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Genome-wide association study of the rate of cognitive decline in Alzheimer’s disease
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Background: Substantial interindividual variability exists in the disease trajectories of Alzheimer's disease (AD) patients. Some decline rapidly whereas others decline slowly, and there are no known explanations for this variability. We describe the first genome‐wide association study to examine rate of cognitive decline in a sample of AD patients with longitudinal measures of cognition. Methods: The discovery sample was 303 AD cases recruited in the Alzheimer's Disease Neuroimaging Initiative and the replication sample was 323 AD cases from the Religious Orders Study and Rush Memory and Aging Project. In the discovery sample, Alzheimer's Disease Assessment Scale–cognitive subscale responses were tested for association with genome‐wide single‐nucleotide polymorphism (SNP) data using linear regression. We tested the 65 most significant SNPs from the discovery sample for association in the replication sample. Results: We identified SNPs in the spondin 1 gene ( SPON1 ), the minor alleles of which were significantly associated with a slower rate of decline (rs11023139, P = 7.0 × 10 −11 ) in the discovery sample. A SPON1 SNP 5.5 kb upstream was associated with decline in the replication sample (rs11606345, P = .002). Conclusion: SPON1 has not been previously associated with AD risk, but is plausibly related because the gene product binds to the amyloid precursor protein and inhibits its cleavage by β‐secretase. These data suggest that SPON1 may be associated with the differential rate of cognitive decline in AD.
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“Analysis of loci that reach GWAS and nominal significance and SNPs in LD with them at D’ ≥ 0.8 reveals predominant associations with traits such as cognitive decline [12, 68, 69], educational attainment [70–73], mean bone density [57, 74], intelligence and performance intelligence quotient [75, 76] and soluble TREM-2 levels [77].”
From Genetic drivers of progression in Alzheimer’s disease are distinct from disease risk (2026) · cites this paper as [12]
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“Lastly, genome wide analyses have often been confounded by the absence of disease confirmatory biomarkers such as amyloid, which is critical given the high rate of clinical misdiagnosis[12–14].”
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“In addition, we found the APOE and APP subnetwork within the vesicle mediated transport pathway which are among the most significant genetic risk factors for AD (Figure 5F).46,47,48”
From SCING: Inference of robust, interpretable gene regulatory networks from single cell and spatial transcriptomics (2023) · cites this paper as [48]
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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
Kok, J. S., Berg, I. J., & Scherder, E. J. A. (2013) · Dementia and Geriatric Cognitive Disorders Extra · doi:10.1159/000353441 · Cited by 20,589
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
A map of human genome variation from population-scale sequencing.
The 1000 Genomes Project Consortium (2010) · Nature · doi:10.1038/nature09534 · Cited by 7,877
METAL: fast and efficient meta-analysis of genomewide association scans
Willer, C. J., Li, Y., & Abecasis, G. R. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq340 · Cited by 5,205
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
A new rating scale for Alzheimer's disease
(1984) · American Journal of Psychiatry · doi:10.1176/ajp.141.11.1356 · Cited by 4,175
Harold, D., Abraham, R., Hollingworth, P., Sims, R., Gerrish, A., Hamshere, M. L., Pahwa, J. S., Moskvina, V., Dowzell, K., Williams, A., et al. (2009) · Nature Genetics · doi:10.1038/ng.440 · Cited by 3,326
A genome-wide association study of type 2 diabetes in Finns detects multiple susceptibility variants
Scott, L. J., Mohlke, K. L., Bonnycastle, L. L., Willer, C. J., Li, Y., Duren, W. L., Erdos, M. R., Stringham, H. M., Chines, P. S., Jackson, A. U., et al. (2007) · Science · doi:10.1126/science.1142382 · Cited by 2,753
Genome-wide association study identifies variants at CLU and CR1 associated with Alzheimer's disease
the European Alzheimer's Disease Initiative Investigators, Lambert, J.-C., Heath, S., Even, G., Campion, D., Sleegers, K., Hiltunen, M., Combarros, O., Zelenika, D., Bullido, M. J., et al. (2009) · Nature Genetics · doi:10.1038/ng.439 · Cited by 2,234
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Li, X., Jin, T., Zhang, M., Yang, H., Huang, X., Zhou, X., Huang, W., Qin, L., Kang, L., Fan, M., et al. (2017) · Oncotarget · doi:10.18632/oncotarget.16362 · Cited by 12
Influence of Anoctamin-4 and -9 on ADAM10 and ADAM17 Sheddase Function
Leitzke, S., Seidel, J., Ahrens, B., Schreiber, R., Kunzelmann, K., Sperrhacke, M., Bhakdi, S., & Reiß, K. (2022) · Membranes · doi:10.3390/membranes12020123 · Cited by 12
Levine, Z., Kalka, I. N., Kolobkov, D. S., Rossman, H., Godneva, A., Shilo, S., Keshet, A., Weissglas‐Volkov, D., Shor, T., Diament, A., et al. (2023) · Med · doi:10.1016/j.medj.2023.12.001 · Cited by 11
Fernandez, S., Burnham, S. C., Milicic, L., Savage, G. R., Maruff, P. T., Peretti, M., Sohrabi, H. R., Lim, Y. Y., Weinborn, M., Ames, D. J., et al. (2021) · Journal of Alzheimer s Disease Reports · doi:10.3233/adr-200246 · Cited by 12
Степанов, В. А., Marusin, A. V., Вагайцева, К. В., Бочарова, А. В., & Makeeva, O. A. (2017) · Genetics Research International · doi:10.1155/2017/6293826 · Cited by 12
Genetic Analysis of Mitochondrial Ribosomal Proteins and Cognitive Aging in Postmenopausal Women
Mozhui, K., Snively, B. M., Rapp, S. R., Wallace, R. B., Williams, R. W., & Johnson, K. (2017) · Frontiers in Genetics · doi:10.3389/fgene.2017.00127 · Cited by 11
Ahmad, S., Imtiaz, M. A., Mishra, A., Wang, R., Herrera-Rivero, M., Bis, J. C., Fornage, M., Roshchupkin, G. V., Hofer, E., Logue, M. W., et al. (2024) · Communications Biology · doi:10.1038/s42003-024-06804-3 · Cited by 11
Wang, J., Qu, S., Wang, W., Guo, L., Zhang, K., Chang, S., & Wang, J. (2016) · Journal of Psychiatric Research · doi:10.1016/j.jpsychires.2016.07.016 · Cited by 10
Udosen, B., Soremekun, O. S., Kamiza, A. B., Machipisa, T., Cissé, C., Omotuyi, O. I., Soliman, M. E. S., Wélé, M., Nashiru, O., Chikowore, T., et al. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24032164 · Cited by 8
Sharie, A. H. A., Zu’bi, Y. O. A., El‐Elimat, T., Al-Kammash, K. R., Lil, A. A., Isawi, I. H., Sharie, S. A., Mousa, B. M. A., Malkawi, A. A. A., & Alali, F. Q. (2023) · Journal of Personalized Medicine · doi:10.3390/jpm13020295 · Cited by 11
Lizama, B. N., Williams, C., North, H. A., Pandey, K., Duong, D., Di Caro, V., Mecca, A. P., Blennow, K., Zetterberg, H., Levey, A. I., et al. (2024) · Alzheimer's & Dementia · doi:10.1002/alz.14152 · Cited by 8
TMEM16 Membrane Proteins in Health and Disease
Yang, H., & Jan, L. Y. (2016) · Elsevier eBooks · doi:10.1016/b978-0-12-802002-9.00007-8 · Cited by 8
Genomewide association study of C-peptide surfaces key regulatory genes in Indians
Bandesh, K., Prasad, G., Giri, A. K., Voruganti, V. S., Butte, N. F., Cole, S. A., Comuzzie, A. G., Tandon, N., & Bharadwaj, D. (2019) · Journal of Genetics · doi:10.1007/s12041-018-1046-1 · Cited by 8
Genome-Wide Association Study Identifies SLAMF1 Affecting the Rate of Memory Decline
Chen, S.-D., Initiative, O. B. O. A. D. N., Li, H., Shen, X., Li, J., Xu, W., Huang, Y., Tan, L., Dong, Q., & Yu, J. (2020) · Journal of Alzheimer s Disease · doi:10.3233/jad-191214 · Cited by 8
Clukay, Hughes, D. A., Kertes, & Mulligan, C. J. (2019) · Human Biology · doi:10.13110/humanbiology.91.2.04 · Cited by 8
Dai, Y., Hsu, Y.-C., Fernandes, B. S., Zhang, K., Li, X., Enduru, N., Liu, A., Manuel, A. M., Jiang, X., Zhao, Z., et al. (2024) · Journal of Alzheimer s Disease · doi:10.3233/jad-231020 · Cited by 9
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Reichhart, N., Milenkovic, V. M., Wetzel, C. H., & Strauß, O. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22052732 · Cited by 7
Lee, S., Park, B., Hong, K., & Jung, D. (2022) · Journal of Cardiovascular Development and Disease · doi:10.3390/jcdd9040104 · Cited by 8
Kim, B.-H., Lee, H., Ham, H., Kim, H. J., Jang, H., Kim, J. P., Park, Y. H., Kim, M., & Seo, S. W. (2023) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2023.1278998 · Cited by 6
Investigation of genetic determinants of cognitive change in later life
Mahedy, L., Anderson, E. L., Tilling, K. M., Thornton, Z. A., Elmore, A. R., Szalma, S., Simen, A. A., Culp, M., Zicha, S., Harel, B., et al. (2024) · Translational Psychiatry · doi:10.1038/s41398-023-02726-6 · Cited by 8
Brain structure and allelic associations in Alzheimer's disease
Moon, S. W., Zhao, L., Matloff, W. J., Hobel, S., Berger, R. P., Kwon, D., Kim, J., Toga, A. W., Dinov, I. D., & Initiative, A. D. N. (2022) · CNS Neuroscience & Therapeutics · doi:10.1111/cns.14073 · Cited by 7
Amber, S., & Zahid, S. (2018) · Gene · doi:10.1016/j.gene.2018.06.011 · Cited by 7
Genetic Regulation of Cytokine Response in Patients with Acute Community-Acquired Pneumonia
Kühnapfel, A., Horn, K., Klotz, U., Kiehntopf, M., Rosolowski, M., Loeffler, M., Ahnert, P., Suttorp, N., Witzenrath, M., & Scholz, M. (2021) · Genes · doi:10.3390/genes13010111 · Cited by 8
Guerri, L., Dobbs, L. K., Silva, D. A. D. S. E., Meyers, A., Ge, A., Lecaj, L., Djakuduel, C., Islek, D., Hipolito, D., Badillo-Martinez, A., et al. (2022) · Biological Psychiatry Global Open Science · doi:10.1016/j.bpsgos.2022.08.010 · Cited by 9
A Urinary Metabolomics Analysis Based on UPLC-MS and Effects of Moxibustion in APP/PS1 Mice
He, R., Liu, J., Huang, C., Liu, J., Cui, H., & Zhao, B. (2020) · Current Alzheimer Research · doi:10.2174/1567205017666201109091759 · Cited by 6
Ray, N. R., Kunkle, B. W., Hamilton‐Nelson, K. L., Kurup, J. T., Rajabli, F., Coşacak, M. İ., Kızıl, Ç., Jean‐Francois, M. N., Cuccaro, M. L., Reyes‐Dumeyer, D., et al. (2023) · Alzheimer s & Dementia · doi:10.1002/alz.078904 · Cited by 6
Self-citationMosaic loss of the Y chromosome in human neurodegenerative and oncological diseases
Kuznetsova, I. L., Uralsky, L. I., Tyazhelova, T. V., Андреева, Т. В., & Рогаев, Е. И. (2023) · Vavilov Journal of Genetics and Breeding · doi:10.18699/vjgb-23-61 · Cited by 6
АНАЛИЗ АССОЦИАЦИЙ ГЕНЕТИЧЕСКИХ МАРКЕРОВ ШИЗОФРЕНИИ И ЕЕ КОГНИТИВНЫХ ЭНДОФЕНОТИПОВ, "Генетика"
Бочарова, А. В., Степанов, В. А., Marusin, A. V., Харьков, В. Н., Вагайцева, К. В., Федоренко, О. Ю., Bokhan, N. A., Semke, A. V., & Ivanova, S. А. (2017) · Генетика · doi:10.7868/s0016675817010039 · Cited by 6
Huang, Z., Iqbal, Z., Zhao, Z., Chen, X., Mahmmod, A., Liu, J., Li, W., & Deng, Z. (2024) · International Journal of Molecular Medicine · doi:10.3892/ijmm.2024.5405 · Cited by 5
Bilecki, W., Latusz, J., Gawlińska, K., Chmelová, M., & Maćkowiak, M. (2021) · Behavioural Brain Research · doi:10.1016/j.bbr.2021.113231 · Cited by 5
Cuéllar-Partida, G., Laurin, C., Ring, S. M., Gaunt, T. R., Relton, C. L., Smith, G. D., & Evans, D. M. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/161471 · Cited by 5
Alzheimer's disease: the large gene instability hypothesisPreprint
Soheili‐Nezhad, S. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/189712 · Cited by 5
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Akgün, B., Cornejo‐Olivas, M., Custodio, N., Mena, P. R., Soto‐Añari, M., Montesinos, R., Adams, L. D., Celis, K., Yang, Z., Huaman, B. C., et al. (2023) · medRxiv · doi:10.1101/2023.11.29.23299201 · Cited by 5
Guo, B., Xie, Z., He, W., Islam, S. M. S., Gottlieb, A., Chen, H., & Zhi, D. (2024) · medRxiv · doi:10.1101/2024.12.06.24318618 · Cited by 5
Regulation of glypican 6-mediated Wnt activation maintains TDP-43 nuclear localization in neurons
Zhang, N., & Sockanathan, S. (2025) · Scientific Reports · doi:10.1038/s41598-025-32069-9 · Cited by 6
Deep causal feature extraction and inference with neuroimaging genetic data
Yao, Y., Charkraborty, D., Zhang, L., Shen, X., Initiative, A. D. N., & Pan, W. (2023) · Statistics in Medicine · doi:10.1002/sim.9824 · Cited by 4
A genome-wide association study of social trust in 33,882 Danish blood donors
Sequeros, C. B., Hansen, T. F., Westergaard, D., Louloudis, I. E., Kalamajski, S., Röder, T., Rohde, P. D., Schwinn, M., Clemmensen, L. K. H., Didriksen, M., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-51636-0 · Cited by 5
Bebek, G., Miyagi, M., Wang, X., Appleby, B. S., Leverenz, J. B., & Pillai, J. A. (2025) · Alzheimer s Research & Therapy · doi:10.1186/s13195-025-01767-x · Cited by 4
Orsi, L., Margaritte‐Jeannin, P., Andrianjafimasy, M. V., Dumas, O., Mohamdi, H., Bouzigon, E., Démenais, F., Matran, R., Zerimech, F., Nadif, R., et al. (2022) · Antioxidants · doi:10.3390/antiox11050802 · Cited by 4
Xavier, C., & Pinto, N. (2025) · Journal of Alzheimer s Disease · doi:10.1177/13872877251317543 · Cited by 4
Plasma proteomic characterization of motoric cognitive risk and mild cognitive impairment
Gómez, G., Sathyan, S., Chen, J., Fornage, M., Schlosser, P., Peng, Z., Cordon, J., Palta, P., Sullivan, K., Tin, A., et al. (2025) · Alzheimer s & Dementia · doi:10.1002/alz.14429 · Cited by 3
Ali, A., Milman, S., Weiss, E. F., Gao, T., Napolioni, V., Barzilai, N., Zhang, Z. D., & Lin, J.-R. (2024) · Alzheimer s & Dementia · doi:10.1002/alz.14379 · Cited by 3
Morenikeji, O. B., Akinyemi, M. O., Wheto, M., OGUNSHOLA, O. J., Badejo, A. A., & Chineke, C. A. (2019) · Journal of Genetics · doi:10.1007/s12041-019-1060-y · Cited by 3
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Brain and CSF Alzheimer’s Biomarkers Are Associated with SERPINE1 Gene Expression
Picard, C., Zetterberg, H., Blennow, K., Villeneuve, S., & Poirier, J. (2025) · Genes · doi:10.3390/genes16070818 · Cited by 4
Neuroimaging Genetics and Network Analysis in Alzheimer’s Disease
Moon, S. W. (2023) · Current Alzheimer Research · doi:10.2174/0115672050265188231107072215 · Cited by 2
Association of immunity-related gene SNPs with Alzheimer’s disease
Bissar‐Tadmouri, N., Kassir, R., Salami, A., & Shamieh, S. E. (2024) · Experimental Biology and Medicine · doi:10.3389/ebm.2024.10303 · Cited by 2
Степанов, В. А., Бочарова, А. В., Вагайцева, К. В., Marusin, A. V., Markova, V. V., Минайчева, Л. И., Zhukova, I. A., Zhukova, N., Alifirova, V. M., & Makeeva, O. A. (2018) · S S Korsakov Journal of Neurology and Psychiatry · doi:10.17116/jnevro20181185192 · Cited by 2
Xi, J., Ding, D., Zhao, Q., Liang, X., Zheng, L., Guo, Q., Hong, Z., Fu, H., Xu, J., & Xiao, Q. (2020) · Current Alzheimer Research · doi:10.2174/1567205017666200317095608 · Cited by 2
Littman, R., Wang, N., Peng, C., & Yang, X. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.09.07.506959 · Cited by 2
Genetic drivers of progression in Alzheimer’s disease are distinct from disease risk
Cohen, C. E., Fernandez, S., Yaman, U., Ehyaei, A. R., Kodosaki, E., Askarova, A., Porter, T., O’Brien, E., Study, A. I. B. A. L., Initiative, A. D. N., et al. (2026) · Alzheimer's Research & Therapy · doi:10.1186/s13195-026-02036-1 · Cited by 1
Compositional epistasis detection using a few prototype disease models
Lu, C., & Zhu, M. (2019) · PLoS ONE · doi:10.1371/journal.pone.0213236 · 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
Ahmad, S., Imtiaz, M. A., Mishra, A., Wang, R., Herrera-Rivero, M., Bis, J. C., Fornage, M., Roshchupkin, G. V., Hofer, E., Logue, M. W., et al. (2022) · medRxiv · doi:10.1101/2022.12.14.22283446 · Cited by 1
Ali, A., Milman, S., Weiss, E. F., Gao, T., Napolioni, V., Barzilai, N., Zhang, Z. D., & Lin, J.-R. (2024) · medRxiv · doi:10.1101/2024.05.21.24307692 · Cited by 1
WNKs regulate mouse behavior and alter central nervous system glucose uptake and insulin signalingPreprint
Jaykumar, A. B., Binns, D. D., Taylor, C. A., Anselmo, A. N., Kankanamalage, S. G., Birnbaum, S. G., Huber, K. M., & Cobb, M. H. (2024) · eLife · doi:10.7554/elife.100097 · Cited by 1
WNKs regulate mouse behavior and alter central nervous system glucose uptake and insulin signalingPreprint
Jaykumar, A. B., Binns, D. D., Taylor, C. A., Anselmo, A. N., Birnbaum, S. G., Huber, K. M., & Cobb, M. H. (2024) · eLife · doi:10.7554/elife.100097.1 · Cited by 2
WNKs regulate mouse behavior and alter central nervous system glucose uptake and insulin signalingPreprint
Jaykumar, A. B., Binns, D. D., Taylor, C. A., Anselmo, A. N., Kankanamalage, S. G., Birnbaum, S. G., Huber, K. M., & Cobb, M. H. (2025) · eLife · doi:10.7554/elife.100097.2 · Cited by 1
Villalba, K. (2014) · FIU - Digital Commons (Florida International University) · doi:10.25148/etd.fi14110727
(2008) · PsycEXTRA Dataset · doi:10.1037/e571372010-001
Alcohol-Induced Differential Variable Genes
Liang, S., Li, J., Li, J., Yang, C., Qiu, W., Yang, J., Sun, Z., & Mao, R. (2017) · IEEE Annual International Computer Software and Applications Conference (COMPSAC) · doi:10.1109/compsac.2017.48
Moreno–Grau, S., Fernández, M. V., de Rojas, I., Hernández, I., Farias, F. G., Budde, J., Quintela, I., Madrid, L., González‐Pérez, A., Montrreal, L., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.02.10.941393
Association of ANKRD55 Gene Polymorphism with HT: A Protective Factor for Disease Susceptibility
Fang, Y., Zhao, J., Zhuang, X., Xu, J., Cai, T., Yang, X., Mu, K., & Zhang, J. (2022) · International Journal of Endocrinology · doi:10.1155/2022/7300796
Matthews, I., Birmbaum, A., Gromova, A., Coutinho, K., McGraw, M., Patterson, D. C., Banks, M. T., Nobles, A. C., Nguyen, N., Miranda, H. C., et al. (2022) · Research Square · doi:10.21203/rs.3.rs-1578234/v1
Predictive Network Analysis Identifies JMJD6 and Other Novel Key Drivers in Alzheimer’s DiseasePreprint
Merchant, J. P., Zhu, K., Henrion, M., Zaidi, S. A. J., Branden, L., Moein, S., Alamprese, M., Pearse, R., Bennett, D. A., Ertekin-Taner, N., et al. (2022) · bioRxiv · doi:10.1101/2022.10.19.512949 · Cited by 1
Self-citationLiu, H., Barnes, J., Pedrosa, E., Herman, N. S., Salas, F., Wang, P., Zheng, D., & Lachman, H. M. (2020) · Figshare · doi:10.6084/m9.figshare.12285422
Liu, H., Barnes, J., Pedrosa, E., Herman, N. S., Salas, F., Wang, P., Zheng, D., & Lachman, H. M. (2020) · Figshare · doi:10.6084/m9.figshare.12285389
Liu, H., Barnes, J., Pedrosa, E., Herman, N. S., Salas, F., Wang, P., Zheng, D., & Lachman, H. M. (2020) · Figshare · doi:10.6084/m9.figshare.12285440
Human genetics: Establishment of cause-and-effect relationships
Saido, T. C. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-28979-8.00013-9
Liu, H., Barnes, J., Pedrosa, E., Herman, N. S., Salas, F., Wang, P., Zheng, D., & Lachman, H. M. (2020) · Figshare · doi:10.6084/m9.figshare.12285410
Liu, H., Barnes, J., Pedrosa, E., Herman, N. S., Salas, F., Wang, P., Zheng, D., & Lachman, H. M. (2020) · Figshare · doi:10.6084/m9.figshare.12285398
Wang, J., Wu, J., Sun, Q., Wu, Q., Li, Y., Duan, S., Yang, L., Wu, W., Wang, Z., Liu, Y., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26596664
Wang, J., Wu, J., Sun, Q., Wu, Q., Li, Y., Duan, S., Yang, L., Wu, W., Wang, Z., Liu, Y., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26596673
Wang, J., Wu, J., Sun, Q., Wu, Q., Li, Y., Duan, S., Yang, L., Wu, W., Wang, Z., Liu, Y., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26596640
Liu, H., Barnes, J., Pedrosa, E., Herman, N. S., Salas, F., Wang, P., Zheng, D., & Lachman, H. M. (2020) · Figshare · doi:10.6084/m9.figshare.12285416
Wang, J., Wu, J., Sun, Q., Wu, Q., Li, Y., Duan, S., Yang, L., Wu, W., Wang, Z., Liu, Y., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26596637
Wang, J., Wu, J., Sun, Q., Wu, Q., Li, Y., Duan, S., Yang, L., Wu, W., Wang, Z., Liu, Y., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26596655
Wang, J., Wu, J., Sun, Q., Wu, Q., Li, Y., Duan, S., Yang, L., Wu, W., Wang, Z., Liu, Y., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26596634
Wang, J., Wu, J., Sun, Q., Wu, Q., Li, Y., Duan, S., Yang, L., Wu, W., Wang, Z., Liu, Y., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26596682
Wang, J., Wu, J., Sun, Q., Wu, Q., Li, Y., Duan, S., Yang, L., Wu, W., Wang, Z., Liu, Y., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26596649
Liu, H., Barnes, J., Pedrosa, E., Herman, N. S., Salas, F., Wang, P., Zheng, D., & Lachman, H. M. (2020) · Figshare · doi:10.6084/m9.figshare.12285431
Wang, J., Wu, J., Sun, Q., Wu, Q., Li, Y., Duan, S., Yang, L., Wu, W., Wang, Z., Liu, Y., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26596643
Wang, J., Wu, J., Sun, Q., Wu, Q., Li, Y., Duan, S., Yang, L., Wu, W., Wang, Z., Liu, Y., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26596646
Wang, J., Wu, J., Sun, Q., Wu, Q., Li, Y., Duan, S., Yang, L., Wu, W., Wang, Z., Liu, Y., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26596679
Wang, J., Wu, J., Sun, Q., Wu, Q., Li, Y., Duan, S., Yang, L., Wu, W., Wang, Z., Liu, Y., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26596661
Wang, J., Wu, J., Sun, Q., Wu, Q., Li, Y., Duan, S., Yang, L., Wu, W., Wang, Z., Liu, Y., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26596676
Wang, J., Wu, J., Sun, Q., Wu, Q., Li, Y., Duan, S., Yang, L., Wu, W., Wang, Z., Liu, Y., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26596658
Wang, J., Wu, J., Sun, Q., Wu, Q., Li, Y., Duan, S., Yang, L., Wu, W., Wang, Z., Liu, Y., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26596652
Wang, J., Wu, J., Sun, Q., Wu, Q., Li, Y., Duan, S., Yang, L., Wu, W., Wang, Z., Liu, Y., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26596670
Wang, J., Wu, J., Sun, Q., Wu, Q., Li, Y., Duan, S., Yang, L., Wu, W., Wang, Z., Liu, Y., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26596667
Chen, X., Zhang, X. R., Xiong, W., Wang, T., Jia, A., Feng, Q., & Huang, M. (2026) · Medical Image Analysis · doi:10.1016/j.media.2026.103952
Hu, Y., Gruen, J. R., & Zhang, H. (2026) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2527955123
Mapping Alzheimer’s disease heterogeneity through exploratory unsupervised learning
Xavier, C., Correia, A. P., Lopes, J., Fernandes, J., Laranjinha, I., & Pinto, N. (2026) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2026.1869804
WNKs regulate mouse behavior and alter central nervous system glucose uptake and insulin signalingPreprint
Jaykumar, A. B., Binns, D. D., Taylor, C. A., Anselmo, A. N., Kankanamalage, S. G., Birnbaum, S. G., Huber, K. M., & Cobb, M. H. (2026) · eLife · doi:10.7554/elife.100097.3
Clinical and genetic determinants of diabetic retinopathy
Liu, Y. (2014) · Discovery Research Portal (University of Dundee)
Metadata
- Authors
- Sherva, R., Tripodis, Y., Bennett, D. A., Chibnik, L. B., Crane, P. K., de Jager, P. L., Farrer, L. A., Saykin, A. J., Shulman, J. M., Naj, A., Green, R. C., & The GENAROAD Consortium, T. A. D. N. I.
- Affiliations
- Boston University (US)
- Department of Medicine (Biomedical Genetics) Boston University School of Medicine Boston MA USA
- Corresponding author
- Sherva, Richard
- ORCID
- Venue
- Alzheimer's & Dementia
- Publisher
- Wiley
- Published
- 2013-03-26
- Volume
- 10
- Issue
- 1
- Pages
- 45-52
- ISSN
- 1552-5260 (ISSN-L)
- 1552-5279
- PMC
- PMC3760995
- OpenAlex
- W2034937783
- Cited by
- 180
- References
- 34
- Citations by year
- 2026: 4 · 2025: 6 · 2024: 23 · 2023: 18 · 2022: 15 · 2021: 15 · 2020: 18 · 2019: 12 · 2018: 14 · 2017: 15 · 2016: 11 · 2015: 11 · 2014: 14 · 2013: 3
- FWCI
- 9.21
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Alzheimer s & Dementia (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health (U01 AG024904)
- National Institute on Aging
- National Institute of Biomedical Imaging and Bioengineering
- Dana Foundation (HG02213, AG027841)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Alzheimer's disease research and treatments
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Keywords
- Alzheimer's disease
- MeSH terms
- Aged, Aged, 80 and over, Alzheimer Disease, Cognition Disorders, Female, Follow-Up Studies, Genotype, Humans, Male, Neuropsychological Tests, Phenotype, Extracellular Matrix Proteins, Polymorphism, Single Nucleotide, Apolipoprotein E4, Genome-Wide Association Study
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