Novel Genetic Variants in TP37, PIK3R1, CALM1, and PLCG2 of the Neurotrophin Signaling Pathway Are Associated with the Progression from Mild Cognitive Impairment to Alzheimer’s Disease
Open accessAbstract
Background: Alzheimer’s disease (AD) is a common neurodegenerative disease and mild cognitive impairment (MCI) is considered as the prodromal stage of AD. Previous studies showed that changes in the neurotrophin signaling pathway could lead to cognitive decline in AD. However, the association of single nucleotide polymorphisms (SNPs) in genes that are involved in this pathway with AD progression from MCI remains unclear. Objective: We investigated the associations between SNPs involved in the neurotrophin signaling pathway with AD progression. Methods: We performed single-locus analysis to identify neurotrophin-signaling-related SNPs associated with the AD progression using 767 patients from the Alzheimer’s Disease Neuroimaging Initiative study and 1,373 patients from the National Alzheimer’s Coordinating Center study. We constructed polygenic risk scores (PRSs) using the identified independent non-APOE SNPs and evaluated its prediction performance on AD progression. Results: We identified 25 SNPs significantly associated with AD progression with Bayesian false-discovery probability ≤0.8. Based on the linkage disequilibrium clumping and expression quantitative trait loci analysis, we found 6 potentially functional SNPs that were associated with AD progression independently. The PRS analysis quantified the combined effects of these SNPs on longitudinal cognitive assessments and biomarkers from cerebrospinal fluid and neuroimaging. The addition of PRSs to the prediction model for 3-year progression to AD from MCI significantly increased the predictive accuracy. Conclusion: Genetic variants in the specific genes of the neurotrophin signaling pathway are predictors of AD progression. eQTL analysis supports that these SNPs regulate expression of key genes involved in the neurotrophin signaling pathway.
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The PLCG2 M28L variant confers a modest increased risk of AD (OR ≈ 1.16).
“other rare PLCG2 variants, such as M28L (rs61749044) that confers a modest increased risk of AD (OR ≈ 1.16) [1, 4, 7–13]”
From PLCγ2 deficiency compromises systemic immune tolerance and erodes myelin homeostasis while enhancing oxidative metabolism in the mouse brain (2026) · cites this paper as [9]
The Results section discusses two other PLCG2 variants, rs4073828 and rs4369658, and mentions five previously reported AD-associated variants without identifying M28L. Searches for M28L, rs61749044, and an odds ratio near 1.16 found no passage in our copy supporting the specific claim. (LLM, conf=0.76)
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Cites
Second-generation PLINK: rising to the challenge of larger and richer datasets
Chang, C. C., Chow, C. C., Tellier, L. C., Vattikuti, S., Purcell, S. M., & Lee, J. J. (2015) · Gigascience · doi:10.1186/s13742-015-0047-8 · Cited by 12,709 · see citing works
How to perform a meta-analysis with R: a practical tutorial
Balduzzi, S., Rücker, G., & Schwarzer, G. (2019) · Evidence Based Mental Health · doi:10.1136/ebmental-2019-300117 · Cited by 5,827 · see citing works
Apolipoprotein E and Alzheimer disease: risk, mechanisms and therapy
Liu, C.-C., Kanekiyo, T., Xu, H., & Bu, G. (2013) · Nature Reviews Neurology · doi:10.1038/nrneurol.2012.263 · Cited by 3,459 · see citing works
A reference panel of 64,976 haplotypes for genotype imputation
the Haplotype Reference Consortium (2016) · Nature Genetics · doi:10.1038/ng.3643 · Cited by 2,836 · see citing works
New insights into the genetic etiology of Alzheimer’s disease and related dementias
Bellenguez, C., Küçükali, F., Jansen, I. E., Kleineidam, L., Moreno-Grau, S., Amin, N., Naj, A. C., Campos-Martin, R., Grenier-Boley, B., Andrade, V., et al. (2022) · Nature Genetics · doi:10.1038/s41588-022-01024-z · Cited by 2,498 · see citing works
Jansen, I. E., Savage, J. E., Watanabe, K., Bryois, J., Williams, D. M., Steinberg, S., Sealock, J., Karlsson, I. K., Hägg, S., Athanasiu, L., et al. (2019) · Nature Genetics · doi:10.1038/s41588-018-0311-9 · Cited by 2,277 · see citing works
Naj, A. C., Jun, G., Beecham, G. W., Wang, L.-S., Vardarajan, B. N., Buros, J., Gallins, P. J., Buxbaum, J. D., Jarvik, G. P., Crane, P. K., et al. (2011) · Nature Genetics · doi:10.1038/ng.801 · Cited by 1,901 · see citing works
Blanche, P., Dartigues, J., & Jacqmin‐Gadda, H. (2013) · Statistics in Medicine · doi:10.1002/sim.5958 · Cited by 1,877 · see citing works
Role of Genes and Environments for Explaining Alzheimer Disease
Gatz, M., Reynolds, C. A., Fratiglioni, L., Johansson, B., Mortimer, J. A., Berg, S., Fiske, A., & Pedersen, N. L. (2006) · Archives of General Psychiatry · doi:10.1001/archpsyc.63.2.168 · Cited by 1,798 · see citing works
PRSice-2: Polygenic Risk Score software for biobank-scale data
Choi, S. W., & O’Reilly, P. F. (2019) · GigaScience · doi:10.1093/gigascience/giz082 · Cited by 1,481 · see citing works
ARUK Consortium, Sims, R., GERAD/PERADES, C., van der Lee, S. J., Naj, A. C., Bellenguez, C., Badarinarayan, N., Jakobsdottir, J., Kunkle, B. W., Boland, A., et al. (2017) · Nature Genetics · doi:10.1038/ng.3916 · Cited by 1,126 · see citing works
Self-citationMortality in the United States, 2020
Murphy, S. L., Kochanek, K. D., Xu, J., & Arias, E. (2021) · doi:10.15620/cdc:112079 · Cited by 864 · see citing works
Mortality in the United States
Murphy, S. L., Xu, J., Kochanek, K. D., & Arias, E. (1840) · New England Journal of Medicine · doi:10.1056/nejm184002260220306 · Cited by 648 · see citing works
A Bayesian Measure of the Probability of False Discovery in Genetic Epidemiology Studies
Wakefield, J. (2007) · The American Journal of Human Genetics · doi:10.1086/519024 · Cited by 486 · see citing works
Wingo, A. P., Liu, Y., Gerasimov, E. S., Gockley, J., Logsdon, B. A., Duong, D. M., Dammer, E. B., Robins, C., Beach, T. G., Reiman, E. M., et al. (2021) · Nature Genetics · doi:10.1038/s41588-020-00773-z · Cited by 366 · see citing works
Phosphorylated tau interactome in the human Alzheimer's disease brain
Drummond, E., Pires, G., MacMurray, C., Askenazi, M., Nayak, S., Bourdon, M., Safar, J., Ueberheide, B., & Wisniewski, T. (2020) · Brain · doi:10.1093/brain/awaa223 · Cited by 357 · see citing works
Common variants in Alzheimer’s disease and risk stratification by polygenic risk scores
de Rojas, I., Moreno-Grau, S., Tesi, N., Grenier-Boley, B., Andrade, V., Jansen, I. E., Pedersen, N. L., Stringa, N., Zettergren, A., Hernández, I., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-22491-8 · Cited by 324 · see citing works
Autosomal Recessive Causes Likely in Early-Onset Alzheimer Disease
Wingo, T. S. (2012) · Archives of Neurology · doi:10.1001/archneurol.2011.221 · Cited by 265 · see citing works
The Genotype-Tissue Expression (GTEx) project
Lonsdale, J., Thomas, J., Salvatore, M., Phillips, R., Lo, E., Shad, S., Hasz, R., Walters, G., Garcia, F., Young, N., et al. (2013) · Nature Genetics · doi:10.1038/ng.2653 · Cited by 263 · see citing works
Ginsberg, S. D., Che, S., Wuu, J., Counts, S., & Mufson, E. J. (2006) · Journal of Neurochemistry · doi:10.1111/j.1471-4159.2006.03764.x · Cited by 255 · see citing works
Novikova, G., Kapoor, M., TCW, J., Abud, E. M., Efthymiou, A. G., Chen, S. X., Cheng, H., Fullard, J. F., Bendl, J., Liu, Y., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-21823-y · Cited by 214 · see citing works
Higher BDNF serum levels predict slower cognitive decline in Alzheimer's disease patients
Laske, C., Stellos, K., Hoffmann, N., Stransky, E., Straten, G., Eschweiler, G. W., & Leyhe, T. (2011) · The International Journal of Neuropsychopharmacology · doi:10.1017/s1461145710001008 · Cited by 180 · see citing works
Innovative Therapy for Alzheimer’s Disease-With Focus on Biodelivery of NGF
Mitra, S., Behbahani, H., & Eriksdotter, M. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.00038 · Cited by 159 · see citing works
Stem cell therapy for Alzheimer's disease
Liu, X., Yang, L.-P., & Zhao, L. (2020) · World Journal of Stem Cells · doi:10.4252/wjsc.v12.i8.787 · Cited by 157 · see citing works
Translating genetic risk of Alzheimer’s disease into mechanistic insight and drug targets
Sierksma, A. S. R., Escott‐Price, V., & Strooper, B. D. (2020) · Science · doi:10.1126/science.abb8575 · Cited by 149 · see citing works
Alzheimer’s disease phospholipase C-gamma-2 (PLCG2) protective variant is a functional hypermorph
Magno, L., Lessard, C. B., Martins, M., Lang, V., Cruz, P., Asi, Y., Katan, M., Bilsland, J., Lashley, T., Chakrabarty, P., et al. (2019) · Alzheimer's Research & Therapy · doi:10.1186/s13195-019-0469-0 · Cited by 143 · cited 1× · see citing works
“2 was a potentially therapeutic method for AD [46].”
Varatharajah, Y., Ramanan, V. K., Iyer, R. K., Vemuri, P., Initiative, A. D. N., Weiner, M. W., Aisen, P., Petersen, R. C., Jack, C. R., Saykin, A. J., et al. (2019) · Scientific Reports · doi:10.1038/s41598-019-38793-3 · Cited by 101 · see citing works
p73 Regulates Neurodegeneration and Phospho-Tau Accumulation during Aging and Alzheimer's Disease
Wetzel, M. K., Naska, S., Laliberté, C. L., Rymar, V. V., Fujitani, M., Biernaskie, J., Cole, C. J., Lerch, J. P., Spring, S., Wang, S., et al. (2008) · Neuron · doi:10.1016/j.neuron.2008.07.021 · Cited by 92 · see citing works
Alzheimer’s disease polygenic risk score as a predictor of conversion from mild-cognitive impairment
Chaudhury, S. R., Brookes, K. J., Patel, T., Fallows, A., Guetta‐Baranes, T., Turton, J. C., Guerreiro, R. J., Brás, J., Hardy, J., Francis, P. T., et al. (2019) · Translational Psychiatry · doi:10.1038/s41398-019-0485-7 · Cited by 89 · see citing works
Ramı́rez, J., Górriz, J. M., Ortíz, A., Martínez-Murcia, F. J., Segovia, F., Salas-Gonzalez, D., Castillo-Barnés, D., Illán, I. A., & Puntonet, C. G. (2017) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2017.12.005 · Cited by 82 · see citing works
Microtubule affinity-regulating kinases are potential druggable targets for Alzheimer’s disease
Annadurai, N., Agrawal, K., Džubák, P., Hajdúch, M., & Das, V. (2017) · Cellular and Molecular Life Sciences · doi:10.1007/s00018-017-2574-1 · Cited by 51 · see citing works
Qi, P., Li, J., Gao, S., Yuan, Y., Sun, Y., Liu, N., Li, Y., Wang, G., Ling, C., & Shi, J. (2020) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2020.589588 · Cited by 45 · see citing works
Bassili, M., Birman, E., Schor, N. F., & Saragovi, H. U. (2009) · Cancer Chemotherapy and Pharmacology · doi:10.1007/s00280-009-1110-x · Cited by 43 · see citing works
TP73allelic expression in human brain and allele frequencies in Alzheimer's disease
Li, Q., Athan, E. S., Wei, M., Yuan, E., Rice, S. L., Vonsattel, J., Mayeux, R. P., & Tycko, B. (2004) · BMC Medical Genetics · doi:10.1186/1471-2350-5-14 · Cited by 41 · see citing works
Basaly, V., Hill, J., Bihaqi, S. W., Marques, E., Slitt, A. L., & Zawia, N. H. (2021) · NeuroToxicology · doi:10.1016/j.neuro.2021.06.008 · Cited by 39 · see citing works
Shu, Z., Mao, D., Xu, Y., Shao, Y., Pang, P., & Gong, X. (2021) · Therapeutic Advances in Neurological Disorders · doi:10.1177/17562864211029551 · Cited by 39 · see citing works
Balancing neurotrophin pathway and sortilin function: Its role in human disease
Al-Yozbaki, M., Acha‐Sagredo, A., George, A., Liloglou, T., & Wilson, C. M. (2020) · Biochimica et Biophysica Acta (BBA) - Reviews on Cancer · doi:10.1016/j.bbcan.2020.188429 · Cited by 37 · see citing works
Platero, C. A., Tobar, M. C., & Initiative, A. D. N. (2020) · Brain Imaging and Behavior · doi:10.1007/s11682-020-00366-8 · Cited by 37 · see citing works
Novikova, G., Andrews, S. J., Renton, A. E., & Marcora, E. M. (2021) · Molecular Neurodegeneration · doi:10.1186/s13024-021-00449-0 · Cited by 30 · see citing works
Whole-Exome Sequencing Analysis of Alzheimer’s Disease in Non-APOE*4 Carriers
Fan, K.-H., Feingold, E., Rosenthal, S. L., Demirci, F. Y., Ganguli, M., López, O. L., & Kamboh, M. I. (2020) · Journal of Alzheimer s Disease · doi:10.3233/jad-200037 · Cited by 29 · see citing works
Stroke risk interacts with Alzheimer's disease biomarkers on brain aging outcomes
Hohman, T. J., Samuels, L. R., Liu, D., Gifford, K. A., Mukherjee, S., Benson, E. M., Abel, T. W., Ruberg, F. L., & Jefferson, A. L. (2015) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2015.05.021 · Cited by 28 · see citing works
Cancino, G. I., Miller, F. D., & Kaplan, D. R. (2012) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2012.04.010 · Cited by 26 · see citing works
Convergent lines of evidence support BIN1 as a risk gene of Alzheimer’s disease
Zhu, J. L., Liu, X., Yin, H., Gao, Y., & Yu, H. (2021) · Human Genomics · doi:10.1186/s40246-021-00307-6 · Cited by 17 · see citing works
Tang, D., Zhao, Y. C., Liu, H., Luo, S., Clarke, J. M., Glass, C., Su, L., Shen, S., Christiani, D. C., Gao, W., et al. (2020) · International Journal of Cancer · doi:10.1002/ijc.32932 · Cited by 14 · see citing works
Self-citationPolygenic Risk Score for Alzheimer’s Disease Is Associated With Ch4 Volume in Normal Subjects
Wang, T., Han, Z., Yang, Y., Tian, R., Zhou, W., Ren, P., Wang, P., Zong, J., Hu, Y., & Jiang, Q. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.00519 · Cited by 12 · see citing works
Neurotrophin Signaling and Alzheimer’s Disease Neurodegeneration − Focus on BDNF/TrkB Signaling
Wong, J. (2013) · InTech eBooks · doi:10.5772/53364 · Cited by 10 · see citing works
A genetic variant in PIK3R1 is associated with pancreatic cancer survival in the Chinese population
Zhu, Y., Ke, J., Gong, Y., Yang, Y., Peng, X., Tian, J., Zou, D., Yang, N., Wang, X., Mei, S., et al. (2019) · Cancer Medicine · doi:10.1002/cam4.2228 · Cited by 8 · see citing works
PLCG2 as a Risk Factor for Alzheimer's Disease.
Tsai, A. P., Dong, C., Preuss, C., Moutinho, M., Lin, P. B.-C., Hajicek, N., Sondek, J., Bissel, S. J., Oblak, A. L., Carter, G. W., et al. (2020) · doi:10.21203/rs.3.rs-38216/v1 · Cited by 7 · see citing works
Zhao, Y. C., Tang, D., Yang, S., Liu, H., Luo, S., Stinchcombe, T. E., Glass, C., Su, L., Shen, S., Christiani, D. C., et al. (2020) · Cancer Epidemiology Biomarkers & Prevention · doi:10.1158/1055-9965.epi-19-1450 · Cited by 6 · see citing works
Self-citation(2020) · doi:10.1101/2020.05.19.104216 · Cited by 5 · see citing works
Rare Intronic Variations inTP73Gene Found in Patients with Alzheimer’sDisease
Bhaumik, P., Ghosh, P., Biswas, A., Ghosh, S., Pal, S., Sarkar, B., & Dey, S. K. (2017) · International Journal of Human Genetics · doi:10.1080/09723757.2017.1421438 · Cited by 2 · see citing works
Network-based Analysis Approach to Prioritize GWAS of CSF in the ADNI Cohort
Meng, X., Wang, C., Li, J., & Liang, H. (2018) · IEEE International Conference on Mechatronics and Automation (ICMA) · doi:10.1109/icma.2018.8484673 · Cited by 1 · see citing works
Flagged first, then by citations
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Unraveling Alzheimer’s disease: insights from single-cell sequencing and spatial transcriptomic
He, Y., Lu, W., Zhou, X. M., Mu, J., & Shen, W. (2024) · Frontiers in Neurology · doi:10.3389/fneur.2024.1515981 · Cited by 38 · see citing works
Ramsden, C. E., Zamora, D., Horowitz, M. S., Jahanipour, J., Calzada, E., Li, X., Keyes, G. S., Murray, H. C., Curtis, M. A., Faull, R. L. M., et al. (2023) · Acta Neuropathologica Communications · doi:10.1186/s40478-023-01693-9 · Cited by 19 · see citing works
Peng, Y., & Zhou, C. (2024) · Drug Design Development and Therapy · doi:10.2147/dddt.s450739 · Cited by 15 · see citing works
Gaunt, J., Zainolabidin, N., Yip, A. K. K., Tan, J.-M., Low, A. Y. T., Chen, A. I., & Ch'ng, T. H. (2023) · Journal of Neuroinflammation · doi:10.1186/s12974-023-02913-8 · Cited by 10 · see citing works
Single-nucleus multi-omics identifies shared and distinct pathways in Pick’s and Alzheimer’s disease
Shi, Z., Das, S., Morabito, S., Stocksdale, J. T., Miyoshi, E., Srinivasan, S. S., Emerson, N., Shahin, A., Rahimzadeh, N., Cao, Z., et al. (2025) · Science Advances · doi:10.1126/sciadv.ads7973 · Cited by 9 · see citing works
Single-nucleus multiome analysis of human cerebellum in Alzheimer’s disease-related dementiaPreprint
Cheng, F., Feng, Y., Flanagan, M. E., Bonakdarpour, B., Jamshidi, P., Castellani, R. J., Mao, Q., Chu, X., Gao, H., Liu, Y., et al. (2024) · Research Square · doi:10.21203/rs.3.rs-4871032/v1 · Cited by 5 · see citing works
Multi-omics analyses reveal novel effects of PLCγ2 deficiency in the mouse brain
Hopp, S. C., Rogers, J. G., Smith, S., Campos, G., Miller, H., Barannikov, S., Kuri, E. G., Wang, H., Han, X., Bieniek, K. F., et al. (2023) · doi:10.1101/2023.12.06.570499 · Cited by 4 · see citing works
Teixeira, L. C. R., Pereira, J. D., Mamede, I., Caramelli, P., Silva, V., Veloso, A., Luizon, M. R., & Gomes, K. B. (2025) · Journal of Neurochemistry · doi:10.1111/jnc.70196 · Cited by 4 · see citing works
Miao, X., Luo, K., Li, Y., Zhou, Y., Wei, M., Zhang, T., Hu, Y., Wang, H., & Jia, J. (2025) · Molecular Neurobiology · doi:10.1007/s12035-025-05574-1 · Cited by 3 · see citing works
Shi, S., & Hu, T. (2024) · Biomedical Chromatography · doi:10.1002/bmc.5921 · Cited by 2 · see citing works
Ramsden, C. E., Zamora, D., Horowitz, M. S., Jahanipour, J., Keyes, G. S., Li, X., Murray, H. C., Curtis, M. A., Faull, R. L. M., Sedlock, A. B., et al. (2023) · medRxiv · doi:10.21203/rs.3.rs-2968020/v1 · Cited by 2 · see citing works
Gangwal, A., ANSARİ, I., Sawale, J. A., & Ansari, A. (2026) · In Silico Research in Biomedicine · doi:10.1016/j.insi.2026.100247 · Cited by 1 · see citing works
Gutierrez-Kuri, E., Marie Garcia-Rogers, J. L., Perez, J., Smith, S., Kenwood, M. R., Archuleta, K. S., Xiao, Y., Campos, G., Barannikov, S., Wang, H., et al. (2026) · doi:10.64898/2026.07.13.738356
Liang, S., Liu, L., Zu, G., Liu, X., & Zhang, Y. (2023) · Research Square · doi:10.21203/rs.3.rs-3435184/v1
Jin, X., Huang, Y., Zhang, Y., Hu, W., Yu, J., Wu, W. D., & Wang, S. (2024) · Research Square · doi:10.21203/rs.3.rs-4553439/v1
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
Muhamad, N. N., Katijjahbe, M. A., Murad, N. A. A., Zaidi, W. A. W., & Saini, S. M. (2026) · Scientific Reports · doi:10.1038/s41598-026-49245-0
Mechanism of Paeonolide in Improving Alzheimer’s Disease Based on Network Pharmacology
曹, 享. (2026) · Advances in Clinical Medicine · doi:10.12677/acm.2026.1662401
- Authors
- Li, H., Initiative, F. T. A. D. N., Liu, H., Lutz, M. W., & Luo, S.
- Affiliations
- Duke University (US)
- Corresponding author
- Luo, Sheng
- Venue
- Journal of Alzheimer's Disease
- Publisher
- SAGE Publications
- Published
- 2023-01-31
- Volume
- 91
- Issue
- 3
- Pages
- 977-987
- ISSN
- 1387-2877 (ISSN-L)
- 1875-8908
- PMC
- PMC9905310
- OpenAlex
- W4311344417
- Cited by
- 17
- References
- 51
- Citations by year
- 2026: 4 · 2025: 3 · 2024: 5 · 2023: 5
- FWCI
- 2.12
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Journal of Alzheimer s Disease (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- U.S. Department of Defense (U01 AG024904, P50 AG005138, W81XWH, W81XWH-12-2-, W81XWH1220012, AG024904)
- Alzheimer's Association (P30 AG013854, P30AG010124, P50 AG023501, U01 AG016976, P50 AG025688, AG010129, P30AG035982, P50 AG005681, P30 AG053760, P30 AG010161, U01 AG024904, P30 AG062421, P50 AG008702, P30 AG062422, P30 AG049638, P30 AG010133, P50 AG005136, W81XWH-12-2-0012, P50 AG005146)
- Bristol-Myers Squibb (U01 AG024904, AG024904, W81XWH-12-2-0012)
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- University of Southern California (P50AG005142, W81XWH-12-2-0012, U01 AG024904)
- Meso Scale Diagnostics (U01 AG024904, AG024904, W81XWH-12-2-0012)
- Alzheimer's Disease Neuroimaging Initiative (W81XWH-12-2–0012, U01AG024904, AG024904, W81XWH-12-2-, P30 AG010129)
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- Genentech (W81XWH-12-2-0012, AG024904, U01 AG024904)
- Canadian Institutes of Health Research (U01 AG024904, W81XWH-12-2-0012, P30 AG010129)
- National Institute on Aging (U01-AG-024904, AG062429, AG 005138, AG-028383, AG010133, AG 016976, P30AG053760, P30AG010129, P30AG010133, AG062421, AG-016573, AG008051, P30-AG062428, P30AG049638, AG047366, W81XWH-12-2–0012, AG-010124, P30AG028383, P30AG012300, AG-005133, AG 025688, R01AG064803, AG008017, P50AG005133, AG 024904, U01AG016976, AG-013854, AG035982, AG049638, P50AG005138, AG062715, AG019610, AG010161, P30AG010124, AG047266, P50AG016573, P50AG047366, AG010129, P30AG008017, P30 AG062428‐01, P30AG010161, P30AG008051, P50AG008702, AG023501, P30AG035982, AG013846, P50AG005681, P50AG005142, P50AG047270, P30AG019610, P50AG005146, AG047270, P30AG013846, P50AG005136, AG062422, P30AG013854, AG053760, P50AG025688, P50AG047266, AG005681, P30AG062429, P30AG062422, P30AG028716, P30AG072958, P30AG062715, U24AG072122, P30AG072947, P30AG062421, P50AG023501)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Keywords
- Alzheimer's disease, mild cognitive impairment
- MeSH terms
- Alzheimer Disease, Bayes Theorem, Humans, Nerve Growth Factors, Transcription Factors, Biomarkers, Amyloid beta-Peptides, tau Proteins, Disease Progression, Neurodegenerative Diseases, Class Ia Phosphatidylinositol 3-Kinase, Cognitive Dysfunction
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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