The present report represents an initial analysis of CNVs in the ADNI dataset and is the first CNV analysis of patients with MCI. After extensive QC, we analyzed CNV calls generated in cases (AD and MCI) compared to controls (HC), using whole genome and candidate gene association approaches.
Comparison of the CNV calls between the three diagnostic groups showed no excess CNV burden (rate of calls) in AD and MCI participants compared to controls. This is consistent with previously published results [9
]. Two AD participants were found to have CNV calls >2
Mb. One AD participant had a duplication on chromosome 11 (Supplementary Figure
3) which includes the gene LUZP2
(leucine zipper protein 2). This gene has been shown to be expressed only in the brain and spinal cord in adult mouse tissues [27
]. The authors of the study also found this gene to be deleted in some patients with Wilms tumor-aniridia-genitourinary anomalies-mental retardation (WAGR) syndrome. Another AD participant had a deletion on chromosome 4 (Supplementary Figure
2), which includes the following genes: NDST4
(N-deacetylase/N-sulfotransferase 4), TRAM1L1
(translocation-associated membrane protein 1-like 1), and MIR1973
(microRNA 1973). None of these genes have been previously associated with AD susceptibility. Further investigation by either cytogenetic techniques such as fluorescence in situ hybridization (FISH) or molecular biology techniques such as real-time quantitative polymerase chain reaction (PCR) and deep resequencing is required to determine the clinical relevance of these regions.
A case/control association analysis was then performed using a candidate gene approach and a whole genome approach to determine if there was an excess of CNV calls partially overlapping genes in AD or MCI participants relative to controls, suggesting potential involvement of these genes in AD or MCI susceptibility.
The candidate gene approach revealed several interesting genes ( and ). The CHRFAM7A
gene had CNV calls in cases (two AD and two MCI) but not in controls. CHRFAM7A
, located on chromosome 15, consists of a partial duplication of the CHRNA7
(cholinergic receptor, nicotinic, alpha 7 ) gene (exons 5-10) fused to a copy of the FAM7A
(family with sequence similarity 7A ) gene (exons A-E) [28
]. The CHRFAM7A
gene contains a polymorphism consisting of a 2-base pair deletion (−2
bp) at position 497-498 of exon 6. This −2
bp polymorphism has been associated with schizophrenia [29
]. The CHRFAM7A
genotype without the −2
bp allele has also been shown to be significantly overrepresented in AD (P
= .011), dementia with Lewy bodies (P
= .001), and Pick's disease (P
< .0001) participants [30
]. Heinzen et al. found a duplication in six out of 276 dementia cases (2%) and one out of 322 controls (0.3%) within the schizophrenia and epilepsy-associated risk region at 15q13.3, affecting the CHRNA7
]. In the present study, we found a deletion in one out of 222 AD participants (0.45%) and one out of 136 MCI participants (0.74%), as well as a duplication in two out of 143 HC participants (1.40%). This gene codes for one of several neuronal cholinergic nicotinic receptors. Genetic variants in CHRNA7
and other cholinergic receptor genes have been implicated in AD susceptibility [9
], and further investigation of this gene family is warranted. The number of CNV calls overlapping the identified genes is small, as we had a small sample size (n
= 501) after QC for analysis limiting power. Nevertheless, all identified genes have been previously investigated in AD studies and thus represent potential candidate genes. Replication studies with larger sample sizes as well as laboratory validation are required to confirm the role of these genes in AD susceptibility.
The whole genome approach revealed three genes at uncorrected P
< .05, as shown in . CSMD1
(CUB and Sushi multiple domains 1) has been shown to be primarily synthesized in the developing central nervous system (CNS) and epithelial tissues [31
]. It is enriched in the nerve growth cone, suggesting that it may be an important regulator of complement activation and inflammation in the developing CNS. HNRNPCL1
(heterogeneous nuclear ribonucleoprotein C-like 1) is predicted to play a role in nucleosome assembly by neutralizing basic proteins such as A and B core hnRNPs (Uniprot: http://www.uniprot.org/
(solute carrier family 35, member F2), also known as lung squamous cell cancer-related protein LSCC-3, is integral to membrane and transport (Gene Ontology: http://www.geneontology.org/
We also identified CNVs overlapping two candidate genes associated with neuropsychiatric disorders: NRXN1
from the whole genome approach in cases, but not in controls. Deletions in the NRXN1
(neurexin 1) gene were observed in four AD participants and three MCI participants; deletions in the ERBB4
(v-erb-a erythroblastic leukemia viral oncogene homolog 4) gene were observed in four AD participants and one MCI participant, respectively. NRXN1
, a member of the neurexin family on chromosome 2, is a cell surface receptor that binds neuroligins. The Ca2+
-dependent neurexin-neuroligin complex is present in the CNS at synapses and is required for efficient neurotransmission and formation of synaptic contacts [32
]. This gene has been found to have reduced expression with AD severity [33
], and its disruption has been shown to be associated with schizophrenia [20
] and autism [24
]. Deletions in this gene have also been shown to predispose to a variety of developmental disorders including autism spectrum disorders, language delays, and mental retardation [36
]. Interestingly, an SNP (rs6463843) flanking the NXPH1
(neurexophilin 1) gene was identified by our group in a GWAS of neuroimaging phenotypes in the ADNI cohort [37
]. The NXPH1
gene, a member of the neurexophilin family, forms a tight complex with alpha neurexins, and the SNP was found to be associated with reduced global and regional grey matter density. The ERBB4
gene, also on chromosome 2, is a member of the type I receptor kinase subfamily, that encodes a receptor for neuregulin 1 (NRG1)
. The neuregulin-ErbB receptor signaling pathway plays a role in development, synaptic function, and neural network activity and has been implicated in schizophrenia [38
]. One AD participant had a large duplication that included 23 genes in the 16p11.2 region (). CNVs in this region have previously been associated with autism [39
], schizophrenia [42
], cognitive impairment and speech/language delay [43
], and obesity [44
], but not AD or MCI. Because the ADNI employed a case/control design, DNA from family members was not available for linkage analysis. This limitation precluded determination as to whether CNVs were de novo
The ADNI cohort provides a unique opportunity for discovery analyses such as this initial CNV analysis. With multiple types of potential biomarkers, including structural and molecular imaging, blood and CSF markers, genetic information, and behavioral data, analysis of the ADNI data has the potential to enhance knowledge of the underlying mechanisms leading to MCI and to AD.
The present study has several limitations related to participant inclusion and exclusion and the software and algorithms used in the analyses. CNV calls in the present report were generated from DNA samples derived only from peripheral blood-78 participants whose DNAs were derived from lymphoblastoid cell lines (LCLs) were excluded. LCLs are generated by transforming peripheral B lymphocytes by the Epstein-Barr virus (EBV). EBV-transformed cells are shown to have significant telomerase activity and develop aneuploidy, along with other cellular changes such as gene mutations and reprogramming in the postimmortal cellular stage of transformation [13
]. Thus, to avoid CNV call discrepancies that may arise between the different DNA sources, we chose to include only those participants whose DNA was derived from peripheral blood. Additional QC was also performed, resulting in only 501 samples that passed all QC checks. To date, no definitive QC criterion has been established to ensure only high-quality samples are included in CNV analyses. Therefore, the QC criterion applied in the present study may have been too stringent leading to the exclusion of samples which otherwise may have had informative CNV data. In future studies, we propose to analyze multiple QC thresholds to determine the optimum QC criteria.
Another limitation is that the CNV calls analyzed in the current study were generated using only one software program (PennCNV). Several detection algorithms including HMMs, segmentation algorithms, t
-tests, and standard deviations of the LRR are available for identifying CNVs from genome-wide SNP array data. A comparison of these methods has been performed by Dellinger et al. Even though the PennCNV program was found to have moderate power in detecting CNVs, it also had a low false positive call rate. The program was found to detect less CNV calls in comparison to other methods and did not accurately detect small CNVs (3-4 SNP CNVs) [46
]. However, in our analyses, we have included CNV calls that had at least 10 SNPs. Obtaining the same CNV calls from another algorithm would help further reduce false positive CNV calls.
The heterogeneity of the MCI group of participants also represents a possible limitation of the present study. Although biomarkers such as CSF and PiB-PET can help differentiate MCI participants who have an AD-like profile from those who have a normal profile, this data was only available for a small number of ADNI-1 participants which would have limited power to detect differences in CNVs. In the next phases of the project (ADNI-GO and ADNI-2), all subjects will have CSF and amyloid PET data, enabling further examination of this issue.