dementia by apolipoprotein E genotypes from a population-based incidence study: the Rotterdam Study. Arch Neurol 55: 964–968. doi: 10.1001/archneur.55.7.964
  14. 14. Verhaaren BF, Vernooij MW, Koudstaal PJ, Uitterlinden AG, Duijn CM, et al.. (2012) Alzheimer’s Disease Genes and Cognition in the Nondemented General Population. Biol Psychiatry.
  15. 15. Cook EH Jr, Scherer SW (2008) Copy-number variations associated with neuropsychiatric conditions. Nature 455: 919–923. doi: 10.1038/nature07458
  16. 16. Heinzen EL, Need AC, Hayden KM, Chiba-Falek O, Roses AD, et al. (2010) Genome-wide scan of copy number variation in late-onset Alzheimer’s disease. J Alzheimers Dis 19: 69–77. doi: 10.3233/JAD-2010-1212
  17. 17. Brouwers N, Cauwenberghe CV, Engelborghs S, Lambert JC, Bettens K, et al. (2012) Alzheimer risk associated with a copy number variation in the complement receptor 1 increasing C3b/C4b binding sites. Mol Psychiatry 17: 223–233. doi: 10.1038/mp.2011.24
  18. 18. Shaw CA, Li Y, Wiszniewska J, Chasse S, Zaidi SN, et al. (2011) Olfactory copy number association with age at onset of Alzheimer disease. Neurology 76: 1302–1309. doi: 10.1212/WNL.0b013e3182166df5
  19. 19. Ghani M, Pinto D, Lee JH, Grinberg Y, Sato C, et al. (2012) Genome-wide survey of large rare copy number variants in Alzheimer’s disease among Caribbean hispanics. G3 (Bethesda) 2: 71–78. doi: 10.1534/g3.111.000869
  20. 20. Swaminathan S, Kim S, Shen L, Risacher SL, Foroud T, et al. (2011) Genomic Copy Number Analysis in Alzheimer’s Disease and Mild Cognitive Impairment: An ADNI Study. Int J Alzheimers Dis 2011: 729478. doi: 10.4061/2011/729478
  21. 21. Swaminathan S, Shen L, Kim S, Inlow M, West JD, et al. (2012) Analysis of Copy Number Variation in Alzheimer’s Disease: the NIA-LOAD/NCRAD Family Study. Curr Alzheimer Res 9: 801–814. doi: 10.2174/156720512802455331
  22. 22. Corneveaux JJ, Myers AJ, Allen AN, Pruzin JJ, Ramirez M, et al. (2010) Association of CR1, CLU and PICALM with Alzheimer’s disease in a cohort of clinically characterized and neuropathologically verified individuals. Hum Mol Genet 19: 3295–3301. doi: 10.1093/hmg/ddq221
  23. 23. Webster JA, Gibbs JR, Clarke J, Ray M, Zhang W, et al. (2009) Genetic control of human brain transcript expression in Alzheimer disease. Am J Hum Genet 84: 445–458. doi: 10.1016/j.ajhg.2009.03.011
  24. 24. Myers AJ, Gibbs JR, Webster JA, Rohrer K, Zhao A, et al. (2007) A survey of genetic human cortical gene expression. Nat Genet 39: 1494–1499. doi: 10.1038/ng.2007.16
  25. 25. Crook R, Hardy J, Duff K (1994) Single-day apolipoprotein E genotyping. J Neurosci Methods 53: 125–127. doi: 10.1016/0165-0270(94)90168-6
  26. 26. Hawkins JR, Khripin Y, Valdes AM, Weaver TA (2002) Miniaturized sealed-tube allele-specific PCR. Hum Mutat 19: 543–553. doi: 10.1002/humu.10060
  27. 27. Saykin AJ, Shen L, Foroud TM, Potkin SG, Swaminathan S, et al. (2010) Alzheimer’s Disease Neuroimaging Initiative biomarkers as quantitative phenotypes: Genetics core aims, progress, and plans. Alzheimers Dement 6: 265–273. doi: 10.1016/j.jalz.2010.03.013
  28. 28. Wang K, Li M, Hadley D, Liu R, Glessner J, et al. (2007) PennCNV: an integrated hidden Markov model designed for high-resolution copy number variation detection in whole-genome SNP genotyping data. Genome Res 17: 1665–1674. doi: 10.1101/gr.6861907
  29. 29. Diskin SJ, Li M, Hou C, Yang S, Glessner J, et al. (2008) Adjustment of genomic waves in signal intensities from whole-genome SNP genotyping platforms. Nucleic Acids Res 36: e126. doi: 10.1093/nar/gkn556
  30. 30. Need AC, Ge D, Weale ME, Maia J, Feng S, et al. (2009) A genome-wide investigation of SNPs and CNVs in schizophrenia. PLoS Genet 5: e1000373. doi: 10.1371/journal.pgen.1000373
  31. 31. Kent WJ, Sugnet CW, Furey TS, Roskin KM, Pringle TH, et al. (2002) The human genome browser at UCSC. Genome Res 12: 996–1006. doi: 10.1101/gr.229102. Article published online before print in May 2002
  32. 32. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, et al. (2007) PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 81: 559–575. doi: 10.1086/519795
  33. 33. Bertram L, McQueen MB, Mullin K, Blacker D, Tanzi RE (2007) Systematic meta-analyses of Alzheimer disease genetic association studies: the AlzGene database. Nat Genet 39: 17–23. doi: 10.1038/ng1934
  34. 34. Wallace BC, Schmid CH, Lau J, Trikalinos TA (2009) Meta-Analyst: software for meta-analysis of binary, continuous and diagnostic data. BMC Med Res Methodol 9: 80. doi: 10.1186/1471-2288-9-80
  35. 35. Borenstein M, Hedges L, Higgins J, Rothstein H (2005) Comprehensive Meta-analysis Version 2, Biostat, Englewood NJ.
  36. 36. Hamza TH, Zabetian CP, Tenesa A, Laederach A, Montimurro J, et al. (2010) Common genetic variation in the HLA region is associated with late-onset sporadic Parkinson’s disease. Nat Genet 42: 781–785. doi: 10.1038/ng.642
  37. 37. Guo Y, Deng X, Zheng W, Xu H, Song Z, et al. (2011) HLA rs3129882 variant in Chinese Han patients with late-onset sporadic Parkinson disease. Neurosci Lett 501: 185–187. doi: 10.1016/j.neulet.2011.05.245
  38. 38. International Multiple Sclerosis Genetics Consortium, Hafler DA, Compston A, Sawcer S, Lander ES, et al. (2007) Risk alleles for multiple sclerosis identified by a genomewide study. N Engl J Med 357: 851–862. doi: 10.1056/NEJMoa073493
  39. 39. Hoppenbrouwers IA, Aulchenko YS, Janssens AC, Ramagopalan SV, Broer L, et al. (2009) Replication of CD58 and CLEC16A as genome-wide significant risk genes for multiple sclerosis. J Hum Genet 54: 676–680. doi: 10.1038/jhg.2009.96
  40. 40. Ma SL, Tang NL, Tam CW, Lui VW, Suen EW, et al. (2008) Association between HLA-A alleles and Alzheimer’s disease in a southern Chinese community. Dement Geriatr Cogn Disord 26: 391–397. doi: 10.1159/000164275
  41. 41. Lehmann DJ, Barnardo MC, Fuggle S, Quiroga I, Sutherland A, et al. (2006) Replication of the association of HLA-B7 with Alzheimer’s disease: a role for homozygosity? J Neuroinflammation 3: 33. doi: 10.1186/1742-2094-3-33
  42. 42. Guerini FR, Tinelli C, Calabrese E, Agliardi C, Zanzottera M, et al. (2009) HLA-A*01 is associated with late onset of Alzheimer’s disease in Italian patients. Int J Immunopathol Pharmacol 22: 991–999.
  43. 43. Listi F, Candore G, Balistreri CR, Grimaldi MP, Orlando V, et al. (2006) Association between the HLA-A2 allele and Alzheimer disease. Rejuvenation Res 9: 99–101. doi: 10.1089/rej.2006.9.99
  44. 44. Riley B, Williamson M, Collier D, Wilkie H, Makoff A (2002) A 3-Mb map of a large Segmental duplication overlapping the alpha7-nicotinic acetylcholine receptor gene (CHRNA7) at human 15q13-q14. Genomics 79: 197–209. doi: 10.1006/geno.2002.6694
  45. 45. Gault J, Robinson M, Berger R, Drebing C, Logel J, et al. (1998) Genomic organization and partial duplication of the human alpha7 neuronal nicotinic acetylcholine receptor gene (CHRNA7). Genomics 52: 173–185. doi: 10.1006/geno.1998.5363
  46. 46. Feher A, Juhasz A, Rimanoczy A, Csibri E, Kalman J, et al. (2009) Association between a genetic variant of the alpha-7 nicotinic acetylcholine receptor subunit and four types of dementia. Dement Geriatr Cogn Disord 28: 56–62. doi: 10.1159/000230036
  47. 47. de Lucas-Cerrillo AM, Maldifassi MC, Arnalich F, Renart J, Atienza G, et al. (2011) Function of partially duplicated human alpha77 nicotinic receptor subunit CHRFAM7A gene: potential implications for the cholinergic anti-inflammatory response. J Biol Chem 286: 594–606. doi: 10.1074/jbc.M110.180067
  48. 48. Araud T, Graw S, Berger R, Lee M, Neveu E, et al. (2011) The chimeric gene CHRFAM7A, a partial duplication of the CHRNA7 gene, is a dominant negative regulator of alpha7*nAChR function. Biochem Pharmacol 82: 904–914. doi: 10.1016/j.bcp.2011.06.018
  49. 49. Seripa D, Matera MG, Franceschi M, Daniele A, Bizzarro A, et al. (2008) The RELN locus in Alzheimer’s disease. J Alzheimers Dis 14: 335–344.
  50. 50. Kramer PL, Xu H, Woltjer RL, Westaway SK, Clark D, et al. (2011) Alzheimer disease pathology in cognitively healthy elderly: a genome-wide study. Neurobiol Aging 32: 2113–2122. doi: 10.1016/j.neurobiolaging.2010.01.010
  51. 51. Rachidi M, Delezoide AL, Delabar JM, Lopes C (2009) A quantitative assessment of gene expression (QAGE) reveals differential overexpression of DOPEY2, a candidate gene for mental retardation, in Down syndrome brain regions. Int J Dev Neurosci 27: 393–398. doi: 10.1016/j.ijdevneu.2009.02.001
  52. 52. Rachidi M, Lopes C, Delezoide AL, Delabar JM (2006) C21orf5, a human candidate gene for brain abnormalities and mental retardation in Down syndrome. Cytogenet Genome Res 112: 16–22. doi: 10.1159/000087509
  53. 53. Martins-de-Souza D, Guest PC, Mann DM, Roeber S, Rahmoune H, et al. (2012) Proteomic analysis identifies dysfunction in cellular transport, energy, and protein metabolism in different brain regions of atypical frontotemporal lobar degeneration. J Proteome Res 11: 2533–2543. doi: 10.1021/pr2012279
  54. 54. Rovelet-Lecrux A, Hannequin D, Raux G, Le Meur N, Laquerriere A, et al. (2006) APP locus duplication causes autosomal dominant early-onset Alzheimer disease with cerebral amyloid angiopathy. Nat Genet 38: 24–26. doi: 10.1038/ng1718
  55. 55. Sleegers K, Brouwers N, Gijselinck I, Theuns J, Goossens D, et al. (2006) APP duplication is sufficient to cause early onset Alzheimer’s dementia with cerebral amyloid angiopathy. Brain 129: 2977–2983. doi: 10.1093/brain/awl203
  56. 56. McNaughton D, Knight W, Guerreiro R, Ryan N, Lowe J, et al.. (2012) Duplication of amyloid precursor protein (APP), but not prion protein (PRNP) gene is a significant cause of early onset dementia in a large UK series. Neurobiol Aging 33: 426 e413–421.
  57. 57. Antonell A, Gelpi E, Sanchez-Valle R, Martinez R, Molinuevo JL, et al. (2012) Breakpoint sequence analysis of an AbetaPP locus duplication associated with autosomal dominant Alzheimer’s disease and severe cerebral amyloid angiopathy. J Alzheimers Dis 28: 303–308. doi: 10.3233/JAD-2011-110911
  58. 58. Autism Genome Project Consortium, Szatmari P, Paterson AD, Zwaigenbaum L, Roberts W, et al (2007) Mapping autism risk loci using genetic linkage and chromosomal rearrangements. Nat Genet 39: 319–328. doi: 10.1038/ng1985
  59. 59. Rujescu D, Ingason A, Cichon S, Pietilainen OP, Barnes MR, et al. (2009) Disruption of the neurexin 1 gene is associated with schizophrenia. Hum Mol Genet 18: 988–996. doi: 10.1093/hmg/ddn351
  60. 60. Gomez Ravetti M, Rosso OA, Berretta R, Moscato P (2010) Uncovering molecular biomarkers that correlate cognitive decline with the changes of hippocampus’ gene expression profiles in Alzheimer’s disease. PLoS One 5: e10153. doi: 10.1371/journal.pone.0010153
  61. 61. Chaudhury AR, Gerecke KM, Wyss JM, Morgan DG, Gordon MN, et al. (2003) Neuregulin-1 and erbB4 immunoreactivity is associated with neuritic plaques in Alzheimer disease brain and in a transgenic model of Alzheimer disease. J Neuropathol Exp Neurol 62: 42–54.
  62. 62. Woo RS, Lee JH, Yu HN, Song DY, Baik TK (2010) Expression of ErbB4 in the apoptotic neurons of Alzheimer’s disease brain. Anat Cell Biol 43: 332–339. doi: 10.5115/acb.2010.43.4.332
  63. 63. Woo RS, Lee JH, Yu HN, Song DY, Baik TK (2011) Expression of ErbB4 in the neurons of Alzheimer’s disease brain and APP/PS1 mice, a model of Alzheimer’s disease. Anat Cell Biol 44: 116–127. doi: 10.5115/acb.2011.44.2.116