The DOI matches its Crossref record; 46 of the 47 references it cites were found in a registry or an index.
Systematic Localization of Common Disease-Associated Variation in Regulatory DNA
Open accessAbstract
Predictions of Genetic Disease: Many genome-wide association studies (GWAS) have identified loci and variants associated with disease, but the ability to predict disease on the basis of these genetic variants remains small. Maurano et al. (p. 1190 ; see the Perspective by Schadt and Chang ; see the cover) characterize the location of GWAS variants in the genome with respect to their proximity to regulatory DNA [marked by deoxyribonuclease I (DNase I) hypersensitive sites] by tissue type, disease, and enrichments in physiologically relevant transcription factor binding sites and networks. They found many noncoding disease associations in regulatory DNA, indicating tissue and developmental-specific regulatory roles for many common genetic variants and thus enabling links to be made between gene regulation and adult-onset disease.
Overview
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
Verification
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 source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“As the majority of schizophrenia-associated variants do not directly index coding changes affecting protein structure”
From DNA methylation meta-analysis reveals cellular alterations in psychosis and markers of treatment-resistant schizophrenia (2021) · cites this paper as Maurano et al., 2012
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“The second challenge is that the majority of MS risk variants appear to localise to gene regulatory regions, rather than coding sequence,55 and specifically to enhancer elements active in stimulated immune cell subsets.56”
From Genome‐wide association studies of multiple sclerosis (2018) · cites this paper as [55]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“It is now widely recognized that a major component of the heritability of nearly all common diseases partitions to these regions, and in particular to regions that have enhancer-like signatures in disease-relevant cell types65.”
From Towards a comprehensive catalogue of validated and target-linked human enhancers (2020) · cites this paper as [65]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Although most causal genetic variants for Mendelian disorders fall in protein-coding regions, the heritable component of common disease risk distributes largely to non-coding regions, and appears to be particularly enriched in enhancers that are specific to disease-relevant cell types.”
From Towards a comprehensive catalogue of validated and target-linked human enhancers (2020) · cites this paper as [65]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“The importance of enhancer variation in human pathology has been further highlighted by genome-wide association studies (GWAS), since GWAS-detected genetic variants associated with complex traits and diseases are typically non-coding, non-promoter associated and highly enriched in enhancers [184, 208, 209].”
From Transcriptional enhancers and their communication with gene promoters (2021) · cites this paper as [209]
Experimental — accuracy not yet measured
The cited source says what the claim attributes to it.
In the citing paper
“The vast majority of these disease-risk genetic variants are located in non-coding regions of the genome (5).”
From Brain cell type–specific enhancer–promoter interactome maps and disease - risk association (2019) · cites this paper as [5]
The cited source says what the claim attributes to it.
In the citing paper
“While the T cell component plays an important role, functional and epigenomic annotation studies have begun to suggest that other elements of the immune system may be involved as well.(7, 8)”
From Multiple sclerosis genomic map implicates peripheral immune cells and microglia in susceptibility (2019) · cites this paper as [8]
The cited source says what the claim attributes to it.
In the citing paper
“Beyond impacting isoform composition through splicing modulation, non-coding variants can influence traits and cause diseases by altering gene expression23,24.”
From Advancing regulatory variant effect prediction with AlphaGenome (2026) · cites this paper as [24]
In the citing paper
“The majority of disease-associated genetic variation lies in non-coding regions (7) enriched for non-coding RNAs and cis regulatory elements that regulate gene expression and splicing of their cognate coding gene targets (8, 9).”
From Transcriptome-wide isoform-level dysregulation in ASD, schizophrenia, and bipolar disorder (2018) · cites this paper as [7]
In the citing paper
“Histone modifications can reliably be measured across the genome using chromatin immunoprecipitation followed by sequencing (ChIP-seq), while chromatin accessibility is typically measured by either DNase hypersensitivity analysis (DNase-seq) [168] or assay for transposase-accessible chromatin using sequencing”
From Genetic architecture of Alzheimer's disease (2020) · cites this paper as [168]
In the citing paper
“A large portion of GWAS variants and even variants in high LD with the lead variant fall within non-coding regions of the genome, making their functional importance difficult to infer [168].”
From Genetic architecture of Alzheimer's disease (2020) · cites this paper as [168]
In the citing paper
“More than 90% of disease-associated SNVs (daSNVs) are localized in noncoding regions with unknown target genes40”
From Three-dimensional genome restructuring across timescales of activity-induced neuronal gene expression (2020) · cites this paper as [40]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
The Sequence Alignment/Map format and SAMtools
Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., Marth, G., Abecasis, G., Durbin, R., & 1000 Genome Project Data Processing Subgroup (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp352 · Cited by 62,041
Gene Ontology: tool for the unification of biology
Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., Davis, A. P., Dolinski, K., Dwight, S. S., Eppig, J. T., et al. (2000) · Nature Genetics · doi:10.1038/75556 · Cited by 43,206
Ultrafast and memory-efficient alignment of short DNA sequences to the human genome.
Langmead, B., Trapnell, C., Pop, M., & Salzberg, S. L. (2009) · Genome Biology · doi:10.1186/gb-2009-10-3-r25 · Cited by 21,849
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
A map of human genome variation from population-scale sequencing.
The 1000 Genomes Project Consortium (2010) · Nature · doi:10.1038/nature09534 · Cited by 7,877
Common polygenic variation contributes to risk of schizophrenia and bipolar disorder
The International Schizophrenia Consortium (2009) · Nature · doi:10.1038/nature08185 · Cited by 4,821
FIMO: scanning for occurrences of a given motif
Grant, C. E., Bailey, T. L., & Noble, W. S. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr064 · Cited by 4,247
Integrating common and rare genetic variation in diverse human populations
The International HapMap 3 Consortium (2010) · Nature · doi:10.1038/nature09298 · Cited by 3,026
King, M.-C., & Wilson, A. C. (1975) · Science · doi:10.1126/science.1090005 · Cited by 2,815
Fetal nutrition and cardiovascular disease in adult life
Barker, D. J. P., Godfrey, K. M., Gluckman, P. D., Harding, J. E., Owens, J. A., & Robinson, J. S. (1993) · The Lancet · doi:10.1016/0140-6736(93)91224-a · Cited by 2,753
Research works printed but not linked: 1
- A. V. Aho B. W. Kernighan P. J. Weinberger The AWK Programming Language (Addison Wesley Reading MA ed. 1 1988).
Cited by
Sorted by citations
Items 301–400 of 1,024
Enhancer Malfunction in Cancer
Herz, H., Hu, D., & Shilatifard, A. (2014) · Molecular Cell · doi:10.1016/j.molcel.2014.02.033 · Cited by 170
Enhancer Logic and Mechanics in Development and Disease
Rickels, R. A., & Shilatifard, A. (2018) · Trends in Cell Biology · doi:10.1016/j.tcb.2018.04.003 · Cited by 171
LRP8-Reelin-regulated neuronal enhancer signature underlying learning and memory formation
Telese, F., Ma, Q., Perez, P. M., Notani, D., Oh, S., Li, W., Comoletti, D., Ohgi, K. A., Taylor, H., & Rosenfeld, M. G. (2015) · Neuron · doi:10.1016/j.neuron.2015.03.033 · Cited by 166
Crowdsourcing biomedical research: leveraging communities as innovation engines
Sáez-Rodríguez, J., Costello, J. C., Friend, S. H., Kellen, M., Mangravite, L. M., Meyer, P., Norman, T., & Stolovitzky, G. A. (2016) · Nature Reviews Genetics · doi:10.1038/nrg.2016.69 · Cited by 173
Multi-species annotation of transcriptome and chromatin structure in domesticated animals
Foissac, S., Djebali, S., Munyard, K., Vialaneix, N., Rau, A., Muret, K., Esquerré, D., Zytnicki, M., Derrien, T., Bardou, P., et al. (2019) · BMC Biology · doi:10.1186/s12915-019-0726-5 · Cited by 162
Mirzaei, S., Paskeh, M. D. A., Okina, E., Gholami, M. H., Hushmandi, K., Hashemi, M., Kalu, A., Zarrabi, A., Nabavi, N., Rabiee, N., et al. (2022) · Journal of Experimental & Clinical Cancer Research · doi:10.1186/s13046-022-02406-1 · Cited by 166
Thandapani, P. (2019) · Pharmacology & Therapeutics · doi:10.1016/j.pharmthera.2019.02.014 · Cited by 167
Origins, Biology, and Diseases of Tissue Macrophages
Cox, N., Pokrovskii, M. V., Vicario, R., & Geissmann, F. (2021) · Annual Review of Immunology · doi:10.1146/annurev-immunol-093019-111748 · Cited by 171
Aubourg, G., Rice, S. J., Bruce-Wootton, P., & Loughlin, J. A. (2021) · Osteoarthritis and Cartilage · doi:10.1016/j.joca.2021.03.002 · Cited by 178
Distinct Gene Regulatory Pathways for Human Innate versus Adaptive Lymphoid Cells
Koues, O. I., Collins, P. L., Cella, M., Robinette, M. L., Porter, S. I., Pyfrom, S., Payton, J. E., Colonna, M., & Oltz, E. M. (2016) · Cell · doi:10.1016/j.cell.2016.04.014 · Cited by 168
Lowe, R., Gemma, C., Beyan, H., Hawa, M. I., Bazeos, A., Leslie, R. D., Montpetit, A., Rakyan, V. K., & Ramagopalan, S. V. (2013) · Epigenetics · doi:10.4161/epi.24362 · Cited by 160
The missing link between genetic association and regulatory function
Connally, N. J., Nazeen, S., Lee, D., Shi, H., Stamatoyannopoulos, J. A., Chun, S., Cotsapas, C. J., Cassa, C. A., & Sunyaev, S. R. (2022) · eLife · doi:10.7554/elife.74970 · Cited by 153
Self-citationTurner, A. W., Hu, S. S., Mosquera, J. V., Feng, W., Hodonsky, C. J., Wong, D., Auguste, G. E., Song, Y., Sol‐Church, K., Farber, E., et al. (2022) · Nature Genetics · doi:10.1038/s41588-022-01069-0 · Cited by 152
3DSNP: a database for linking human noncoding SNPs to their three-dimensional interacting genes
Lu, Y., Quan, C., Chen, H., Bo, X., & Zhang, C. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkw1022 · Cited by 133
Schmidt, E. M., Zhang, J., Zhou, W., Chen, J., Mohlke, K. L., Chen, Y., & Willer, C. J. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv201 · Cited by 152
Bell, C. G., Gao, F., Yuan, W., Roos, L., Acton, R., Xia, Y., Bell, J. T., Ward, K. J., Mangino, M., Hysi, P. G., et al. (2017) · Nature Communications · doi:10.1038/s41467-017-01586-1 · Cited by 157
Multiplexed assays of variant effects contribute to a growing genotype–phenotype atlas
Weile, J., & Roth, F. P. (2018) · Human Genetics · doi:10.1007/s00439-018-1916-x · Cited by 162
A linear mixed-model approach to study multivariate gene–environment interactions
Moore, R., Casale, F. P., Bonder, M. J., Horta, D., Franke, L., Barroso, I., & Stegle, O. (2018) · Nature Genetics · doi:10.1038/s41588-018-0271-0 · Cited by 154
Kusnadi, A., Park, S. H., Yuan, R., Pannellini, T., Giannopoulou, E., Oliver, D., Lu, T., Park-Min, K.-H., & Ivashkiv, L. B. (2019) · Immunity · doi:10.1016/j.immuni.2019.06.005 · Cited by 157
Genome organization and long-range regulation of gene expression by enhancers
Smallwood, A., & Ren, B. (2013) · Current Opinion in Cell Biology · doi:10.1016/j.ceb.2013.02.005 · Cited by 155
Enhancer grammar in development, evolution, and disease: dependencies and interplay
Jindal, G. A., & Farley, E. K. (2021) · Developmental Cell · doi:10.1016/j.devcel.2021.02.016 · Cited by 152
Gazal, S., Loh, P.-R., Finucane, H. K., Ganna, A., Schoech, A., Sunyaev, S., & Price, A. L. (2018) · Nature Genetics · doi:10.1038/s41588-018-0231-8 · Cited by 154
Self-citationSuper-enhancers in transcriptional regulation and genome organization
Wang, X., Cairns, M. J., & Yan, J. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz1038 · Cited by 154
Candidate gene association studies: a comprehensive guide to useful in silicotools
Patnala, R., Clements, J. A., & Batra, J. (2013) · BMC Genetics · doi:10.1186/1471-2156-14-39 · Cited by 154
Type 2 Diabetes Variants Disrupt Function of SLC16A11 through Two Distinct Mechanisms
Rusu, V., Hoch, E., Mercader, J. M., Tenen, D. E., Gymrek, M., Hartigan, C. R., DeRan, M., von Grotthuss, M., Fontanillas, P., Spooner, A., et al. (2017) · Cell · doi:10.1016/j.cell.2017.06.011 · Cited by 152
The Role of HOX Transcription Factors in Cancer Predisposition and Progression
Li, B., Huang, Q., & Wei, G. (2019) · Cancers · doi:10.3390/cancers11040528 · Cited by 149
lentiMPRA and MPRAflow for high-throughput functional characterization of gene regulatory elements
Gordon, M. G., Inoue, F., Martin, B., Schubach, M., Agarwal, V., Whalen, S., Feng, S., Zhao, J., Ashuach, T., Ziffra, R. S., et al. (2020) · Nature Protocols · doi:10.1038/s41596-020-0333-5 · Cited by 144
Large-scale transcriptome-wide association study identifies new prostate cancer risk regions
Mancuso, N., Gayther, S. A., Gusev, A., Zheng, W., Penney, K. L., Kote‐Jarai, Z., Eeles, R. A., Freedman, M. L., Haiman, C. A., Paşaniuc, B., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-06302-1 · Cited by 149
Long Noncoding RNA and Epithelial Mesenchymal Transition in Cancer
Gugnoni, M., & Ciarrocchi, A. (2019) · International Journal of Molecular Sciences · doi:10.3390/ijms20081924 · Cited by 149
Li, B., & Ritchie, M. D. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.713230 · Cited by 155
Guo, H., Fortune, M. D., Burren, O. S., Schofield, E. C., Todd, J. A., & Wallace, C. (2015) · Human Molecular Genetics · doi:10.1093/hmg/ddv077 · Cited by 139
Márquez‐Luna, C., Gazal, S., Loh, P., Kim, S. S., Furlotte, N. A., Auton, A., Agee, M., Alipanahi, B., Bell, R. K., Bryc, K., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-25171-9 · Cited by 151
Whole-exome SNP array identifies 15 new susceptibility loci for psoriasis
Zuo, X., Sun, L., Yin, X., Gao, J., Sheng, Y., Xu, J., Zhang, J., He, C., Qiu, Y., Wen, G., et al. (2015) · Nature Communications · doi:10.1038/ncomms7793 · Cited by 150
Mechanisms of tissue and cell-type specificity in heritable traits and diseases
Hekselman, I., & Yeger‐Lotem, E. (2020) · Nature Reviews Genetics · doi:10.1038/s41576-019-0200-9 · Cited by 156
Hannon, E., Dempster, E. L., Mansell, G., Burrage, J., Bass, N., Bohlken, M. M., Corvin, A., Curtis, C. J., Dempster, D., Di Forti, M., et al. (2021) · eLife · doi:10.7554/elife.58430 · Cited by 132
“As the majority of schizophrenia-associated variants do not directly index coding changes affecting protein structure, there remains uncertainty about the causal genes involved in disease pathogenesis and how their function is dysregulated ( Maurano et al., 2012 ).”
Feng, C., Song, C., Liu, Y., Qian, F., Gao, Y., Ning, Z., Wang, Q., Jiang, Y., Li, Y., Li, M., et al. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkz881 · Cited by 147
GRASP v2.0: an update on the Genome-Wide Repository of Associations between SNPs and phenotypes
Eicher, J. D., Landowski, C., Stackhouse, B., Sloan, A. A., Chen, W., Jensen, N., Lien, J.-P., Leslie, R., & Johnson, A. D. (2014) · Nucleic Acids Research · doi:10.1093/nar/gku1202 · Cited by 148
Self-citationPastukh, V. M., Roberts, J. T., Clark, D. W., Bardwell, G. C., Patel, M., Al‐Mehdi, A., Borchert, G. M., & Gillespie, M. N. (2015) · American Journal of Physiology-Lung Cellular and Molecular Physiology · doi:10.1152/ajplung.00236.2015 · Cited by 147
Type 2 diabetes: genetic data sharing to advance complex disease research
Flannick, J. A., & Florez, J. C. (2016) · Nature Reviews Genetics · doi:10.1038/nrg.2016.56 · Cited by 148
Plant Enhancers: A Call for Discovery
Weber, B., Zicola, J., Oka, R., & Stam, M. E. (2016) · Trends in Plant Science · doi:10.1016/j.tplants.2016.07.013 · Cited by 144
O’Brien, H., Hannon, E. J., Hill, M., Toste, C., Robertson, M. J., Morgan, J., McLaughlin, G. M., Lewis, C. M., Schalkwyk, L. C., Hall, L. S., et al. (2018) · Genome biology · doi:10.1186/s13059-018-1567-1 · Cited by 141
Massively parallel characterization of transcriptional regulatory elements
Agarwal, V., Inoue, F., Schubach, M., Penzar, D., Martin, B. K., Dash, P. M., Keukeleire, P., Zhang, Z., Sohota, A., Zhao, J., et al. (2025) · Nature · doi:10.1038/s41586-024-08430-9 · Cited by 146
Integrative functional genomics identifies regulatory mechanisms at coronary artery disease loci
Miller, C. L., Pjanic, M., Wang, T., Nguyen, T., Cohain, A. T., Lee, J. D., Matic, L. P., Hedin, U., Kundu, R. K., Majmudar, D., et al. (2016) · Nature Communications · doi:10.1038/ncomms12092 · Cited by 141
Greenwald, W. W. Y., Li, H., Benaglio, P., Jakubosky, D. A., Matsui, H., Schmitt, A. D., Selvaraj, S., D’Antonio, M., D’Antonio‐Chronowska, A., Smith, E. N., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-08940-5 · Cited by 144
Catalina, M. D., Owen, K. A., Labonte, A. C., Grammer, A. C., & Lipsky, P. E. (2019) · Journal of Autoimmunity · doi:10.1016/j.jaut.2019.102359 · Cited by 146
A promoter interaction map for cardiovascular disease genetics
Montefiori, L. E., Sobreira, D. R., Sakabe, N. J., Aneas, I., Joslin, A. C., Hansen, G. T., Bozek, G., Moskowitz, I. P., McNally, E. M., & Nóbrega, M. A. (2018) · eLife · doi:10.7554/elife.35788 · Cited by 145
Bauer, D. E., & Orkin, S. H. (2015) · Current Opinion in Genetics & Development · doi:10.1016/j.gde.2015.08.001 · Cited by 143
Deletions of chromosomal regulatory boundaries are associated with congenital disease
Ibn-Salem, J., Köhler, S., Love, M. I., Chung, H.-R., Huang, N., Hurles, M. E., Haendel, M., Washington, N. L., Smedley, D., Mungall, C. J., et al. (2014) · Genome Biology · doi:10.1186/s13059-014-0423-1 · Cited by 143
Gasperini, M., Findlay, G. M., McKenna, A., Milbank, J. H., Lee, C., Zhang, M. D., Cusanovich, D. A., & Shendure, J. (2017) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2017.06.010 · Cited by 143
Beagan, J. A., Pastuzyn, E. D., Fernandez, L. R., Guo, M. H., Feng, K., Titus, K. R., Chandrashekar, H., Shepherd, J. D., & Phillips-Cremins, J. E. (2020) · Nature Neuroscience · doi:10.1038/s41593-020-0634-6 · Cited by 143
Stem Cells, Genome Editing, and the Path to Translational Medicine
Soldner, F., & Jaenisch, R. (2018) · Cell · doi:10.1016/j.cell.2018.09.010 · Cited by 144
Pampari, A., Shcherbina, A., Kvon, E. Z., Kosicki, M., Nair, S., Kundu, S., Kathiria, A. S., Risca, V. I., Kuningas, K., Alasoo, K., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.12.25.630221 · Cited by 183
Common and rare forms of diabetes mellitus: towards a continuum of diabetes subtypes
Flannick, J. A., Johansson, S., & Njølstad, P. R. (2016) · Nature Reviews Endocrinology · doi:10.1038/nrendo.2016.50 · Cited by 148
Power, false discovery rate and Winner’s Curse in eQTL studies
Huang, Q. Q., Ritchie, S. C., Brożyńska, M., & Inouye, M. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky780 · Cited by 143
Chatterjee, S., Kapoor, A., Akiyama, J. A., Auer, D. R., Lee, D., Gabriel, S., Berrios, C., Pennacchio, L. A., & Chakravarti, A. (2016) · Cell · doi:10.1016/j.cell.2016.09.005 · Cited by 135
Cardiac cell type–specific gene regulatory programs and disease risk association
Hocker, J. D., Poirion, O., Zhu, F., Buchanan, J., Zhang, K., Chiou, J., Wang, T., Zhang, Q., Hou, X., Li, Y. E., et al. (2021) · Science Advances · doi:10.1126/sciadv.abf1444 · Cited by 136
High-Resolution Epigenomic Atlas of Human Embryonic Craniofacial Development
Wilderman, A., VanOudenhove, J., Kron, J., Noonan, J. P., & Cotney, J. (2018) · Cell Reports · doi:10.1016/j.celrep.2018.03.129 · Cited by 139
Twenty-eight loci that influence serum urate levels: analysis of association with gout
Phipps‐Green, A. J., Merriman, M. E., Topless, R., Altaf, S., Montgomery, G. W., Franklin, C., Jones, G. T., van Rij, A., White, D., Stamp, L. K., et al. (2014) · Annals of the Rheumatic Diseases · doi:10.1136/annrheumdis-2014-205877 · Cited by 140
Gene Regulatory Elements, Major Drivers of Human Disease
Chatterjee, S., & Ahituv, N. (2017) · Annual Review of Genomics and Human Genetics · doi:10.1146/annurev-genom-091416-035537 · Cited by 144
Human Genetics of Addiction: New Insights and Future Directions
Hancock, D. B., Markunas, C. A., Bierut, L. J., & Johnson, E. O. (2018) · Current Psychiatry Reports · doi:10.1007/s11920-018-0873-3 · Cited by 145
Complementary Alu sequences mediate enhancer–promoter selectivity
Liang, L., Cao, C., Ji, L., Cai, Z., Wang, D., Ye, R., Chen, J., Yu, X., Zhou, J., Bai, Z., et al. (2023) · Nature · doi:10.1038/s41586-023-06323-x · Cited by 147
Characterizing the genetic basis of methylome diversity in histologically normal human lung tissue
Shi, J., Marconett, C. N., Duan, J., Hyland, P. L., Li, P., Wang, Z., Wheeler, W., Zhou, B., Campan, M., Lee, D. S., et al. (2014) · Nature Communications · doi:10.1038/ncomms4365 · Cited by 137
Hook, P. W., McClymont, S. A., Cannon, G. H., Law, W. D., Morton, A. J., Goff, L. A., & McCallion, A. S. (2018) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2018.02.001 · Cited by 136
Control of Immune Cell Homeostasis and Function by lncRNAs
Mowel, W. K., Kotzin, J. J., McCright, S. J., Neal, V. D., & Henao‐Mejia, J. (2017) · Trends in Immunology · doi:10.1016/j.it.2017.08.009 · Cited by 139
Case for genome sequencing in infants and children with rare, undiagnosed or genetic diseases
Bick, D. P., Jones, M. C., Taylor, S. L., Taft, R. J., & Belmont, J. W. (2019) · Journal of Medical Genetics · doi:10.1136/jmedgenet-2019-106111 · Cited by 145
Genetic determinants of gut microbiota composition and bile acid profiles in mice
Kemis, J. H., Linke, V., Barrett, K. L., Boehm, F. J., Traeger, L. L., Keller, M. P., Rabaglia, M. E., Schueler, K. L., Stapleton, D. S., Gatti, D. M., et al. (2019) · PLOS Genetics · doi:10.1371/journal.pgen.1008073 · Cited by 136
Functional footprinting of regulatory DNA
Vierstra, J., Reik, A., Chang, K.-H., Stehling-Sun, S., Zhou, Y., Hinkley, S. J., Paschon, D. E., Zhang, L., Psatha, N., Bendana, Y. R., et al. (2015) · Nature Methods · doi:10.1038/nmeth.3554 · Cited by 133
Self-citationGenome-wide Association Studies: Findings at the Major Histocompatibility Complex Locus in Psychosis
Corvin, A. P., & Morris, D. W. (2013) · Biological Psychiatry · doi:10.1016/j.biopsych.2013.09.018 · Cited by 136
Friendship and natural selection
Christakis, N. A., & Fowler, J. H. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1400825111 · Cited by 139
Hawkes, J. E., Nguyen, G. H., Fujita, M., Florell, S. R., Duffin, K. C., Krueger, G. G., & O’Connell, R. M. (2016) · Journal of Investigative Dermatology · doi:10.1038/jid.2015.409 · Cited by 139
McClay, J. L., Shabalin, A. A., Dozmorov, M. G., Adkins, D. E., Kumar, G., Nerella, S., Clark, S. L., Bergen, S. E., Hultman, C. M., Magnusson, P. K. E., et al. (2015) · Genome biology · doi:10.1186/s13059-015-0842-7 · Cited by 141
Regulatory polymorphisms modulate the expression of HLA class II molecules and promote autoimmunity
Raj, P., Rai, E., Song, R., Khan, S., Wakeland, B., Viswanathan, K., Arana, C., Liang, C., Zhang, B., Dozmorov, I. M., et al. (2016) · eLife · doi:10.7554/elife.12089 · Cited by 133
Self-citationBesides Pathology: Long Non-Coding RNA in Cell and Tissue Homeostasis
Salviano‐Silva, A., Lobo‐Alves, S. C., de Almeida, R. C., Malheiros, D., & Petzl‐Erler, M. L. (2018) · Non-Coding RNA · doi:10.3390/ncrna4010003 · Cited by 135
Genetics of Migraine: Insights into the Molecular Basis of Migraine Disorders
Sutherland, H. G. E., & Griffiths, L. R. (2017) · Headache The Journal of Head and Face Pain · doi:10.1111/head.13053 · Cited by 140
Cho, M. H., Hobbs, B. D., & Silverman, E. K. (2022) · The Lancet Respiratory Medicine · doi:10.1016/s2213-2600(21)00510-5 · Cited by 146
Precise modulation of transcription factor levels identifies features underlying dosage sensitivity
Naqvi, S., Kim, S., Hoskens, H., Matthews, H. S., Spritz, R. A., Klein, O. D., Hallgrímsson, B., Swigut, T., Claes, P., Pritchard, J. K., et al. (2023) · Nature Genetics · doi:10.1038/s41588-023-01366-2 · Cited by 132
Genotype to phenotype via network analysis
Carter, H., Hofree, M., & Ideker, T. (2013) · Current Opinion in Genetics & Development · doi:10.1016/j.gde.2013.10.003 · Cited by 132
Epigenome-wide analysis links SMAD3 methylation at birth to asthma in children of asthmatic mothers
DeVries, A., Wlasiuk, G., Miller, S. J., Bosco, A., Stern, D. A., Lohman, I. C., Rothers, J., Jones, A. C., Nicodemus‐Johnson, J., Vasquez, M. M., et al. (2016) · Journal of Allergy and Clinical Immunology · doi:10.1016/j.jaci.2016.10.041 · Cited by 132
Chromatin activity at GWAS loci identifies T cell states driving complex immune diseases
Soskic, B., Cano-Gamez, E., Smyth, D. J., Rowan, W. C., Nakić, N., Esparza-Gordillo, J., Bossini‐Castillo, L., Tough, D. F., Larminie, C. G. C., Bronson, P. G., et al. (2019) · Nature Genetics · doi:10.1038/s41588-019-0493-9 · Cited by 129
A practical view of fine-mapping and gene prioritization in the post-genome-wide association era
Broekema, R., Bakker, O. B., & Jonkers, I. H. (2020) · Open Biology · doi:10.1098/rsob.190221 · Cited by 141
An encyclopedia of enhancer-gene regulatory interactions in the human genome
Gschwind, A. R., Mualim, K. S., Karbalayghareh, A., Sheth, M. U., Dey, K. K., Jagoda, E., Nurtdinov, R. N., Xi, W., Tan, A. S., Jones, H., et al. (2023) · doi:10.1101/2023.11.09.563812 · Cited by 119
Self-citationTakata, A., Ionita‐Laza, I., Gogos, J. A., Xu, B., & Karayiorgou, M. (2016) · Neuron · doi:10.1016/j.neuron.2016.02.024 · Cited by 129
Functional regulatory variants implicate distinct transcriptional networks in dementia
Cooper, Y. A., Teyssier, N., Dräger, N. M., Guo, Q., Davis, J. E., Sattler, S. M., Yang, Z., Patel, A. M., Wu, S. J., Kosuri, S., et al. (2022) · Science · doi:10.1126/science.abi8654 · Cited by 121
A predictive modeling approach for cell line-specific long-range regulatory interactions
Roy, S., Siahpirani, A. F., Chasman, D. A., Knaack, S., Ay, F., Stewart, R. M., Wilson, M. D., & Sridharan, R. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv865 · Cited by 123
eFORGE: A Tool for Identifying Cell Type-Specific Signal in Epigenomic Data
Breeze, C. E., Paul, D. S., van Dongen, J., Butcher, L. M., Ambrose, J. C., Barrett, J. J. E., Lowe, R., Rakyan, V. K., Iotchkova, V., Frontini, M., et al. (2016) · Cell Reports · doi:10.1016/j.celrep.2016.10.059 · Cited by 125
Whole genome sequencing in psychiatric disorders: the WGSPD consortium
(WGSPD), W. G. S. F. P. D., Sanders, S., Neale, B. M., Huang, H., Werling, D. M., An, J., Dong, S., Abecasis, G. R., Arguello, P. A., Blangero, J., et al. (2017) · Nature Neuroscience · doi:10.1038/s41593-017-0017-9 · Cited by 128
Gisselbrecht, S. S., Palagi, A., Kurland, J. V., Rogers, J. M., Ozadam, H., Zhan, Y., Dekker, J., & Bulyk, M. L. (2020) · Molecular Cell · doi:10.1016/j.molcel.2019.10.004 · Cited by 128
17q21 asthma-risk variants switch CTCF binding and regulate IL-2 production by T cells
Schmiedel, B. J., Seumois, G., Samaniego-Castruita, D., Cayford, J., Schulten, V., Chavez, L., Ay, F., Sette, A., Peters, B., & Vijayanand, P. (2016) · Nature Communications · doi:10.1038/ncomms13426 · Cited by 127
Andersson, R. (2015) · BioEssays · doi:10.1002/bies.201400162 · Cited by 127
Claussnitzer, M., Dankel, S. N., Klocke, B., Grallert, H., Glunk, V., Berulava, T., Lee, H., Oskolkov, N., Fadista, J., Ehlers, K., et al. (2014) · Cell · doi:10.1016/j.cell.2013.10.058 · Cited by 133
Allele-specific open chromatin in human iPSC neurons elucidates functional disease variants
Zhang, S., Zhang, H., Zhou, Y., Qiao, M., Zhao, S., Kozlova, A., Shi, J., Sanders, A. R., Wang, G., Luo, K., et al. (2020) · Science · doi:10.1126/science.aay3983 · Cited by 127
Genetic architecture of Alzheimer's disease
Neuner, S. M., TCW, J., & Goate, A. M. (2020) · Neurobiology of Disease · doi:10.1016/j.nbd.2020.104976 · Cited by 126
“Histone modifications can reliably be measured across the genome using chromatin immunoprecipitation followed by sequencing (ChIP-seq), while chromatin accessibility is typically measured by either DNase hypersensitivity analysis (DNase-seq) (Maurano et al., 2012) or assay for transposaseaccessible chromatin using seq…”
BackgroundMethod52 Genetic Loci Influencing Myocardial Mass
van der Harst, P., van Setten, J., Verweij, N., Vogler, G., Franke, L., Maurano, M. T., Wang, X., Leach, I. M., Eijgelsheim, M., Sotoodehnia, N., et al. (2016) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2016.07.729 · Cited by 121
Self-citationThe Role of Epigenetic Factors in Psoriasis
Dopytalska, K., Ciechanowicz, P., Wiszniewski, K., Szymańska, E., & Walecka, I. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22179294 · Cited by 126
Ryan, R. J. H., Drier, Y., Whitton, H. J., Cotton, M. J., Kaur, J., Issner, R., Gillespie, S. M., Epstein, C. B., Nardi, V., Sohani, A. R., et al. (2015) · Cancer Discovery · doi:10.1158/2159-8290.cd-15-0370 · Cited by 127
3DIV: A 3D-genome Interaction Viewer and database
Yang, D., Jang, I., Choi, J., Kim, M.-S., Lee, A., Kim, H.-W., Eom, J., Kim, D., Jung, I., & Lee, B.-W. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkx1017 · Cited by 123
Mosquera, J. V., Auguste, G., Wong, D., Turner, A. W., Hodonsky, C. J., Alvarez-Yela, A. C., Song, Y., Cheng, Q., Lino Cardenas, C. L., Theofilatos, K., et al. (2023) · Cell Reports · doi:10.1016/j.celrep.2023.113380 · Cited by 123
Faraone, S. V., Spencer, T. J., Madras, B. K., Zhang‐James, Y., & Biederman, J. (2013) · Molecular Psychiatry · doi:10.1038/mp.2013.126 · Cited by 131
The Power of Meta-Analysis in Genome-Wide Association Studies
Panagiotou, O. A., Willer, C. J., Hirschhorn, J. N., & Ioannidis, J. P. A. (2013) · Annual Review of Genomics and Human Genetics · doi:10.1146/annurev-genom-091212-153520 · Cited by 124
Elling, R., Chan, J., & Fitzgerald, K. A. (2016) · European Journal of Immunology · doi:10.1002/eji.201444558 · Cited by 123
Metadata
- Authors
- Maurano, M. T., Humbert, R., Rynes, E., Thurman, R. E., Haugen, E., Wang, H., Reynolds, A. P., Sandstrom, R., Qu, H., Brody, J., Shafer, A., Neri, F., Lee, K., Kutyavin, T., Stehling-Sun, S., Johnson, A. K., Canfield, T. K., Giste, E., Diegel, M., Bates, D., Hansen, R. S., Neph, S., Sabo, P. J., Heimfeld, S., Raubitschek, A., Ziegler, S., Cotsapas, C., Sotoodehnia, N., Glass, I., Sunyaev, S. R., Kaul, R., & Stamatoyannopoulos, J. A.
- Affiliations
- University of Washington (US)
- Chinese Academy of Sciences (CN)
- Fred Hutch Cancer Center (US)
- Benaroya Research Institute (US)
- Yale University (US)
- Brigham and Women's Hospital (US)
- Corresponding author
- Maurano, Matthew T.
- Humbert, Richard
- Rynes, Eric
- Stamatoyannopoulos, John A.
- ORCID
- Venue
- Science
- Published
- 2012-09-07
- Volume
- 337
- Issue
- 6099
- Pages
- 1190-1195
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- PMC
- PMC3771521
- OpenAlex
- W2078059415
- Cited by
- 3,716
- References
- 47
- Citations by year
- 2026: 215 · 2025: 261 · 2024: 296 · 2023: 260 · 2022: 288 · 2021: 352 · 2020: 306 · 2019: 273 · 2018: 270 · 2017: 252 · 2016: 269 · 2015: 249 · 2014: 239 · 2013: 163 · 2012: 17
- FWCI
- 126.66
- Type
- journal article
- Language
- en
- Open access
- green
- europepmc.org
- Locations
- Science (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Heart, Lung, and Blood Institute (R01HL088456)
- National Institute of Mental Health (F31MH094073)
- National Human Genome Research Institute (U54HG004592)
- National Institute of Diabetes and Digestive and Kidney Diseases (P30DK056465)
- National Institute of Environmental Health Sciences (U01ES01156, U01ES017156)
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (R24HD000836)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Epigenetics and DNA Methylation
- Keywords
- non-coding variants, regulatory DNA, DNase I hypersensitive sites, common diseases, fetal development, prenatal exposure, transcription factor binding sites, regulatory networks, Crohn's disease, multiple sclerosis, electrocardiogram, disease-associated variants
- MeSH terms
- Alleles, Chromatin, Crohn Disease, Deoxyribonuclease I, Disease, DNA, Electrocardiography, Fetus, Humans, Multiple Sclerosis, Phenotype, Promoter Regions, Genetic, Regulatory Sequences, Nucleic Acid, Transcription Factors, Genetic Variation, Genome, Human, Polymorphism, Single Nucleotide, Fetal Development, Regulatory Elements, Transcriptional, Gene Regulatory Networks, Genome-Wide Association Study
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
