Enhancer variants associated with Alzheimer’s disease affect gene expression via chromatin looping
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Abstract
BACKGROUND: Genome-wide association studies (GWASs) have identified single-nucleotide polymorphisms (SNPs) that may be genetic factors underlying Alzheimer's disease (AD). However, how these AD-associated SNPs (AD SNPs) contribute to the pathogenesis of this disease is poorly understood because most of them are located in non-coding regions, such as introns and intergenic regions. Previous studies reported that some disease-associated SNPs affect regulatory elements including enhancers. We hypothesized that non-coding AD SNPs are located in enhancers and affect gene expression levels via chromatin loops. METHODS: from the GWAS catalog database. Of these, we selected 392 SNPs within non-coding regions. Next, we checked whether those non-coding AD SNPs were located in enhancers that typically regulate gene expression levels using publicly available data for enhancers that were predicted in 127 human tissues or cell types. We sought expression quantitative trait locus (eQTL) genes affected by non-coding AD SNPs within enhancers because enhancers are regulatory elements that influence the gene expression levels. To elucidate how the non-coding AD SNPs within enhancers affect the gene expression levels, we identified chromatin-chromatin interactions by Hi-C experiments. RESULTS: We report the following findings: (1) nearly 30% of non-coding AD SNPs are located in enhancers; (2) eQTL genes affected by non-coding AD SNPs within enhancers are associated with amyloid beta clearance, synaptic transmission, and immune responses; (3) 95% of the AD SNPs located in enhancers co-localize with their eQTL genes in topologically associating domains suggesting that regulation may occur through chromatin higher-order structures; (4) rs1476679 spatially contacts the promoters of eQTL genes via CTCF-CTCF interactions; (5) the effect of other AD SNPs such as rs7364180 is likely to be, at least in part, indirect through regulation of transcription factors that in turn regulate AD associated genes. CONCLUSION: Our results suggest that non-coding AD SNPs may affect the function of enhancers thereby influencing the expression levels of surrounding or distant genes via chromatin loops. This result may explain how some non-coding AD SNPs contribute to AD pathogenesis.
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Disease-associated SNPs in non-coding areas of the genome influence genes associated with diseases including Alzheimer's Disease (AD).
“disease-associated SNPs in non-coding areas of the genome influence genes associated with diseases including Alzheimer’s Disease (AD)”
From Neuronal DNA double-strand breaks lead to genome structural variations and 3D genome disruption in neurodegeneration (2023) · cites this paper as [49]
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Cites
An integrated encyclopedia of DNA elements in the human genome
The ENCODE Project Consortium (2012) · Nature · doi:10.1038/nature11247 · Cited by 18,072 · see citing works
Neuropathological stageing of Alzheimer-related changes.
Braak, H., & Braak, E. (1991) · Acta Neuropathologica · doi:10.1007/bf00308809 · Cited by 16,123 · see citing works
Statistical significance for genomewide studies
Storey, J. D., & Tibshirani, R. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1530509100 · Cited by 9,692 · cited 1× · see citing works
“Each p-value was corrected for multiple testing across genes on the same chromosome using Storey’s qvalue [30].”
MethodA 3D Map of the Human Genome at Kilobase Resolution Reveals Principles of Chromatin Looping
Rao, S. S. P., Huntley, M. H., Durand, N. C., Stamenova, E. K., Bochkov, I. D., Robinson, J. T., Sanborn, A. L., Machol, I., Omer, A. D., Lander, E. S., et al. (2014) · Cell · doi:10.1016/j.cell.2014.11.021 · Cited by 9,212 · cited 9× · see citing works
“Identification of topologically associated domains (TADs) Identification of TADs in the SK-N-SH and U-251MG cell lines was performed using the R software packages HiCdat and HiCseg [39,40].”
BackgroundInfluentialComprehensive Mapping of Long-Range Interactions Reveals Folding Principles of the Human Genome
Lieberman-Aiden, E., van Berkum, N. L., Williams, L., Imakaev, M., Ragoczy, T., Telling, A., Amit, I., Lajoie, B. R., Sabo, P. J., Dorschner, M. O., et al. (2009) · Science · doi:10.1126/science.1181369 · Cited by 8,898 · cited 2× · see citing works
“Genome-wide association studies (GWASs) have been performed to search for other genetic factors associated with AD [2-7].”
BackgroundMethodTopological domains in mammalian genomes identified by analysis of chromatin interactions
Dixon, J. R., Selvaraj, S., Yue, F., Kim, A., Li, Y., Shen, Y., Hu, M., Liu, J. S., & Ren, B. (2012) · Nature · doi:10.1038/nature11082 · Cited by 6,829 · cited 9× · see citing works
“Identification of topologically associated domains (TADs) Identification of TADs in the SK-N-SH and U-251MG cell lines was performed using the R software packages HiCdat and HiCseg [39,40].”
BackgroundInfluentialIntegrative analysis of 111 reference human epigenomes
Roadmap Epigenomics Consortium, Kundaje, A., Meuleman, W., Ernst, J., Bilenky, M., Yen, A., Heravi-Moussavi, A., Kheradpour, P., Zhang, Z., Wang, J., et al. (2015) · Nature · doi:10.1038/nature14248 · Cited by 6,368 · see citing works
The Genotype-Tissue Expression (GTEx) pilot analysis: Multitissue gene regulation in humans
The GTEx Consortium, Ardlie, K. G., Deluca, D. S., Segrè, A. V., Sullivan, T. J., Young, T. R., Gelfand, E. T., Trowbridge, C. A., Maller, J. B., Tukiainen, T., et al. (2015) · Science · doi:10.1126/science.1262110 · Cited by 5,062 · see citing works
Meta-Analysis of 74,046 Individuals Identifies 11 New Susceptibility Loci for Alzheimer’s Disease
(EADI), E. A. D. I., Lambert, J.-C., (GERAD), G. A. E. R. I. A. D., Alzheimer's Disease Genetic Consortium (ADGC), (CHARGE), C. F. H. A. A. R. I. G. E., Ibrahim-Verbaas, C. A., Harold, D., Naj, A. C., Sims, R., Bellenguez, C., et al. (2013) · Nature Genetics · doi:10.1038/ng.2802 · Cited by 4,336 · cited 15× · see citing works
“Nearly 30% of non-coding AD SNPs are located in enhancers (A) Circle chart showing the genomic region location of AD SNPs from the GWAS catalog database (p-value 1.00 × 10−6).”
BackgroundInfluentialGenetic effects on gene expression across human tissues
GTEx Consortium (2017) · Nature · doi:10.1038/nature24277 · Cited by 4,036 · see citing works
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 · see citing works
Spatial partitioning of the regulatory landscape of the X-inactivation centre
Nora, E. P., Lajoie, B. R., Schulz, E. G., Giorgetti, L., Okamoto, I., Servant, N., Piolot, T., van Berkum, N. L., Meisig, J., Sedat, J., et al. (2012) · Nature · doi:10.1038/nature11049 · Cited by 2,895 · cited 9× · see citing works
“Identification of topologically associated domains (TADs) Identification of TADs in the SK-N-SH and U-251MG cell lines was performed using the R software packages HiCdat and HiCseg [39,40].”
BackgroundInfluentialThe NHGRI GWAS Catalog, a curated resource of SNP-trait associations
Welter, D., MacArthur, J., Morales, J., Burdett, T., Hall, P., Junkins, H., Klemm, A., Flicek, P., Manolio, T., Hindorff, L., et al. (2014) · Nucleic Acids Research · doi:10.1093/nar/gkt1229 · Cited by 2,707 · cited 1× · see citing works
“lected AD SNPs from the GWAS catalog database [25].”
BackgroundChromHMM: automating chromatin-state discovery and characterization
Ernst, J., & Kellis, M. (2012) · Nature Methods · doi:10.1038/nmeth.1906 · Cited by 2,489 · cited 3× · see citing works
“Z, and DNase I-hypersensitive sites) using ChromHMM and ChromImpute [23, 24].”
BackgroundMethodJansen, 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 · cited 10× · see citing works
“Thus, NYAP1 may be related to AD pathology.”
BackgroundInfluentialGenome-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 · see citing works
Initiative, T. A. D. N., Hollingworth, P., CHARGE consortium, EADI1 consortium, Harold, D., Sims, R., Gerrish, A., Lambert, J.-C., Carrasquillo, M. M., Abraham, R., et al. (2011) · Nature Genetics · doi:10.1038/ng.803 · Cited by 1,997 · see citing works
TRRUST v2: an expanded reference database of human and mouse transcriptional regulatory interactions
Han, H., Cho, J.-W., Lee, S., Yun, A., Kim, H., Bae, D., Yang, S., Kim, C. Y., Lee, M., Kim, E., et al. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkx1013 · Cited by 1,983 · cited 1× · see citing works
“To examine the genes that are regulated by SREBF2, whose product is a TF, we used TRRUST, which is a TF-target interaction database based on text mining and manual curation [53].”
MethodNaj, 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
Integrated Systems Approach Identifies Genetic Nodes and Networks in Late-Onset Alzheimer’s Disease
Zhang, B., Gaiteri, C., Bodea, L.-G., Wang, Z., McElwee, J., Podtelezhnikov, A. A., Zhang, C., Xie, T., Tran, L., Dobrin, R., et al. (2013) · Cell · doi:10.1016/j.cell.2013.03.030 · Cited by 1,657 · cited 6× · see citing works
“For this analysis, we used three publicly available datasets (syn5550404 [32], GSE5281 [33], and GSE44770 [34]) that include gene expression data analyzed in nine human brain tissues (prefrontal cortex, temporal cortex, visual cortex, entorhinal cortex, hippocampus, medial temporal gyrus, posterior cingulate, superior…”
MethodInfluentialIterative correction of Hi-C data reveals hallmarks of chromosome organization
Imakaev, M., Fudenberg, G., McCord, R. P., Naumova, N., Goloborodko, A., Lajoie, B. R., Dekker, J., & Mirny, L. A. (2012) · Nature Methods · doi:10.1038/nmeth.2148 · Cited by 1,423 · cited 1× · see citing works
“[36], we mapped the sequenced reads to the human reference genome (hg19) using Bowtie2 and used the “hiclib” library (provided by the Leonid Mirny Laboratory (https://bitbucket.”
MethodGenome-wide Analysis of Genetic Loci Associated With Alzheimer Disease
Seshadri, S. (2010) · JAMA · doi:10.1001/jama.2010.574 · Cited by 1,219 · see citing works
Cohesin mediates transcriptional insulation by CCCTC-binding factor
Wendt, K. S., Yoshida, K., Itoh, T., Bando, M., Koch, B., Schirghuber, E., Tsutsumi, S., Nagae, G., Ishihara, K., Mishiro, T., et al. (2008) · Nature · doi:10.1038/nature06634 · Cited by 1,193 · cited 1× · see citing works
“CTCF is a key factor to form chromatin loops and protects promoters against acting by chance from distant enhancers [47].”
BackgroundCTCF: an architectural protein bridging genome topology and function
Ong, C., & Corces, V. G. (2014) · Nature Reviews Genetics · doi:10.1038/nrg3663 · Cited by 1,044 · cited 9× · see citing works
“Identification of topologically associated domains (TADs) Identification of TADs in the SK-N-SH and U-251MG cell lines was performed using the R software packages HiCdat and HiCseg [39,40].”
BackgroundInfluentialMapping long-range promoter contacts in human cells with high-resolution capture Hi-C
Mifsud, B., Tavares-Cadete, F., Young, A. N., Sugar, R., Schoenfelder, S., Ferreira, L., Wingett, S. W., Andrews, S., Grey, W., Ewels, P. A., et al. (2015) · Nature Genetics · doi:10.1038/ng.3286 · Cited by 990 · cited 15× · see citing works
“Furthermore, 13 SNPs in SK-N-SH and 14 SNPs in U-251 MG co-localized in the same TAD with more than 80% of the eQTL genes associated with that particular SNP.”
BackgroundInfluentialCTCF-Mediated Human 3D Genome Architecture Reveals Chromatin Topology for Transcription
Tang, Z., Luo, O. J., Li, X., Zheng, M., Zhu, J. J., Szalaj, P., Trzaskoma, P., Magalska, A., Wlodarczyk, J., Ruszczycki, B., et al. (2015) · Cell · doi:10.1016/j.cell.2015.11.024 · Cited by 982 · cited 1× · see citing works
“Mutations in CTCF-binding sites are associated with diseases [19, 66].”
BackgroundMeta- and Orthogonal Integration of Influenza “OMICs” Data Defines a Role for UBR4 in Virus Budding
Tripathi, S., Pohl, M. O., Zhou, Y., Rodríguez-Frandsen, A., Wang, G., Stein, D. A., Moulton, H. M., DeJesus, P., Che, J., Mulder, L. C. F., et al. (2015) · Cell Host & Microbe · doi:10.1016/j.chom.2015.11.002 · Cited by 846 · see citing works
Insulin and neurodegenerative disease: shared and specific mechanisms
Craft, S., & Stennis Watson, G. (2004) · The Lancet Neurology · doi:10.1016/s1474-4422(04)00681-7 · Cited by 830 · see citing works
Linking disease associations with regulatory information in the human genomePreprint
Schaub, M. A., Boyle, A. P., Kundaje, A. B., Batzoglou, S., & Snyder, M. P. (2012) · Genome Research · doi:10.1101/gr.136127.111 · Cited by 715 · cited 1× · see citing works
“These AD-associated SNPs (AD SNPs) could be tag SNPs of surrounding functional exonic variants [11]; however, a fine-mapping study of BIN1, CLU, CR1, and PICALM, which are the closest genes to several AD SNPs, showed no direct association with AD pathogenesis [12].”
ResultGenetic variability in the regulation of gene expression in ten regions of the human brain
UK Brain Expression Consortium, Ramasamy, A., North American Brain Expression Consortium, Trabzuni, D., Guelfi, S., Varghese, V., Smith, C., Walker, R., De, T., Coin, L., et al. (2014) · Nature Neuroscience · doi:10.1038/nn.3801 · Cited by 644 · cited 1× · see citing works
“To this end, the genes influenced by the non-coding AD SNPs (hereafter referred to as eQTL genes) were collected from the GTEx Portal [26, 27] and BRAINEAC databases [28].”
MethodLiang, W. S., Reiman, E. M., Valla, J., Dunckley, T., Beach, T. G., Grover, A., Niedzielko, T. L., Schneider, L. E., Mastroeni, D., Caselli, R., et al. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0709259105 · Cited by 585 · cited 7× · see citing works
“For this analysis, we used three publicly available datasets (syn5550404 [32], GSE5281 [33], and GSE44770 [34]) that include gene expression data analyzed in nine human brain tissues (prefrontal cortex, temporal cortex, visual cortex, entorhinal cortex, hippocampus, medial temporal gyrus, posterior cingulate, superior…”
MethodInfluentialChromosome conformation elucidates regulatory relationships in developing human brain
Won, H., de la Torre-Ubieta, L., Stein, J. L., Parikshak, N. N., Huang, J., Opland, C. K., Gandal, M. J., Sutton, G. J., Hormozdiari, F., Lu, D., et al. (2016) · Nature · doi:10.1038/nature19847 · Cited by 584 · cited 15× · see citing works
“Furthermore, 13 SNPs in SK-N-SH and 14 SNPs in U-251 MG co-localized in the same TAD with more than 80% of the eQTL genes associated with that particular SNP.”
BackgroundInfluentialMurrell, A., Heeson, S., & Reik, W. (2004) · Nature Genetics · doi:10.1038/ng1402 · Cited by 556 · cited 2× · see citing works
“Genome-wide association studies (GWASs) have been performed to search for other genetic factors associated with AD [2-7].”
BackgroundMethodWang, Y., Song, F., Zhang, B., Zhang, L., Xu, J., Kuang, D., Li, D., Choudhary, M. N. K., Li, Y., Hu, M., et al. (2018) · Genome biology · doi:10.1186/s13059-018-1519-9 · Cited by 552 · cited 2× · see citing works
“We analyzed publicly available data and validated this extensive interacting region through chromatin interaction analysis using paired-end tag sequencing (ChlA-PET), which is experimental method used to identify chromatin loops, in the 3D Genome Browser [46] (Additional file 9: Supplement Figure S1).”
MethodKalhor, R., Tjong, H., Jayathilaka, N., Alber, F., & Chen, L. (2011) · Nature Biotechnology · doi:10.1038/nbt.2057 · Cited by 532 · cited 1× · see citing works
“To detect TADs, we performed tethered conformation capture (TCC), which is a variant of the Hi-C method that is used for the identification of comprehensive chromatin loops through paired-end sequencing [35].”
MethodParkinson-associated risk variant in distal enhancer of α-synuclein modulates target gene expression
Soldner, F., Stelzer, Y., Shivalila, C., Abraham, B. J., Latourelle, J. C., Barrasa, M. I., Goldmann, J. E., Myers, R. H., Young, R. A., & Jaenisch, R. (2016) · Nature · doi:10.1038/nature17939 · Cited by 496 · cited 2× · see citing works
“showed that a non-coding risk variant rs356168, which is associated with Parkinson’s disease (PD), is located in an enhancer region and upregulates the expression level of a PD-susceptibility gene SNCA [13].”
Heritability for Alzheimer’s disease: the study of dementia in Swedish twins
Gatz, M., Pedersen, N. L., Berg, S., Johansson, B., Johansson, K., Mortimer, J. A., Posner, S. F., Viitanen, M., Winblad, B., & Ahlbom, A. (1997) · The Journals of Gerontology Series A: Biological Sciences and Medical Sciences · doi:10.1093/gerona/52a.2.m117 · Cited by 413 · cited 1× · see citing works
“itability of AD is estimated to be between 58 and 79% [1].”
BackgroundThe GENCODE pseudogene resource
Pei, B.-K., Sisu, C., Frankish, A., Howald, C., Habegger, L., Mu, X., Harte, R., Balasubramanian, S., Tanzer, A., Diekhans, M., et al. (2012) · Genome Biology · doi:10.1186/gb-2012-13-9-r51 · Cited by 355 · see citing works
Large-scale imputation of epigenomic datasets for systematic annotation of diverse human tissues
Ernst, J., & Kellis, M. (2015) · Nature Biotechnology · doi:10.1038/nbt.3157 · Cited by 352 · cited 3× · see citing works
“Z, and DNase I-hypersensitive sites) using ChromHMM and ChromImpute [23, 24].”
BackgroundMethodThe cerebellum in Alzheimer’s disease: evaluating its role in cognitive decline
Jacobs, H. I. L., Hopkins, D. A., Mayrhofer, H. C., Bruner, E., van Leeuwen, F. W., Raaijmakers, W., & Schmahmann, J. D. (2017) · Brain · doi:10.1093/brain/awx194 · Cited by 339 · cited 1× · see citing works
“Studies of functional MRI have reported network-based degeneration in the cerebellum of AD patients [57, 58].”
BackgroundThe 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 · cited 4× · see citing works
“org/) [26, 27] and the BRAINEAC database (http://www.”
MethodInfluentialA Role for Noncoding Variation in Schizophrenia
Roussos, P., Mitchell, A. C., Voloudakis, G., Fullard, J. F., Pothula, V. M., Tsang, J., Stahl, E. A., Georgakopoulos, A., Ruderfer, D. M., Charney, A., et al. (2014) · Cell Reports · doi:10.1016/j.celrep.2014.10.015 · Cited by 251 · see citing works
Lyoo, C. H., Ikawa, M., Liow, J., Zoghbi, S. S., Morse, C. L., Pike, V. W., Fujita, M., Innis, R. B., & Kreisl, W. C. (2015) · Journal of Nuclear Medicine · doi:10.2967/jnumed.114.146027 · Cited by 237 · cited 1× · see citing works
“The cerebellum in AD does not present neurofibrillary tangles, which are intracellular aggregations of hyperphosphorylated tau protein [54] and has ever been often used as a reference brain region in AD studies [55, 56].”
Guo, C. C., Tan, R. H., Hodges, J. R., Hu, X., Sami, S. A. K., & Hornberger, M. (2016) · Brain · doi:10.1093/brain/aww003 · Cited by 206 · cited 1× · see citing works
“Studies of functional MRI have reported network-based degeneration in the cerebellum of AD patients [57, 58].”
BackgroundFrench, J. D., Ghoussaini, M., Edwards, S. L., Meyer, K. B., Michailidou, K., Ahmed, S., Khan, S., Maranian, M. J., O’Reilly, M., Hillman, K. M., et al. (2013) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2013.01.002 · Cited by 202 · see citing works
Convergent genetic and expression data implicate immunity in Alzheimer's disease
International Genomics of Alzheimer's Disease Consortium (IGAP), Jones, L., Lambert, J., Wang, L., Choi, S., Harold, D., Vedernikov, A., Escott‐Price, V., Stone, T., Richards, A., et al. (2015) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2014.05.1757 · Cited by 199 · see citing works
Lee, J. H., Cheng, R., Barral, S., Reitz, C., Medrano, M., Lantigua, R., Jiménez-Velazquez, I. Z., Rogaeva, E., St. George-Hyslop, P. H., & Mayeux, R. (2011) · Archives of Neurology · doi:10.1001/archneurol.2010.292 · Cited by 190 · see citing works
A CTCF Code for 3D Genome Architecture
Nichols, M. H., & Corces, V. G. (2015) · Cell · doi:10.1016/j.cell.2015.07.053 · Cited by 186 · see citing works
Diabetes and Insulin in Regulation of Brain Cholesterol Metabolism
Suzuki, R., Lee, K., Jing, E., Biddinger, S. B., McDonald, J. G., Montine, T. J., Craft, S., & Kahn, C. R. (2010) · Cell Metabolism · doi:10.1016/j.cmet.2010.11.006 · Cited by 183 · cited 1× · see citing works
“In addition, previous studies have shown that SREBF2 controls brain cholesterol synthesis and is involved in diabetes, which is associated with an increased risk of AD [61, 62], and that AD model mice overexpressing SREBF2 show Aβ accumulation and neurofibrillary tangle formation [63].”
BackgroundRegional protein expression in human Alzheimer’s brain correlates with disease severity
Xu, J., Patassini, S., Rustogi, N., Riba‐Garcia, I., Hale, B. D., Phillips, A. M., Waldvogel, H. J., Haines, R., Bradbury, P., Stevens, A., et al. (2019) · Communications Biology · doi:10.1038/s42003-018-0254-9 · Cited by 182 · cited 1× · see citing works
“Additionally, a comprehensive proteome study suggested that the cerebellum is affected by different pathways compared to the other brain regions [59].”
BackgroundGenomic variants, genes, and pathways of Alzheimer's disease: An overview
Naj, A. C., & Schellenberg, G. D. (2016) · American Journal of Medical Genetics Part B Neuropsychiatric Genetics · doi:10.1002/ajmg.b.32499 · Cited by 178 · see citing works
Identification of a Novel Human Sterol-Sensitive ATP-Binding Cassette Transporter (ABCA7)
Kaminski, W. E., Orsó, E., Diederich, W., Klucken, J., Drobnik, W., & Schmitz, G. (2000) · Biochemical and Biophysical Research Communications · doi:10.1006/bbrc.2000.2954 · Cited by 166 · cited 1× · see citing works
“ABCA7 is highly expressed in human monocytes that induced into macrophages [67].”
BackgroundTwo-dimensional segmentation for analyzing Hi-C data
Lévy‐Leduc, C., Delattre, M., Mary‐Huard, T., & Robin, S. S. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu443 · Cited by 152 · cited 6× · see citing works
“Identification of topologically associated domains (TADs) Identification of TADs in the SK-N-SH and U-251MG cell lines was performed using the R software packages HiCdat and HiCseg [39,40].”
MethodInfluentialBarbero‐Camps, E., Fernández, A., Martínez-Gili, L., Fernández-Checa, J. C., & Colell, A. (2013) · Human Molecular Genetics · doi:10.1093/hmg/ddt201 · Cited by 127 · see citing works
Cerebellar atrophy in Alzheimer's disease—clinicopathological correlations
Węgiel, J., Wı́sniewski, H. M., Dziewíatkowski, J., Badmajew, E., Tarnawski, M. J., Reisberg, ., Mlodzik, B., de Leon, M. J., & Miller, D. C. (1999) · Brain Research · doi:10.1016/s0006-8993(98)01279-7 · Cited by 123 · see citing works
Wang, Y., Zhang, B., Zhang, L., et al. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/112268 · Cited by 118 · see citing works
Genomic interactions: Chromatin loops and gene meeting points in transcriptional regulation
Sexton, T., Bantignies, F., & Cavalli, G. (2009) · Seminars in Cell and Developmental Biology · doi:10.1016/j.semcdb.2009.06.004 · Cited by 117 · see citing works
Shih, S.-J., Allan, C., Grehan, S., Tse, E., Moran, C., & Taylor, J. M. (2000) · Journal of Biological Chemistry · doi:10.1074/jbc.m005468200 · Cited by 112 · cited 1× · see citing works
“This region is known as multienhancer 1 and affects APOE expression in various tissues or cell types, including macrophages, adipose tissue, and a neuronal cell line [41, 42].”
BackgroundAlzheimer’s disease risk polymorphisms regulate gene expression in the ZCWPW1 and the CELF1 loci
Karch, C. M., Ezerskiy, L. A., Bertelsen, S., Alzheimer’s Disease Genetics Consortium (ADGC), & Goate, A. M. (2016) · PLoS ONE · doi:10.1371/journal.pone.0148717 · Cited by 111 · cited 4× · see citing works
“Previous eQTL studies of AD have indicated that rs1476679 was associated with PILRB and GATS gene expression levels [15, 52]; however, it is not known how rs1476679 regulates these gene expression levels.”
BackgroundMethodResultInfluentialA Dementia-Associated Risk Variant near TMEM106B Alters Chromatin Architecture and Gene Expression
Gallagher, M. D., Posavi, M., Huang, P., Unger, T. L., Berlyand, Y., Gruenewald, A. L., Chesi, A., Manduchi, E., Wells, A. D., Grant, S. F. A., et al. (2017) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2017.09.004 · Cited by 109 · see citing works
Bekris, L. M., Lutz, F., & Yu, C. (2011) · Journal of Human Genetics · doi:10.1038/jhg.2011.123 · Cited by 98 · cited 1× · see citing works
“This region is known as multienhancer 1 and affects APOE expression in various tissues or cell types, including macrophages, adipose tissue, and a neuronal cell line [41, 42].”
BackgroundABCA7 Mediates Phagocytic Clearance of Amyloid-β in the Brain
Fu, Y., Hsiao, J.-H. T., Paxinos, G., Halliday, G. M., & Kim, W. S. (2016) · Journal of Alzheimer s Disease · doi:10.3233/jad-160456 · Cited by 98 · cited 1× · see citing works
“Additionally, the expression level of ABCA7 is also high in human microglia [68].”
BackgroundFreer, R., Sormanni, P., Vecchi, G., Ciryam, P., Dobson, C. M., & Vendruscolo, M. (2016) · Science Advances · doi:10.1126/sciadv.1600947 · Cited by 97 · cited 1× · see citing works
“The cerebellum in AD does not present neurofibrillary tangles, which are intracellular aggregations of hyperphosphorylated tau protein [54] and has ever been often used as a reference brain region in AD studies [55, 56].”
Identification of cis-regulatory elements for MECP2 expression
Liu, J., & Francke, U. (2006) · Human Molecular Genetics · doi:10.1093/hmg/ddl099 · Cited by 78 · cited 2× · see citing works
“Genome-wide association studies (GWASs) have been performed to search for other genetic factors associated with AD [2-7].”
BackgroundMethodNYAP: a phosphoprotein family that links PI3K to WAVE1 signalling in neurons
Yokoyama, K., Tezuka, T., Kotani, M., Nakazawa, T., Hoshina, N., Shimoda, Y., Kakuta, S., Sudo, K., Watanabe, K., Iwakura, Y., et al. (2011) · The EMBO Journal · doi:10.1038/emboj.2011.348 · Cited by 75 · see citing works
Late-onset Alzheimer disease risk variants mark brain regulatory loci
Allen, M., Kachadoorian, M., Carrasquillo, M. M., Karhade, A., Manly, L., Burgess, J. D., Wang, C., Serie, D., Wang, X., Siuda, J., et al. (2015) · Neurology Genetics · doi:10.1212/nxg.0000000000000012 · Cited by 73 · cited 2× · see citing works
“pression levels [15, 52]; however, it is not known how”
BackgroundResultKauwe, J. S. K., Cruchaga, C., Karch, C. M. M., Sadler, B. E., Lee, M., Mayo, K., Latu, W., Su'a, M., Fagan, A. M., Holtzman, D. M., et al. (2011) · PLoS ONE · doi:10.1371/journal.pone.0015918 · Cited by 72 · cited 1× · see citing works
“These AD-associated SNPs (AD SNPs) could be tag SNPs of surrounding functional exonic variants [11]; however, a fine-mapping study of BIN1, CLU, CR1, and PICALM, which are the closest genes to several AD SNPs, showed no direct association with AD pathogenesis [12].”
ResultHiCdat: a fast and easy-to-use Hi-C data analysis tool
Schmid, M. W., Grob, S., & Grossniklaus, U. (2015) · BMC Bioinformatics · doi:10.1186/s12859-015-0678-x · Cited by 65 · cited 6× · see citing works
“Identification of topologically associated domains (TADs) Identification of TADs in the SK-N-SH and U-251MG cell lines was performed using the R software packages HiCdat and HiCseg [39,40].”
MethodInfluentialRosenthal, S. L., Barmada, M. M., Wang, X., Demirci, F. Y., & Kamboh, M. I. (2014) · PLoS ONE · doi:10.1371/journal.pone.0095152 · Cited by 45 · cited 1× · see citing works
“It is reported that some SNPs in AD influenced gene expression levels as in AD [14, 15].”
BackgroundfourSig: a method for determining chromosomal interactions in 4C-Seq data
Williams, R. L., Starmer, J., Mugford, J. W., Calabrese, J. M., Mieczkowski, P. A., Yee, D., & Magnuson, T. (2014) · Nucleic Acids Research · doi:10.1093/nar/gku156 · Cited by 41 · cited 2× · see citing works
“To identify significant chromatin interactions, we applied the R software package fourSig [37].”
MethodKunkle, B., Grenier-Boley, B., Sims, R., Bis, J., Naj, A., Boland, A., Vronskaya, M., van der Lee, S., Amlie-Wolf, A., Bellenguez, C., et al. (2018) · doi:10.1101/294629 · Cited by 35 · see citing works
Lambert, J. (2013) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2013.04.040 · Cited by 32 · see citing works
FTO Obesity Variant and Adipocyte Browning in Humans
Claussnitzer, M., Hui, C.-C., & Kellis, M. (2016) · New England Journal of Medicine · doi:10.1056/nejmc1513316 · Cited by 32 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- [50]Tang Z Luo OJ Li X Zheng M Zhu JJ Szalaj P CTCF-mediated human 3D genome architecture reveals chromatin topology for transcription Cell 2015 163 1611 1627 10.1016/j.cell.2015.11.024 26686651 PMC4734140 · DOI · Paper page
Cited by
Sorted by citations
Dileep, V., Boix, C. A., Mathys, H., Marco, A., Welch, G. M., Meharena, H. S., Loon, A., Jeloka, R., Peng, Z., Bennett, D. A., et al. (2023) · Cell · doi:10.1016/j.cell.2023.08.038 · Cited by 130 · see citing works
Wang, M., Song, W., Chen, M., Wang, Q., Zhou, X., Xu, P., Krek, A., Yoon, Y., Ho, L., Orr, M. E., et al. (2022) · Molecular Neurodegeneration · doi:10.1186/s13024-022-00517-z · Cited by 100 · see citing works
Griswold, A. J., Celis, K., Bussies, P. L., Rajabli, F., Whitehead, P. L., Hamilton‐Nelson, K. L., Beecham, G. W., Dykxhoorn, D. M., Nuytemans, K., Wang, L., et al. (2021) · Alzheimer's & Dementia · doi:10.1002/alz.12287 · Cited by 87 · see citing works
Transcription factors: building hubs in the 3D space
Giammartino, D. C. D., Polyzos, A., & Apostolou, E. (2020) · Cell Cycle · doi:10.1080/15384101.2020.1805238 · Cited by 66 · see citing works
“Using 3D genomics assays several groups started to assign non-coding genetic variants, associated with disease, and putative enhancers to their interacting target genes in the contexts of cancer [132], neuronal disorders [133,134], autoimmune and cardiovascular diseases [36].”
BackgroundA mega-analysis of expression quantitative trait loci in retinal tissue
Strunz, T., Kiel, C., Graßmann, F., Ratnapriya, R., Kwicklis, M., Karlstetter, M., Fauser, S., Arend, N., Swaroop, A., Langmann, T., et al. (2020) · PLoS Genetics · doi:10.1371/journal.pgen.1008934 · Cited by 57 · see citing works
“Topological chromatin interactions were previously reported for this regulatory cluster [24].”
BackgroundEnhancer-gene specificity in development and disease
Pachano, T., Haro, E., & Rada-Iglesias, Á. (2022) · Development · doi:10.1242/dev.186536 · Cited by 55 · see citing works
Missing heritability in Parkinson’s disease: the emerging role of non-coding genetic variation
Ohnmacht, J., May, P., Sinkkonen, L., & Krüger, R. (2020) · Journal of Neural Transmission · doi:10.1007/s00702-020-02184-0 · Cited by 57 · see citing works
McFarland, K. N., & Chakrabarty, P. (2022) · Neurotherapeutics · doi:10.1007/s13311-021-01179-3 · Cited by 48 · see citing works
“Variants also exert effects on transcript levels over a greater distance – greater than 1 Mb from the location of the SNP – and in AD almost 30% of non-coding variants reside in enhancer regions and are thought to act by modulating the higher-order chromatin structure [165].”
BackgroundJia, H., Liu, G., Li, J., Zhang, J., Sun, P., Zhao, S., Zhou, X. C., Lu, M., & Hu, J. (2020) · Journal of Experimental Botany · doi:10.1093/jxb/eraa172 · Cited by 46 · see citing works
Uncovering the impact of noncoding variants in neurodegenerative brain diseases
Frydas, A., Wauters, E., van der Zee, J., & Broeckhoven, C. V. (2021) · Trends in Genetics · doi:10.1016/j.tig.2021.08.010 · Cited by 42 · see citing works
“Tethered conformation capture experiments in neuroblastoma and astrocytoma cells show that enhancer variants colocalize with their eQTL genes in the same topologically associated domain [56].”
BackgroundInfluentialGao, C., Shen, X., Tan, Y., & Chen, S. (2022) · Journal of Neuroinflammation · doi:10.1186/s12974-022-02580-1 · Cited by 43 · see citing works
“In fact, some enhancerlocated SNPs influence the expression of surrounding or distant genes by contacting gene promoters and regulating specific transcription factor activities via chromatin higher-order structures [92].”
BackgroundGene networks and systems biology in Alzheimer's disease: Insights from multi‐omics approaches
Rahimzadeh, N., Srinivasan, S. S., Zhang, J., & Swarup, V. (2024) · Alzheimer s & Dementia · doi:10.1002/alz.13790 · Cited by 36 · see citing works
“101 Interestingly, the majority of these genes are differentially expressed in AD and colocalize with their eQTL variants within the same topological associated domains (TADs).”
BackgroundAn epigenome atlas of neural progenitors within the embryonic mouse forebrain
Rhodes, C. T., Thompson, J. J., Mitra, A., Asokumar, D., Lee, D. R., Lee, D. J., Zhang, Y., Jason, E., Dale, R., Rocha, P. P., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-31793-4 · Cited by 33 · see citing works
Defining functional variants associated with Alzheimer’s disease in the induced immune response
Harwood, J. C., Leonenko, G., Sims, R., Escott‐Price, V., Williams, J. H., & Holmans, P. (2021) · Brain Communications · doi:10.1093/braincomms/fcab083 · Cited by 32 · see citing works
Nuclear dynamics and stress responses in Alzheimer’s disease
Iatrou, A., Clark, E. M., & Wang, Y. (2021) · Molecular Neurodegeneration · doi:10.1186/s13024-021-00489-6 · Cited by 27 · see citing works
“The rs7364180 AD variant alters the expression of the transcription factor SREFB2 and then indirectly regulates 20 AD risk genes through a cascade of transcriptional events [80].”
BackgroundInfluentialNeuroepigenetics of ageing and neurodegeneration-associated dementia: An updated review
Mir, F. A., Amanullah, A., Jain, B. P., Hyderi, Z., & Gautam, A. (2023) · Ageing Research Reviews · doi:10.1016/j.arr.2023.102067 · Cited by 28 · see citing works
3D genome alterations in T cells associated with disease activity of systemic lupus erythematosus
Zhao, M., Feng, D., Hu, L., Liu, L., Wu, J., Hu, Z. Q., Long, H., Kuang, Q., Ouyang, L., & Lu, Q. (2022) · Annals of the Rheumatic Diseases · doi:10.1136/ard-2022-222653 · Cited by 26 · see citing works
Alzheimer’s Disease Variant Portal: A Catalog of Genetic Findings for Alzheimer’s Disease
Kuksa, P. P., Liu, C., Fu, W., Qu, L., Zhao, Y., Katanić, Ž., Clark, K., Kuzma, A. B., Ho, P., Tzeng, K., et al. (2022) · Journal of Alzheimer s Disease · doi:10.3233/jad-215055 · Cited by 24 · see citing works
Song, H., Liu, Y., Tan, Y., Zhang, Y., Jin, W. Y., Chen, L., Wu, S., Yan, J., Li, J., Chen, Z., et al. (2022) · Blood · doi:10.1182/blood.2021014945 · Cited by 23 · see citing works
Ctcf haploinsufficiency mediates intron retention in a tissue-specific manner
Alharbi, A. B., Schmitz, U., Marshall, A. D., Vanichkina, D., Nagarajah, R., Vellozzi, M., Wong, J. J. L., Bailey, C. G., & Rasko, J. E. J. (2020) · RNA Biology · doi:10.1080/15476286.2020.1796052 · Cited by 21 · see citing works
Prabhu, B. N. B., Kanchamreddy, S. H., Sharma, A., Bhat, S. K., Bhat, P. V., Kabekkodu, S. P., Satyamoorthy, K., & Rai, P. S. (2021) · Journal of Endocrinological Investigation · doi:10.1007/s40618-021-01498-4 · Cited by 23 · see citing works
Alcalá‐Vida, R., Awada, A., Boutillier, A., & Mérienne, K. (2020) · Neurobiology of Disease · doi:10.1016/j.nbd.2020.105155 · Cited by 22 · see citing works
Xu, M., Liu, Q., Bi, R., Li, Y., Li, H., Kang, W.-B., Yan, Z., Zheng, Q., Sun, C., Ye, M., et al. (2023) · Biological Psychiatry · doi:10.1016/j.biopsych.2023.05.020 · Cited by 21 · see citing works
Chromatin Alterations in Neurological Disorders and Strategies of (Epi)Genome Rescue
Janowski, M., Milewska, M., Zare, P., & Pękowska, A. (2021) · Pharmaceuticals · doi:10.3390/ph14080765 · Cited by 21 · see citing works
Li, Y., Xu, M., Xiang, B.-L., Li, X., Zhang, D.-F., Zhao, H., Bi, R., & Yao, Y.-G. (2023) · Neuropsychopharmacology · doi:10.1038/s41386-023-01542-2 · Cited by 20 · see citing works
“The fi rst cohort included 221 AD patients with an early age at onset (age ≤ 55) and/or an AD familial history (FOAD), in which 169 individuals had been reported in our previous studies [15, 41].”
Gamache, J., Gingerich, D., Shwab, E. K., Barrera, J., Garrett, M. E., Hume, C., Crawford, G. E., Ashley-Koch, A. E., & Chiba-Falek, O. (2023) · Cell & Bioscience · doi:10.1186/s13578-023-01120-5 · Cited by 20 · see citing works
“Previous studies suggested that some noncoding LOAD SNPs are located in regulatory elements such as enhancers, affecting their functions, and thereby impacting gene expression [18], including expression of distal genes [19].”
BackgroundThree-dimensional chromatin architecture datasets for aging and Alzheimer’s disease
Meng, G.-F., Xu, H., Lu, D., Li, S.-S., Zhao, Z.-Z., Li, H.-H., & Zhang, W. (2023) · Scientific Data · doi:10.1038/s41597-023-01948-z · Cited by 18 · see citing works
Haplotype architecture of the Alzheimer's risk in the APOE region via co‐skewness
Kulminski, A. M., Philipp, I., Loika, Y., He, L., & Culminskaya, I. V. (2020) · Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring · doi:10.1002/dad2.12129 · Cited by 18 · see citing works
Epigenetics in Neurodegenerative Diseases
van Zundert, B., & Montecino, M. A. (2025) · Sub-cellular biochemistry/Subcellular biochemistry · doi:10.1007/978-3-031-75980-2_3 · Cited by 18 · see citing works
Pan, G., King, A. E., Wu, F., Simpson, S., Woodhouse, A., Phipps, A., & Vickers, J. C. (2021) · Ageing Research Reviews · doi:10.1016/j.arr.2021.101402 · Cited by 17 · see citing works
Lutz, M. W., & Chiba‐Falek, O. (2022) · Alzheimer s & Dementia Translational Research & Clinical Interventions · doi:10.1002/trc2.12244 · Cited by 16 · see citing works
Deep learning approaches for noncoding variant prioritization in neurodegenerative diseases
Lan, A. Y., & Corces, M. R. (2022) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2022.1027224 · Cited by 16 · see citing works
Three-dimensional chromatin organization in brain function and dysfunction
Dileep, V., & Tsai, L.-H. (2021) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2021.04.006 · Cited by 14 · see citing works
Inglis, G. A. S., Zhou, Y., Patterson, D. G., Scharer, C. D., Han, Y., Boss, J. M., Wen, Z., & Escayg, A. (2020) · Human Molecular Genetics · doi:10.1093/hmg/ddaa150 · Cited by 12 · see citing works
“and contributing toward increased SZ risk (79,80).”
Oh, J., Jo, S., Park, K. W., Lee, E., Lee, S. H., Hwang, Y. S., Jeon, H. R., Ryu, Y., Yoon, H. J., Chun, S., et al. (2023) · Experimental & Molecular Medicine · doi:10.1038/s12276-023-00952-y · Cited by 13 · see citing works
Wu, T.-R., Jiao, C.-N., Cui, X., Wang, Y., Zheng, C.-H., & Liu, J. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3383885 · Cited by 13 · see citing works
“…inthegeneticmechanismofdiseasesandaresuitableforthe geneticanatomyofcomplextraitsanddiseases[3]. sMRIcan provideinformationonbraintissuestructure,i.e.,graymatter (GM), white matter (WM) and cerebrospinal fluid (CSF), which is useful for the…”
BackgroundLoop Interrupted: Dysfunctional Chromatin Relations in Neurological Diseases
Behrends, M., & Engmann, O. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.732033 · Cited by 13 · see citing works
“(Kikuchi et al., 2019) SNP rs1476679 rs7364180 forms chromatin loops”
BackgroundInfluentialGenetic architecture of natural variation of cardiac performance from flies to humans
Saha, S., Spinelli, L., Mondragón, J. A. C., Kervadec, A., Lynott, M. R., Kremmer, L., Röder, L., Krifa, S., Torres, M., Brun, C., et al. (2022) · eLife · doi:10.7554/elife.82459 · Cited by 12 · see citing works
Jablonski, K. P., Carron, L., Mozziconacci, J., Forné, T., Hütt, M. ‐., & Lesne, A. (2022) · Human Genomics · doi:10.1186/s40246-022-00375-2 · Cited by 12 · see citing works
Wang, X., Huang, K., Yang, F., Chen, D., Cai, S., & Huang, L. (2021) · Behavioural Brain Research · doi:10.1016/j.bbr.2021.113330 · Cited by 9 · see citing works
“…to the group of neurodegenerative diseases, a single nucleotide polymorphism (SNP) in a CTCF-binding site modifies the surrounding chromatin conformation and spatially regulates the expression level of a causative gene of transmembrane protein 106B (TMEM106B), leading to neuronal death [52].”
An Emerging Role for Enhancer RNAs in Brain Disorders
Patel, A., & Dharap, A. A. (2024) · NeuroMolecular Medicine · doi:10.1007/s12017-024-08776-3 · Cited by 8 · see citing works
“Nota-bly, enhancer variants in Alzheimer’s Disease (AD) have been found to disrupt chromatin looping (Kikuchi et al., 2019), which is an epigenetic mechanism previously shown to be regulated by eRNA binding (Hsieh et al., 2014; Li et al., 2013; Tsai et al., 2018).”
Zebrafish regulatory genomic resources for disease modelling and regeneration
Jiménez-González, A., Baranas̆ić, D., & Müller, F. (2023) · Disease Models & Mechanisms · doi:10.1242/dmm.050280 · Cited by 6 · see citing works
“Mutations can also impact enhancer tethering to its target gene promoter by enhancer looping (Box 1), as shown in Alzheimer ’ s disease (Kikuchi et al., 2019).”
Jąkalski, M., Bruhn‐Olszewska, B., Rychlicka-Buniowska, E., Davies, H., Sarkisyan, D., Siedlar, M., Baran, J., Węglarczyk, K., Jaszczyński, J., Ryś, J., et al. (2025) · Cellular and Molecular Life Sciences · doi:10.1007/s00018-025-05618-8 · Cited by 6 · see citing works
“Recent studies showed dysregulation of epigenetic processes in AD and that regulation may occur through chromatin higher-order structures [47, 48, 91].”
Yuan, S., Li, H., Gu, Y. F., & Sun, X. S. (2021) · Biomedicines · doi:10.3390/biomedicines9060658 · Cited by 5 · see citing works
“SNPs within enhancers can alter transcription factor binding and alter enhancer–promoter interactions, leading to dysregulation of gene expression and diseases [34], such as AD [35,36].”
BackgroundIdentifying genetic markers enriched by brain imaging endophenotypes in Alzheimer’s disease
Kim, M., Wu, R., Yao, X., Saykin, A. J., Moore, J. H., Shen, L., & Initiative, A. D. N. (2022) · BMC Medical Genomics · doi:10.1186/s12920-022-01323-8 · Cited by 5 · see citing works
Multiple human enhancer RNAs contain long translated open reading framesPreprint
Vlasov, P. A., Ogami, K., Valenzuela, E., Kawaguchi, R. K., Jovanovic, M., & Manley, J. L. (2025) · Genes & Development · doi:10.1101/gad.352944.125 · Cited by 5 · see citing works
Targeting Genome Stability to Mitigate Human Aging and Disease
Toiber, D., & Schumacher, B. (2025) · Annual Review of Pathology Mechanisms of Disease · doi:10.1146/annurev-pathmechdis-042624-105942 · Cited by 4 · see citing works
Connecting dementia risk loci to the CSF proteome identifies pathophysiological leads for dementia
Reus, L. M., Jansen, I. E., Tijms, B. M., Visser, P. J., Tesi, N., van der Lee, S. J., Vermunt, L., Peeters, C. F. W., Groot, L. A. D., Hok‐A‐Hin, Y. S., et al. (2024) · Brain · doi:10.1093/brain/awae090 · Cited by 5 · see citing works
3D interactions with the growth hormone locus in cellular signalling and cancer-related pathways
Jain, L., Fadason, T., Schierding, W. S., Vickers, M. H., O'Sullivan, J. M., & Perry, J. K. (2020) · Journal of Molecular Endocrinology · doi:10.1530/jme-20-0010 · Cited by 4 · see citing works
Papanicolaou, N., & Bonetti, A. (2020) · SLAS DISCOVERY · doi:10.1177/2472555220926158 · Cited by 4 · see citing works
Non-coding variation in dementias: mechanisms, insights, and challenges
Rogers, B. B., & Cochran, J. N. (2025) · npj Dementia · doi:10.1038/s44400-025-00012-4 · Cited by 2 · see citing works
“Approximately90%ofGWASvariantsarelocatedinnon-codingregionsof thegenome,with30%ofAD-associatedvariantsestimatedtofallwithin enhancerregulatoryelements 34,40,112,113 .”
ZCWPW1 loss-of-function variants in Alzheimer’s DiseasePreprint
Küçükali, F., Nußbaumer, K., Dongen, J. V., Hens, E., Bellenguez, C., Grenier‐Boley, B., Daian, D., Boland, A., Deleuze, J., Lambert, J., et al. (2021) · medRxiv · doi:10.1101/2021.08.13.21261426 · Cited by 3 · see citing works
Wang, S., Luo, G., Sun, G., Zhang, M., Qin, Y., Lu, J., Xu, H. M., & Li, Z. (2025) · Journal of Alzheimer s Disease · doi:10.1177/13872877251326273 · Cited by 2 · see citing works
“30 Given the divergent conclusions regarding the relationship between rs7561528 and AD risk, it has become crucial to reevaluate this association by incorporating a large-scale, multicenter study, complemented with functional analysis.”
Ghorbani, F., de Boer, E. N., Fokkens, M. R., de Boer-Bergsma, J. J., Verschuuren‐Bemelmans, C. C., Wierenga, E., Kasaei, H., Noordermeer, D., Verbeek, D. S., Westers, H., et al. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms252011205 · Cited by 2 · see citing works
Loss of Y is associated with multi-omic changes in immune cells from Alzheimer’s disease patientsPreprint
Jąkalski, M., Rychlicka-Buniowska, E., Davies, H., Sarkisyan, D., Siedlar, M., Baran, J., Węglarczyk, K., Jaszczyński, J., Ryś, J., Gedraitis, V., et al. (2023) · medRxiv · doi:10.1101/2023.02.07.23285520 · Cited by 2 · see citing works
Integrating Multimorbidity into a Whole-Body Understanding of Disease Using Spatial Genomics
Gokuladhas, S., Zaied, R. E., Schierding, W. S., Farrow, S. L., Fadason, T., & O'Sullivan, J. M. (2022) · Results and problems in cell differentiation · doi:10.1007/978-3-031-06573-6_5 · Cited by 2 · see citing works
A multiple-causal-gene-cluster model underlying GWAS signals of Alzheimer’s diseasePreprint
Xu, M., Liu, Q., Bi, R., Li, Y., Zeng, C., Yan, Z., Zheng, Q., Li, X., Sun, C., Ye, M., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.05.14.444131 · Cited by 1 · see citing works
Contribution of 3D genome topological domains to genetic risk of cancersPreprint
Jablonski, K. P., Carron, L., Mozziconacci, J., Forné, T., Hütt, M. ‐., & Lesne, A. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.07.26.453813 · Cited by 1 · see citing works
Park, J.-H., Kim, Y., & Kim, J.-P. (2020) · Korean Leprosy Bulletin · doi:10.33161/klb.2020.53.1.23
Gamache, J., Gingerich, D. C., Shwab, E. K., Barrera, J., Garrett, M. E., Hume, C., Crawford, G. E., Ashley‐Koch, A. E., & Chiba‐Falek, O. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.05.12.540037
Zhao, S., Yang, Q., & Gao, C. (2025) · Revista de investigaci�n Cl�nica · doi:10.24875/ric.24000246
Prakash, A., & Banerjee, M. (2025) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqaf080
“…labels for rs7364180 and rs12411216 were found to be significant. rs7364180 is found to be associated with many eQTL genes in brain tissues [ 28 ]. rs12411216 is reported to be a probable regulatory risk variant for mild cognitive impairment in Parkinson’s disease [ 29 ]. rs7364180 is…”
Tao, Y., Wan, G., & Zhou, X. (2025) · BMC Medical Imaging · doi:10.1186/s12880-025-01782-2
“In severe cases, personality changes will occur [2].”
Epigenetics and pharmacoepigenetics of age-related neurodegenerative disorders
Cacabelos, R., Martínez-Iglesias, O. A., Cacabelos, N., Cacabelos, P., & Naidoo, V. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-18722-3.00032-3
Tanegashima, T., Shiota, M., Akamatsu, S., Miyake, H., Takahashi, M., Oya, M., Tsuchiya, N., Masumori, N., Matsuyama, H., Obara, W., et al. (2025) · Cancer Science · doi:10.1111/cas.70204
“For instance, SNPs in promoter or enhancer regions can alter transcription factor binding sites, thereby modulating gene [27, 28].”
Gamache, J., Gingerich, D. C., Shwab, E. K., Barrera, J., Garrett, M. E., Hume, C., Crawford, G. E., Ashley‐Koch, A. E., & Chiba‐Falek, O. (2024) · Figshare · doi:10.6084/m9.figshare.26622151
Gamache, J., Gingerich, D. C., Shwab, E. K., Barrera, J., Garrett, M. E., Hume, C., Crawford, G. E., Ashley‐Koch, A. E., & Chiba‐Falek, O. (2024) · Figshare · doi:10.6084/m9.figshare.26622154
Ji, J., Li, Z., Xing, A., Luo, G., Zhai, X., Xu, W., Li, J., Tan, T., Jia, R., Yan, Y., et al. (2026) · Brain Imaging and Behavior · doi:10.1007/s11682-026-01114-0
“The poten - tial mechanisms may include the following aspects: affecting enhancer function and disrupting the expression of distant genes (Kikuchi et al., 2019 ); genetic variations that affect promoter-enhancer interactions (Giambartolomei et al., 2021); genetic variations that change 3D genome…”
Dissection of coordinated interferon response by promoter elements with enhancer activity
Santiago‐Algarra, D. (2020) · theses.fr (ABES) · doi:10.70675/a064ee17zbea2z412azb9b6z12839deb2851
The role of the serum response factor in the pathogenesis of Huntington's disease
Awada, A. (2021) · theses.fr (ABES) · doi:10.70675/fabef4b5z9d78z440bz99f2z9be7d0814a03
Développement d’un test sanguin pour le dépistage de la maladie d’Alzheimer
Torres, F. D. (2022) · theses.fr (ABES) · doi:10.70675/afbb97a5z1636z46abz844czb340616aeda1
Species-wide survey of transcript and protein abundance variation in yeast
Teyssonnière, É. (2023) · theses.fr (ABES) · doi:10.70675/9c5f617cz3bcfz4064z9e8eza0b9f5bca082
The impact of 3D chromatin remodeling on cellular senescence and aging
Chen, W., Zhan, Q., Xiao, F., Ju, Z., & Chen, Z. (2025) · Aging Research · doi:10.26599/agr.2025.9340072
Apolipoprotein E (ApoE): Structural insights, functionality, and genetic variability
Viswalingam, V., Kumar, D., Tewari, S., & Ganeshpurkar, A. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-40563-1.00001-6
Viswalingam, V., & Kumar, D. (2026) · Springer eBooks · doi:10.1007/978-3-032-27324-6_8
- Authors
- Kikuchi, M., Hara, N., Hasegawa, M., Miyashita, A., Kuwano, R., Ikeuchi, T., & Nakaya, A.
- Affiliations
- The University of Osaka (JP)
- Brain Research Institute, Niigata University (JP)
- Corresponding author
- Kikuchi, Masataka
- Nakaya, Akihiro
- Venue
- BMC Medical Genomics
- Published
- 2019-09-09
- Volume
- 12
- Issue
- 1
- Pages
- 128-128
- ISSN
- 1755-8794 (ISSN-L)
- PMC
- PMC6734281
- OpenAlex
- W2972895026
- Cited by
- 74
- References
- 72
- Citations by year
- 2026: 3 · 2025: 12 · 2024: 6 · 2023: 11 · 2022: 15 · 2021: 15 · 2020: 12
- FWCI
- 7.20
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- BMC Medical Genomics (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- Japan Science and Technology Agency (16K07222, 17K15049, 22129004, 24310144, 24651221)
- Japan Agency for Medical Research and Development (An Integrated Database of Clinical and Genomic Information, A Research and Development Grants for Dementia)
- Topics
- Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Keywords
- Alzheimer's disease, enhancer variants, chromatin looping, topologically associating domains, expression quantitative trait loci, amyloid-beta clearance, synaptic transmission, immune response, transcription factor regulation, enhancer-promoter interactions
- MeSH terms
- CCCTC-Binding Factor, Alzheimer Disease, Binding Sites, Chromatin, Enhancer Elements, Genetic, Gene Expression Regulation, Humans, Nucleic Acid Conformation, Genetic Variation, Polymorphism, Single Nucleotide, Quantitative Trait Loci
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