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Hippocampal Atrophy as a Quantitative Trait in a Genome-Wide Association Study Identifying Novel Susceptibility Genes for Alzheimer's Disease
Open accessAbstract
BACKGROUND: With the exception of APOE epsilon4 allele, the common genetic risk factors for sporadic Alzheimer's Disease (AD) are unknown. METHODS AND FINDINGS: We completed a genome-wide association study on 381 participants in the ADNI (Alzheimer's Disease Neuroimaging Initiative) study. Samples were genotyped using the Illumina Human610-Quad BeadChip. 516,645 unique Single Nucleotide Polymorphisms (SNPs) were included in the analysis following quality control measures. The genotype data and raw genetic data are freely available for download (LONI, http://www.loni.ucla.edu/ADNI/Data/). Two analyses were completed: a standard case-control analysis, and a novel approach using hippocampal atrophy measured on MRI as an objectively defined, quantitative phenotype. A General Linear Model was applied to identify SNPs for which there was an interaction between the genotype and diagnosis on the quantitative trait. The case-control analysis identified APOE and a new risk gene, TOMM40 (translocase of outer mitochondrial membrane 40), at a genome-wide significance level of < or =10(-6) (10(-11) for a haplotype). TOMM40 risk alleles were approximately twice as frequent in AD subjects as controls. The quantitative trait analysis identified 21 genes or chromosomal areas with at least one SNP with a p-value < or =10(-6), which can be considered potential "new" candidate loci to explore in the etiology of sporadic AD. These candidates included EFNA5, CAND1, MAGI2, ARSB, and PRUNE2, genes involved in the regulation of protein degradation, apoptosis, neuronal loss and neurodevelopment. Thus, we identified common genetic variants associated with the increased risk of developing AD in the ADNI cohort, and present publicly available genome-wide data. Supportive evidence based on case-control studies and biological plausibility by gene annotation is provided. Currently no available sample with both imaging and genetic data is available for replication. CONCLUSIONS: Using hippocampal atrophy as a quantitative phenotype in a genome-wide scan, we have identified candidate risk genes for sporadic Alzheimer's disease that merit further investigation.
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“from a strict association perspective it does appear that the TOMM40 gene, and by extension its protein product, could potentially impact LOAD.”
From The Alzheimer’s disease mitochondrial cascade hypothesis: progress and perspectives (2014) · cites this paper as [148]
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“a large number of novel loci and genes have been nominated by these analyses [222, 229–231]”
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Cites
McKhann, G., Drachman, D., Folstein, M., Katzman, R., Price, D., & Stadlan, E. M. (1984) · Neurology · doi:10.1212/wnl.34.7.939 · Cited by 27,915
Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard, O., Delcroix, N., Mazoyer, B., & Joliot, M. (2002) · NeuroImage · doi:10.1006/nimg.2001.0978 · Cited by 16,319
Haploview: analysis and visualization of LD and haplotype maps
Barrett, J. C., Fry, B., Maller, J., & Daly, M. J. (2004) · Bioinformatics · doi:10.1093/bioinformatics/bth457 · Cited by 14,551
Advances in functional and structural MR image analysis and implementation as FSL
Smith, S. M., Jenkinson, M., Woolrich, M. W., Beckmann, C. F., Behrens, T. E. J., Johansen-Berg, H., Bannister, P. R., De Luca, M., Drobnjak, I., Flitney, D. E., et al. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2004.07.051 · Cited by 13,630
Principal components analysis corrects for stratification in genome-wide association studies
Price, A. L., Patterson, N. J., Plenge, R. M., Weinblatt, M. E., Shadick, N. A., & Reich, D. (2006) · Nature Genetics · doi:10.1038/ng1847 · Cited by 10,248
Voxel-Based Morphometry—The Methods
Ashburner, J., & Friston, K. J. (2000) · NeuroImage · doi:10.1006/nimg.2000.0582 · Cited by 8,676
A second generation human haplotype map of over 3.1 million SNPs
The International HapMap Consortium (2007) · Nature · doi:10.1038/nature06258 · Cited by 4,438
Research criteria for the diagnosis of Alzheimer's disease: revising the NINCDS–ADRDA criteria
Dubois, B., Feldman, H. H., Jacova, C., DeKosky, S. T., Barberger-Gateau, P., Cummings, J., Delacourte, A., Galasko, D., Gauthier, S., Jicha, G., et al. (2007) · The Lancet Neurology · doi:10.1016/s1474-4422(07)70178-3 · Cited by 4,411
The Alzheimer’s disease neuroimaging initiative (ADNI): MRI methods
Jack, C. R., Bernstein, M. A., Fox, N. C., Thompson, P., Alexander, G., Harvey, D., Borowski, B., Britson, P. J., L. Whitwell, J., Ward, C., et al. (2008) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.21049 · Cited by 4,347
Genome-wide association studies for common diseases and complex traits
Hirschhorn, J. N., & Daly, M. J. (2005) · Nature Reviews Genetics · doi:10.1038/nrg1521 · Cited by 2,923
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Items 301–371 of 371
Axelsen, T. M., Bager, C. L., Bihlet, A. R., Karsdal, M. A., Henriksen, K., & Tang, M. E. (2023) · The Journal of Prevention of Alzheimer s Disease · doi:10.14283/jpad.2023.36 · Cited by 3
Multidimensional Heritability Analysis of Neuroanatomical ShapePreprint
Ge, T., Reuter, M., Winkler, A. M., Holmes, A. J., Lee, P. H., Tirrell, L. S., Roffman, J. L., Buckner, R. L., Smoller, J. W., & Sabuncu, M. R. (2015) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/033407 · Cited by 3
Pinakhina, D. V., Kasyanov, E. D., Rukavishnikov, G. V., Larin, A. K., Veselovsky, V. A., Rakitko, A., Незнанов, Н. Г., Kibitov, A. O., Mazo, G., & Artomov, M. (2024) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2024.1416009 · Cited by 2
Hu, L., Li, J., Guo, H., Su, L., Dong, P., Huang, J., Liu, Y., Liu, X., Luo, Z., Xiong, W., et al. (2025) · Advanced Science · doi:10.1002/advs.202503408 · Cited by 3
Genetic analysis of imaging-derived phenotypes
Bian, Y., & Akey, J. M. (2026) · Nature Reviews Genetics · doi:10.1038/s41576-026-00989-5 · Cited by 4
Combinational Drug Repurposing from Genetic Networks Applied to Alzheimer’s Disease
Nabirotchkin, S., Bouaziz, J., Glibert, F., Mandel, J., Foucquier, J., Hajj, R., Callizot, N., Cholet, N., Guedj, M., & Cohen, D. (2022) · Journal of Alzheimer s Disease · doi:10.3233/jad-220120 · Cited by 2
Gottschalk, W. K., Mahon, S., Hodgson, D., Barrera, J., Hill, D., Wei, A. L., Kumar, M., Dai, K. Z., Anderson, L., Mihovilovic, M., et al. (2023) · Journal of Alzheimer s Disease · doi:10.3233/jad-230451 · Cited by 2
Neuroimaging Genetics and Network Analysis in Alzheimer’s Disease
Moon, S. W. (2023) · Current Alzheimer Research · doi:10.2174/0115672050265188231107072215 · Cited by 2
Imaging Genetics of Neuropsychiatric Disease
Chen, J. A., & Coppola, G. (2015) · Elsevier eBooks · doi:10.1016/b978-0-12-397025-1.00130-5 · Cited by 2
Gaetano, C. D., Matullo, G., Piazza, A., Ursino, M., & Gasparini, M. (2013) · Evolutionary Bioinformatics · doi:10.4137/ebo.s10211 · Cited by 2
L’homéopathie ouvre de nouvelles voies de recherche dans la maladie d’Alzheimer
Billot, J.-P. (2012) · La Revue d Homéopathie · doi:10.1016/j.revhom.2012.07.001 · Cited by 2
Farrell, C., & Brookes, K. J. (2022) · Journal of Neurology and Neuromedicine · doi:10.29245/2572.942x/2022/2.1284 · Cited by 2
An AS‐qPCR‐based method for the detection of Alzheimer's disease‐related SNPs
Chen, J., Shi, B., Li, Y., Feng, Y., Ni, J., Shi, J., Luo, C., Wang, J., & Tian, J. (2022) · Journal of Cellular Biochemistry · doi:10.1002/jcb.30350 · Cited by 2
Porter, T., Armstrong, A. M., Milicic, L., Vacher, M. A., Fernandez, S., O’Brien, E. K., Doré, V., Bourgeat, P., Shishegar, R., Xia, Y., et al. (2025) · Research Square · doi:10.21203/rs.3.rs-7540786/v1 · Cited by 2
Genetic and Genomic Aspects of Alzheimer's Disease
Chua, S.-M., Ng, B.-Y., & Lee, T.-S. W. (2010) · Proceedings of Singapore Healthcare · doi:10.1177/201010581001900408 · Cited by 1
Düz, E., İlgün, A., Bozkurt, F. B., & Çakır, T. (2024) · European Journal of Neuroscience · doi:10.1111/ejn.16600 · Cited by 1
Nadanaka, S., Imamoto, Y., Takarada, T., Tanaka, M., & Kitagawa, H. (2025) · Cells · doi:10.3390/cells14211686 · Cited by 1
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Vacher, M., Porter, T., Milicic, L., Bourgeat, P., Doré, V., Villemagne, V. L., Laws, S. M., & Doecke, J. D. (2022) · Journal of Alzheimer s Disease · doi:10.3233/jad-210644 · Cited by 1
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Falco, A. D., Iolascon, A., Ascione, F., & Piscopo, C. (2023) · Genes · doi:10.3390/genes14051116 · Cited by 1
Lima, A. O., Malheiros, J., Afonso, J., Petrini, J., Coutinho, L., Diniz, W. J. D. S., Bressani, F. A., Tizioto, P., de Oliveira, P. N. D., Ribeiro, J., et al. (2022) · Mammalian Genome · doi:10.1007/s00335-022-09960-1 · Cited by 1
Ancestralidade da população de São Paulo e correlação com alterações neuropatológicas no idoso
Schlesinger, D. (2010) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/t.41.2010.tde-17122010-142628 · Cited by 1
Neurogenesis in the adult brain, gene networks, and Alzheimer's Disease
Horgusluoglu, E. (2017) · IUScholarWorks (Indiana University) · doi:10.7912/c2/1972 · Cited by 1
Beaton, D., Rieck, J. R., Alhazmi, F. H., ADNI, & Abdi, H. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/235945 · Cited by 1
A 3-fold kernel approach for characterizing Late Onset Alzheimer’s DiseasePreprint
Squillario, M., Tomasi, F., Tozzo, V., Barla, A., Uberti, D. L., & Initiative, A. D. N. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/397760 · Cited by 1
Nuytemans, K., Vasquez, M. L., Wang, L., Booven, D. V., Griswold, A. J., Rajabli, F., Celis, K., Oron, O., Hofmann, N. K., Rolati, S., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.08.23.457375 · Cited by 1
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Brand, C. M. (2021) · Scholars' Bank (University of Oregon) · Cited by 1
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Stokes, M. E. (2014) · D-Scholarship@Pitt (University of Pittsburgh)
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Swaminathan, S. (2013) · IUScholarWorks (Indiana University) · doi:10.7912/c2/1949
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von Otter, M. (2009) · Gothenburg University Publications Electronic Archive (Gothenburg University)
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Tales, A., Bayer, A. J., Krajcik, S., & Jellinger, K. A. (2012) · International Journal of Alzheimer s Disease · doi:10.1155/2012/262305
Turton, J. C., & Morgan, K. (2013) · Springer eBooks · doi:10.1007/978-1-4614-7309-1_6
Rothenberg, K. G., Siedlak, S. L., Lee, H., Zhu, X., Perry, G. L., & Smith, M. A. (2011) · Future Neurology · doi:10.2217/fnl.10.81
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Wallace, D. C. (2011) · Cold Spring Harbor Symposia on Quantitative Biology · doi:10.1101/sqb.2011.76.010462 · Cited by 132
Ramanan, V. K. (2014) · IUScholarWorks (Indiana University) · doi:10.7912/c2/1951
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Lin, J.-A. (2019) · Carolina Digital Repository (University of North Carolina at Chapel Hill) · doi:10.17615/2jaw-zt11
Sochat, V., David, M. M., & Wall, D. P. (2015) · PubMed Central
Picky Partial Least Squares for Feature Selection from Alzheimer's Data
Wästerlid, T., & Li, B. (2014) · doi:10.1109/trustcom.2014.91
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Ferencz, B. (2017) · Figshare
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Yang, T. (2017)
Hawley, B. (2012) · uO Research (University of Ottawa) · doi:10.20381/ruor-6225
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Spector, S. A. (2013) · Elsevier eBooks · doi:10.1016/b978-0-12-411464-7.00020-1
Yurkiewich, A. J. (2012) · uO Research (University of Ottawa) · doi:10.20381/ruor-5448
Genetic Risk Factors for Complex Forms of Alzheimer’s Disease
Bellenguez, C., & Lambert, J. (2018) · Springer eBooks · doi:10.1007/978-3-319-72938-1_4
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Yao, X. (2018) · IUScholarWorks (Indiana University) · doi:10.7912/c2/903
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Avgan, N. (2018) · Queensland University of Technology · doi:10.5204/thesis.eprints.122903
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Marín, B., Alquézar-Baeta, C., Hernández, M., & Mayordomo, E. (2018) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm.2018.8621358
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Bashir, A. (2019) · Global Journal of Pediatrics & Neonatal Care · doi:10.33552/gjpnc.2019.01.000503
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Paul, S. E., Elsayed, N. M., Bondy, E., & Bogdan, R. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-813156-5.00030-3
Belbin, O., Carrasquillo, M. M., Crump, M. P., Culley, O. J., Hunter, T. A., Ma, L., Bisceglio, G. D., Dickson, D. W., Younkin, S. G., Morgan, K., et al. (2011)
Nikolova, Y. S. (2014) · DukeSpace (Duke University)
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Risacher, S. L., Wishart, H. A., & Saykin, A. J. (2014) · Springer eBooks · doi:10.1007/978-1-4939-1995-6_10
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Leung, S. K., Walker, E., Policicchio, S. S. S., Dahir, A., Vellame, D. S., Smith, A. R., Swarbrick, R., Lunnon, K., Dempster, E. L., Ahmed, Z., et al. (2026) · npj Dementia · doi:10.1038/s44400-026-00074-y · Cited by 2
Hebert, S., Pederson, E. N., & Ouyang, Z. (2025) · Bioinformatics and Biology Insights · doi:10.1177/11779322251358309
Chen, S., Lee, E.-G., Leong, L., Tulloch, J., & Yu, C. (2025) · Research Square · doi:10.21203/rs.3.rs-7634101/v1
Shafquat, A., Crystal, R. G., & Mezey, J. G. (2020) · Figshare · doi:10.6084/m9.figshare.12270359
Li, L., Yu, X., Sheng, C., Jiang, X., Zhang, Q., Han, Y., & Jiang, J. (2022) · Figshare · doi:10.6084/m9.figshare.21130048
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Wang, W. (2017) · theses.fr (ABES) · doi:10.70675/fda08f82z3032z4960z8fc3zb205890acc42
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Grossman, I., Lutz, M. W., Crenshaw, D. G., Saunders, A. M., Chiba‐Falek, O., Gottschalk, W. K., & Roses, A. D. (2012) · Advances in predictive, preventive and personalised medicine · doi:10.1007/978-94-007-5866-7_7
Metadata
- Authors
- Potkin, S. G., Guffanti, G., Lakatos, A., Turner, J. A., Kruggel, F., Fallon, J. H., Saykin, A. J., Orro, A., Lupoli, S., Salvi, E., Weiner, M., Macciardi, F., & Initiative, F. T. A. D. N.
- Affiliations
- University of California, Irvine (US)
- University of Milan (IT)
- Indiana University Indianapolis (US)
- Institute of Biomedical Technologies (IT)
- Vita-Salute San Raffaele University (IT)
- University of California, San Francisco (US)
- Corresponding author
- Potkin, Steven G.
- ORCID
- Venue
- PLoS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2009-08-07
- Volume
- 4
- Issue
- 8
- Pages
- e6501
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC2719581
- OpenAlex
- W2162988145
- Cited by
- 376
- References
- 71
- Citations by year
- 2026: 2 · 2025: 8 · 2024: 15 · 2023: 17 · 2022: 15 · 2021: 6 · 2020: 22 · 2019: 14 · 2018: 21 · 2017: 25 · 2016: 21 · 2015: 27 · 2014: 28 · 2013: 36 · 2012: 36
- FWCI
- 25.99
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PLoS ONE (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Archivio Istituzionale della Ricerca (Universita Degli Studi Di Milano) (submittedVersion, other-oa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- eScholarship (California Digital Library) (submittedVersion, cc-by)
- Figshare (submittedVersion, cc-by)
- IUScholarWorks (Indiana University) (submittedVersion, cc-by)
- Funders
- Foundation for the National Institutes of Health (U01AG024904, AG024904)
- Alzheimer's Association (U01 AG024904)
- Bristol-Myers Squibb (U01 AG024904, AG024904)
- Eli Lilly and Company (U01 AG024904, AG024904)
- Pfizer (AG024904, U01 AG024904)
- AstraZeneca
- GlaxoSmithKline
- Alzheimer's Disease Neuroimaging Initiative (3U01AG024904-03S5, ADNI U01 AG024904-01, U01AG024904, AG024904)
- Novartis Pharmaceuticals Corporation (U01 AG024904, AG024904)
- Northern California Institute for Research and Education (U01 AG024904)
- Vanda Pharmaceuticals
- Elan (U01 AG024904)
- Eisai (U01 AG024904, AG024904)
- National Institutes of Health (3u01ag024904-03s5, U01-AG024904, AG024904, ADNI U01 AG024904)
- U.S. Food and Drug Administration
- University of California, Los Angeles
- University of California, San Diego
- National Institute on Alcohol Abuse and Alcoholism
- National Institute on Aging (U01-AG-024904, U01 AG024904-01, AG 024904, 3U01AG024904-03S5, U19AG010483, U24AG021886, 3U01AG024904)
- National Institute of Biomedical Imaging and Bioengineering (U01 AG024904, AG024904)
- National Center for Research Resources (U24RR021992, FBIRN U24-RR021992, RR021992, P20RR020837, TIGC-P20 RR020837-01, U01 AG024904)
- Topics
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Alzheimer's disease research and treatments
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Keywords
- Alzheimer's disease, hippocampal atrophy, genetic susceptibility, genome-wide association study, quantitative traits, apolipoprotein E, TOMM40, ephrin-A5, CAND1, MAGI2, arsB, single nucleotide polymorphisms, MRI phenotypes, sporadic Alzheimer's disease, ADNI
- MeSH terms
- Adult, Alzheimer Disease, Female, Hippocampus, Humans, Magnetic Resonance Imaging, Male, Case-Control Studies, Genetic Predisposition to Disease, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Genome-Wide Association Study
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