Genome-wide meta-analysis, fine-mapping and integrative prioritization implicate new Alzheimer’s disease risk genes
Open accessPublished version of a preprint
Abstract
Genome-wide association studies have discovered numerous genomic loci associated with Alzheimer's disease (AD); yet the causal genes and variants are incompletely identified. We performed an updated genome-wide AD meta-analysis, which identified 37 risk loci, including new associations near CCDC6, TSPAN14, NCK2 and SPRED2. Using three SNP-level fine-mapping methods, we identified 21 SNPs with >50% probability each of being causally involved in AD risk and others strongly suggested by functional annotation. We followed this with colocalization analyses across 109 gene expression quantitative trait loci datasets and prioritization of genes by using protein interaction networks and tissue-specific expression. Combining this information into a quantitative score, we found that evidence converged on likely causal genes, including the above four genes, and those at previously discovered AD loci, including BIN1, APH1B, PTK2B, PILRA and CASS4.
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?
Nine loci (APP, CCDC6, GRN, LILRB2, NCK2, TNIP1, TMEM106B, TSPAN14 and SHARPIN) were recently reported in three articles using part of the GWAS data included in the study.
“Nine of these loci (APP, CCDC6, GRN, LILRB2, NCK2, TNIP1, TMEM106B, TSPAN14 and SHARPIN) were recently reported in three articles using part of the GWAS data included in our study11–13.”
From New insights into the genetic etiology of Alzheimer’s disease and related dementias (2022) · cites this paper as [11]
The Results section (“Meta-analysis discovers 37 loci associated with Alzheimer’s disease risk”; page unavailable) identifies several listed loci and describes overlap with earlier GWAS reports. It does not establish that all nine named loci were reported in three articles using part of the citing study’s GWAS data. (LLM, conf=0.78)
Experimental — accuracy not yet measuredMicroglia are implicated in the pathology and genetic risk of several central nervous system diseases, including Alzheimer's disease.
“Microglia, the resident immune cells of the brain, are implicated in the pathology and genetic risk of several CNS diseases, including AD.46,47,48,49”
From hdWGCNA identifies co-expression networks in high-dimensional transcriptomics data (2023) · cites this paper as [48]
The Results section (‘AD risk is enriched near genes with high microglial gene expression’; page not provided) reports enrichment of Alzheimer’s disease risk near genes highly expressed in microglia. The paper also discusses a possible role for microglial activation in AD, but the passages do not extend that finding to several CNS diseases. (LLM, conf=0.88)
Experimental — accuracy not yet measuredIndependent association signals detected in the CD33, HLA, PICALM and RHOH loci are new compared to refs. 4-6,9.
“Compared to refs. 4–6,9, the independent association signals detected in the CD33, HLA, PICALM and RHOH loci are new”
From Consensus meta-analysis of genome-wide association studies for Alzheimer’s disease and related dementias (2026) · cites this paper as [9]
The Results section ('Meta-analysis discovers 37 loci associated with Alzheimer’s disease risk'; page unavailable) reports independent signals at several loci, supporting the general finding. It does not identify CD33, HLA, PICALM and RHOH as loci with new independent signals or establish their novelty relative to refs. 4–6,9. The Discussion’s four novel associations concern different loci and do not resolve that comparison. (LLM, conf=0.82)
Experimental — accuracy not yet measuredSeveral AD GWAS loci implicate B cell relevant genes including SCIMP, BLNK, PLCG2, HLA class II region and IGHG1.
“Indeed, several AD GWAS loci implicate B cell relevant genes as well, albeit with varying strength of evidence and confidence in causal gene assignment. These include SCIMP , BLNK , PLCG2 , HLA class II region and IGHG1 [26 -32].”
From Is there a role for B cells in Alzheimer's disease? (2026) · cites this paper as [29]
The Results (pages unavailable) identify PLCG2 among AD genes supported by network scores and report an AD-associated locus near IGHG1. The Online Methods also discuss an HLA locus, but the passages do not identify it as a class II signal. Our copy does not substantiate the inclusion of SCIMP or BLNK, or characterize these loci as B-cell-relevant. (LLM, conf=0.85)
Experimental — accuracy not yet measuredHLA class II locus encompassing HLA-DRB1/HLA-DQA1 is a well established AD risk locus, although extensive linkage disequilibrium makes it difficult to pin down the specific causal gene.
“The HLA class II locus encom passing HLA-DRB1/HLA-DQA1 is also a well established AD risk locus, although extensive linkage disequilibrium across this region makes it difficult to pin down the specific causal gene.”
From Is there a role for B cells in Alzheimer's disease? (2026) · cites this paper as [29]
The Online Methods treat HLA as an AD risk locus, and Extended Data Fig. 2 cautions that its fine-mapping results should be interpreted carefully. But our copy does not identify the locus as encompassing HLA-DRB1/HLA-DQA1 or say that extensive LD across that region prevents identification of the causal gene; the fine-mapping discussion instead points to sensitivity to the reference panel. (LLM, conf=0.84)
Experimental — accuracy not yet measured
27 of 36 AD risk loci colocalized with expression quantitative trait loci in multiple tissues.
“a genome-wide fine-mapping study revealed that 27 of 36 AD risk loci colocalized with expression quantitative trait loci in multiple tissues [50].”
From Association of SPI1 haplotypes with altered SPI1 gene expression and Alzheimer’s disease risk (2022) · cites this paper as [50]
Two-sample MR using 36 AD risk polymorphisms as instruments and CHIP as the outcome found no evidence of a causal effect of AD on CHIP (OR = 0.97, P = 0.26).
“we also performed two-sample MR using 36 polymorphisms for risk of AD as the instruments18 and CHIP as the outcome, which found no evidence of a causal effect in this direction (OR = 0.97 per 1 log-odds increase in risk of AD, P = 0.26 using the weighted median estimator).”
From Clonal hematopoiesis is associated with protection from Alzheimer’s disease (2023) · cites this paper as [18]
IGHG1 is less well established as a causal AD gene and is more accurately described as residing within an AD associated locus rather than as a validated risk gene.
“IGHG1 , in contrast, is less well established as a causal AD gene and is more accurately described as residing within an AD associated locus rather than as a validated risk gene.”
From Is there a role for B cells in Alzheimer's disease? (2026) · cites this paper as [29]
If missing in Bellenguez et al., 2022 summary statistics, the Schwartzentruber et al., 2021 summary statistics were used.
“If missing in the Bellenguez et al., 2022 summary statistics, the Schwartzentruber et al., 2021 summary statistics were used.”
From A genetic and proteomic comparison of key AD biomarkers across tissues (2024) · cites this paper as Schwartzentruber et al., 2021
The analyses nominate alternative candidates at three AD risk loci: CR2 at 1q32.2, PILRA at 7q22.1, and PRSS8 at 16p11.2.
“Our analyses also nominate alternative candidates at three AD risk loci. First, the association at 1q32.2 has been linked to CR153–55, but we observed a colocalized association for both CR1 and CR2 levels with AD risk (Supp. Fig. 23).”
From Proteogenomic analysis of human cerebrospinal fluid identifies neurologically relevant regulation and implicates causal proteins for Alzheimer’s disease (2024) · cites this paper as [59]
In Alzheimer's disease, many genetic loci have been identified, but for some the functional gene is not clear.
“In AD, many genetic loci have been identified, but for some the functional gene is not clear23,59.”
From Proteogenomic analysis of human cerebrospinal fluid identifies neurologically relevant regulation and implicates causal proteins for Alzheimer’s disease (2024) · cites this paper as [59]
MR using 24 CHIP-associated polymorphisms found that higher genetic risk of CHIP was associated with reduced odds of AD using the weighted median estimator (OR = 0.90 per 1 log-odds increase, P = 3.3 × 10−4).
“One- and two-sample MR using 24 CHIP-associated polymorphisms18 as the instruments (Supplementary Table 7) found that higher genetic risk of CHIP was associated with reduced odds of AD using the weighted median estimator (OR = 0.90 per 1 log-odds increase in risk of CHIP, P = 3.3 × 10−4, Fig. 2c).”
From Clonal hematopoiesis is associated with protection from Alzheimer’s disease (2023) · cites this paper as [18]
The Results section reports an Alzheimer’s disease GWAS meta-analysis, and Online Methods describes fine-mapping and AD meta-analysis, but neither reports the claimed CHIP Mendelian-randomization result. Searches for CHIP, Mendelian randomization, the weighted-median estimate, and the stated P value found no passage addressing it. (LLM, conf=0.76)
Experimental — accuracy not yet measuredSCIMP is expressed across myeloid and B cell lineages and has been identified as an AD-associated locus.
“SCIMP is expressed accross myeloid and B cell lineages and has been identified as an AD-associated locus.”
From Is there a role for B cells in Alzheimer's disease? (2026) · cites this paper as [29]
The Results sections on AD risk loci and gene-expression colocalization discuss other genes, but the passages found do not identify SCIMP as an AD-associated locus or describe its expression in myeloid and B-cell lineages. Searches for “SCIMP” and for those cell lineages found no passage addressing the claim. (LLM, conf=0.72)
Experimental — accuracy not yet measuredBLNK is a central adaptor downstream of BCR, and PLCG2 is a major BCR proximal signaling node implicated in germinal center and memory B cell formation.
“Two further loci provide more direct mechanistic links to B cell receptor (BCR) signaling: BLNK is a central adaptor downstream of the BCR, and PLCG2 is a major BCR proximal signaling node implicated in germinal center and memory B cell formation.”
From Is there a role for B cells in Alzheimer's disease? (2026) · cites this paper as [29]
The Results section on network evidence names PLCG2 as an Alzheimer’s disease gene with a high network score, but does not describe its role in B-cell receptor signaling. Searches for BLNK, BCR signaling, and germinal-center or memory B-cell formation found no passage addressing the claimed mechanisms in our copy. (LLM, conf=0.76)
Experimental — accuracy not yet measuredLinked peaks identified in microglia were significantly enriched for heritability of AD across five different studies.
“Consistent with previous studies,55,56 linked peaks identified in microglia were significantly enriched for heritability of AD across five different studies2,3,4,5,6”
From Single nucleus multiomics identifies ZEB1 and MAFB as candidate regulators of Alzheimer’s disease-specific cis-regulatory elements (2023) · cites this paper as [4]
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
Fast and accurate short read alignment with Burrows-Wheeler transform
Li, H., & Durbin, R. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp324 · Cited by 58,607 · see citing works
BEDTools: a flexible suite of utilities for comparing genomic features
Quinlan, A. R., & Hall, I. M. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq033 · Cited by 27,437 · see citing works
Regularization Paths for Generalized Linear Models via Coordinate Descent
Friedman, J., Hastie, T., & Tibshirani, R. (2010) · Journal of Statistical Software · doi:10.18637/jss.v033.i01 · Cited by 19,927 · see citing works
Model-based Analysis of ChIP-Seq (MACS)
Zhang, Y., Liu, T., Meyer, C. A., Eeckhoute, J., Johnson, D. S., Bernstein, B. E., Nusbaum, C., Myers, R. M., Brown, M., Li, W., et al. (2008) · Genome Biology · doi:10.1186/gb-2008-9-9-r137 · Cited by 18,386 · see citing works
Integrating single-cell transcriptomic data across different conditions, technologies, and species
Butler, A., Hoffman, P., Smibert, P., Papalexi, E., & Satija, R. (2018) · Nature Biotechnology · doi:10.1038/nbt.4096 · Cited by 13,174 · see citing works
Fast and accurate long-read alignment with Burrows-Wheeler transform.
Li, H., & Durbin, R. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btp698 · Cited by 12,063 · see citing works
The igraph software package for complex network research
Csárdi, G., & Nepusz, T. (2006) · Medical Entomology and Zoology · Cited by 10,078 · see citing works
The UK Biobank resource with deep phenotyping and genomic data
Bycroft, C., Freeman, C., Petkova, D., Band, G., Elliott, L. T., Sharp, K., Motyer, A., Vukcevic, D., Delaneau, O., O’Connell, J., et al. (2018) · Nature · doi:10.1038/s41586-018-0579-z · Cited by 8,876 · see citing works
Self-citationThe Ensembl Variant Effect Predictor
McLaren, W., Gil, L., Hunt, S. E., Riat, H. S., Ritchie, G. R. S., Thormann, A., Flicek, P., & Cunningham, F. (2016) · Genome Biology · doi:10.1186/s13059-016-0974-4 · Cited by 8,099 · see citing works
Szklarczyk, D., Morris, J. H., Cook, H., Kuhn, M., Wyder, S., Simonovic, M., Santos, A., Doncheva, N. T., Roth, A., Bork, P., et al. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkw937 · Cited by 6,980 · see citing works
Integrative 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
LD Score regression distinguishes confounding from polygenicity in genome-wide association studies
Schizophrenia Working Group of the Psychiatric Genomics Consortium, Bulik-Sullivan, B. K., Loh, P.-R., Finucane, H. K., Ripke, S., Yang, J., Patterson, N., Daly, M. J., Price, A. L., & Neale, B. M. (2015) · Nature Genetics · doi:10.1038/ng.3211 · Cited by 5,559 · 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
Raudvere, U., Kolberg, L., Kuzmin, I., Arak, T., Adler, P., Peterson, H., & Vilo, J. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz369 · Cited by 5,045 · 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 · see citing works
Giambartolomei, C., Vukcevic, D., Schadt, E. E., Franke, L., Hingorani, A. D., Wallace, C., & Plagnol, V. (2014) · PLoS Genetics · doi:10.1371/journal.pgen.1004383 · Cited by 4,261 · see citing works
Robust relationship inference in genome-wide association studies
Manichaikul, A., Mychaleckyj, J. C., Rich, S. S., Daly, K., Sale, M., & Chen, W.-M. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq559 · Cited by 3,541 · see citing works
<i>TREM2</i> Variants in Alzheimer's Disease
Guerreiro, R., Wojtas, A., Bras, J., Carrasquillo, M., Rogaeva, E., Majounie, E., Cruchaga, C., Sassi, C., Kauwe, J. S. K., Younkin, S., et al. (2013) · New England Journal of Medicine · doi:10.1056/nejmoa1211851 · Cited by 3,020 · see citing works
Alzheimer 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
Predicting splicing from primary sequence with deep learning
Jaganathan, K., Kyriazopoulou Panagiotopoulou, S., McRae, J. F., Darbandi, S. F., Knowles, D., Li, Y. I., Kosmicki, J. A., Arbelaez, J., Cui, W., Schwartz, G. B., et al. (2019) · Cell · doi:10.1016/j.cell.2018.12.015 · Cited by 2,853 · see citing works
Liu, J. Z., van Sommeren, S., Huang, H., Ng, S. C., Alberts, R., Takahashi, A., Ripke, S., Lee, J. C., Jostins, L., Shah, T., et al. (2015) · Nature Genetics · doi:10.1038/ng.3359 · Cited by 2,603 · see citing works
Self-citationVariant of <i>TREM2</i> Associated with the Risk of Alzheimer's Disease
Jonsson, T., Stefansson, H., Steinberg, S., Jonsdottir, I., Jonsson, P. V., Snaedal, J., Bjornsson, S., Huttenlocher, J., Levey, A. I., Lah, J. J., et al. (2013) · New England Journal of Medicine · doi:10.1056/nejmoa1211103 · Cited by 2,558 · see citing works
An atlas of active enhancers across human cell types and tissues
The FANTOM Consortium, Andersson, R., Gebhard, C., Miguel-Escalada, I., Hoof, I., Bornholdt, J., Boyd, M., Chen, Y., Zhao, X., Schmidl, C., et al. (2014) · Nature · doi:10.1038/nature12787 · Cited by 2,550 · see citing works
Schizophrenia risk from complex variation of complement component 4
Sekar, A., Bialas, A. R., de Rivera, H., Davis, A., Hammond, T. R., Kamitaki, N., Tooley, K., Presumey, J., Baum, M., Van Doren, V., et al. (2016) · Nature · doi:10.1038/nature16549 · Cited by 2,394 · see citing works
Predicting effects of noncoding variants with deep learning–based sequence model
Zhou, J., & Troyanskaya, O. G. (2015) · Nature Methods · doi:10.1038/nmeth.3547 · Cited by 2,338 · 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
Transcriptome and genome sequencing uncovers functional variation in humans
The Geuvadis Consortium, Lappalainen, T., Sammeth, M., Friedländer, M. R., ‘t Hoen, P. A. C., Monlong, J., Rivas, M. A., Gonzàlez-Porta, M., Kurbatova, N., Griebel, T., et al. (2013) · Nature · doi:10.1038/nature12531 · Cited by 1,888 · see citing works
The MIntAct project—IntAct as a common curation platform for 11 molecular interaction databases
Orchard, S. E., Ammari, M. G., Aranda, B., Breuza, L., Briganti, L., Broackes-Carter, F. C., Campbell, N. H., Chavali, G. B., Chen, C. C. L., del‐Toro, N., et al. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt1115 · Cited by 1,835 · see citing works
Conserved cell types with divergent features in human versus mouse cortex
Hodge, R. D., Bakken, T. E., Miller, J. A., Smith, K. A., Barkan, E. R., Graybuck, L. T., Close, J. L., Long, B., Johansen, N., Penn, O., et al. (2019) · Nature · doi:10.1038/s41586-019-1506-7 · Cited by 1,775 · see citing works
Identifying a High Fraction of the Human Genome to be under Selective Constraint Using GERP++
Davydov, E. V., Goode, D. L., Sirota, M., Cooper, G. M., Sidow, A., & Batzoglou, S. (2010) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1001025 · Cited by 1,757 · see citing works
Genetic Investigation of ANthropometric Traits (GIANT) Consortium, Yang, J., DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium, Ferreira, T., Morris, A. P., Medland, S. E., Madden, P. A. F., Heath, A. C., Martin, N. G., Montgomery, G. W., et al. (2012) · Nature Genetics · doi:10.1038/ng.2213 · Cited by 1,712 · 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 · see citing works
Efficient Bayesian mixed-model analysis increases association power in large cohorts
Loh, P.-R., Tucker, G., Bulik-Sullivan, B. K., Vilhjálmsson, B. J., Finucane, H. K., Salem, R. M., Chasman, D. I., Ridker, P. M., Neale, B. M., Berger, B., et al. (2015) · Nature Genetics · doi:10.1038/ng.3190 · Cited by 1,507 · see citing works
<i>FTO</i> Obesity Variant Circuitry and Adipocyte Browning in Humans
Claussnitzer, M., Dankel, S. N., Kim, K.-H., Quon, G., Meuleman, W., Haugen, C., Glunk, V., Sousa, I. S., Beaudry, J. L., Puviindran, V., et al. (2015) · New England Journal of Medicine · doi:10.1056/nejmoa1502214 · Cited by 1,332 · see citing works
Self-citationAn environment-dependent transcriptional network specifies human microglia identity
Gosselin, D., Skola, D., Coufal, N. G., Holtman, I. R., Schlachetzki, J. C. M., Sajti, E., Jaeger, B. N., O’Connor, C., Fitzpatrick, C., Pasillas, M. P., et al. (2017) · Science · doi:10.1126/science.aal3222 · Cited by 1,220 · see citing works
ARUK Consortium, Sims, R., GERAD/PERADES, C., van der Lee, S. J., Naj, A. C., Bellenguez, C., Badarinarayan, N., Jakobsdottir, J., Kunkle, B. W., Boland, A., et al. (2017) · Nature Genetics · doi:10.1038/ng.3916 · Cited by 1,126 · see citing works
A meta-analysis of genome-wide association studies identifies 17 new Parkinson's disease risk loci
Chang, D., Nalls, M. A., Hallgrímsdóttir, I. B., Hunkapiller, J., van der Brug, M., Cai, F., International Parkinson's Disease Genomics Consortium, 23andMe Research Team, Kerchner, G. A., Ayalon, G., et al. (2017) · Nature Genetics · doi:10.1038/ng.3955 · Cited by 1,112 · see citing works
The BioGRID interaction database: 2017 update
Chatr‐aryamontri, A., Oughtred, R., Boucher, L., Rust, J., Chang, C., Kolas, N. K., O’Donnell, L., Oster, S. K., Theesfeld, C. L., Sellam, A., et al. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw1102 · Cited by 960 · see citing works
Impact of Genetic Polymorphisms on Human Immune Cell Gene Expression
Schmiedel, B. J., Singh, D., Madrigal, A., Valdovino-Gonzalez, A. G., White, B. M., Zapardiel-Gonzalo, J., Ha, B., Altay, G., Greenbaum, J. A., McVicker, G., et al. (2018) · Cell · doi:10.1016/j.cell.2018.10.022 · Cited by 867 · 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
FINEMAP: efficient variable selection using summary data from genome-wide association studies
Benner, C., Spencer, C. C. A., Havulinna, A. S., Salomaa, V., Ripatti, S., & Pirinen, M. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw018 · Cited by 815 · see citing works
Comparative cellular analysis of motor cortex in human, marmoset and mouse
Bakken, T. E., Jorstad, N. L., Hu, Q., Lake, B. B., Tian, W., Kalmbach, B. E., Crow, M., Hodge, R. D., Krienen, F. M., Sorensen, S. A., et al. (2021) · Nature · doi:10.1038/s41586-021-03465-8 · Cited by 812 · see citing works
Evolution of cellular diversity in primary motor cortex of human, marmoset monkey, and mouse
Bakken, T. E., Jorstad, N. L., Hu, Q., Lake, B. B., Tian, W., Kalmbach, B. E., Crow, M., Hodge, R. D., Krienen, F. M., Sorensen, S. A., et al. (2020) · doi:10.1101/2020.03.31.016972 · Cited by 809 · see citing works
Innate Immune Activity Conditions the Effect of Regulatory Variants upon Monocyte Gene Expression
Fairfax, B. P., Humburg, P., Makino, S., Naranbhai, V., Wong, D., Lau, E., Jostins, L., Plant, K., Andrews, R., McGee, C., et al. (2014) · Science · doi:10.1126/science.1246949 · Cited by 764 · see citing works
CrossMap: a versatile tool for coordinate conversion between genome assemblies
Zhao, H., Sun, Z., Wang, J., Huang, H., Kocher, J., & Wang, L. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt730 · Cited by 718 · see citing works
Genetic Drivers of Epigenetic and Transcriptional Variation in Human Immune Cells
Chen, L., Ge, B., Casale, F. P., Vasquez, L., Kwan, T., Garrido-Martín, D., Watt, S., Yan, Y., Kundu, K., Ecker, S., et al. (2016) · Cell · doi:10.1016/j.cell.2016.10.026 · Cited by 679 · see citing works
An atlas of genetic associations in UK Biobank
Canela‐Xandri, O., Rawlik, K., & Tenesa, A. (2018) · Nature Genetics · doi:10.1038/s41588-018-0248-z · Cited by 657 · see citing works
Lanoiselée, H.-M., Nicolas, G., Wallon, D., Rovelet-Lecrux, A., Lacour, M., Rousseau, S., Richard, A.-C., Pasquier, F., Rollin-Sillaire, A., Martinaud, O., et al. (2017) · PLOS Medicine · doi:10.1371/journal.pmed.1002270 · Cited by 616 · see citing works
Fine-mapping inflammatory bowel disease loci to single-variant resolution
International Inflammatory Bowel Disease Genetics Consortium, Huang, H., Fang, M., Jostins, L., Umićević Mirkov, M., Boucher, G., Anderson, C. A., Andersen, V., Cleynen, I., Cortes, A., et al. (2017) · Nature · doi:10.1038/nature22969 · Cited by 611 · see citing works
Common genetic variation drives molecular heterogeneity in human iPSCs
Kilpinen, H., Goncalves, A., Leha, A., Afzal, V., Alasoo, K., Ashford, S., Bala, S., Bensaddek, D., Casale, F. P., Culley, O. J., et al. (2017) · Nature · doi:10.1038/nature22403 · Cited by 586 · see citing works
Self-citationGWAS 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
Battle, A., Mostafavi, S., Zhu, X., Potash, J. B., Weissman, M. M., McCormick, C., Haudenschild, C. D., Beckman, K. B., Shi, J., Mei, R., et al. (2014) · Genome Research · doi:10.1101/gr.155192.113 · Cited by 547 · see citing works
Cell type–specific genetic regulation of gene expression across human tissues
Kim-Hellmuth, S., Aguet, F., Oliva, M., Muñoz-Aguirre, M., Kasela, S., Wucher, V., Castel, S. E., Hamel, A. R., Viñuela, A., Roberts, A. L., et al. (2020) · Science · doi:10.1126/science.aaz8528 · Cited by 530 · see citing works
Joint Analysis of Functional Genomic Data and Genome-wide Association Studies of 18 Human Traits
Pickrell, J. K. (2014) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2014.03.004 · Cited by 520 · see citing works
Genetic Ancestry and Natural Selection Drive Population Differences in Immune Responses to Pathogens
Nédélec, Y., Sanz, J., Baharian, G., Szpiech, Z. A., Pacis, A., Dumaine, A., Grenier, J.-C., Freiman, A., Sams, A. J., Hebert, S., et al. (2016) · Cell · doi:10.1016/j.cell.2016.09.025 · Cited by 512 · see citing works
Open Targets: a platform for therapeutic target identification and validation
Koscielny, G., An, P., Carvalho‐Silva, D., Cham, J. A., Fumis, L., Gasparyan, R., Hasan, S., Karamanis, N., Maguire, M., Papa, E., et al. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw1055 · Cited by 500 · see citing works
Fairfax, B. P., Makino, S., Radhakrishnan, J., Plant, K., Leslie, S., Dilthey, A., Ellis, P., Langford, C., Vannberg, F. O., & Knight, J. C. (2012) · Nature Genetics · doi:10.1038/ng.2205 · Cited by 486 · see citing works
Bayesian refinement of association signals for 14 loci in 3 common diseases
Maller, J., McVean, G., Byrnes, J., Vukcevic, D., Palin, K., Su, Z., Howson, J. M. M., Auton, A., Myers, S. R., Morris, A. P., et al. (2012) · Nature Genetics · doi:10.1038/ng.2435 · Cited by 481 · see citing works
Integrating Functional Data to Prioritize Causal Variants in Statistical Fine-Mapping Studies
Kichaev, G., Yang, W.-Y., Lindström, S., Hormozdiari, F., Eskin, E., Price, A. L., Kraft, P. L., & Paşaniuc, B. (2014) · PLoS Genetics · doi:10.1371/journal.pgen.1004722 · Cited by 476 · see citing works
Genetic adaptation and Neandertal admixture shaped the immune system of human populations
Quach, H., Rotival, M., Pothlichet, J., Loh, Y.-H. E., Dannemann, M., Zidane, N., Laval, G., Patin, E., Harmant, C., Lopez, M., et al. (2016) · Cell · doi:10.1016/j.cell.2016.09.024 · Cited by 475 · 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
Passive and active DNA methylation and the interplay with genetic variation in gene regulation
Gutierrez-Arcelus, M., Lappalainen, T., Montgomery, S. B., Buil, A., Ongen, H., Yurovsky, A., Bryois, J., Giger, T., Romano, L., Planchon, A., et al. (2013) · eLife · doi:10.7554/elife.00523 · Cited by 440 · see citing works
A compendium of uniformly processed human gene expression and splicing quantitative trait loci
Kerimov, N., Hayhurst, J. D., Peikova, K., Manning, J. R., Walter, P., Kolberg, L., Samoviča, M., Sakthivel, M. P., Kuzmin, I., Trevanion, S. J., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00924-w · Cited by 440 · see citing works
Raj, T., Li, Y. I., Wong, G., Humphrey, J., Wang, M., Ramdhani, S., Wang, Y.-C., Ng, B., Gupta, I., Haroutunian, V., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0238-1 · Cited by 430 · see citing works
Bayes factors for genome‐wide association studies: comparison with P ‐values
Wakefield, J. (2008) · Genetic Epidemiology · doi:10.1002/gepi.20359 · Cited by 397 · see citing works
The GTEx Consortium atlas of genetic regulatory effects across human tissuesPreprint
Aguet, F., Barbeira, A., Bonazzola, R., Brown, A. A., Castel, S. E., Jo, B., Kasela, S., Kim-Hellmuth, S., Liang, Y., Oliva, M., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/787903 · Cited by 379 · see citing works
The BrainSeq Consortium, Jaffe, A. E., Straub, R. E., Shin, J. H., Tao, R., Gao, Y., Collado-Torres, L., Kam-Thong, T., Xi, H. S., Quan, J., et al. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0197-y · Cited by 364 · see citing works
HIPSCI Consortium, Alasoo, K., Rodrigues, J., Mukhopadhyay, S., Knights, A. J., Mann, A. L., Kundu, K., Hale, C., Dougan, G., & Gaffney, D. J. (2018) · Nature Genetics · doi:10.1038/s41588-018-0046-7 · Cited by 338 · see citing works
Self-citationDesign of integrated system for heterogeneous network query terminal
Gao, Y. U., Chen, Y., & Guo, Y. (2009) · Journal of Computer Applications · doi:10.3724/sp.j.1087.2009.02191 · Cited by 324 · see citing works
Loss-of-function variants in ABCA7 confer risk of Alzheimer's disease
DemGene, Steinberg, S., Stefansson, H., Jonsson, T., Johannsdottir, H., Ingason, A., Helgason, H., Sulem, P., Magnusson, O. T., Gudjonsson, S. A., et al. (2015) · Nature Genetics · doi:10.1038/ng.3246 · Cited by 324 · see citing works
Sieberts, S. K., Perumal, T. M., Carrasquillo, M. M., Allen, M., Reddy, J. S., Hoffman, G. E., Dang, K. K., Calley, J., Ebert, P. J., Eddy, J., et al. (2020) · Scientific Data · doi:10.1038/s41597-020-00642-8 · Cited by 318 · see citing works
Self-citationTowards Scaling Fully Personalized PageRank: Algorithms, Lower Bounds, and Experiments
Fogaras, D., Rácz, B., Csalogány, K., & Sarlós, T. (2005) · Internet Mathematics · doi:10.1080/15427951.2005.10129104 · Cited by 283 · see citing works
Loss of Bin1 Promotes the Propagation of Tau Pathology
Calafate, S., Flavin, W., Verstreken, P., & Moechars, D. (2016) · Cell Reports · doi:10.1016/j.celrep.2016.09.063 · Cited by 265 · see citing works
CD33 Alzheimer’s risk-altering polymorphism, CD33 expression, and exon 2 splicing
Malik, M., Simpson, J. F., Parikh, I., Wilfred, B. R., Fardo, D. W., Nelson, P. T., & Estus, S. (2013) · The Journal of Neuroscience · doi:10.1523/jneurosci.1224-13.2013 · Cited by 262 · see citing works
Fine-mapping cellular QTLs with RASQUAL and ATAC-seq
Kumasaka, N., Knights, A. J., & Gaffney, D. J. (2015) · Nature Genetics · doi:10.1038/ng.3467 · Cited by 259 · see citing works
Self-citationChun, S., Casparino, A., Patsopoulos, N. A., Croteau-Chonka, D. C., Raby, B. A., De Jager, P. L., Sunyaev, S. R., & Cotsapas, C. (2017) · Nature Genetics · doi:10.1038/ng.3795 · Cited by 259 · see citing works
Schlepckow, K., Kleinberger, G., Fukumori, A., Feederle, R., Lichtenthaler, S. F., Steiner, H., & Haass, C. (2017) · EMBO Molecular Medicine · doi:10.15252/emmm.201707672 · Cited by 258 · see citing works
Benner, C., Havulinna, A. S., Järvelin, M., Salomaa, V. V., Ripatti, S., & Pirinen, M. (2017) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2017.08.012 · Cited by 244 · see citing works
Genome-wide association study of Alzheimer's disease
Kamboh, M. I., Demirci, F. Y., Wang, X., Minster, R. L., Carrasquillo, M. M., Pankratz, V. S., Younkin, S. G., Saykin, A. J., Initiative, A. D. N., Jun, G., et al. (2012) · Translational Psychiatry · doi:10.1038/tp.2012.45 · Cited by 242 · see citing works
Case–control association mapping by proxy using family history of disease
Liu, J. Z., Erlich, Y., & Pickrell, J. K. (2017) · Nature Genetics · doi:10.1038/ng.3766 · Cited by 238 · see citing works
Self-citationIBD risk loci are enriched in multigenic regulatory modules encompassing putative causative genes
Momozawa, Y., Dmitrieva, J., Théâtre, E., Deffontaine, V., Rahmouni, S., Charloteaux, B., Crins, F., Docampo, E., Elansary, M., Gori, A.-S., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-04365-8 · Cited by 233 · see citing works
Molecular and functional variation in iPSC-derived sensory neurons
HIPSCI Consortium, Schwartzentruber, J., Foskolou, S., Kilpinen, H., Rodrigues, J., Alasoo, K., Knights, A. J., Patel, M., Goncalves, A., Ferreira, R., et al. (2018) · Nature Genetics · doi:10.1038/s41588-017-0005-8 · Cited by 223 · see citing works
Self-citationA 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
Self-citationGene-gene and gene-environment interactions detected by transcriptome sequence analysis in twins
Buil, A., Brown, A. A., Lappalainen, T., Viñuela, A., Davies, M. N., Zheng, H.-F., Richards, J. B., Glass, D., Small, K. S., Durbin, R. M., et al. (2014) · Nature Genetics · doi:10.1038/ng.3162 · Cited by 210 · see citing works
A genetics-led approach defines the drug target landscape of 30 immune-related traits
Fang, H., Wolf, H. D., Knezevic, B., Burnham, K. L., Osgood, J. A., Sanniti, A., Lara, A. L., Kasela, S., Cesco, S. D., Wegner, J. K., et al. (2019) · Nature Genetics · doi:10.1038/s41588-019-0456-1 · Cited by 207 · see citing works
Transethnic genome‐wide scan identifies novel Alzheimer's disease loci
Jun, G. R., Chung, J., Mez, J., Barber, R., Beecham, G. W., Bennett, D. A., Buxbaum, J. D., Byrd, G. S., Carrasquillo, M. M., Crane, P. K., et al. (2017) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2016.12.012 · Cited by 197 · see citing works
van de Bunt, M., Fox, J. E. M., Dai, X.-Q., Barrett, A., Grey, C. L., Li, L., Bennett, A. J., Johnson, P. R. V., Rajotte, R. V., Gaulton, K. J., et al. (2015) · PLoS Genetics · doi:10.1371/journal.pgen.1005694 · Cited by 174 · see citing works
CD33 modulates TREM2: convergence of Alzheimer loci
Chan, G., White, C. C., Winn, P. A., Cimpean, M., Replogle, J. M., Glick, L. R., Cuerdon, N. E., Ryan, K. J., Johnson, K. A., Schneider, J. A., et al. (2015) · Nature Neuroscience · doi:10.1038/nn.4126 · Cited by 162 · see citing works
Moreno–Grau, S., de Rojas, I., Hernández, I., Quintela, I., Montrreal, L., Alegret, M., Hernández‐Olasagarre, B., Madrid, L., González‐Pérez, A., Maroñas, O., et al. (2019) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2019.06.4950 · Cited by 156 · see citing works
Genomic modulators of gene expression in human neutrophils
Naranbhai, V., Fairfax, B. P., Makino, S., Humburg, P., Wong, D., Ng, E., Hill, A. V. S., & Knight, J. C. (2015) · Nature Communications · doi:10.1038/ncomms8545 · Cited by 145 · see citing works
Raj, T., Ryan, K. J., Replogle, J. M., Chibnik, L. B., Rosenkrantz, L., Tang, A., Rothamel, K., Stranger, B. E., Bennett, D. A., Evans, D. A., et al. (2014) · Human Molecular Genetics · doi:10.1093/hmg/ddt666 · Cited by 144 · see citing works
Nordestgaard, L. T., Tybjærg‐Hansen, A., Nordestgaard, B. G., & Frikke‐Schmidt, R. (2015) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2015.04.006 · Cited by 139 · see citing works
3‐O‐Sulfation of Heparan Sulfate Enhances Tau Interaction and Cellular Uptake
Zhao, J., Zhu, Y., Song, X., Xiao, Y., Su, G., Liu, X., Wang, Z., Xu, Y., Liu, J., Eliezer, D., et al. (2019) · Angewandte Chemie International Edition · doi:10.1002/anie.201913029 · Cited by 111 · see citing works
Self-citationStacey, D., Fauman, E. B., Ziemek, D., Sun, B. B., Harshfield, E. L., Wood, A., Butterworth, A. S., Suhre, K., & Paul, D. S. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky837 · Cited by 107 · see citing works
Pirinen, M., Donnelly, P., & Spencer, C. C. A. (2013) · The Annals of Applied Statistics · doi:10.1214/12-aoas586 · Cited by 99 · see citing works
Lu, Q., Powles, R. L., Abdallah, S., Ou, D., Wang, Q., Hu, Y., Lu, Y., Liu, W., Li, B., Mukherjee, S., et al. (2017) · PLOS Genetics · doi:10.1371/journal.pgen.1006933 · Cited by 97 · see citing works
An intronic VNTR affects splicing of ABCA7 and increases risk of Alzheimer’s disease
On Behalf of the BELNEU Consortium, De Roeck, A., Duchateau, L., Van Dongen, J., Cacace, R., Bjerke, M., Van den Bossche, T., Cras, P., Vandenberghe, R., De Deyn, P. P., et al. (2018) · Acta Neuropathologica · doi:10.1007/s00401-018-1841-z · Cited by 93 · see citing works
Rathore, N., Ramani, S. R., Pantua, H., Payandeh, J., Bhangale, T., Wuster, A., Kapoor, M., Sun, Y., Kapadia, S. B., Gonzalez, L., et al. (2018) · PLOS Genetics · doi:10.1371/journal.pgen.1007427 · Cited by 93 · see citing works
Risk factor SORL1: from genetic association to functional validation in Alzheimer’s disease
Andersen, O. M., Rudolph, I., & Willnow, T. E. (2016) · Acta Neuropathologica · doi:10.1007/s00401-016-1615-4 · Cited by 89 · see citing works
Somatic variants in autosomal dominant genes are a rare cause of sporadic Alzheimer’s disease
Nicolas, G., Acuña‐Hidalgo, R., Keogh, M. J., Quenez, O., Steehouwer, M., Lelieveld, S., Rousseau, S., Richard, A., Oud, M. S., Marguet, F., et al. (2018) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2018.06.3056 · Cited by 81 · see citing works
Claes, C., Daele, J. V. D., Boon, R., Schouteden, S., Colombo, A. V., Monasor, L. S., Fiers, M. W. E. J., Ordovás, L., Nami, F., Bohrmann, B., et al. (2018) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2018.09.006 · Cited by 81 · see citing works
Alzheimer's Disease Risk Factor Pyk2 Mediates Amyloid-β-Induced Synaptic Dysfunction and Loss
Salazar, S. V., Cox, T. O., Lee, S., Brody, A. H., Chyung, A. S., Haas, L. T., & Strittmatter, S. M. (2019) · The Journal of Neuroscience · doi:10.1523/jneurosci.1873-18.2018 · Cited by 77 · see citing works
Bernstein, A. I., Lin, Y., Street, R. C., Lin, L., Dai, Q., Yu, L., Bao, H., Gearing, M., Lah, J. J., Nelson, P. T., et al. (2016) · Human Molecular Genetics · doi:10.1093/hmg/ddw109 · Cited by 73 · see citing works
Exploiting the GTEx resources to decipher the mechanisms at GWAS lociPreprint
Barbeira, A., Bonazzola, R., Gamazon, E. R., Liang, Y., Park, Y., Kim-Hellmuth, S., Wang, G. T., Jiang, Z., Zhou, D., Hormozdiari, F., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/814350 · Cited by 70 · see citing works
Witoelar, A., Rongve, A., Almdahl, I. S., Ulstein, I. D., Engvig, A., White, L. R., Selbæk, G., Stordal, E., Andersen, F., Brækhus, A., et al. (2018) · Scientific Reports · doi:10.1038/s41598-018-36429-6 · Cited by 68 · see citing works
A Microglial Signature Directing Human Aging and Neurodegeneration-Related Gene Networks
Mukherjee, S., Klaus, C. R., Pricop‐Jeckstadt, M., Miller, J. A., & Struebing, F. L. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.00002 · Cited by 67 · see citing works
A robust clustering algorithm for identifying problematic samples in genome-wide association studies
Bellenguez, C., Strange, A., Freeman, C., Donnelly, P., & Spencer, C. C. A. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr599 · Cited by 66 · see citing works
eQTL Catalogue: a compendium of uniformly processed human gene expression and splicing QTLsPreprint
Kerimov, N., Hayhurst, J., Peikova, K., Manning, J., Walter, P., Kolberg, L., Samoviča, M., Sakthivel, M. P., Kuzmin, I., Trevanion, S., et al. (2020) · bioRxiv · doi:10.1101/2020.01.29.924266 · Cited by 65 · see citing works
Amin, L., & Harris, D. A. (2021) · Nature Communications · doi:10.1038/s41467-021-23507-z · Cited by 57 · see citing works
Nylocks, K. M., Michopoulos, V., Rothbaum, A. O., Almli, L. M., Gillespie, C. F., Wingo, A. P., Schwartz, A. C., Habib, L., Gamwell, K. L., Marvar, P. J., et al. (2015) · American Journal of Medical Genetics Part B Neuropsychiatric Genetics · doi:10.1002/ajmg.b.32313 · Cited by 49 · see citing works
Inactivation of γ‐secretases leads to accumulation of substrates and non‐Alzheimer neurodegeneration
Acx, H., Serneels, L., Radaelli, E., Muyldermans, S., Vincke, C., Pepermans, E., Müller, U., Chávez‐Gutiérrez, L., & Strooper, B. D. (2017) · EMBO Molecular Medicine · doi:10.15252/emmm.201707561 · Cited by 48 · see citing works
A map of transcriptional heterogeneity and regulatory variation in human microgliaPreprint
Young, A. M. H., Kumasaka, N., Calvert, F., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2019.12.20.874099 · Cited by 47 · see citing works
Self-citationLeung, Y. Y., Toledo, J. B., Nefedov, A., Robi Polikar, A., Raghavan, N., Xie, S. X., Farnum, M. A., Schultz, T., Baek, Y., Deerlin, V. M. V., et al. (2015) · Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring · doi:10.1016/j.dadm.2015.06.008 · Cited by 46 · see citing works
Rasmussen, I. J., Tybjærg‐Hansen, A., Rasmussen, K. L., Nordestgaard, B. G., & Frikke‐Schmidt, R. (2019) · European Journal of Epidemiology · doi:10.1007/s10654-019-00498-2 · Cited by 46 · see citing works
Influence of Coding Variability in APP-Aβ Metabolism Genes in Sporadic Alzheimer’s Disease
Sassi, C., Ridge, P. G., Nalls, M. A., Gibbs, J. R., Ding, J., Lupton, M. K., Troakes, C., Lunnon, K., Al‐Sarraj, S., Brown, K. S., et al. (2016) · PLoS ONE · doi:10.1371/journal.pone.0150079 · Cited by 42 · see citing works
Brain xQTL map: Integrating the genetic architecture of the human brain transcriptome and epigenomePreprint
Ng, B., White, C. C., Klein, H., Sieberts, S., Aubin, C., Patrick, E., Xu, J., Yu, L., Gaiteri, C., Bennett, D. A. A., et al. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/142927 · Cited by 39 · see citing works
Ohkura, T., Yoshimura, T., Fujisawa, M., Ohara, T., Marutani, R., Usami, K., & Matsukawa, A. (2019) · Frontiers in Immunology · doi:10.3389/fimmu.2019.00017 · Cited by 39 · see citing works
Sweet, R. A., Nickerson, K. M., Cullen, J. L., Wang, Y., & Shlomchik, M. J. (2017) · The Journal of Immunology · doi:10.4049/jimmunol.1600861 · Cited by 31 · see citing works
Ghanbari, M., Munshi, S. T., Ma, B., Lendemeijer, B., Bansal, S., Adams, H. H. H., Wang, W., Goth, K., Slump, D. E., van den Hout, M. C. G. N., et al. (2019) · Human Mutation · doi:10.1002/humu.23872 · Cited by 31 · see citing works
Regulation of Leukocytes by TspanC8 Tetraspanins and the “Molecular Scissor” ADAM10
Matthews, A. L., Koo, C. Z., Szyroka, J., Harrison, N. J., Kanhere, A., & Tomlinson, M. G. (2018) · Frontiers in Immunology · doi:10.3389/fimmu.2018.01451 · Cited by 30 · see citing works
Novikova, G., Kapoor, M., TCW, J., Abud, E. M., Efthymiou, A. G., Cheng, H., Fullard, J. F., Bendl, J., Roussos, P., Poon, W. W., et al. (2019) · doi:10.1101/694281 · Cited by 27 · see citing works
Chung, C., Wang, R.-Y., Fann, C. S. J., Chen, J., Jong, Y.-S., Jou, Y., Yang, H., Kang, C.-S., Chen, C., Chang, H.-C., et al. (2013) · PLoS ONE · doi:10.1371/journal.pone.0056119 · Cited by 26 · see citing works
Sieberts, S. K., Perumal, T. M., Carrasquillo, M. M., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/638544 · Cited by 24 · see citing works
Biundo, F., Ishiwari, K., Prete, D. D., & D'adamio, L. (2016) · Oncotarget · doi:10.18632/oncotarget.7389 · Cited by 15 · see citing works
Nott, A., Holtman, I., Coufal, N., Schlachetzki, J., Yu, M., Hu, R., Han, C. Z., Pena, M., Xiao, J., Wu, Y., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/778183 · Cited by 11 · see citing works
Patel, K. R., Zhu, K., Henrion, M., Beckmann, N. D., Moein, S., Alamprese, M. L., Allen, M., Wang, X., Chan, G., Pertel, T., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.06.09.143529 · Cited by 9 · see citing works
Negative evidence for a role of APH1B T27I variant in Alzheimer’s disease
Zhang, X., Zhang, C. M., Prokopenko, D., et al. (2020) · Human Molecular Genetics · doi:10.1093/hmg/ddaa017 · Cited by 8 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- [53]Alasoo K Shared genetic effects on chromatin and gene expression indicate a role for enhancer priming in immune response Nat Genet 2018 50 424 431 29379200 PMC6548559
Cited by
Sorted by citations
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
Self-citationWightman, D. P., Jansen, I. E., Savage, J. E., Shadrin, A. A., Bahrami, S., Holland, D., Rongve, A., Børte, S., Winsvold, B. S., Drange, O. K., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00921-z · Cited by 1,011 · see citing works
hdWGCNA identifies co-expression networks in high-dimensional transcriptomics data
Morabito, S., Reese, F., Rahimzadeh, N., Miyoshi, E., & Swarup, V. (2023) · Cell Reports Methods · doi:10.1016/j.crmeth.2023.100498 · Cited by 773 · see citing works
The complex genetic architecture of Alzheimer's disease: novel insights and future directions
Andrews, S. J., Renton, A. E., Fulton-Howard, B., Podlesny-Drabiniok, A., Marcora, E., & Goate, A. M. (2023) · eBioMedicine · doi:10.1016/j.ebiom.2023.104511 · Cited by 377 · see citing works
Więckowska-Gacek, A., Mietelska-Porowska, A., Wydrych, M., & Wojda, U. (2021) · Ageing Research Reviews · doi:10.1016/j.arr.2021.101397 · Cited by 343 · see citing works
Tauopathies: new perspectives and challenges
Zhang, Y., Wu, K.-M., Yang, L., Dong, Q., & Yu, J.-T. (2022) · Molecular Neurodegeneration · doi:10.1186/s13024-022-00533-z · Cited by 323 · 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
Self-citationGene–environment interactions in Alzheimer disease: the emerging role of epigenetics
Migliore, L., & Coppedè, F. (2022) · Nature Reviews Neurology · doi:10.1038/s41582-022-00714-w · Cited by 217 · see citing works
Microglial Phagocytosis: A Disease-Associated Process Emerging from Alzheimer’s Disease Genetics
Podleśny-Drabiniok, A., Marcora, E., & Goate, A. M. (2020) · Trends in Neurosciences · doi:10.1016/j.tins.2020.10.002 · Cited by 211 · see citing works
Epigenomic dissection of Alzheimer’s disease pinpoints causal variants and reveals epigenome erosion
Xiong, X., James, B. T., Boix, C. A., Park, Y. P., Galani, K., Victor, M. B., Sun, N., Hou, L., Ho, L.-L., Mantero, J., et al. (2023) · Cell · doi:10.1016/j.cell.2023.08.040 · Cited by 205 · see citing works
Clonal hematopoiesis is associated with protection from Alzheimer’s disease
Bouzid, H., Belk, J. A., Jan, M., Qi, Y., Sarnowski, C., Wirth, S., Ma, L., Chrostek, M. R., Ahmad, H., Nachun, D., et al. (2023) · Nature Medicine · doi:10.1038/s41591-023-02397-2 · Cited by 169 · see citing works
“Summary statistics from a large AD genome-wide association study (GWAS) and GWAS-by-proxy metaanalysis(16) were then used to perform Mendelian randomization using the inverse-variance weighted”
Genetics 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
Absence of microglia promotes diverse pathologies and early lethality in Alzheimer’s disease mice
Kiani Shabestari, S., Morabito, S., Danhash, E. P., McQuade, A., Sanchez, J. R., Miyoshi, E., Chadarevian, J. P., Claes, C., Coburn, M. A., Hasselmann, J., et al. (2022) · Cell Reports · doi:10.1016/j.celrep.2022.110961 · Cited by 167 · see citing works
Fang, J., Zhang, P., Wang, Q., Chiang, C.-W., Zhou, Y., Hou, Y., Xu, J., Chen, R., Zhang, B., Lewis, S. J., et al. (2022) · Alzheimer's Research & Therapy · doi:10.1186/s13195-021-00951-z · Cited by 140 · see citing works
Western, D., Timsina, J., Wang, L., Wang, C., Yang, C., Phillips, B., Wang, Y., Liu, M., Ali, M., Beric, A., et al. (2024) · Nature Genetics · doi:10.1038/s41588-024-01972-8 · Cited by 111 · see citing works
Tang, A. S., Rankin, K. P., Cerono, G., Miramontes, S., Mills, H., Roger, J., Zeng, B., Nelson, C., Soman, K., Woldemariam, S., et al. (2024) · Nature Aging · doi:10.1038/s43587-024-00573-8 · Cited by 86 · see citing works
Anderson, A. G., Rogers, B. B., Loupe, J. M., Rodriguez-Nunez, I., Roberts, S. C., White, L. M., Brazell, J. N., Bunney, W. E., Bunney, B. G., Watson, S. J., et al. (2023) · Cell Genomics · doi:10.1016/j.xgen.2023.100263 · Cited by 82 · see citing works
Jo, T., Nho, K., Bice, P., Saykin, A. J., & Initiative, F. T. A. D. N. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac022 · Cited by 80 · see citing works
Functional characterization of Alzheimer’s disease genetic variants in microglia
Yang, X., Wen, J., Yang, H., Jones, I. R., Zhu, X., Liu, W., Li, B., Clelland, C. D., Luo, W., Wong, M. Y., et al. (2023) · Nature Genetics · doi:10.1038/s41588-023-01506-8 · Cited by 71 · see citing works
The role of genetic risk factors of Alzheimer’s disease in synaptic dysfunction
Fu, W.-Y., & Ip, N. Y. (2023) · Seminars in Cell & Developmental Biology · doi:10.1016/j.semcdb.2022.07.011 · Cited by 48 · see citing works
Artificial intelligence for dementia genetics and omics
Bettencourt, C., Skene, N., Bandres‐Ciga, S., Anderson, E., Winchester, L. M., Foote, I. F., Schwartzentruber, J., Botia, J. A., Nalls, M., Singleton, A., et al. (2023) · Alzheimer's & Dementia · doi:10.1002/alz.13427 · Cited by 30 · see citing works
Self-citationAssociation of SPI1 haplotypes with altered SPI1 gene expression and Alzheimer’s disease risk
Cao, H., Initiative, A. D. N., Zhou, X., Chen, Y., Ip, F. C. F., Chen, Y., Lai, N. C. H., Lo, R. M. N., Tong, E. P. S., Mok, V. C. T., et al. (2022) · Journal of Alzheimer s Disease · doi:10.3233/jad-215311 · Cited by 28 · see citing works
Sun, J., Chen, F., She, L., Zeng, Y., Tang, H., Ye, B., Zheng, W., Xiong, L., Li, L., Li, L., et al. (2025) · Acta Pharmaceutica Sinica B · doi:10.1016/j.apsb.2024.11.020 · Cited by 24 · see citing works
EADB, Bellenguez, C., Castillo Morales, A., Amin, N., van der Lee, S. J., Muntaner, M., Parveen, K., Küçükali, F., Grenier-Boley, B., Heikkinen, S., et al. (2026) · Nature Genetics · doi:10.1038/s41588-026-02583-1 · Cited by 7 · see citing works
“As in previous GWAS findings, genes enriched for ADRD or AD association signals in the main, no-proxy and no-biobank meta-analyses were overexpressed in microglia across four datasets spanning different brain regions (Supplementary Note, Supplementary Tables 9 and 10 and Supplementary Figs.”
A genetic and proteomic comparison of key AD biomarkers across tissues
Marsh, T. W., Western, D., Timsina, J., Gorijala, P., Yang, C., Pastor, P., Liu, M., Morris, J. C., Bateman, R. J., Schindler, S. E., et al. (2024) · Alzheimer's & Dementia · doi:10.1002/alz.14139 · Cited by 6 · see citing works
Gutierrez-Kuri, E., Marie Garcia-Rogers, J. L., Perez, J., Smith, S., Kenwood, M. R., Archuleta, K. S., Xiao, Y., Campos, G., Barannikov, S., Wang, H., et al. (2026) · doi:10.64898/2026.07.13.738356
Is there a role for B cells in Alzheimer's disease?
Charavner, E.-L., Fillatreau, S., & El Behi, M. (2026) · Cellular Immunology · doi:10.1016/j.cellimm.2026.105154
Wang, J. D. J., Teo, A. Y. T., Xiao, B., Chao, Y., Zhou, Z. D., Tan, B. J.-W., Chan, L.-L., Rosalie, E., & Tan, E.-K. (2026) · Translational Neurodegeneration · doi:10.1186/s40035-026-00568-y
Alzheimer’s genetic risk factor CASS4 controls microglial focal adhesion biology and amyloidosis
Walsh, A. E., Davis, B. A., Samuels, J. D., Mills, W. A., Thanos, J. M., Johnson, A. M., Moore, K. A., Puchalski, M. A., Steacy, E. J., Wendell, B. W., et al. (2026) · Journal of Experimental Medicine · doi:10.1084/jem.20260104
- Authors
- Schwartzentruber, J., Cooper, S., Liu, J. Z., Barrio-Hernandez, I., Bello, E., Kumasaka, N., Young, A. M. H., Franklin, R. J. M., Johnson, T., Estrada, K., Gaffney, D. J., Beltrao, P., & Bassett, A.
- Affiliations
- European Bioinformatics Institute (GB)
- Wellcome Sanger Institute (GB)
- Biogen (United States) (US)
- Wellcome/MRC Cambridge Stem Cell Institute (GB)
- GlaxoSmithKline (United Kingdom) (GB)
- BioMarin (United States) (US)
- Corresponding author
- Schwartzentruber, Jeremy
- Bassett, Andrew
- ORCID
- Venue
- Nature Genetics
- Published
- 2021-02-15
- Volume
- 53
- Issue
- 3
- Pages
- 392-402
- ISSN
- 1061-4036 (ISSN-L)
- 1546-1718
- PMC
- PMC7610386
- OpenAlex
- W3115346491
- Cited by
- 532
- References
- 128
- Citations by year
- 2026: 75 · 2025: 105 · 2024: 117 · 2023: 96 · 2022: 86 · 2021: 50 · 2020: 3
- FWCI
- 56.94
- Type
- journal article
- Language
- en
- Update notices
- correction (2021-02-26)
- Open access
- green
- doi.org
- Locations
- Nature Genetics (publishedVersion)
- medRxiv (acceptedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Apollo (University of Cambridge) (submittedVersion)
- Apollo
- Funders
- Multiple Sclerosis Society (50)
- Dr. Miriam and Sheldon G. Adelson Medical Research Foundation
- Wellcome Trust (203151/Z/16/Z, PhD for clinicians fellowship)
- Topics
- Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Keywords
- Alzheimer's disease, genetic susceptibility loci, causal variant identification, fine mapping, colocalization, expression quantitative trait loci, protein-protein interaction network, tissue-specific gene expression, CCDC6, Nck2, SPRED2, BIN1, Pyk2, susceptibility genes
- MeSH terms
- Alzheimer Disease, Chromosome Mapping, Cytoskeletal Proteins, Humans, Risk Factors, Oncogene Proteins, Linkage Disequilibrium, Gene Expression, Microglia, Genetic Predisposition to Disease, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Adaptor Proteins, Signal Transducing, Genome-Wide Association Study, Tetraspanins, Protein Interaction Maps
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
