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 901–1,000 of 1,024
Impact of genetic risk loci for multiple sclerosis on expression of proximal genes in patients
James, T., Lindén, M., Morikawa, H., Fernandes, S. J., Ruhrmann, S., Huss, M., Brandi, M., Piehl, F., Jagodic, M., Tegner, J. N., et al. (2018) · Human Molecular Genetics · doi:10.1093/hmg/ddy001 · Cited by 47
Jiang, K., Zhu, L., Buck, M. J., Chen, Y., Carrier, B., Liu, T., & Jarvis, J. N. (2015) · Arthritis & Rheumatology · doi:10.1002/art.39135 · Cited by 47
Interpretation of allele-specific chromatin accessibility using cell state–aware deep learning
Atak, Z. K., Taskiran, I. I., Demeulemeester, J., Flerin, C., Mauduit, D., Minnoye, L., Hulselmans, G., Christiaens, V., Ghanem, G.-E., Wouters, J., et al. (2021) · Genome Research · doi:10.1101/gr.260851.120 · Cited by 48
Dixon, P. H., Levine, A. P., Cebola, I., Chan, M., Amin, A. S., Aich, A., Mozere, M., Maude, H., Mitchell, A., Zhang, J., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-29931-z · Cited by 47
Burkhardt, R., Kirsten, H., Beutner, F., Holdt, L. M., Gross, A., Teren, A., Tönjes, A., Becker, S., Krohn, K., Kovacs, P. L., et al. (2015) · PLoS Genetics · doi:10.1371/journal.pgen.1005510 · Cited by 46
Genetics of immune-mediated disorders: from genome-wide association to molecular mechanism
Kumar, V., Wijmenga, C., & Xavier, R. J. (2014) · Current Opinion in Immunology · doi:10.1016/j.coi.2014.09.007 · Cited by 47
Epigenomic elements enriched in the promoters of autoimmunity susceptibility genes
Dozmorov, M. G., Wren, J. D., & Alarcón-Riquelme, M. E. (2013) · Epigenetics · doi:10.4161/epi.27021 · Cited by 48
The genetics of juvenile idiopathic arthritis: current understanding and future prospects
Cobb, J. E., Hinks, A., & Thomson, W. (2013) · Rheumatology · doi:10.1093/rheumatology/ket314 · Cited by 52
Resolving mechanisms of immune‐mediated disease in primary CD4 T cells
Bourges, C., Groff, A. F., Burren, O. S., Gerhardinger, C., Mattioli, K., Hutchinson, A., Hu, T. X., Anand, T., Epping, M. W., Wallace, C., et al. (2020) · EMBO Molecular Medicine · doi:10.15252/emmm.202012112 · Cited by 45
Gibson, G. C. (2015) · Science · doi:10.1126/science.aab3002 · Cited by 50
CHiCAGO: Robust Detection of DNA Looping Interactions in Capture Hi-C dataPreprint
Cairns, J., Freire-Pritchett, P., Wingett, S., Várnai, C., Dimond, A., Plagnol, V., Zerbino, D. R., Schoenfelder, S., Javierre, B. M., Osborne, C. S., et al. (2015) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/028068 · Cited by 47
Nakaoka, H., Gurumurthy, A., Hayano, T., Ahmadloo, S., Omer, W. H., Yoshihara, K., Yamamoto, A., Kurose, K., Enomoto, T., Akira, S., et al. (2016) · PLOS Genetics · doi:10.1371/journal.pgen.1005893 · Cited by 46
Sørensen, I. F., Edwards, S. M., Rohde, P. D., & Sørensen, P. (2017) · Scientific Reports · doi:10.1038/s41598-017-02281-3 · Cited by 47
Integrative analysis of 3604 GWAS reveals multiple novel cell type-specific regulatory associations
Breeze, C. E., Haugen, E., Reynolds, A. P., Teschendorff, A. E., van Dongen, J., Lan, Q., Rothman, N., Bourque, G., Dunham, I., Beck, S., et al. (2022) · Genome biology · doi:10.1186/s13059-021-02560-3 · Cited by 44
Self-citationKreimer, A., Ashuach, T., Inoue, F., Khodaverdian, A., Deng, C., Yosef, N., & Ahituv, N. (2022) · Nature Communications · doi:10.1038/s41467-022-28659-0 · Cited by 44
Genetic determinants of risk and progression in multiple sclerosis
Didonna, A., & Oksenberg, J. R. (2015) · Clinica Chimica Acta · doi:10.1016/j.cca.2015.01.034 · Cited by 49
Coetzee, S. G., Shen, H. C., Hazelett, D. J., Lawrenson, K., Kuchenbaecker, K. B., Tyrer, J. P., Rhie, S. K., Levanon, K., Karst, A. M., Drapkin, R., et al. (2015) · Human Molecular Genetics · doi:10.1093/hmg/ddv101 · Cited by 44
Predicting enhancer activity and variant impact using gkm‐SVM
Beer, M. (2017) · Human Mutation · doi:10.1002/humu.23185 · Cited by 47
Genome-wide association study identifies a novel locus for cannabis dependence
Arpana Agrawal, A., Chou, Y.-L., Carey, C. E., Baranger, D. A. A., Zhang, B., Sherva, R., Wetherill, L. F., Kapoor, M., Wang, J.-C., Bertelsen, S. E., et al. (2017) · Molecular Psychiatry · doi:10.1038/mp.2017.200 · Cited by 46
Smith, E. N., D’Antonio‐Chronowska, A., Greenwald, W. W. Y., Borja, V., Aguiar, L. R., Pogue, R. E., Matsui, H., Benaglio, P., Borooah, S., D’Antonio, M., et al. (2019) · Stem Cell Reports · doi:10.1016/j.stemcr.2019.04.012 · Cited by 48
Chen, Z., Wen, W., Beeghly-Fadiel, A. C., Shu, X., Díez‐Obrero, V., Long, J., Bao, J., Wang, J., Liu, Q., Cai, Q., et al. (2019) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2019.07.006 · Cited by 43
A screen of 1,049 schizophrenia and 30 Alzheimer's‐associated variants for regulatory potential
Myint, L., Wang, R., Boukas, L., Hansen, K. D., Goff, L. A., & Avramopoulos, D. (2019) · American Journal of Medical Genetics Part B Neuropsychiatric Genetics · doi:10.1002/ajmg.b.32761 · Cited by 44
Jia, H., Liu, G., Li, J., Zhang, J., Sun, P., Zhao, S., Zhou, X. C., Lu, M., & Hu, J. (2020) · Journal of Experimental Botany · doi:10.1093/jxb/eraa172 · Cited by 46
Cell Type-Specific Intralocus Interactions Reveal Oligodendrocyte Mechanisms in MS
Factor, D. C., Barbeau, A. M., Allan, K. C., Hu, L. R., Madhavan, M., Hoang, A. T., Hazel, K. E. A., Hall, P. A., Nisraiyya, S., Najm, F. J., et al. (2020) · Cell · doi:10.1016/j.cell.2020.03.002 · Cited by 48
VARAdb: a comprehensive variation annotation database for human
Pan, Q., Liu, Y.-J., Bai, X., Han, X., Jiang, Y., Ai, B., Shi, S.-S., Wang, F., Xu, M., Wang, Y., et al. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkaa922 · Cited by 45
GBAT: a gene-based association test for robust detection of trans-gene regulation
Liu, X.-Y., Mefford, J., Dahl, A. W., He, Y., Subramaniam, M., Battle, A., Price, A. L., & Zaitlen, N. (2020) · Genome Biology · doi:10.1186/s13059-020-02120-1 · Cited by 26
Aditham, A. K., Markin, C. J., Mokhtari, D. A., DelRosso, N. V., & Fordyce, P. M. (2020) · Cell Systems · doi:10.1016/j.cels.2020.11.012 · Cited by 45
Evolutionary conservation in noncoding genomic regions
Leypold, N. A., & Speicher, M. R. (2021) · Trends in Genetics · doi:10.1016/j.tig.2021.06.007 · Cited by 51
Soliai, M. M., Kato, A., Helling, B. A., Stanhope, C. T., Norton, J. E., Naughton, K. A., Klinger, A. I., Thompson, E. E., Clay, S. M., Kim, S., et al. (2021) · Genome Medicine · doi:10.1186/s13073-021-00967-y · Cited by 44
Nature vs. nurture: FOXP3, genetics, and tissue environment shape Treg function
Raugh, A., Allard, D., & Bettini, M. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.911151 · Cited by 46
Cheng, W., van der Meer, D., Parker, N., Hindley, G., O’Connell, K. S., Wang, Y., Shadrin, A. A., Alnæs, D., Bahrami, S., Lin, A., et al. (2022) · Molecular Psychiatry · doi:10.1038/s41380-022-01751-z · Cited by 45
Massively parallel functional dissection of schizophrenia-associated noncoding genetic variants
Rummel, C. K., Gagliardi, M., Ahmad, R., Herholt, A., Jiménez-Barrón, L. T., Murek, V., Weigert, L., Hausruckinger, A., Maidl, S., Hauger, B., et al. (2023) · Cell · doi:10.1016/j.cell.2023.09.015 · Cited by 49
Genetic regulation of cell type–specific chromatin accessibility shapes brain disease etiology
Zeng, B., Bendl, J. M., Deng, C., Lee, D., Misir, R., Reach, S. M., Kleopoulos, S. P., Auluck, P. K., Marenco, S., Lewis, D. A., et al. (2024) · Science · doi:10.1126/science.adh4265 · Cited by 48
Haglund, A., Zuber, V., Abouzeid, M., Yang, Y., Ko, J. H., Wiemann, L., Otero‐Jimenez, M., Muhammed, L., Feleke, R., Nott, A., et al. (2025) · Nature Genetics · doi:10.1038/s41588-024-02050-9 · Cited by 46
Gasser, D. L., Winkler, C. A., Peng, M., An, P., McKenzie, L. M., Kirk, G. D., Shi, Y., Xie, L. X., Marbois, B. N., Clarke, C. F., et al. (2013) · American Journal of Physiology-Renal Physiology · doi:10.1152/ajprenal.00143.2013 · Cited by 45
Genome-Wide Discovery of Drug-Dependent Human Liver Regulatory Elements
Smith, R. P., Eckalbar, W. L., Morrissey, K. M., Luizon, M. R., Hoffmann, T. J., Sun, X., Jones, S. L., Aldred, S. F., Ramamoorthy, A., Desta, Z., et al. (2014) · PLoS Genetics · doi:10.1371/journal.pgen.1004648 · Cited by 43
From Identification to Characterization of the Multiple Sclerosis Susceptibility Gene CLEC16A
Berge, T., Leikfoss, I., & Harbo, H. (2013) · International Journal of Molecular Sciences · doi:10.3390/ijms14034476 · Cited by 45
pENCODE: A Plant Encyclopedia of DNA Elements
Lane, A. K., Niederhuth, C. E., Ji, L., & Schmitz, R. J. (2014) · Annual Review of Genetics · doi:10.1146/annurev-genet-120213-092443 · Cited by 42
Higgins, G. A., Allyn-Feuer, A., Barbour, E., & Athey, B. D. (2015) · Pharmacogenomics · doi:10.2217/pgs.15.106 · Cited by 46
Genome instability: a conserved mechanism of ageing?
Vijg, J., Dong, X., Milholland, B., & Zhang, L. (2017) · Essays in Biochemistry · doi:10.1042/ebc20160082 · Cited by 45
The contribution of alternative splicing to genetic risk for psychiatric disorders
Reble, E., Dineen, A., & Barr, C. L. (2017) · Genes Brain & Behavior · doi:10.1111/gbb.12430 · Cited by 47
Deconstructing and targeting the genomic architecture of human neurodegeneration
Jager, P. L. D., Yang, H., & Bennett, D. A. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0240-z · Cited by 46
Movilli, J., Kolkman, R. W., Rozzi, A., Corradini, R., Segerink, L. I., & Huskens, J. (2020) · Langmuir · doi:10.1021/acs.langmuir.0c00144 · Cited by 43
Cazares, T. A., Rizvi, F. W., Iyer, B., Chen, X., Kotliar, M., Bejjani, A. T., Wayman, J. A., Donmez, O. A., Wronowski, B., Parameswaran, S., et al. (2023) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010863 · Cited by 44
Single-cell genomics improves the discovery of risk variants and genes of atrial fibrillation
Selewa, A., Luo, K., Wasney, M., Smith, L. A., Sun, X., Tang, C., Eckart, H., Moskowitz, I. P. G., Basu, A., He, X., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-40505-5 · Cited by 45
The chromatin landscape of pathogenic transcriptional cell states in rheumatoid arthritis
Weinand, K., Sakaue, S., Nathan, A., Jonsson, A. H., Zhang, F., Watts, G. F. M., Suqri, M. A., Zhu, Z., Accelerating Medicines Partnership Program: Rheumatoid Arthritis and Systemic Lupus Erythematosus (AMP RA/SLE) Network, Albrecht, J., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-48620-7 · Cited by 51
Gene regulatory networks in disease and ageing
Avila, P. U., Padvitski, T., Leote, A. C., Chen, H., Sáez-Rodríguez, J., Kann, M., & Beyer, A. (2024) · Nature Reviews Nephrology · doi:10.1038/s41581-024-00849-7 · Cited by 47
Variants at IRX4 as prostate cancer expression quantitative trait loci
Xu, X., Hussain, W. M., Vijai, J., Offit, K., Rubin, M. A., Demichelis, F., & Klein, R. J. (2013) · European Journal of Human Genetics · doi:10.1038/ejhg.2013.195 · Cited by 44
Christiansen, I. K., Sandve, G. K., Schmitz, M., Dürst, M., & Hovig, E. J. (2015) · PLoS ONE · doi:10.1371/journal.pone.0119566 · Cited by 55
Uhl, J. D., Zandvakili, A., & Gebelein, B. (2016) · PLOS Genetics · doi:10.1371/journal.pgen.1005981 · Cited by 44
En route to precision medicine through the integration of biological sex into pharmacogenomics
Gaignebet, L., & Kararigas, G. (2017) · Clinical Science · doi:10.1042/cs20160379 · Cited by 43
Molecular windows into the human brain for psychiatric disorders
Egervári, G., Kozlenkov, A., Dracheva, S. M., & Hurd, Y. L. (2018) · Molecular Psychiatry · doi:10.1038/s41380-018-0125-2 · Cited by 40
The role of 3D genome organization in disease: From compartments to single nucleotides
Chakraborty, A., & Ay, F. (2018) · Seminars in Cell and Developmental Biology · doi:10.1016/j.semcdb.2018.07.005 · Cited by 39
Biological relevance of computationally predicted pathogenicity of noncoding variants
Liu, L., Sanderford, M. D., Patel, R., Chandrashekar, P. B., Gibson, G. C., & Kumar, S. (2019) · Nature Communications · doi:10.1038/s41467-018-08270-y · Cited by 45
Mortlock, S., Restuadi, R., Levien, R., Girling, J. E., Holdsworth‐Carson, S. J., Healey, M., Zhu, Z., Qi, T., Wu, Y., Lukowski, S. W., et al. (2019) · Clinical Epigenetics · doi:10.1186/s13148-019-0648-7 · Cited by 42
Computational Biology Solutions to Identify Enhancers-target Gene Pairs
Hariprakash, J. M., & Ferrari, F. (2019) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2019.06.012 · Cited by 41
Multiple Roles of Exosomal Long Noncoding RNAs in Cancers
Zhao, W., Liu, Y., Zhang, C., & Duan, C. (2019) · BioMed Research International · doi:10.1155/2019/1460572 · Cited by 46
Lalonde, S., Codina-Fauteux, V.-A., de Bellefon, S. M., Leblanc, F., Beaudoin, M., Simon, M.-M., Dali, R., Kwan, T., Lo, K. S., Pastinen, T., et al. (2019) · Genome biology · doi:10.1186/s13059-019-1749-5 · Cited by 44
Regulation of inflammation in diabetes: From genetics to epigenomics evidence
Diedisheim, M., Carcarino, E., Vandiedonck, C., Roussel, R., Gautier, J., & Venteclef, N. (2020) · Molecular Metabolism · doi:10.1016/j.molmet.2020.101041 · Cited by 48
Three decades of ASD genetics: building a foundation for neurobiological understanding and treatment
Eyring, K. W., & Geschwind, D. H. (2021) · Human Molecular Genetics · doi:10.1093/hmg/ddab176 · Cited by 46
Sharma, S., Chhabra, D., Kho, A. T., Hayden, L. P., Tantisira, K. G., & Weiss, S. T. (2014) · Thorax · doi:10.1136/thoraxjnl-2014-205166 · Cited by 43
Han, F., Liu, X., Chen, C., Liu, Y., Du, M., Guan, Y., Zhang, Y., Wang, D., Ahmed, M., Li, X., et al. (2020) · Cell Research · doi:10.1038/s41422-020-0303-z · Cited by 43
Breeze, C. E., Haugen, E., Gutiérrez‐Arcelus, M., Yao, X., Teschendorff, A. E., Beck, S., Dunham, I., Stamatoyannopoulos, J. A., Franceschini, N., Machiela, M. J., et al. (2024) · Genome biology · doi:10.1186/s13059-023-03126-1 · Cited by 45
Self-citationvan Weerd, J. H., Badi, I., van den Boogaard, M., Stefanovic, S., van de Werken, H. J. G., Gómez-Velázquez, M., Badía-Careaga, C., Manzanares, M., de Laat, W. L., Barnett, P., et al. (2014) · Circulation Research · doi:10.1161/circresaha.115.303591 · Cited by 45
Allele-Specific Methylation Occurs at Genetic Variants Associated with Complex Disease
Hutchinson, J. N., Raj, T., Fagerness, J., Stahl, E. A., Viloria, F. T., Gimelbrant, A. A., Seddon, J. M., Daly, M. J., Chess, A. J., & Plenge, R. M. (2014) · PLoS ONE · doi:10.1371/journal.pone.0098464 · Cited by 44
Berge, T., Leikfoss, I. S., Brorson, I. S., Bos, S. D., Page, C. M., Gustavsen, M. W., Bjølgerud, A., Holmøy, T., Celius, E. G., Damoiseaux, J. G. M. C., et al. (2016) · Genes and Immunity · doi:10.1038/gene.2015.61 · Cited by 44
Cowan, J. R., Tariq, M., Shaw, C. A., Rao, M., Belmont, J. W., Lalani, S. R., Smolarek, T. A., & Ware, S. M. (2016) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2015.0406 · Cited by 43
Decoding the role of regulatory element polymorphisms in complex disease
Vockley, C. M., Barrera, A., & Reddy, T. E. (2016) · Current Opinion in Genetics & Development · doi:10.1016/j.gde.2016.10.007 · Cited by 43
Properties of STAT1 and IRF1 enhancers and the influence of SNPs
Hassan, M. A. E., Huang, K., Eswara, M. B. K., Xu, Z., Tao, Y., Aubry, A., Ni, Z., Livne-Bar, I., Sangwan, M., Ahmad, M. S., et al. (2017) · BMC Molecular Biology · doi:10.1186/s12867-017-0084-1 · Cited by 45
Mechanisms of Type 2 Diabetes Risk Loci
Gaulton, K. J. (2017) · Current Diabetes Reports · doi:10.1007/s11892-017-0908-x · Cited by 43
SNP rs2071095 in LincRNA H19 is associated with breast cancer risk
Cui, P., Zhao, Y., Chu, X., He, N., Zheng, H., Han, J., Song, F., & Chen, K. (2018) · Breast Cancer Research and Treatment · doi:10.1007/s10549-018-4814-y · Cited by 43
Enhancer RNAs: a missing regulatory layer in gene transcription
Mao, R., Wu, Y., Ming, Y., Xu, Y., Wang, S., Chen, X., Wang, X., & Fan, Y. (2018) · Science China Life Sciences · doi:10.1007/s11427-017-9370-9 · Cited by 42
Oct1/Pou2f1 is selectively required for colon regeneration and regulates colon malignancy
Vázquez-Arreguín, K., Bensard, C., Schell, J. C., Swanson, E., Chen, X., Rutter, J. P., & Tantin, D. R. (2019) · PLoS Genetics · doi:10.1371/journal.pgen.1007687 · Cited by 43
Recent Advances in the Genetics of Fractures in Osteoporosis
Koromani, F., Trajanoska, K., Rivadeneira, F., & Oei, L. (2019) · Frontiers in Endocrinology · doi:10.3389/fendo.2019.00337 · Cited by 43
BarkBase: Epigenomic Annotation of Canine Genomes
Megquier, K., Genereux, D. P., Hekman, J. P., Swofford, R., Turner-Maier, J., Johnson, J., Alonso, J., Li, X., Morrill, K. M., Anguish, L. J., et al. (2019) · Genes · doi:10.3390/genes10060433 · Cited by 42
Hill, W. D., Davies, G., Harris, S. E., Hagenaars, S. P., The neuroCHARGE Cognitive Working group, Davies, G., Deary, I. J., Debette, S., Verbaas, C. I., Bressler, J., et al. (2016) · Translational Psychiatry · doi:10.1038/tp.2016.246 · Cited by 43
Benaglio, P., D’Antonio‐Chronowska, A., Ma, W., Yang, F., Greenwald, W. W. Y., Donovan, M. K. R., DeBoever, C., Li, H., Drees, F., Singhal, S., et al. (2019) · Nature Genetics · doi:10.1038/s41588-019-0499-3 · Cited by 42
Self-citationLutz, S., Brion, C., Kliebhan, M., & Albert, F. W. (2019) · PLoS Genetics · doi:10.1371/journal.pgen.1008375 · Cited by 46
Chromatin interactome mapping at 139 independent breast cancer risk signals
Beesley, J., Sivakumaran, H., Marjaneh, M. M., Lima, L. G., Hillman, K. M., Kaufmann, S., Tuano, N., Hussein, N., Ham, S., Mukhopadhyay, P., et al. (2020) · Genome biology · doi:10.1186/s13059-019-1877-y · Cited by 41
Evaluating the informativeness of deep learning annotations for human complex diseases
Dey, K. K., van de Geijn, B., Kim, S. S., Hormozdiari, F., Kelley, D. R., & Price, A. L. (2020) · Nature Communications · doi:10.1038/s41467-020-18515-4 · Cited by 42
A GO catalogue of human DNA-binding transcription factors
Lovering, R. C., Gaudet, P., Acencio, M. L., Ignatchenko, A., Jolma, A., Fornés, O., Kuiper, M. T. R., Kulakovskiy, I. V., Lægreid, A., Martin, M. J., et al. (2021) · Biochimica et Biophysica Acta (BBA) - Gene Regulatory Mechanisms · doi:10.1016/j.bbagrm.2021.194765 · Cited by 40
Regulation of the Late Onset alzheimer’s Disease Associated HLA-DQA1/DRB1 Expression
Zhang, X., Zou, M., Wu, Y., Jiang, D., Wu, T., Zhao, Y., Wu, D., Cui, J., & Li, G. (2022) · American Journal of Alzheimer s Disease & Other Dementias® · doi:10.1177/15333175221085066 · Cited by 45
Antoniani, C., Romano, O., & Miccio, A. (2017) · Stem Cells Translational Medicine · doi:10.1002/sctm.17-0192 · Cited by 50
Chandrashekar, P. B., Alatkar, S., Wang, J., Hoffman, G. E., He, C., Jin, T., Khullar, S., Bendl, J., Fullard, J. F., Roussos, P., et al. (2023) · Genome Medicine · doi:10.1186/s13073-023-01248-6 · Cited by 37
Lineage-specific accelerated sequences underlying primate evolution
Bi, X., Zhou, L., Zhang, J.-J., Feng, S., Hu, M. Y., Cooper, D. N., Lin, J., Li, J., Wu, D., & Zhang, G. (2023) · Science Advances · doi:10.1126/sciadv.adc9507 · Cited by 46
Zhang, W., Zhang, M., Xu, Z., Yan, H., Wang, H., Jiang, J., Wan, J., Tang, B., Liu, C., Chen, C., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-40861-2 · Cited by 46
Self-citationComprehensive Functional Annotation of Seventy-One Breast Cancer Risk Loci
Rhie, S. K., Coetzee, S. G., Noushmehr, H., Yan, C., Kim, J. M., Haiman, C. A., & Coetzee, G. A. (2013) · PLoS ONE · doi:10.1371/journal.pone.0063925 · Cited by 43
Pyun, J.-A., Kim, S., Cho, N. H., Koh, I., Lee, J., Shin, C., & Kwack, K. (2013) · Menopause The Journal of The North American Menopause Society · doi:10.1097/gme.0b013e3182a433f7 · Cited by 42
Genome-enabled prediction of quantitative traits in chickens using genomic annotation
Morota, G., Abdollahi-Arpanahi, R., Kranis, A., & Gianola, D. (2014) · BMC Genomics · doi:10.1186/1471-2164-15-109 · Cited by 45
Approaches for establishing the function of regulatory genetic variants involved in disease
Knight, J. C. (2014) · Genome Medicine · doi:10.1186/s13073-014-0092-4 · Cited by 45
Larson, N. B., McDonnell, S. K., French, A. J., Fogarty, Z. C., Cheville, J. C., Middha, S., Riska, S. M., Baheti, S., Nair, A., Wang, L., et al. (2015) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2015.04.015 · Cited by 44
Genetic variants alter T-bet binding and gene expression in mucosal inflammatory disease
Soderquest, K., Hertweck, A., Giambartolomei, C., Henderson, S. R., Mohamed, R., Goldberg, R., Perucha, E., Franke, L., Herrero, J., Plagnol, V., et al. (2017) · PLoS Genetics · doi:10.1371/journal.pgen.1006587 · Cited by 44
Predicting gene expression in massively parallel reporter assays: A comparative study
Kreimer, A., Zeng, H., Edwards, M. D., Guo, Y., Tian, K., Shin, S., Welch, R., Wainberg, M., Mohan, R., Sinnott-Armstrong, N., et al. (2017) · Human Mutation · doi:10.1002/humu.23197 · Cited by 41
Sex influences eQTL effects of SLE and Sjögren’s syndrome-associated genetic polymorphisms
Lindén, M., Sepúlveda, J. I. R., James, T., Thorlacius, G. E., Brauner, S., Gómez-Cabrero, D., Olsson, T. P., Kockum, I. S., & Wahren‐Herlenius, M. (2017) · Biology of Sex Differences · doi:10.1186/s13293-017-0153-7 · Cited by 42
Detecting Differential Transcription Factor Activity from ATAC-Seq Data
Tripodi, I. J., Allen, M. A., & Dowell, R. D. (2018) · Molecules · doi:10.3390/molecules23051136 · Cited by 37
Fu, S., Wang, Q., Moore, J. E., Purcaro, M. J., Pratt, H., Fan, K., Gu, C., Jiang, C., Zhu, R., Kundaje, A. B., et al. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky753 · Cited by 39
The Open Chromatin Landscape of Non–Small Cell Lung Carcinoma
Wang, Z., Tu, K., Lin, X., Luo, K., Luo, W., Tang, J., Lu, K., Hu, X., He, Y., Qiao, W., et al. (2019) · Cancer Research · doi:10.1158/0008-5472.can-18-3663 · Cited by 42
LocusFocus: Web-based colocalization for the annotation and functional follow-up of GWAS
Panjwani, N., Wang, F., Wang, C., He, G., Mastromatteo, S., Bao, A., Gong, J., Rommens, J. M., Sun, L., & Strug, L. J. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008336 · Cited by 42
Sakabe, N. J., Aneas, I., Knoblauch, N., Sobreira, D. R., Clark, N., Paz, C., Horth, C., Ziffra, R. S., Kaur, H., Liu, X., et al. (2020) · Science Advances · doi:10.1126/sciadv.abc8696 · Cited by 38
Selective Requirement of MYB for Oncogenic Hyperactivation of a Translocated Enhancer in Leukemia
Smeenk, L., Ottema, S., Mulet‐Lazaro, R., Ebert, A., Havermans, M., Varea, A. A., Fellner, M., Pastoors, D., van Herk, S., Erpelinck-Verschueren, C. A. J., et al. (2021) · Cancer Discovery · doi:10.1158/2159-8290.cd-20-1793 · Cited by 41
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
