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Volume 10 Supplement 1

Selected articles from the 6th Translational Bioinformatics Conference (TBC 2016): medical genomics

  • Research
  • Open Access

Association analysis of rare variants near the APOE region with CSF and neuroimaging biomarkers of Alzheimer’s disease

  • 1, 3, 12Email author,
  • 1, 15,
  • 2,
  • 1, 12,
  • 1, 3, 12,
  • 13,
  • 14,
  • 1, 2, 3, 12,
  • 4,
  • 4,
  • 5,
  • 6,
  • 7,
  • 8, 9,
  • 10,
  • 11,
  • 1, 2, 3, 12Email author and
BMC Medical GenomicsBMC series – open, inclusive and trusted201710 (Suppl 1) :29

https://doi.org/10.1186/s12920-017-0267-0

©  The Author(s). 2017

  • Published:

Abstract

Background

The APOE ε4 allele is the most significant common genetic risk factor for late-onset Alzheimer’s disease (LOAD). The region surrounding APOE on chromosome 19 has also shown consistent association with LOAD. However, no common variants in the region remain significant after adjusting for APOE genotype. We report a rare variant association analysis of genes in the vicinity of APOE with cerebrospinal fluid (CSF) and neuroimaging biomarkers of LOAD.

Methods

Whole genome sequencing (WGS) was performed on 817 blood DNA samples from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Sequence data from 757 non-Hispanic Caucasian participants was used in the present analysis. We extracted all rare variants (MAF (minor allele frequency) < 0.05) within a 312 kb window in APOE’s vicinity encompassing 12 genes. We assessed CSF and neuroimaging (MRI and PET) biomarkers as LOAD-related quantitative endophenotypes. Gene-based analyses of rare variants were performed using the optimal Sequence Kernel Association Test (SKAT-O).

Results

A total of 3,334 rare variants (MAF < 0.05) were found within the APOE region. Among them, 72 rare non-synonymous variants were observed. Eight genes spanning the APOE region were significantly associated with CSF Aβ1-42 (p < 1.0 × 10−3). After controlling for APOE genotype and adjusting for multiple comparisons, 4 genes (CBLC, BCAM, APOE, and RELB) remained significant. Whole-brain surface-based analysis identified highly significant clusters associated with rare variants of CBLC in the temporal lobe region including the entorhinal cortex, as well as frontal lobe regions. Whole-brain voxel-wise analysis of amyloid PET identified significant clusters in the bilateral frontal and parietal lobes showing associations of rare variants of RELB with cortical amyloid burden.

Conclusions

Rare variants within genes spanning the APOE region are significantly associated with LOAD-related CSF Aβ1-42 and neuroimaging biomarkers after adjusting for APOE genotype. These findings warrant further investigation and illustrate the role of next generation sequencing and quantitative endophenotypes in assessing rare variants which may help explain missing heritability in AD and other complex diseases.

Keywords

  • Whole genome sequencing
  • Rare variants
  • Near APOE
  • ADNI
  • CSF
  • Neuroimaging

Background

The number of individuals with late-onset Alzheimer’s disease (LOAD) is rapidly increasing and predicted to triple by 2050 with the increasing population of aging adults [1]. The heritability of LOAD was predicted to be up to 80% based on twin studies [2] and large-scale genome-wide association studies (GWAS) have recently led to the identification and confirmation of approximately 22 LOAD-associated genes including APOE (Apolipoprotein E), the best established and most significant susceptibility gene for LOAD [3]. The association of APOE with LOAD has been replicated and validated in many studies from different populations [4]. The APOE ε4 allele increases an individual’s risk for developing LOAD and also reduces age-at-onset in patients with LOAD in a dose-dependent manner, while the APOE ε2 allele appears to reduce the risk for LOAD [5]. Furthermore, GWAS studies have repeatedly identified several susceptibility loci for LOAD near the 19q13 on the chromosome 19 including APOE and TOMM40 (translocase of outer mitochondrial membrane 40 homolog) [3, 6]. In particular, TOMM40 has the second most significant SNP (single nucleotide polymorphism) associated with LOAD and multiple LOAD-related neuroimaging phenotypes in the 19q13 region [7–9]. However, conditional analyses strongly suggested that this effect is due to APOE [10, 11]. As APOE and TOMM40 are in strong linkage disequilibrium (LD), it is not easy to attribute an APOE-independent role of TOMM40 in the risk of LOAD development, although TOMM40 is essential for protein trafficking into mitochondria and mitochondrial dysfunction has been widely implicated in LOAD pathophysiology. Several groups investigated the association between a variable length poly-T polymorphism (poly-T) at rs10524523 within TOMM40 and LOAD, and yielded contrasting results [12–16]. Recently, Jun et al. comprehensively evaluated the association of risk and age at onset of LOAD with common SNPs (MAF (minor allele frequency) > 5%) and poly-T repeat in the APOE region using approximately 23,000 cases and controls, and found no significant independent association after adjusting for APOE genotype [16]. Highly significant results, after adjusting for APOE genotype, are unlikely in view of the very strong LD in this region.

Up to 50% of LOAD heritability remain unexplained by all of the known LOAD susceptibility genes including APOE and a substantial missing heritability for LOAD remains to be identified [17]. The advent of high throughput next generation sequencing such as whole genome sequencing (WGS) to identify variation in human genes has created unprecedented opportunities to discover genetic factors that influence disease risk in the field of human genetics [18, 19]. Several recent reports show that deep re-sequencing of GWAS-implicated loci and WGS-based association studies can identify independent functional rare variants with large effects on diseases including LOAD pathogenesis [20–22].

Two neuropathological hallmarks of the AD brain are extracellular amyloid-β plaques and intracellular neurofibrillary tangles. Studies have shown decreased concentrations of the CSF Aβ1–42 peptide and increased concentrations of total tau (t-tau) and hyperphosphorylated tau (p-tau) in AD compared with cognitively normal elders [23, 24]. Here we performed a gene-based association analysis of rare variants within genes in the vicinity of APOE with cerebrospinal fluid (CSF) and LOAD-related neuroimaging markers using a WGS data set (N = 757) from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort. Our results strongly suggest rare variants in the region surrounding APOE on chromosome 19 were significantly associated with LOAD-related CSF Aβ1-42 and neuroimaging biomarkers.

Methods

Study participants

All individuals included in this study were participants of the longitudinal Alzheimer’s Disease Neuroimaging Initiative (ADNI) initiated in 2004, especially its subsequent extensions (ADNI-GO/2). Information about ADNI has been published previously and can be found at http://www.adni-info.org [25, 26]. All data were downloaded from the ADNI data repository (http://www.loni.usc.edu/ADNI/). All participants provided written informed consent at the time of enrollment for imaging and genetic sample collection and study protocols were approved by each participating sites’ Institutional Review Board (IRB).

For the control for population substructure, we restricted our analyses to participants with non-Hispanic Caucasian ancestry determined by using HapMap 3 genotype data and the multidimensional scaling (MDS) analysis (http://www.hapmap.org) [18, 19, 27]. Participants aged 55–90 to be used in this analysis include 259 cognitively normal older individuals (CN), 219 individuals with early mild cognitive impairment (MCI), 232 individuals diagnosed with late MCI, and 47 individuals diagnosed with AD.

Whole genome sequencing (WGS) analysis

WGS data from 817 ADNI participants were downloaded from the ADNI data repository (http://www.loni.usc.edu/ADNI/). An established next generation sequencing analysis pipeline based on GATK previously described was used to process ADNI WGS data performed on blood-derived genomic DNA samples and sequenced on the Illumina HiSeq2000 using paired-end read chemistry and read lengths of 100 bp at 30-40X coverage (http://www.illumina.com) [28]. We extracted all variants (SNPs and short indels) within a 312 kb region in APOE’s vicinity including 12 genes.

Neuroimaging analysis

T1-weighted brain MRI scans were processed using previously described automated MRI analysis techniques [29], whole-brain voxel-based morphometry (VBM) and FreeSurfer software [30, 31]. [18F]Florbetapir PET scans were pre-processed as described [30] and intensity normalized by the whole cerebellum. These normalizations yielded standardized uptake value ratio (SUVR) images [32].

Statistical analysis

The SKAT-O software was used to perform a gene-based association analysis of all WGS-identified rare SNPs and short indels (MAF < 0.05) in the APOE cluster region [33]. We performed an association analysis first using only all SNPs and second using all SNPs plus short indels. Baseline CSF measurements (Amyloid-β 1–42 peptide (Aβ1-42), total tau (t-tau), and tau phosphorylated at the threonine 181 (p-tau181p)) were downloaded [34]. GWAS of CSF biomarkers found that several SNPs in TOMM40 and APOE are significantly associated with Aβ1-42 [34]. Thus, for the CSF analysis, we used CSF Aβ1-42 as a quantitative phenotype and age, gender, and APOE genotype as covariates. For the neuroimaging analysis, age, gender, year of education, MRI field strength, total intracranial volume (ICV), and APOE genotype were as covariates. We considered associations with p < 0.0042 (=0.05/12) to be significant in order to control for multiple comparisons.

Results

Sequencing of chromosome 19q13 region

Within a 312 kb window in APOE’s vicinity spanning 12 genes, we found 683 common variants (618 SNPs and 65 indels) and 3,334 rare variants (3,040 SNPs and 294 indels) (Table 1). Among 4,017 variants, there are 147 exonic and 2,159 intronic variants. Of 147 exonic variants, we found 1 frameshift and 3 nonframeshift indels, 72 nonsynonymous and 51 synonymous SNPs, and 20 unknown variants.
Table 1

Number of common and rare variants (SNPs and Indels) of 12 genes near the APOE region

Gene

Common variant (MAF ≥ 5%)

Rare variant (MAF < 5%)

SNP

Indel

SNP

Indel

BCL3

67

9

408

42

CBLC

28

5

361

52

BCAM

41

2

327

25

PVRL2

190

29

513

65

TOMM40

32

2

154

11

APOE

13

1

51

3

APOC1

23

2

102

7

APOC1P1

21

3

113

9

APOC4

19

4

90

13

APOC2

27

2

61

3

CLPTM1

105

7

456

34

RELB

52

7

404

30

Total

618

65

3,040

294

Association of rare variants near the APOE region with CSF Aβ1-42

Gene-based association analysis of rare SNPs near the APOE region identified three genes (TOMM40, APOE, and APOC1) that achieved a genome-wide significant association with CSF Aβ1-42 (p < 5 × 10−7) (Table 2) and the most significant association was between APOC1 and CSF Aβ1-42. After controlling for APOE genotype and adjusting for multiple comparisons based on a Bonferroni threshold (p < 0.05/12 = 0.0042), 4 genes (CBLC, BCAM, APOE, and RELB) remain significant. The strongest significant association was observed at the BCAM gene (p = 0.0006). There were about 10% short indels of all rare variants near the APOE region. The results of gene-based association of both rare SNPs and short indels near the APOE region with CSF Aβ1-42 were almost same as the association results of only rare SNPs (Table 2).
Table 2

Gene-based association results (p-values) of rare variants (MAF < 5%; SNPs and Indels) of genes near the APOE region with CSF biomarker Aβ1-42 with and without adjusting for APOE genotypes

Gene

SNP + Indel

SNP

p-value

p-value adjusted for APOE genotype

p-value

p-value adjusted for APOE genotype

BCL3

8.38E-04

0.0056

7.60E-04

0.0054

CBLC

7.07E-05

0.0011

7.35E-05

0.0013

BCAM

1.97E-04

0.0005

2.49E-04

0.0006

PVRL2

3.55E-05

0.3842

4.40E-05

0.4605

TOMM40

6.84E-07

0.0922

5.01E-07

0.0880

APOE

3.35E-10

0.0039

4.08E-07

0.0036

APOC1

6.18E-11

0.2394

2.85E-11

0.1636

APOC1P1

4.43E-02

0.0145

6.16E-02

0.0097

APOC4

2.11E-02

0.2062

1.60E-02

0.1642

APOC2

2.23E-01

0.5363

1.64E-01

0.5102

CLPTM1

1.02E-02

0.0438

9.12E-03

0.0377

RELB

2.36E-04

0.0053

1.51E-04

0.0042

Association of rare variants near the APOE region with neuroimaging (MRI, PET)

To examine the LOAD-related neuroimaging biomarker association of all rare variants in 3 genes (CBLC, BCAM, and RELB) significantly associated with CSF Aβ1-42 after adjusting for APOE genotype, a detailed whole-brain multivariate analysis of cortical thickness (MRI) and amyloid-β burden ([18F]-florbetapir PET) was performed to detect brain regions of associations of a single polygenic risk score. A single polygenic risk score was determined by collapsing all rare variants and counting minor alleles with a dominant genetic model. Figure 1 displays the results of the main effect of all rare variants after adjusting for APOE genotype in a surface-based cortical thickness whole brain analysis. Highly significant clusters associated with the risk score were found in temporal lobes including the entorhinal cortex, where AD pathology primarily begins, frontal lobe regions for CBLC, and temporal lobe regions for BCAM and RELB, where subjects having high risk scores showed thinner mean cortical thickness compared with the participants having lower risk scores. A polygenic risk score of all rare variants in 3 genes (CBLC, BCAM, RELB) was associated with multifocal brain atrophy, predominantly in the temporal and bilateral frontal lobes (Fig. 1d). Fig. 2 shows the association of all rare variants in RELB with cortical amyloid burden from voxel-wise analysis of the effect of rare variants on amyloid accumulation measured by [18F]-florbetapir PET at a voxel-wise threshold of p < 0.005 (uncorrected). The color scale indicates regions where the risk scores were associated with higher amyloid burden after adjusting for APOE genotype. The significant clusters were observed in the bilateral frontal and parietal lobes.
Fig. 1
Fig. 1

Surface-based whole-brain analysis results. A whole-brain multivariate analysis of cortical thickness was performed on a vertex-by-vertex basis to visualize the topography of genetic association in an unbiased manner. Statistical maps were thresholded using a random field theory adjustment to a corrected significance level of p = 0.05. a CBLC. b RELB. c BCAM. d CBLC + RELB + BCAM

Fig. 2
Fig. 2

Voxel-wise analysis results of [18F]Florbetapir positron emission tomography (PET). A whole-brain analysis of cerebral amyloid deposition was performed on a voxel-by-voxel basis to visualize the topography of genetic association (RELB) in an unbiased manner. Figure is displayed at an uncorrected p value <0.005 and minimum voxel size (k) = 27 voxels

Association of common SNPs near the APOE region with CSF Aβ1-42

The association analysis of common SNPs near the APOE region was performed using PLINK set-based tests and permutation while considering the linkage disequilibrium structure of SNPs and identified one significant gene (BCL3) passed a Bonferroni threshold after adjusting for APOE genotype (p = 0.0005; Table 3). The association results remain almost unchanged when both common SNPs and short indels were used.
Table 3

Gene-based association results (p-values) of common variants (MAF ≥ 5%; SNPs and Indels) of genes near the APOE region with CSF biomarker Aβ1-42 with and without adjusting for APOE genotypes

Gene

SNP + Indel

SNP

p-value

p-value adjusted for APOE genotype

p-value

p-value adjusted for APOE genotype

BCL3

0.0013

0.0005

0.0019

0.0005

CBLC

0.0006

0.0122

0.0005

0.0128

BCAM

0.0014

0.0132

0.0016

0.0131

PVRL2

<1.0E-05

0.6852

<1.0E-05

0.6665

TOMM40

<1.0E-05

1.0000

<1.0E-05

1.0000

APOE

<1.0E-05

0.1380

<1.0E-05

1.0000

APOC1

<1.0E-05

1.0000

<1.0E-05

1.0000

APOC1P1

<1.0R-05

1.000

<1.0E-05

1.0000

APOC4

0.1718

1.000

0.1437

1.0000

APOC2

0.0406

0.0621

0.0152

0.0570

CLPTM1

0.0198

0.0467

0.0331

0.0464

RELB

0.0515

0.6551

0.2485

0.6095

Discussion and Conclusions

We show for the first time to our knowledge that rare variants within genes near the APOE region are significantly associated with a LOAD biomarker CSF Aβ1-42 after adjusting for APOE genotype. Our results indicated that four genes (CBLC, BCAM, APOE, and RELB) remained significant after correcting for multiple comparisons. In addition, gene-based association analysis of common variants identified one significant gene BCL3. Whole-brain surface-based analysis identified highly significant clusters associated with rare variants of CBLC in temporal lobe regions including the entorhinal cortex and frontal lobe regions.

BCL3 (B-cell CLL/lymphoma 3) gene functions as a transcriptional co-activator involved in cell replication and apoptosis that activates through its association with NF-κB homodimers [35]. BCL3 gene is associated with genetic linkage with late-onset Familial Alzheimer’s disease as well as chronic lymphocytic leukemia [36–38]. RELB (RELB proto-oncogene, NF-κB subunit) gene is a member of NF-κB family of transcriptional factors. Among its related pathways are immune system and interleukin-3, 5 and GM-CSF signaling. NF-κB plays a central role in the inflammatory and immune responses and controls cell proliferation and protects the cell from apoptosis [39]. NF-κB is a major transcription factor and activated in AD patients. Amyloid beta accumulation is a potential activator of NF-κB in primary neurons [40]. CBLC (Cbl proto-oncogene C, E3 ubiquitin protein ligase) gene is the member of the Cbl family of E3 ubiquitin ligases. Cbl proteins play an important role in cell signaling through the ubiquitination and subsequent downregulation of the tyrosine kinases. BCAM (basal cell adhesion molecule) gene encodes a glycoprotein expressed on cell surfaces [41]. BCAM is a member of the immunoglobulin superfamily and a receptor for the extracellular matrix protein, laminin α-5. BCAM may play a role in intracellular signaling. BCAM is related to the Lutheran glycoprotein, which is a specific marker of brain capillary endothelium, which forms the blood brain barrier (BBB) in vivo [42, 43].

ADNI is a unique cohort and the only large WGS data set of LOAD with CSF Aβ1-42 and neuroimaging data also available. However, a limitation of the present report is that we used a modest sample size (n = 757) of whole genome sequencing data for genetic analysis. Therefore, validation in independent and larger cohorts is warranted.

In conclusion, we used whole genome sequencing to perform an association analysis of rare variants within genes near the APOE region with CSF Aβ1-42 and neuroimaging biomarkers of LOAD. Importantly, our results implicate this region or these genes contain additional explanatory information with regard to LOAD endophenotypes above and beyond that conferred by APOE genotype. Overall, combining whole genome sequencing and LOAD-related quantitative endophenotypes adds to the growing understanding of the genetics of LOAD and holds promise for discovery of rare variants involved in neurodegeneration and other brain disorders, further nominating novel potential diagnostic and therapeutic targets.

Declarations

Acknowledgements

Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.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.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf. Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; BioClinica, Inc.; Biogen Idec Inc.; Bristol-Myers Squibb Company; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; GE Healthcare; Innogenetics, N.V.; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Medpace, Inc.; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Synarc Inc.; and Takeda Pharmaceutical Company. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions 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. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California. Samples from the National Cell Repository for AD (NCRAD), which receives government support under a cooperative agreement grant (U24 AG21886) awarded by the National Institute on Aging (AIG), were used in this study. Funding for the WGS was provided by the Alzheimer’s Association and the Brin Wojcicki Foundation.

Funding

Additional support for data analysis was provided by NLM R00 LM011384, NIA R01 AG19771, NIA P30 AG10133, NLM R01 LM011360, NSF IIS-1117335, DOD W81XWH-14-2-0151, NCAA 14132004, NIGMS P50GM115318, and NCATS UL1 TR001108. Publication of this article has been funded by NLM R00 LM011384.

Availability of data and materials

Demographic information, raw neuroimaging scan data, APOE and whole genome sequencing data, neuropsychological test scores, and diagnostic information are available from the ADNI data repository (http://www.loni.usc.edu/ADNI/).

Authors’ contributions

All authors contributed substantively to this work. KN, EH, SK, SLR, LS, and AJS were involved in study conception and design. KN, EH, DK, SL, and AJS were involved in data organization, whole genome sequencing analysis and statistical analyses. TF, PSA, RCP, RCG, CRJ, LMS, JQT, RCG, AWT, MWW and AJS were involved in coordination and data collection and processing for ADNI. KN and AJS drafted the report and prepared all figures and tables. All authors were involved in reviewing and editing of the manuscript and approved it.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Ethics approval and consent to participate

Written informed consent was obtained at the time of enrollment for imaging and genetic sample collection and protocols of consent forms were approved by each participating sites’ Institutional Review Board (IRB).

About this supplement

This article has been published as part of BMC Medical Genomics Volume 10 Supplement 1, 2017: Selected articles from the 6th Translational Bioinformatics Conference (TBC 2016): medical genomics. The full contents of the supplement are available online at https://bmcmedgenomics.biomedcentral.com/articles/supplements/volume-10-supplement-1.

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Authors’ Affiliations

(1)
Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
(2)
Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
(3)
Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, USA
(4)
Department of Pathology and Laboratory Medicine, University of Pennsylvania School of Medicine, Philadelphia, PA, USA
(5)
Department of Neuroscience, University of California-San Diego, San Diego, CA, USA
(6)
Department of Neurology, Mayo Clinic Minnesota, Rochester, MN, USA
(7)
Department of Radiology, Mayo Clinic Minnesota, Rochester, MN, USA
(8)
Departments of Radiology, Medicine, and Psychiatry, University of California-San Francisco, San Francisco, CA, USA
(9)
Department of Veterans Affairs Medical Center, San Francisco, CA, USA
(10)
Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
(11)
The Institute for Neuroimaging and Informatics and Laboratory of Neuro Imaging, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
(12)
Indiana Alzheimer’s Disease Center, Indiana University School of Medicine, Indianapolis, IN, USA
(13)
Department of Biomedical and Translational Informatics, Geisinger Health System, Danville, PA, USA
(14)
Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI, USA
(15)
Department of Electrical and Computer Engineering, State University of New York at Oswego, Oswego, NY, USA

References

  1. Hebert LE, Scherr PA, Bienias JL, Bennett DA, Evans DA. Alzheimer disease in the US population: prevalence estimates using the 2000 census. Arch Neurol. 2003;60(8):1119–22.View ArticlePubMedGoogle Scholar
  2. Bergem AL, Engedal K, Kringlen E. The role of heredity in late-onset Alzheimer disease and vascular dementia. A twin study. Arch Gen Psychiatry. 1997;54(3):264–70.View ArticlePubMedGoogle Scholar
  3. Lambert JC, Ibrahim-Verbaas CA, Harold D, Naj AC, Sims R, Bellenguez C, DeStafano AL, Bis JC, Beecham GW, Grenier-Boley B, et al. Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer’s disease. Nat Genet. 2013;45(12):1452–8.View ArticlePubMedPubMed CentralGoogle Scholar
  4. Ward A, Crean S, Mercaldi CJ, Collins JM, Boyd D, Cook MN, Arrighi HM. Prevalence of apolipoprotein E4 genotype and homozygotes (APOE e4/4) among patients diagnosed with Alzheimer’s disease: a systematic review and meta-analysis. Neuroepidemiology. 2012;38(1):1–17.View ArticlePubMedGoogle Scholar
  5. Liu CC, Kanekiyo T, Xu H, Bu G. Apolipoprotein E and Alzheimer disease: risk, mechanisms and therapy. Nat Rev Neurol. 2013;9(2):106–18.View ArticlePubMedPubMed CentralGoogle Scholar
  6. Kamboh MI, Demirci FY, Wang X, Minster RL, Carrasquillo MM, Pankratz VS, Younkin SG, Saykin AJ, Alzheimer’s Disease Neuroimaging I, Jun G, et al. Genome-wide association study of Alzheimer’s disease. Transl Psychiatry. 2012;2:e117.View ArticlePubMedPubMed CentralGoogle Scholar
  7. Saykin AJ, Shen L, Foroud TM, Potkin SG, Swaminathan S, Kim S, Risacher SL, Nho K, Huentelman MJ, Craig DW, et al. Alzheimer’s Disease Neuroimaging Initiative biomarkers as quantitative phenotypes: Genetics core aims, progress, and plans. Alzheimers Dement. 2010;6(3):265–73.View ArticlePubMedPubMed CentralGoogle Scholar
  8. Shen L, Kim S, Risacher SL, Nho K, Swaminathan S, West JD, Foroud T, Pankratz N, Moore JH, Sloan CD, et al. Whole genome association study of brain-wide imaging phenotypes for identifying quantitative trait loci in MCI and AD: A study of the ADNI cohort. NeuroImage. 2010;53(3):1051–63.View ArticlePubMedPubMed CentralGoogle Scholar
  9. Ferencz B, Laukka EJ, Lovden M, Kalpouzos G, Keller L, Graff C, Wahlund LO, Fratiglioni L, Backman L. The influence of APOE and TOMM40 polymorphisms on hippocampal volume and episodic memory in old age. Front Hum Neurosci. 2013;7:198.View ArticlePubMedPubMed CentralGoogle Scholar
  10. Guerreiro RJ, Hardy J. TOMM40 association with Alzheimer disease: tales of APOE and linkage disequilibrium. Arch Neurol. 2012;69(10):1243–4.View ArticlePubMedGoogle Scholar
  11. Cervantes S, Samaranch L, Vidal-Taboada JM, Lamet I, Bullido MJ, Frank-Garcia A, Coria F, Lleo A, Clarimon J, Lorenzo E, et al. Genetic variation in APOE cluster region and Alzheimer’s disease risk. Neurobiol Aging. 2011;32(11):2107 e2107–2117.View ArticleGoogle Scholar
  12. Roses AD. An inherited variable poly-T repeat genotype in TOMM40 in Alzheimer disease. Arch Neurol. 2010;67(5):536–41.View ArticlePubMedPubMed CentralGoogle Scholar
  13. Roses AD, Lutz MW, Amrine-Madsen H, Saunders AM, Crenshaw DG, Sundseth SS, Huentelman MJ, Welsh-Bohmer KA, Reiman EM. A TOMM40 variable-length polymorphism predicts the age of late-onset Alzheimer’s disease. Pharmacogenomics J. 2010;10(5):375–84.View ArticlePubMedGoogle Scholar
  14. Johnson SC, La Rue A, Hermann BP, Xu G, Koscik RL, Jonaitis EM, Bendlin BB, Hogan KJ, Roses AD, Saunders AM, et al. The effect of TOMM40 poly-T length on gray matter volume and cognition in middle-aged persons with APOE epsilon3/epsilon3 genotype. Alzheimers Dement. 2011;7(4):456–65.View ArticlePubMedPubMed CentralGoogle Scholar
  15. Cruchaga C, Nowotny P, Kauwe JS, Ridge PG, Mayo K, Bertelsen S, Hinrichs A, Fagan AM, Holtzman DM, Morris JC, et al. Association and expression analyses with single-nucleotide polymorphisms in TOMM40 in Alzheimer disease. Arch Neurol. 2011;68(8):1013–9.View ArticlePubMedPubMed CentralGoogle Scholar
  16. Jun G, Vardarajan BN, Buros J, Yu CE, Hawk MV, Dombroski BA, Crane PK, Larson EB, Alzheimer’s Disease Genetics C, Mayeux R, et al. Comprehensive search for Alzheimer disease susceptibility loci in the APOE region. Arch Neurol. 2012;69(10):1270–9.View ArticlePubMedPubMed CentralGoogle Scholar
  17. Tanzi RE. The genetics of Alzheimer disease. Cold Spring Harb Perspect Med. 2012;2(10):a006296.View ArticlePubMedPubMed CentralGoogle Scholar
  18. Nho K, Corneveaux JJ, Kim S, Lin H, Risacher SL, Shen L, Swaminathan S, Ramanan VK, Liu Y, Foroud T, et al. Identification of functional variants from whole-exome sequencing, combined with neuroimaging genetics. Mol Psychiatry. 2013;18(7):739.View ArticlePubMedPubMed CentralGoogle Scholar
  19. Nho K, Corneveaux JJ, Kim S, Lin H, Risacher SL, Shen L, Swaminathan S, Ramanan VK, Liu Y, Foroud T, et al. Whole-exome sequencing and imaging genetics identify functional variants for rate of change in hippocampal volume in mild cognitive impairment. Mol Psychiatry. 2013;18(7):781–7.View ArticlePubMedPubMed CentralGoogle Scholar
  20. Guerreiro R, Hardy J. TREM2 and neurodegenerative disease. N Engl J Med. 2013;369(16):1569–70.PubMedGoogle Scholar
  21. Guerreiro R, Wojtas A, Bras J, Carrasquillo M, Rogaeva E, Majounie E, Cruchaga C, Sassi C, Kauwe JS, Younkin S, et al. TREM2 variants in Alzheimer’s disease. N Engl J Med. 2013;368(2):117–27.View ArticlePubMedGoogle Scholar
  22. Cruchaga C, Karch CM, Jin SC, Benitez BA, Cai Y, Guerreiro R, Harari O, Norton J, Budde J, Bertelsen S, et al. Rare coding variants in the phospholipase D3 gene confer risk for Alzheimer’s disease. Nature. 2014;505(7484):550–4.View ArticlePubMedGoogle Scholar
  23. Anoop A, Singh PK, Jacob RS, Maji SK. CSF Biomarkers for Alzheimer’s Disease Diagnosis. Int J Alzheimers Dis. 2010;23:2010.Google Scholar
  24. Forlenza OV, Radanovic M, Talib LL, Aprahamian I, Diniz BS, Zetterberg H, Gattaz WF. Cerebrospinal fluid biomarkers in Alzheimer’s disease: Diagnostic accuracy and prediction of dementia. Alzheimers Dement (Amst). 2015;1(4):455–63.Google Scholar
  25. Saykin AJ, Shen L, Yao X, Kim S, Nho K, et al. Genetic studies of quantitative MCI and AD phenotypes in ADNI: Progress, opportunities, and plans. Alzheimers Dement. 2015;11:792–814. PMID: 26194313.Google Scholar
  26. Weiner MW, Aisen PS, Jack Jr CR, Jagust WJ, Trojanowski JQ, Shaw L, Saykin AJ, Morris JC, Cairns N, Beckett LA, et al. The Alzheimer’s disease neuroimaging initiative: progress report and future plans. Alzheimers Dement. 2010;6(3):202–11. e207.View ArticlePubMedPubMed CentralGoogle Scholar
  27. Price AL, Zaitlen NA, Reich D, Patterson N. New approaches to population stratification in genome-wide association studies. Nat Rev Genet. 2010;11(7):459–63.View ArticlePubMedPubMed CentralGoogle Scholar
  28. Nho K, West JD, Li H, Henschel R, Bharthur A, Tavares MC, Saykin AJ. Comparison of Multi-Sample Variant Calling Methods for Whole Genome Sequencing. IEEE Int Conf Systems Biol. 2014;2014:59–62.PubMedPubMed CentralGoogle Scholar
  29. Jack Jr CR, Bernstein MA, Fox NC, Thompson P, Alexander G, Harvey D, Borowski B, Britson PJ, LW J, Ward C, et al. The Alzheimer’s Disease Neuroimaging Initiative (ADNI): MRI methods. J Magn Reson Imaging. 2008;27(4):685–91.View ArticlePubMedPubMed CentralGoogle Scholar
  30. Risacher SL, Kim S, Nho K, Foroud T, Shen L, Petersen RC, Jack Jr CR, Beckett LA, Aisen PS, Koeppe RA, et al. APOE effect on Alzheimer’s disease biomarkers in older adults with significant memory concern. Alzheimers Dement. 2015;11(12):1417–29.View ArticlePubMedPubMed CentralGoogle Scholar
  31. Nho K, Saykin AJ, Alzheimer’s Disease Neuroimaging I, Nelson PT. Hippocampal Sclerosis of Aging, a Common Alzheimer’s Disease ‘Mimic’: Risk Genotypes are Associated with Brain Atrophy Outside the Temporal Lobe. J Alzheimers Dis. 2016;52(1):373–83.View ArticlePubMedPubMed CentralGoogle Scholar
  32. Jagust WJ, Bandy D, Chen K, Foster NL, Landau SM, Mathis CA, Price JC, Reiman EM, Skovronsky D, Koeppe RA, et al. The Alzheimer’s Disease Neuroimaging Initiative positron emission tomography core. Alzheimers Dement. 2010;6(3):221–9.View ArticlePubMedPubMed CentralGoogle Scholar
  33. Lee S, Emond MJ, Bamshad MJ, Barnes KC, Rieder MJ, Nickerson DA, Team NGESP-ELP, Christiani DC, Wurfel MM, Lin X. Optimal unified approach for rare-variant association testing with application to small-sample case-control whole-exome sequencing studies. Am J Hum Genet. 2012;91(2):224–37.View ArticlePubMedPubMed CentralGoogle Scholar
  34. Kim S, Swaminathan S, Shen L, Risacher SL, Nho K, Foroud T, Shaw LM, Trojanowski JQ, Potkin SG, Huentelman MJ, et al. Genome-wide association study of CSF biomarkers Abeta1-42, t-tau, and p-tau181p in the ADNI cohort. Neurology. 2011;76(1):69–79.View ArticlePubMedGoogle Scholar
  35. Porcellini E, Carbone I, Ianni M, Licastro F. Alzheimer’s disease gene signature says: beware of brain viral infections. Immun Ageing. 2010;7:16.View ArticlePubMedPubMed CentralGoogle Scholar
  36. Pericak-Vance MA, Bebout JL, Gaskell Jr PC, Yamaoka LH, Hung WY, Alberts MJ, Walker AP, Bartlett RJ, Haynes CA, Welsh KA, et al. Linkage studies in familial Alzheimer disease: evidence for chromosome 19 linkage. Am J Hum Genet. 1991;48(6):1034–50.PubMedPubMed CentralGoogle Scholar
  37. St George-Hyslop PH, Ohno H, Gusella JF, McKeithan T. The BCL3 locus on chromosome 19 displays an informative microsatellite polymorphism. Nucleic Acids Res. 1992;20(4):927.View ArticlePubMedGoogle Scholar
  38. Miller LL, Ploughman LM, Marazita ML. Heterogeneity testing for Alzheimer’s disease within and between data sets. Genet Epidemiol. 1993;10(6):443–7.View ArticlePubMedGoogle Scholar
  39. Baud V, Collares D. Post-Translational Modifications of RelB NF-kappaB Subunit and Associated Functions. Cells. 2016;5(2):22.View ArticlePubMed CentralGoogle Scholar
  40. Kaltschmidt B, Uherek M, Volk B, Baeuerle PA, Kaltschmidt C. Transcription factor NF-kappaB is activated in primary neurons by amyloid beta peptides and in neurons surrounding early plaques from patients with Alzheimer disease. Proc Natl Acad Sci U S A. 1997;94(6):2642–7.View ArticlePubMedPubMed CentralGoogle Scholar
  41. Latini FR, Bastos AU, Arnoni CP, Muniz JG, Person RM, Baleotti Jr W, Barreto JA, Castilho L, Cerutti JM. DARC (Duffy) and BCAM (Lutheran) reduced expression in thyroid cancer. Blood Cells Mol Dis. 2013;50(3):161–5.View ArticlePubMedGoogle Scholar
  42. Boado RJ, Li JY, Pardridge WM. Selective Lutheran glycoprotein gene expression at the blood-brain barrier in normal brain and in human brain tumors. J Cereb Blood Flow Metab. 2000;20(7):1096–102.View ArticlePubMedGoogle Scholar
  43. Provias J, Jeynes B. The role of the blood-brain barrier in the pathogenesis of senile plaques in Alzheimer’s disease. Int J Alzheimers Dis. 2014;2014:191863.PubMedPubMed CentralGoogle Scholar

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