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Stem cell-derived neurons reflect features of protein networks, neuropathology, and cognitive outcome of their aged human donors.

Neuron

Lagomarsino VN, Pearse RV, Liu L, Hsieh YC, Fernandez MA, Vinton EA, Paull D, Felsky D, Tasaki S, Gaiteri C, Vardarajan B, Lee H, Muratore CR, Benoit CR, Chou V, Fancher SB, He A, Merchant JP, Duong DM, Martinez H, Zhou M, Bah F, Vicent MA, Stricker JMS, Xu J, Dammer EB, Levey AI, Chibnik LB, Menon V, Seyfried NT, De Jager PL, Noggle S, Selkoe DJ, Bennett DA, Young-Pearse TL.
PMID: 34473944
Neuron. 2021 Nov 03;109(21):3402-3420.e9. doi: 10.1016/j.neuron.2021.08.003. Epub 2021 Sep 01.

We have generated a controlled and manipulable resource that captures genetic risk for Alzheimer's disease: iPSC lines from 53 individuals coupled with RNA and proteomic profiling of both iPSC-derived neurons and brain tissue of the same individuals. Data collected...

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Lagomarsino VN, Pearse RV, Liu L, et al. Stem cell-derived neurons reflect features of protein networks, neuropathology, and cognitive outcome of their aged human donors. Neuron. 2021;109(21):3402-3420.e9doi: 10.1016/j.neuron.2021.08.003.
Lagomarsino, V. N., Pearse, R. V., Liu, L., Hsieh, Y. C., Fernandez, M. A., Vinton, E. A., Paull, D., Felsky, D., Tasaki, S., Gaiteri, C., Vardarajan, B., Lee, H., Muratore, C. R., Benoit, C. R., Chou, V., Fancher, S. B., He, A., Merchant, J. P., Duong, D. M., Martinez, H., Zhou, M., Bah, F., Vicent, M. A., Stricker, J. M. S., Xu, J., Dammer, E. B., Levey, A. I., Chibnik, L. B., Menon, V., Seyfried, N. T., De Jager, P. L., Noggle, S., Selkoe, D. J., Bennett, D. A., & Young-Pearse, T. L. (2021). Stem cell-derived neurons reflect features of protein networks, neuropathology, and cognitive outcome of their aged human donors. Neuron, 109(21), 3402-3420.e9. https://doi.org/10.1016/j.neuron.2021.08.003
Lagomarsino, Valentina N, et al. "Stem cell-derived neurons reflect features of protein networks, neuropathology, and cognitive outcome of their aged human donors." Neuron vol. 109,21 (2021): 3402-3420.e9. doi: https://doi.org/10.1016/j.neuron.2021.08.003
Lagomarsino VN, Pearse RV, Liu L, Hsieh YC, Fernandez MA, Vinton EA, Paull D, Felsky D, Tasaki S, Gaiteri C, Vardarajan B, Lee H, Muratore CR, Benoit CR, Chou V, Fancher SB, He A, Merchant JP, Duong DM, Martinez H, Zhou M, Bah F, Vicent MA, Stricker JMS, Xu J, Dammer EB, Levey AI, Chibnik LB, Menon V, Seyfried NT, De Jager PL, Noggle S, Selkoe DJ, Bennett DA, Young-Pearse TL. Stem cell-derived neurons reflect features of protein networks, neuropathology, and cognitive outcome of their aged human donors. Neuron. 2021 Nov 03;109(21):3402-3420.e9. doi: 10.1016/j.neuron.2021.08.003. Epub 2021 Sep 01. PMID: 34473944; PMCID: PMC8571042.
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Integrated Genomic, Transcriptomic and Proteomic Analysis for Identifying Markers of Alzheimer's Disease.

Diagnostics (Basel, Switzerland)

Madrid L, Labrador SC, González-Pérez A, Sáez ME, The Alzheimer's Disease Neuroimaging Initiative Adni.
PMID: 34943540
Diagnostics (Basel). 2021 Dec 08;11(12). doi: 10.3390/diagnostics11122303.

There is an urgent need to identify biomarkers for Alzheimer's disease (AD), but the identification of reliable blood-based biomarkers has proven to be much more difficult than initially expected. The current availability of high-throughput multi-omics data opens new possibilities...

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Madrid L, Labrador SC, González-Pérez A, et al. Integrated Genomic, Transcriptomic and Proteomic Analysis for Identifying Markers of Alzheimer's Disease. Diagnostics (Basel). 2021;11(12)doi: 10.3390/diagnostics11122303.
Madrid, L., Labrador, S. C., González-Pérez, A., Sáez, M. E., & The Alzheimer's Disease Neuroimaging Initiative Adni. (2021). Integrated Genomic, Transcriptomic and Proteomic Analysis for Identifying Markers of Alzheimer's Disease. Diagnostics (Basel, Switzerland), 11(12), . https://doi.org/10.3390/diagnostics11122303
Madrid, Laura, et al. "Integrated Genomic, Transcriptomic and Proteomic Analysis for Identifying Markers of Alzheimer's Disease." Diagnostics (Basel, Switzerland) vol. 11,12 (2021). doi: https://doi.org/10.3390/diagnostics11122303
Madrid L, Labrador SC, González-Pérez A, Sáez ME, The Alzheimer's Disease Neuroimaging Initiative Adni. Integrated Genomic, Transcriptomic and Proteomic Analysis for Identifying Markers of Alzheimer's Disease. Diagnostics (Basel). 2021 Dec 08;11(12). doi: 10.3390/diagnostics11122303. PMID: 34943540; PMCID: PMC8700271.
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Modeling multifunctionality of genes with secondary gene co-expression networks in human brain provides novel disease insights.

Bioinformatics (Oxford, England)

Sánchez JA, Gil-Martinez AL, Cisterna A, García-Ruíz S, Gómez-Pascual A, Reynolds RH, Nalls M, Hardy J, Ryten M, Botía JA.
PMID: 33734320
Bioinformatics. 2021 Mar 17; doi: 10.1093/bioinformatics/btab175. Epub 2021 Mar 17.

MOTIVATION: Co-expression networks are a powerful gene expression analysis method to study how genes co-express together in clusters with functional coherence that usually resemble specific cell type behaviour for the genes involved. They can be applied to bulk-tissue gene...

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Sánchez JA, Gil-Martinez AL, Cisterna A, et al. Modeling multifunctionality of genes with secondary gene co-expression networks in human brain provides novel disease insights. Bioinformatics. 2021;doi: 10.1093/bioinformatics/btab175.
Sánchez, J. A., Gil-Martinez, A. L., Cisterna, A., García-Ruíz, S., Gómez-Pascual, A., Reynolds, R. H., Nalls, M., Hardy, J., Ryten, M., & Botía, J. A. (2021). Modeling multifunctionality of genes with secondary gene co-expression networks in human brain provides novel disease insights. Bioinformatics (Oxford, England), . https://doi.org/10.1093/bioinformatics/btab175
Sánchez, Juan A, et al. "Modeling multifunctionality of genes with secondary gene co-expression networks in human brain provides novel disease insights." Bioinformatics (Oxford, England) vol. (2021). doi: https://doi.org/10.1093/bioinformatics/btab175
Sánchez JA, Gil-Martinez AL, Cisterna A, García-Ruíz S, Gómez-Pascual A, Reynolds RH, Nalls M, Hardy J, Ryten M, Botía JA. Modeling multifunctionality of genes with secondary gene co-expression networks in human brain provides novel disease insights. Bioinformatics. 2021 Mar 17; doi: 10.1093/bioinformatics/btab175. Epub 2021 Mar 17. PMID: 33734320; PMCID: PMC8479669.
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Rare variants in the endocytic pathway are associated with Alzheimer's disease, its related phenotypes, and functional consequences.

PLoS genetics

Zhan L, Li J, Jew B, Sul JH.
PMID: 34516545
PLoS Genet. 2021 Sep 13;17(9):e1009772. doi: 10.1371/journal.pgen.1009772. eCollection 2021 Sep.

Late-onset Alzheimer's disease (LOAD) is the most common type of dementia causing irreversible brain damage to the elderly and presents a major public health challenge. Clinical research and genome-wide association studies have suggested a potential contribution of the endocytic...

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Zhan L, Li J, Jew B, et al. Rare variants in the endocytic pathway are associated with Alzheimer's disease, its related phenotypes, and functional consequences. PLoS Genet. 2021;17(9):e1009772doi: 10.1371/journal.pgen.1009772.
Zhan, L., Li, J., Jew, B., & Sul, J. H. (2021). Rare variants in the endocytic pathway are associated with Alzheimer's disease, its related phenotypes, and functional consequences. PLoS genetics, 17(9), e1009772. https://doi.org/10.1371/journal.pgen.1009772
Zhan, Lingyu, et al. "Rare variants in the endocytic pathway are associated with Alzheimer's disease, its related phenotypes, and functional consequences." PLoS genetics vol. 17,9 (2021): e1009772. doi: https://doi.org/10.1371/journal.pgen.1009772
Zhan L, Li J, Jew B, Sul JH. Rare variants in the endocytic pathway are associated with Alzheimer's disease, its related phenotypes, and functional consequences. PLoS Genet. 2021 Sep 13;17(9):e1009772. doi: 10.1371/journal.pgen.1009772. eCollection 2021 Sep. PMID: 34516545; PMCID: PMC8460036.
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Analysis of genes (TMEM106B, GRN, ABCC9, KCNMB2, and APOE) implicated in risk for LATE-NC and hippocampal sclerosis provides pathogenetic insights: a retrospective genetic association study.

Acta neuropathologica communications

Dugan AJ, Nelson PT, Katsumata Y, Shade LMP, Boehme KL, Teylan MA, Cykowski MD, Mukherjee S, Kauwe JSK, Hohman TJ, Schneider JA, Fardo DW.
PMID: 34526147
Acta Neuropathol Commun. 2021 Sep 15;9(1):152. doi: 10.1186/s40478-021-01250-2.

Limbic-predominant age-related TDP-43 encephalopathy neuropathologic change (LATE-NC) is the most prevalent subtype of TDP-43 proteinopathy, affecting up to 1/3rd of aged persons. LATE-NC often co-occurs with hippocampal sclerosis (HS) pathology. It is currently unknown why some individuals with LATE-NC...

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Dugan AJ, Nelson PT, Katsumata Y, et al. Analysis of genes (TMEM106B, GRN, ABCC9, KCNMB2, and APOE) implicated in risk for LATE-NC and hippocampal sclerosis provides pathogenetic insights: a retrospective genetic association study. Acta Neuropathol Commun. 2021;9(1):152doi: 10.1186/s40478-021-01250-2.
Dugan, A. J., Nelson, P. T., Katsumata, Y., Shade, L. M. P., Boehme, K. L., Teylan, M. A., Cykowski, M. D., Mukherjee, S., Kauwe, J. S. K., Hohman, T. J., Schneider, J. A., & Fardo, D. W. (2021). Analysis of genes (TMEM106B, GRN, ABCC9, KCNMB2, and APOE) implicated in risk for LATE-NC and hippocampal sclerosis provides pathogenetic insights: a retrospective genetic association study. Acta neuropathologica communications, 9(1), 152. https://doi.org/10.1186/s40478-021-01250-2
Dugan, Adam J, et al. "Analysis of genes (TMEM106B, GRN, ABCC9, KCNMB2, and APOE) implicated in risk for LATE-NC and hippocampal sclerosis provides pathogenetic insights: a retrospective genetic association study." Acta neuropathologica communications vol. 9,1 (2021): 152. doi: https://doi.org/10.1186/s40478-021-01250-2
Dugan AJ, Nelson PT, Katsumata Y, Shade LMP, Boehme KL, Teylan MA, Cykowski MD, Mukherjee S, Kauwe JSK, Hohman TJ, Schneider JA, Fardo DW. Analysis of genes (TMEM106B, GRN, ABCC9, KCNMB2, and APOE) implicated in risk for LATE-NC and hippocampal sclerosis provides pathogenetic insights: a retrospective genetic association study. Acta Neuropathol Commun. 2021 Sep 15;9(1):152. doi: 10.1186/s40478-021-01250-2. PMID: 34526147; PMCID: PMC8442328.
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Integrative-omics for discovery of network-level disease biomarkers: a case study in Alzheimer's disease.

Briefings in bioinformatics

Xie L, He B, Varathan P, Nho K, Risacher SL, Saykin AJ, Salama P, Yan J.
PMID: 33971669
Brief Bioinform. 2021 Nov 05;22(6). doi: 10.1093/bib/bbab121.

A large number of genetic variations have been identified to be associated with Alzheimer's disease (AD) and related quantitative traits. However, majority of existing studies focused on single types of omics data, lacking the power of generating a community...

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Xie L, He B, Varathan P, et al. Integrative-omics for discovery of network-level disease biomarkers: a case study in Alzheimer's disease. Brief Bioinform. 2021;22(6)doi: 10.1093/bib/bbab121.
Xie, L., He, B., Varathan, P., Nho, K., Risacher, S. L., Saykin, A. J., Salama, P., & Yan, J. (2021). Integrative-omics for discovery of network-level disease biomarkers: a case study in Alzheimer's disease. Briefings in bioinformatics, 22(6), . https://doi.org/10.1093/bib/bbab121
Xie, Linhui, et al. "Integrative-omics for discovery of network-level disease biomarkers: a case study in Alzheimer's disease." Briefings in bioinformatics vol. 22,6 (2021). doi: https://doi.org/10.1093/bib/bbab121
Xie L, He B, Varathan P, Nho K, Risacher SL, Saykin AJ, Salama P, Yan J. Integrative-omics for discovery of network-level disease biomarkers: a case study in Alzheimer's disease. Brief Bioinform. 2021 Nov 05;22(6). doi: 10.1093/bib/bbab121. PMID: 33971669; PMCID: PMC8574309.
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Molecular subtyping of Alzheimer's disease using RNA sequencing data reveals novel mechanisms and targets.

Science advances

Neff RA, Wang M, Vatansever S, Guo L, Ming C, Wang Q, Wang E, Horgusluoglu-Moloch E, Song WM, Li A, Castranio EL, Tcw J, Ho L, Goate A, Fossati V, Noggle S, Gandy S, Ehrlich ME, Katsel P, Schadt E, Cai D, Brennand KJ, Haroutunian V, Zhang B.
PMID: 33523961
Sci Adv. 2021 Jan 06;7(2). doi: 10.1126/sciadv.abb5398. Print 2021 Jan.

Alzheimer's disease (AD), the most common form of dementia, is recognized as a heterogeneous disease with diverse pathophysiologic mechanisms. In this study, we interrogate the molecular heterogeneity of AD by analyzing 1543 transcriptomes across five brain regions in two...

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Neff RA, Wang M, Vatansever S, et al. Molecular subtyping of Alzheimer's disease using RNA sequencing data reveals novel mechanisms and targets. Sci Adv. 2021;7(2)doi: 10.1126/sciadv.abb5398.
Neff, R. A., Wang, M., Vatansever, S., Guo, L., Ming, C., Wang, Q., Wang, E., Horgusluoglu-Moloch, E., Song, W. M., Li, A., Castranio, E. L., Tcw, J., Ho, L., Goate, A., Fossati, V., Noggle, S., Gandy, S., Ehrlich, M. E., Katsel, P., Schadt, E., Cai, D., Brennand, K. J., Haroutunian, V., & Zhang, B. (2021). Molecular subtyping of Alzheimer's disease using RNA sequencing data reveals novel mechanisms and targets. Science advances, 7(2), . https://doi.org/10.1126/sciadv.abb5398
Neff, Ryan A, et al. "Molecular subtyping of Alzheimer's disease using RNA sequencing data reveals novel mechanisms and targets." Science advances vol. 7,2 (2021). doi: https://doi.org/10.1126/sciadv.abb5398
Neff RA, Wang M, Vatansever S, Guo L, Ming C, Wang Q, Wang E, Horgusluoglu-Moloch E, Song WM, Li A, Castranio EL, Tcw J, Ho L, Goate A, Fossati V, Noggle S, Gandy S, Ehrlich ME, Katsel P, Schadt E, Cai D, Brennand KJ, Haroutunian V, Zhang B. Molecular subtyping of Alzheimer's disease using RNA sequencing data reveals novel mechanisms and targets. Sci Adv. 2021 Jan 06;7(2). doi: 10.1126/sciadv.abb5398. Print 2021 Jan. PMID: 33523961; PMCID: PMC7787497.
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Molecular subtyping of Alzheimer's disease using RNA sequencing data reveals novel mechanisms and targets.

Science advances

Neff RA, Wang M, Vatansever S, Guo L, Ming C, Wang Q, Wang E, Horgusluoglu-Moloch E, Song WM, Li A, Castranio EL, Tcw J, Ho L, Goate A, Fossati V, Noggle S, Gandy S, Ehrlich ME, Katsel P, Schadt E, Cai D, Brennand KJ, Haroutunian V, Zhang B.
PMID: 33523961
Sci Adv. 2021 Jan 06;7(2). doi: 10.1126/sciadv.abb5398. Print 2021 Jan.

Alzheimer's disease (AD), the most common form of dementia, is recognized as a heterogeneous disease with diverse pathophysiologic mechanisms. In this study, we interrogate the molecular heterogeneity of AD by analyzing 1543 transcriptomes across five brain regions in two...

Cite
Neff RA, Wang M, Vatansever S, et al. Molecular subtyping of Alzheimer's disease using RNA sequencing data reveals novel mechanisms and targets. Sci Adv. 2021;7(2)doi: 10.1126/sciadv.abb5398.
Neff, R. A., Wang, M., Vatansever, S., Guo, L., Ming, C., Wang, Q., Wang, E., Horgusluoglu-Moloch, E., Song, W. M., Li, A., Castranio, E. L., Tcw, J., Ho, L., Goate, A., Fossati, V., Noggle, S., Gandy, S., Ehrlich, M. E., Katsel, P., Schadt, E., Cai, D., Brennand, K. J., Haroutunian, V., & Zhang, B. (2021). Molecular subtyping of Alzheimer's disease using RNA sequencing data reveals novel mechanisms and targets. Science advances, 7(2), . https://doi.org/10.1126/sciadv.abb5398
Neff, Ryan A, et al. "Molecular subtyping of Alzheimer's disease using RNA sequencing data reveals novel mechanisms and targets." Science advances vol. 7,2 (2021). doi: https://doi.org/10.1126/sciadv.abb5398
Neff RA, Wang M, Vatansever S, Guo L, Ming C, Wang Q, Wang E, Horgusluoglu-Moloch E, Song WM, Li A, Castranio EL, Tcw J, Ho L, Goate A, Fossati V, Noggle S, Gandy S, Ehrlich ME, Katsel P, Schadt E, Cai D, Brennand KJ, Haroutunian V, Zhang B. Molecular subtyping of Alzheimer's disease using RNA sequencing data reveals novel mechanisms and targets. Sci Adv. 2021 Jan 06;7(2). doi: 10.1126/sciadv.abb5398. Print 2021 Jan. PMID: 33523961; PMCID: PMC7787497.
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Identification of cell-type-specific marker genes from co-expression patterns in tissue samples.

Bioinformatics (Oxford, England)

Qiu Y, Wang J, Lei J, Roeder K.
PMID: 33904573
Bioinformatics. 2021 Apr 27; doi: 10.1093/bioinformatics/btab257. Epub 2021 Apr 27.

MOTIVATION: Marker genes, defined as genes that are expressed primarily in a single cell type, can be identified from the single cell transcriptome; however, such data are not always available for the many uses of marker genes, such as...

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Qiu Y, Wang J, Lei J, et al. Identification of cell-type-specific marker genes from co-expression patterns in tissue samples. Bioinformatics. 2021;doi: 10.1093/bioinformatics/btab257.
Qiu, Y., Wang, J., Lei, J., & Roeder, K. (2021). Identification of cell-type-specific marker genes from co-expression patterns in tissue samples. Bioinformatics (Oxford, England), . https://doi.org/10.1093/bioinformatics/btab257
Qiu, Yixuan, et al. "Identification of cell-type-specific marker genes from co-expression patterns in tissue samples." Bioinformatics (Oxford, England) vol. (2021). doi: https://doi.org/10.1093/bioinformatics/btab257
Qiu Y, Wang J, Lei J, Roeder K. Identification of cell-type-specific marker genes from co-expression patterns in tissue samples. Bioinformatics. 2021 Apr 27; doi: 10.1093/bioinformatics/btab257. Epub 2021 Apr 27. PMID: 33904573; PMCID: PMC8504631.
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Dysregulation of the secretory pathway connects Alzheimer's disease genetics to aggregate formation.

Cell systems

Kuo CC, Chiang AWT, Baghdassarian HM, Lewis NE.
PMID: 34171228
Cell Syst. 2021 Sep 22;12(9):873-884.e4. doi: 10.1016/j.cels.2021.06.001. Epub 2021 Jun 24.

Amyloid disorders such as Alzheimer's disease (AD) involve the aggregation of secreted proteins. However, it is largely unclear how secretory-pathway proteins contribute to amyloid formation. We developed a systems biology framework integrating expression data with protein-protein interaction networks to...

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Kuo CC, Chiang AWT, Baghdassarian HM, et al. Dysregulation of the secretory pathway connects Alzheimer's disease genetics to aggregate formation. Cell Syst. 2021;12(9):873-884.e4doi: 10.1016/j.cels.2021.06.001.
Kuo, C. C., Chiang, A. W. T., Baghdassarian, H. M., & Lewis, N. E. (2021). Dysregulation of the secretory pathway connects Alzheimer's disease genetics to aggregate formation. Cell systems, 12(9), 873-884.e4. https://doi.org/10.1016/j.cels.2021.06.001
Kuo, Chih-Chung, et al. "Dysregulation of the secretory pathway connects Alzheimer's disease genetics to aggregate formation." Cell systems vol. 12,9 (2021): 873-884.e4. doi: https://doi.org/10.1016/j.cels.2021.06.001
Kuo CC, Chiang AWT, Baghdassarian HM, Lewis NE. Dysregulation of the secretory pathway connects Alzheimer's disease genetics to aggregate formation. Cell Syst. 2021 Sep 22;12(9):873-884.e4. doi: 10.1016/j.cels.2021.06.001. Epub 2021 Jun 24. PMID: 34171228; PMCID: PMC8505362.
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Integrative brain transcriptome analysis links complement component 4 and HSPA2 to the APOE ε2 protective effect in Alzheimer disease.

Molecular psychiatry

Panitch R, Hu J, Chung J, Zhu C, Meng G, Xia W, Bennett DA, Lunetta KL, Ikezu T, Au R, Stein TD, Farrer LA, Jun GR.
PMID: 34480088
Mol Psychiatry. 2021 Oct;26(10):6054-6064. doi: 10.1038/s41380-021-01266-z. Epub 2021 Sep 03.

Mechanisms underlying the protective effect of apolipoprotein E (APOE) ε2 against Alzheimer disease (AD) are not well understood. We analyzed gene expression data derived from autopsied brains donated by 982 individuals including 135 APOE ɛ2/ɛ3 carriers. Complement pathway genes...

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Panitch R, Hu J, Chung J, et al. Integrative brain transcriptome analysis links complement component 4 and HSPA2 to the APOE ε2 protective effect in Alzheimer disease. Mol Psychiatry. 2021;26(10):6054-6064doi: 10.1038/s41380-021-01266-z.
Panitch, R., Hu, J., Chung, J., Zhu, C., Meng, G., Xia, W., Bennett, D. A., Lunetta, K. L., Ikezu, T., Au, R., Stein, T. D., Farrer, L. A., & Jun, G. R. (2021). Integrative brain transcriptome analysis links complement component 4 and HSPA2 to the APOE ε2 protective effect in Alzheimer disease. Molecular psychiatry, 26(10), 6054-6064. https://doi.org/10.1038/s41380-021-01266-z
Panitch, Rebecca, et al. "Integrative brain transcriptome analysis links complement component 4 and HSPA2 to the APOE ε2 protective effect in Alzheimer disease." Molecular psychiatry vol. 26,10 (2021): 6054-6064. doi: https://doi.org/10.1038/s41380-021-01266-z
Panitch R, Hu J, Chung J, Zhu C, Meng G, Xia W, Bennett DA, Lunetta KL, Ikezu T, Au R, Stein TD, Farrer LA, Jun GR. Integrative brain transcriptome analysis links complement component 4 and HSPA2 to the APOE ε2 protective effect in Alzheimer disease. Mol Psychiatry. 2021 Oct;26(10):6054-6064. doi: 10.1038/s41380-021-01266-z. Epub 2021 Sep 03. PMID: 34480088; PMCID: PMC8758485.
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Association between WWOX/MAF variants and dementia-related neuropathologic endophenotypes.

Neurobiology of aging

Dugan AJ, Nelson PT, Katsumata Y, Shade LMP, Teylan MA, Boehme KL, Mukherjee S, Kauwe JSK, Hohman TJ, Schneider JA, Fardo DW.
PMID: 34852950
Neurobiol Aging. 2022 Mar;111:95-106. doi: 10.1016/j.neurobiolaging.2021.10.011. Epub 2021 Oct 29.

The genetic locus containing the WWOX and MAF genes was implicated as a clinical Alzheimer's disease (AD) risk locus in two recent large meta-analytic genome wide association studies (GWAS). In a prior GWAS, we identified a variant in WWOX...

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Dugan AJ, Nelson PT, Katsumata Y, et al. Association between WWOX/MAF variants and dementia-related neuropathologic endophenotypes. Neurobiol Aging. 2021;111:95-106doi: 10.1016/j.neurobiolaging.2021.10.011.
Dugan, A. J., Nelson, P. T., Katsumata, Y., Shade, L. M. P., Teylan, M. A., Boehme, K. L., Mukherjee, S., Kauwe, J. S. K., Hohman, T. J., Schneider, J. A., Fardo, D. W. (2022). Association between WWOX/MAF variants and dementia-related neuropathologic endophenotypes. Neurobiology of aging, 11195-106. https://doi.org/10.1016/j.neurobiolaging.2021.10.011
Dugan, Adam J, et al. "Association between WWOX/MAF variants and dementia-related neuropathologic endophenotypes." Neurobiology of aging vol. 111 (2022): 95-106. doi: https://doi.org/10.1016/j.neurobiolaging.2021.10.011
Dugan AJ, Nelson PT, Katsumata Y, Shade LMP, Teylan MA, Boehme KL, Mukherjee S, Kauwe JSK, Hohman TJ, Schneider JA, Fardo DW. Association between WWOX/MAF variants and dementia-related neuropathologic endophenotypes. Neurobiol Aging. 2022 Mar;111:95-106. doi: 10.1016/j.neurobiolaging.2021.10.011. Epub 2021 Oct 29. PMID: 34852950; PMCID: PMC8761217.
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