SingleBrain: A Meta-Analysis of Single-Nucleus eQTLs Linking Genetic Risk to Brain Disorders
Open accessAbstract
Most genetic risk variants for neurological diseases are located in non-coding regulatory regions, where they may often act as expression quantitative trait loci (eQTLs), modulating gene expression and influencing disease susceptibility. However, eQTL studies in bulk brain tissue or specific cell types lack the resolution to capture the brain’s cellular diversity. Single-nucleus RNA sequencing (snRNA-seq) offers high-resolution mapping of eQTLs across diverse brain cell types. Here, we performed a meta-analysis, “SingleBrain,” integrating publicly available snRNA-seq and genotype data from four cohorts, totaling 5.8 million nuclei from 983 individuals. We mapped cis -eQTLs across major brain cell types and subtypes and employed statistical colocalization and Mendelian randomization to identify genes mediating neurological disease risk. We observed up to a 10-fold increase in cis -eQTLs compared to previous studies and uncovered novel cell type-specific genes linked to Alzheimer’s disease, Parkinson’s disease, and schizophrenia that were previously undetectable in bulk tissue analyses. Additionally, we prioritized putative causal variants for each locus through fine-mapping and integration with cell type-specific enhancer and promoter regulatory elements. SingleBrain represents a comprehensive single-cell eQTL resource, advancing insights into the genetic regulation of brain disorders.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The directional effect of the AD risk allele of rs10792832 on PICALM expression was consistent with other hMG eQTL findings.
“The directional effect of the AD risk allele of rs10792832 on PICALM expression was consistent with other hMG eQTL findings8,9,25.”
From PICALM Alzheimer’s risk allele causes aberrant lipid droplets in microglia (2025) · cites this paper as [25]
The Results section discusses AD-associated microglial eQTLs, but the passages found do not mention rs10792832 or PICALM. Searches for both terms and for a directional comparison with other human microglial eQTL findings found no passage that addresses the claim. (LLM, conf=0.72)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
PLINK: a tool set for whole-genome association and population-based linkage analyses.
Purcell, S., Neale, B., Todd-Brown, K., Thomas, L., Ferreira, M. A. R., Bender, D., Maller, J., Sklar, P., de Bakker, P. I. W., Daly, M. J., et al. (2007) · The American Journal of Human Genetics · doi:10.1086/519795 · Cited by 34,089 · see citing works
A global reference for human genetic variation
The 1000 Genomes Project Consortium, authors, C., Auton, A., Abecasis, G. R., Steering committee, Altshuler, D. M., Durbin, R. M., Abecasis, G. R., Bentley, D. R., Chakravarti, A., et al. (2015) · Nature · doi:10.1038/nature15393 · Cited by 18,247 · see citing works
Fast, sensitive and accurate integration of single-cell data with Harmony
Korsunsky, I., Millard, N., Fan, J., Slowikowski, K., Zhang, F., Wei, K., Baglaenko, Y., Brenner, M., Loh, P.-R., & Raychaudhuri, S. (2019) · Nature Methods · doi:10.1038/s41592-019-0619-0 · Cited by 10,488 · see citing works
Bowden, J., Davey Smith, G., & Burgess, S. (2015) · International Journal of Epidemiology · doi:10.1093/ije/dyv080 · Cited by 9,816 · see citing works
Bowden, J., Davey Smith, G., Haycock, P. C., & Burgess, S. (2016) · Genetic Epidemiology · doi:10.1002/gepi.21965 · Cited by 8,922 · see citing works
SCANPY: large-scale single-cell gene expression data analysis
Wolf, F. A., Angerer, P., & Theis, F. J. (2018) · Genome Biology · doi:10.1186/s13059-017-1382-0 · Cited by 8,881 · see citing works
GCTA: a tool for genome-wide complex trait analysis
Yang, J., Lee, S. H., Goddard, M. E., & Visscher, P. M. (2011) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2010.11.011 · Cited by 8,279 · see citing works
The MR-Base platform supports systematic causal inference across the human phenome
Hemani, G., Zheng, J., Elsworth, B., Wade, K. H., Haberland, V., Baird, D., Laurin, C., Burgess, S., Bowden, J., Langdon, R., et al. (2018) · eLife · doi:10.7554/elife.34408 · Cited by 7,959 · see citing works
The GTEx Consortium atlas of genetic regulatory effects across human tissues
The GTEx Consortium, Aguet, F., Anand, S., Ardlie, K. G., Gabriel, S., Getz, G. A., Graubert, A., Hadley, K., Handsaker, R. E., Huang, K. H., et al. (2020) · Science · doi:10.1126/science.aaz1776 · Cited by 5,547 · see citing works
Software for Computing and Annotating Genomic Ranges
Lawrence, M. F., Huber, W. K., Pagès, H., Aboyoun, P., Carlson, M. R. J., Gentleman, R. C., Morgan, M. T., & Carey, V. J. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003118 · Cited by 4,438 · see citing works
Genetic effects on gene expression across human tissues
GTEx Consortium (2017) · Nature · doi:10.1038/nature24277 · Cited by 4,036 · see citing works
Robust inference in summary data Mendelian randomization via the zero modal pleiotropy assumption
Hartwig, F. P., Davey Smith, G., & Bowden, J. (2017) · International Journal of Epidemiology · doi:10.1093/ije/dyx102 · Cited by 3,485 · see citing works
Mapping genomic loci implicates genes and synaptic biology in schizophrenia
Trubetskoy, V., Pardiñas, A. F., Qi, T., Panagiotaropoulou, G., Awasthi, S., Bigdeli, T. B., Bryois, J., Chen, C.-Y., Dennison, C. A., Hall, L. S., et al. (2022) · Nature · doi:10.1038/s41586-022-04434-5 · Cited by 2,964 · see citing works
Self-citationAlzheimer Disease Genetics Consortium (ADGC), Kunkle, B. W., The European Alzheimer’s Disease Initiative (EADI), Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium (CHARGE), Genetic and Environmental Risk in AD/Defining Genetic, P. A. E. R. F. A. D. C. (., Grenier-Boley, B., Sims, R., Bis, J. C., Damotte, V., Naj, A. C., et al. (2019) · Nature Genetics · doi:10.1038/s41588-019-0358-2 · Cited by 2,946 · see citing works
Scrublet: computational identification of cell doublets in single-cell transcriptomic data
Wolock, S. L., Lopez, R., & Klein, A. M. (2019) · Cell Systems · doi:10.1016/j.cels.2018.11.005 · Cited by 2,758 · 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
Nalls, M. A., Blauwendraat, C., Vallerga, C. L., Heilbron, K., Bandres-Ciga, S., Chang, D., Tan, M., Kia, D. A., Noyce, A. J., Xue, A., et al. (2019) · The Lancet Neurology · doi:10.1016/s1474-4422(19)30320-5 · Cited by 2,454 · 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
Microglia states and nomenclature: A field at its crossroads
Paolicelli, R. C., Sierra, A., Stevens, B., Tremblay, M.-E., Aguzzi, A., Ajami, B., Amit, I., Audinat, E., Bechmann, I., Bennett, M., et al. (2022) · Neuron · doi:10.1016/j.neuron.2022.10.020 · Cited by 1,973 · see citing works
Self-citationMullins, N., Forstner, A. J., O’Connell, K. S., Coombes, B., Coleman, J. R. I., Qiao, Z., Als, T. D., Bigdeli, T. B., Børte, S., Bryois, J., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00857-4 · Cited by 1,517 · see citing works
Self-citationMultiple sclerosis genomic map implicates peripheral immune cells and microglia in susceptibility
International Multiple Sclerosis Genetics Consortium, Patsopoulos, N. A., Baranzini, S. E., Santaniello, A., Shoostari, P., Cotsapas, C., Wong, G., Beecham, A. H., James, T., Replogle, J., et al. (2019) · Science · doi:10.1126/science.aav7188 · Cited by 1,471 · see citing works
Gene expression elucidates functional impact of polygenic risk for schizophrenia
Fromer, M., Roussos, P., Sieberts, S. K., Johnson, J. S., Kavanagh, D. H., Perumal, T. M., Ruderfer, D. M., Oh, E. C., Topol, A., Shah, H. R., et al. (2016) · Nature Neuroscience · doi:10.1038/nn.4399 · Cited by 1,091 · see citing works
Self-citationA structural variation reference for medical and population genetics
Collins, R. L., Brand, H., Karczewski, K. J., Zhao, X., Alföldi, J., Francioli, L. C., Khera, A. V., Lowther, C., Gauthier, L. D., Wang, H., et al. (2020) · Nature · doi:10.1038/s41586-020-2287-8 · Cited by 1,037 · see citing works
Brain cell type–specific enhancer–promoter interactome maps and disease <b>-</b> risk association
Nott, A., Holtman, I. R., Coufal, N. G., Schlachetzki, J. C. M., Yu, M., Hu, R., Han, C. Z., Pena, M., Xiao, J., Wu, Y., et al. (2019) · Science · doi:10.1126/science.aay0793 · Cited by 823 · see citing works
Common Regulatory Variation Impacts Gene Expression in a Cell Type–Dependent Manner
Dimas, A. S., Deutsch, S., Stranger, B. E., Montgomery, S. B., Borel, C., Attar, H., Ingle, C., Beazley, C., Arcelus, M. G., Sekowska, M., et al. (2009) · Science · doi:10.1126/science.1174148 · Cited by 746 · see citing works
GWAS on family history of Alzheimer’s disease
Marioni, R. E., Harris, S. E., Zhang, Q., McRae, A. F., Hagenaars, S. P., Hill, W. D., Davies, G., Ritchie, C. W., Gale, C. R., Starr, J. M., et al. (2018) · Translational Psychiatry · doi:10.1038/s41398-018-0150-6 · Cited by 564 · see citing works
van Rheenen, W., van der Spek, R. A. A., Bakker, M. K., van Vugt, J. J. F. A., Hop, P. J., Zwamborn, R. A. J., de Klein, N., Westra, H.-J., Bakker, O. B., Deelen, P., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00973-1 · Cited by 542 · see citing works
Stegle, O., Parts, L., Durbin, R., & Winn, J. (2010) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000770 · Cited by 497 · see citing works
Mathys, H., Peng, Z., Boix, C. A., Victor, M. B., Leary, N., Babu, S., Abdelhady, G., Jiang, X., Ng, A. P., Ghafari, K., et al. (2023) · Cell · doi:10.1016/j.cell.2023.08.039 · Cited by 495 · see citing works
Human microglial state dynamics in Alzheimer’s disease progression
Sun, N., Victor, M. B., Park, Y. P., Xiong, X., Scannail, A. N., Leary, N., Prosper, S., Viswanathan, S., Luna, X., Boix, C. A., et al. (2023) · Cell · doi:10.1016/j.cell.2023.08.037 · Cited by 462 · see citing works
An xQTL map integrates the genetic architecture of the human brain's transcriptome and epigenome
Ng, B., White, C. C., Klein, H.-U., Sieberts, S. K., McCabe, C., Patrick, E., Xu, J., Yu, L., Gaiteri, C., Bennett, D. A., et al. (2017) · Nature Neuroscience · doi:10.1038/nn.4632 · Cited by 452 · see citing works
Myers, T. A., Chanock, S. J., & Machiela, M. J. (2020) · Frontiers in Genetics · doi:10.3389/fgene.2020.00157 · Cited by 429 · see citing works
GTEx Consortium, Gamazon, E. R., Segrè, A. V., van de Bunt, M., Wen, X., Xi, H. S., Hormozdiari, F., Ongen, H., Konkashbaev, A., Derks, E. M., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0154-4 · Cited by 412 · see citing works
Integrated multimodal cell atlas of Alzheimer’s disease
Gabitto, M. I., Travaglini, K. J., Rachleff, V. M., Kaplan, E. S., Long, B., Ariza, J., Ding, Y., Mahoney, J. T., Dee, N., Goldy, J., et al. (2024) · Nature Neuroscience · doi:10.1038/s41593-024-01774-5 · Cited by 397 · cited 9× · see citing works
“For all datasets 11,12,14,15 , annotated cells and samples as poor-quality were excluded.”
InfluentialFunctionally informed fine-mapping and polygenic localization of complex trait heritability
Weissbrod, O., Hormozdiari, F., Benner, C., Cui, R., Ulirsch, J., Gazal, S., Schoech, A. P., van de Geijn, B., Reshef, Y., Márquez-Luna, C., et al. (2020) · Nature Genetics · doi:10.1038/s41588-020-00735-5 · Cited by 364 · see citing works
Mapping of long-range chromatin interactions by proximity ligation-assisted ChIP-seq
Fang, R., Yu, M., Li, G., Chee, S., Liu, T., Schmitt, A. D., & Ren, B. (2016) · Cell Research · doi:10.1038/cr.2016.137 · Cited by 344 · see citing works
Single-Nucleus RNA-Seq Is Not Suitable for Detection of Microglial Activation Genes in Humans
Thrupp, N., Sala Frigerio, C., Wolfs, L., Skene, N. G., Fattorelli, N., Poovathingal, S., Fourne, Y., Matthews, P. M., Theys, T., Mancuso, R., et al. (2020) · Cell Reports · doi:10.1016/j.celrep.2020.108189 · Cited by 329 · cited 1× · see citing works
“The lower abundance of certain genes in nuclear RNA compared to whole-cell RNA can hinder their detection in snRNA-seq data, particularly for genes with low nuclear expression or those depleted in nuclei preparation 33 .”
Bryois, J., Calini, D., Macnair, W., Foo, L., Urich, E., Ortmann, W., Iglesias, V. A., Selvaraj, S., Nutma, E., Marzin, M., et al. (2022) · Nature Neuroscience · doi:10.1038/s41593-022-01128-z · Cited by 311 · see citing works
Population-scale single-cell RNA-seq profiling across dopaminergic neuron differentiation
Jerber, J., Seaton, D. D., Cuomo, A. S. E., Kumasaka, N., Haldane, J., Steer, J., Patel, M. J., Pearce, D., Andersson, M. H. L., Bonder, M. J., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00801-6 · Cited by 288 · see citing works
Lopes, K. D. P., Snijders, G. J. L., Humphrey, J., Allan, A., Sneeboer, M. A. M., Navarro, E., Schilder, B. M., Vialle, R. A., Parks, M., Missall, R., et al. (2022) · Nature Genetics · doi:10.1038/s41588-021-00976-y · Cited by 280 · see citing works
Self-citationQuantifying genetic effects on disease mediated by assayed gene expression levels
Yao, D. W., O’Connor, L. J., Price, A. L., & Gusev, A. (2020) · Nature Genetics · doi:10.1038/s41588-020-0625-2 · Cited by 274 · see citing works
de Klein, N., Tsai, E. A., Vochteloo, M., Baird, D., Huang, Y., Chen, C.-Y., van Dam, S., Oelen, R., Deelen, P., Bakker, O. B., et al. (2023) · Nature Genetics · doi:10.1038/s41588-023-01300-6 · Cited by 272 · see citing works
A Map of Transcriptional Heterogeneity and Regulatory Variation in Human Microglia
Young, A. M. H., Kumasaka, N., Calvert, F., Hammond, T. R., Knights, A., Panousis, N., Park, J. S., Schwartzentruber, J., Liu, J., Kundu, K., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00875-2 · Cited by 213 · see citing works
Cellular communities reveal trajectories of brain ageing and Alzheimer’s disease
Green, G. S., Fujita, M., Yang, H.-S., Taga, M., Cain, A., McCabe, C., Comandante-Lou, N., White, C. C., Schmidtner, A. K., Zeng, L., et al. (2024) · Nature · doi:10.1038/s41586-024-07871-6 · Cited by 204 · see citing works
Cell subtype-specific effects of genetic variation in the Alzheimer’s disease brain
Fujita, M., Gao, Z., Zeng, L., Aubin, C., White, C. C., Ng, B., Green, G. S., Rozenblatt–Rosen, O., Phillips, D., Amir-Zilberstein, L., et al. (2024) · Nature Genetics · doi:10.1038/s41588-024-01685-y · Cited by 189 · see citing works
Single-cell genomics and regulatory networks for 388 human brains
Emani, P. S., Liu, J. J., Clarke, D., Jensen, M., Warrell, J., Gupta, C., Meng, R., Lee, C.-Y., Xu, S., Dursun, C., et al. (2024) · Science · doi:10.1126/science.adi5199 · Cited by 168 · see citing works
Self-citationGenetics of the human microglia regulome refines Alzheimer’s disease risk loci
Kosoy, R., Fullard, J. F., Zeng, B., Bendl, J., Dong, P., Rahman, S., Kleopoulos, S. P., Shao, Z., Girdhar, K., Humphrey, J., et al. (2022) · Nature Genetics · doi:10.1038/s41588-022-01149-1 · Cited by 168 · see citing works
Self-citationPedersen, B. S., Bhetariya, P. J., Brown, J., Kravitz, S. N., Marth, G., Jensen, R. L., Bronner, M. P., Underhill, H. R., & Quinlan, A. R. (2020) · Genome Medicine · doi:10.1186/s13073-020-00761-2 · Cited by 137 · see citing works
Zeng, B., Bendl, J. M., Kosoy, R., Fullard, J. F., Hoffman, G. E., & Roussos, P. (2022) · Nature Genetics · doi:10.1038/s41588-021-00987-9 · Cited by 89 · see citing works
Tuddenham, J. F., Taga, M. F., Haage, V. C., Marshe, V. S., Roostaei, T., White, C. C., Lee, A., Fujita, M., Khairallah, A., Zhang, Y., et al. (2024) · Nature Neuroscience · doi:10.1038/s41593-024-01764-7 · Cited by 78 · see citing works
Nonparametric Bayesian Estimation of Positive False Discovery Rates
Tang, Y., Subhashis Ghosal, B., & Roy, A. (2007) · Biometrics · doi:10.1111/j.1541-0420.2007.00819.x · Cited by 52 · see citing works
Systematic investigation of allelic regulatory activity of schizophrenia-associated common variants
McAfee, J. C., Lee, S., Lee, J., Bell, J., Krupa, O., Davis, J. E., Insigne, K. D., Bond, M. L., Zhao, N., Boyle, A. P., et al. (2023) · Cell Genomics · doi:10.1016/j.xgen.2023.100404 · Cited by 49 · see citing works
Self-citationZinc Finger RNA-Binding Protein Zn72D Regulates ADAR-Mediated RNA Editing in Neurons
Sapiro, A. L., Freund, E. C., Restrepo, L., Qiao, H., Bhate, A., Li, Q., Ni, J., Mosca, T. J., & Li, J. B. (2020) · Cell Reports · doi:10.1016/j.celrep.2020.107654 · Cited by 34 · see citing works
Cooler, S., & Schwartz, G. W. (2020) · Nature Neuroscience · doi:10.1038/s41593-020-00747-8 · Cited by 26 · see citing works
Humphrey, J., Brophy, E., Kosoy, R., Zeng, B., Coccia, E., Mattei, D., Ravi, A., Efthymiou, A. G., Navarro, E., Muller, B. Z., et al. (2023) · medRxiv · doi:10.1101/2023.12.01.23299073 · Cited by 5 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
PICALM Alzheimer’s risk allele causes aberrant lipid droplets in microglia
Kozlova, A., Zhang, S., Sudwarts, A., Zhang, H., Smirnou, S., Byeon, S. K., Thapa, C., Sun, X., Stephenson, K., Zhao, X., et al. (2025) · Nature · doi:10.1038/s41586-025-09486-x · Cited by 53 · see citing works
“The directional effect of the AD risk allele of rs10792832 on PICALM expression was consistent with other hMG eQTL findings 8,9,25 .”
Genomic analyses reveal new insights into Alzheimer's diseasePreprint
Uffelmann, E., Wightman, D. P., Bahrami, S., Shadrin, A. A., Fominykh, V., Ojima, T., Jiang, C., Benner, C., Moreno, E., Campos, A. I., et al. (2025) · medRxiv · doi:10.1101/2025.10.10.25337470 · Cited by 7 · see citing works
Mosaic chromosomal alterations in blood are associated with an increased risk of Alzheimer’s disease
Naito, T., Hirata, K., Jang, B., Lakhani, C. M., Buonfiglioli, A., Lee, W.-P., Valladares, O., Wang, L.-S., Okada, Y., Won, H.-H., et al. (2025) · doi:10.1101/2025.05.29.25328544 · Cited by 4 · see citing works
Self-citationCell type-specific associations with Alzheimer’s Disease conserved across racial and ethnic groupsPreprint
Luquez, T., Algoo, J., Chiu, R., Mares, J. A., Yadav, A., Lam, M., Gaur, P., Lai, X., Lee, D. I., Paryani, F., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.04.14.648597 · Cited by 3 · see citing works
Single-cell Multiome Analysis of Chromatin State and Transcriptome in the Human Basal GangliaPreprint
Chang, L., Li, K., Xie, Y., Zhong, G., Rink, J. A., Báez-Becerra, C. T., Lie, A., Indralingam, H. S., Dong, K., Loe, T. H., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.03.703645 · Cited by 4 · see citing works
Zeng, Y., Cook, N., Yang, C., Sivasankaran, S. K., Fujita, M., Gardell, Z. A., Guen, Y. L., Shigemizu, D., Ozaki, K., Morizono, T., et al. (2026) · Alzheimer s & Dementia · doi:10.1002/alz.71552 · Cited by 2 · see citing works
Kikuchi, M., Miyashita, A., Hirota, Y., Hara, N., Hasegawa, M., Sakakibara, Y., Sekiya, M., Ozaki, K., Niida, S., Morishima, M., et al. (2025) · medRxiv · doi:10.1101/2025.10.06.25336925 · Cited by 1 · see citing works
Xie, Y., Chang, L., Zhong, G., Rink, J. A., Baez-Becerra, T., Armand, E. J., Ding, W., Li, K., Bonne, É., Lie, A., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.02.703166 · Cited by 4 · see citing works
Migration genetics link excitatory neurons to ancient selection and economic growthPreprint
Casten, L. G., Tener, A. J., Elsadany, M., Yang, J. S., Strang, J. F., & Michaelson, J. J. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.05.703995
Das, A., Lakhani, C. M., Mazeeva, V. M., Raj, T., & Knowles, D. A. (2026) · medRxiv · doi:10.64898/2026.06.15.26354742
Self-citationtraceCB: Trans-ancestry cell-type-specific eQTLs mapping by integrating scRNA-seq and bulk dataPreprint
Jiang, W., Xiao, J., & Cai, M. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.06.20.733502
- Authors
- Jang, B., Kailash, B., Tokolyi, A., Cuddleston, W. H., Ravi, A., Jung, S.-H., Naito, T., Kim, B., Kim, M. S., Cho, M., Park, M.-S., Rosen, M., Blanchard, J., Humphrey, J., Knowles, D. A., Won, H.-H., & Raj, T.
- Affiliations
- Samsung Medical Center (KR)
- New York Genome Center (US)
- Icahn School of Medicine at Mount Sinai (US)
- University of California, San Francisco (US)
- Kangwon National University (KR)
- Broad Institute (US)
- Corresponding author
- Won, Hong-Hee
- Raj, Towfique
- ORCID
- Venue
- medRxiv
- Publisher
- openRxiv
- Published
- 2025-03-07
- PMC
- PMC11908325
- OpenAlex
- W4408216097
- Cited by
- 11
- References
- 55
- Citations by year
- 2026: 6 · 2025: 5
- Type
- posted content
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- medRxiv (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (submittedVersion, cc-by-nc-nd)
- PubMed Central (submittedVersion, cc-by-nc-nd)
- Funders
- Georgia Clinical and Translational Science Alliance (UL1TR004419)
- National Research Foundation
- National Research Foundation of Korea (RS-2023-00223277)
- Ministry of Science and ICT, South Korea (RS‐2023‐00223277)
- National Institutes of Health (1r21ag063130-01a1, 1s10od026880-01, 1rf1ag065926-01a1, 1r56ag055824-01a1, 1s10od030463-01, AG066514, NIA R01-AG054005, RF1-AG065926, UL1TR004419)
- Samsung
- National Institute on Aging (U01AG058635, R21-AG063130, U01AG068880, NIA R21-AG063130, R01AG054005, R56- AG055824, RF1-AG065926, P30AG066514)
- National Institute of Neurological Disorders and Stroke (U54NS123743, R01NS116006)
- National Center for Advancing Translational Sciences (S10OD026880, S10OD030463)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Keywords
- single-nucleus RNA sequencing, expression quantitative trait loci, cis-eQTL, brain cell types, Alzheimer's disease, Parkinson's disease, schizophrenia, non-coding regulatory variants, colocalization, Mendelian randomization, fine mapping
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 ›
Are you an author, or do you see a problem with this page? Request a review
