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
Flagged first, then by citations
1 of the 47 references couldn’t be matched to a known paper.
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 701–800 of 1,024
Single-cell chromatin accessibility reveals malignant regulatory programs in primary human cancers
Sundaram, L., Kumar, A., Zatzman, M., Salcedo, A., Ravindra, N. G., Shams, S., Louie, B. H., Bagdatli, S. T., Myers, M. A., Sarmashghi, S., et al. (2024) · Science · doi:10.1126/science.adk9217 · Cited by 66
Lundin, K. E. A., & Sollid, L. M. (2014) · Current Opinion in Gastroenterology · doi:10.1097/mog.0000000000000041 · Cited by 71
Lessons from Functional Analysis of Genome-Wide Association Studies
Sur, I., Tuupanen, S., Whitington, T., Aaltonen, L. A., & Taipale, J. (2013) · Cancer Research · doi:10.1158/0008-5472.can-13-0789 · Cited by 68
Gusev, A., Mancuso, N., Finucane, H. K., Reshef, Y., Song, L., Safi, A., Oh, E. C. T., McCarroll, S. A., Neale, B. M., Ophoff, R. A., et al. (2016) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/067355 · Cited by 66
Schofield, E. C., Carver, T., Achuthan, P., Freire-Pritchett, P., Spivakov, M., Todd, J. A., & Burren, O. S. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw173 · Cited by 65
Jasinska, A. J., Zelaya, I., Service, S. K., Peterson, C. B., Cantor, R. M., Choi, O., DeYoung, J. A., Eskin, E., Fairbanks, L. A., Fears, S. C., et al. (2017) · Nature Genetics · doi:10.1038/ng.3959 · Cited by 67
Expression estimation and eQTL mapping for HLA genes with a personalized pipeline
Aguiar, V. R. C., Cesar, J. E. D. S., Delaneau, O., Dermitzakis, E. T., & Meyer, D. (2019) · PLoS Genetics · doi:10.1371/journal.pgen.1008091 · Cited by 64
Involvement of long noncoding RNAs in the pathogenesis of autoimmune diseases
Zou, Y., & Xu, H. (2020) · Journal of Translational Autoimmunity · doi:10.1016/j.jtauto.2020.100044 · Cited by 65
Chromatin accessibility during human first-trimester neurodevelopment
Mannens, C. C. A., Hu, L., Lönnerberg, P., Schipper, M., Reagor, C. C., Li, X., He, X., Barker, R. A., Sundström, E., Posthuma, D., et al. (2024) · Nature · doi:10.1038/s41586-024-07234-1 · Cited by 69
ImmVar project: Insights and design considerations for future studies of “healthy” immune variation
Jager, P. L. D., Hacohen, N., Mathis, D. J., Regev, A., Stranger, B. E., & Benoist, C. O. (2015) · Seminars in Immunology · doi:10.1016/j.smim.2015.03.003 · Cited by 65
Schork, A. J., Wang, Y., Thompson, W. K., Dale, A. M., & Andreassen, O. A. (2015) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2015.10.008 · Cited by 64
Targeted transgene integration overcomes variability of position effects in zebrafish
Roberts, J. A., Miguel-Escalada, I., Slovik, K. J., Walsh, K. T., Hadzhiev, Y., Sanges, R., Stupka, E., Marsh, E. K., Balciuniene, J., Balciunas, D., et al. (2014) · Development · doi:10.1242/dev.100347 · Cited by 69
Amariuta, T., Luo, Y., Gazal, S., Davenport, E. E., van de Geijn, B., Ishigaki, K., Westra, H.-J., Teslovich, N. C., Okada, Y., Yamamoto, K., et al. (2019) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2019.03.012 · Cited by 60
White, M. A. (2015) · Genomics · doi:10.1016/j.ygeno.2015.06.003 · Cited by 65
Applying CRISPR–Cas9 tools to identify and characterize transcriptional enhancers
Lopes, R., Korkmaz, G., & Agami, R. (2016) · Nature Reviews Molecular Cell Biology · doi:10.1038/nrm.2016.79 · Cited by 65
A Chromatin Basis for Cell Lineage and Disease Risk in the Human Pancreas
Arda, H. E., Tsai, J., Rosli, Y., Giresi, P. G., Bottino, R., Greenleaf, W. J., Chang, H. Y., & Kim, S. K. (2018) · Cell Systems · doi:10.1016/j.cels.2018.07.007 · Cited by 62
Discovery of regulatory noncoding variants in individual cancer genomes by using cis-X
Liu, Y., Li, C., Shen, S., Chen, X., Szlachta, K., Edmonson, M. N., Shao, Y., Ma, X., Hyle, J., Wright, S., et al. (2020) · Nature Genetics · doi:10.1038/s41588-020-0659-5 · Cited by 64
Zhao, H., Tu, Z., Liu, Y., Zong, Z., Li, J., Liu, H., Xiong, F., Zhan, J., Hu, X., & Xie, W. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab383 · Cited by 64
A transcriptome-wide association study identifies susceptibility genes for Parkinson’s disease
Yao, S., Zhang, X., Zou, S.-C., Zhu, Y. X., Li, B., Kuang, W., Guo, Y., Li, X., Li, L., & Wang, X. (2021) · npj Parkinson s Disease · doi:10.1038/s41531-021-00221-7 · Cited by 65
Tissue-specific enhancer–gene maps from multimodal single-cell data identify causal disease alleles
Sakaue, S., Weinand, K., Isaac, S., Dey, K. K., Jagadeesh, K. A., Kanai, M., Watts, G. F. M., Zhu, Z., Accelerating Medicines Partnership® RA/SLE Program and Network, Albrecht, J., et al. (2024) · Nature Genetics · doi:10.1038/s41588-024-01682-1 · Cited by 65
Chromatin priming of genes in development: Concepts, mechanisms and consequences
Bonifer, C., & Cockerill, P. N. (2017) · Experimental Hematology · doi:10.1016/j.exphem.2017.01.003 · Cited by 63
Genetic association of molecular traits: A help to identify causative variants in complex diseases
Vandiedonck, C. (2017) · Clinical Genetics · doi:10.1111/cge.13187 · Cited by 63
A directed learning strategy integrating multiple omic data improves genomic prediction
Hu, X., Xie, W., Wu, C., & Xu, S. (2019) · Plant Biotechnology Journal · doi:10.1111/pbi.13117 · Cited by 64
Gain-of-Function Mutations: An Emerging Advantage for Cancer Biology
Li, Y., Zhang, Y., Li, X., Yi, S. S., & Xu, J. (2019) · Trends in Biochemical Sciences · doi:10.1016/j.tibs.2019.03.009 · Cited by 65
Pearl, J. R., Colantuoni, C., Bergey, D. E., Funk, C. C., Shannon, P. T., Basu, B., Casella, A. M., Oshone, R. T., Hood, L., Price, N. D., et al. (2019) · Cell Systems · doi:10.1016/j.cels.2019.01.002 · Cited by 60
Hoffman, G. E., Bendl, J. M., Girdhar, K., Schadt, E. E., & Roussos, P. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz808 · Cited by 63
Cheung, W., Shao, X., Morin, A., Siroux, V., Kwan, T., Ge, B., Aïssi, D., Chen, L., Vasquez, L. J., Allum, F., et al. (2019) · Genome biology · doi:10.1186/s13059-019-1702-7 · Cited by 65
Joshi, H. J., Hansen, L. H., Narimatsu, Y., Freeze, H. H., Henrissat, B., Bennett, E., Wandall, H. H., Clausen, H., & Schjoldager, K. T.-B. G. (2018) · Glycobiology · doi:10.1093/glycob/cwy015 · Cited by 62
“Stripe” transcription factors provide accessibility to co-binding partners in mammalian genomes
Zhao, Y., Vartak, S. V., Conte, A., Wang, X., Garcia, D. A., Stevens, E., Jung, S. K., Kieffer-Kwon, K.-R., Vian, L., Stodola, T. J., et al. (2022) · Molecular Cell · doi:10.1016/j.molcel.2022.06.029 · Cited by 62
Yardımcı, G. G., Frank, C. L., Crawford, G. E., & Ohler, U. (2014) · Nucleic Acids Research · doi:10.1093/nar/gku810 · Cited by 61
Gu, J., Stevens, M. W., Xing, X., Li, D., Zhang, B., Payton, J. E., Oltz, E. M., Jarvis, J. N., Jiang, K., Cicero, T. J., et al. (2016) · G3 Genes Genomes Genetics · doi:10.1534/g3.115.025437 · Cited by 64
Molecular regulation of peripheral B cells and their progeny in immunityPreprint
Boothby, M. R., Hodges, E. C., & Thomas, J. W. (2019) · Genes & Development · doi:10.1101/gad.320192.118 · Cited by 68
The regulatory landscape of early maize inflorescence development
Parvathaneni, R. K., Bertolini, E., Shamimuzzaman, M., Vera, D. L., Lung, P.-Y., Rice, B. R., Zhang, J., Brown, P. J., Lipka, A. E., Bass, H. W., et al. (2020) · Genome biology · doi:10.1186/s13059-020-02070-8 · Cited by 67
Genetic risk factors of ME/CFS: a critical review
Dibble, J. J., McGrath, S. J., & Ponting, C. P. (2020) · Human Molecular Genetics · doi:10.1093/hmg/ddaa169 · Cited by 62
Ma, M., Ru, Y., Chuang, L.-S. F., Hsu, N.-Y., Shi, L., Hakenberg, J., Cheng, W., Uzilov, A. V., Ding, W., Glicksberg, B. S., et al. (2015) · BMC Genomics · doi:10.1186/1471-2164-16-s8-s3 · Cited by 61
Genetic association signal near NTN 4 in T ourette syndrome
Paschou, P., Yu, D., Gerber, G. F., Evans, P. D., Tsetsos, F., Davis, L. K., Karagiannidis, I., Chaponis, J., Gamazon, E. R., Müller‐Vahl, K., et al. (2014) · Annals of Neurology · doi:10.1002/ana.24215 · Cited by 61
Functional characterization of CYP2D6 enhancer polymorphisms
Wang, D., Papp, A. C., & Sun, X. (2014) · Human Molecular Genetics · doi:10.1093/hmg/ddu566 · Cited by 62
Dissecting evolution and disease using comparative vertebrate genomics
Meadows, J. R. S., & Lindblad‐Toh, K. (2017) · Nature Reviews Genetics · doi:10.1038/nrg.2017.51 · Cited by 60
Arnes, L., Liu, Z., Wang, J., Maurer, H. C., Sagalovskiy, I. R., Sánchez-Martín, M., Bommakanti, N. K., Garofalo, D. C., Balderes, D. A., Sussel, L., et al. (2018) · Gut · doi:10.1136/gutjnl-2017-314353 · Cited by 64
Colocalization analyses of genomic elements: approaches, recommendations and challenges
Kanduri, C., Bock, C., Gundersen, S., Hovig, E. J., & Sandve, G. K. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty835 · Cited by 64
The NIH Common Fund/Roadmap Epigenomics Program: Successes of a comprehensive consortium
Satterlee, J. S., Chadwick, L. H., Tyson, F. L., McAllister, K., Beaver, J. M., Birnbaum, L. S., Volkow, N. D., Wilder, E. L., Anderson, J. M., & Roy, A. L. (2019) · Science Advances · doi:10.1126/sciadv.aaw6507 · Cited by 62
GBshape: a genome browser database for DNA shape annotations
Chiu, T.-P., Yang, L., Zhou, T., Main, B. J., Parker, S. C. J., Nuzhdin, S. V., Tullius, T. D., & Rohs, R. (2014) · Nucleic Acids Research · doi:10.1093/nar/gku977 · Cited by 62
Corradin, O., Cohen, A. J., Luppino, J. M., Bayles, I., Schumacher, F. R., & Scacheri, P. C. (2016) · Nature Genetics · doi:10.1038/ng.3674 · Cited by 63
A versatile platform for locus-scale genome rewriting and verification
Brosh, R., Laurent, J. M., Ordóñez, R., Huang, E., Hogan, M. S., Hitchcock, A. M., MITCHELL, L. A., Pinglay, S., Cadley, J. A., Luther, R. D., et al. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2023952118 · Cited by 63
Self-citationSystemic lupus erythematosus as a genetic disease
Harley, I. T. W., & Sawalha, A. H. (2022) · Clinical Immunology · doi:10.1016/j.clim.2022.108953 · Cited by 68
SNPs in 3′UTR miRNA Target Sequences Associated with Individual Drug Susceptibility
RYKOVA, E. Y., Ershov, N., Damarov, I. S., & Merkulova, T. I. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms232213725 · Cited by 70
Ledó, N., Ko, Y.-A., Park, A. S. D., Kang, H. M., Han, S. Y., Choi, P., & Suszták, K. (2014) · Journal of the American Society of Nephrology · doi:10.1681/asn.2014010028 · Cited by 60
Walsh, A. M., Whitaker, J. W., Huang, C., Cherkas, Y., Lamberth, S. L., Brodmerkel, C. M., Curran, M. E., & Dobrin, R. (2016) · Genome biology · doi:10.1186/s13059-016-0948-6 · Cited by 58
Hameed, I., Masoodi, S. R., Malik, P. A., Mir, S. A., Ghazanfar, K., & Ganai, B. A. (2018) · Gene · doi:10.1016/j.gene.2018.03.095 · Cited by 61
Lou, J., Gong, J., Ke, J., Tian, J., Zhang, Y., Li, J., Yang, Y., Zhu, Y., Gong, Y., Lü, L., et al. (2016) · Carcinogenesis · doi:10.1093/carcin/bgw204 · Cited by 61
Stadhouders, R., Li, B. W. S., de Bruijn, M. J. W., Gómez, A., Rao, T. N., Fehling, H. J., van IJcken, W. F. J., Lim, A. I., Santo, J. P. D., Graf, T., et al. (2018) · Journal of Allergy and Clinical Immunology · doi:10.1016/j.jaci.2017.12.1006 · Cited by 60
Genetic and Epigenetic Fine Mapping of Complex Trait Associated Loci in the Human Liver
Çalışkan, M., Manduchi, E., Rao, H. S., Segert, J. A., Beltrame, M. H., Trizzino, M., Park, Y., Baker, S. W., Chesi, A., Johnson, M. E., et al. (2019) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2019.05.010 · Cited by 57
Transcriptomic Insight Into the Polygenic Mechanisms Underlying Psychiatric Disorders
Hernandez, L. M., Kim, M., Hoftman, G. D., Haney, J. R., de la Torre-Ubieta, L., Paşaniuc, B., & Gandal, M. J. (2020) · Biological Psychiatry · doi:10.1016/j.biopsych.2020.06.005 · Cited by 59
In vivo functional screening for systems-level integrative cancer genomics
Weber, J., Braun, C. J., Saur, D., & Rad, R. (2020) · Nature reviews. Cancer · doi:10.1038/s41568-020-0275-9 · Cited by 60
Circular RNAs: The Brain Transcriptome Comes Full Circle
Gokoolparsadh, A., Loy, C. T., Halliday, G. M., & Voineagu, I. (2020) · Trends in Neurosciences · doi:10.1016/j.tins.2020.07.007 · Cited by 61
Kerimov, N., Tambets, R., Hayhurst, J. D., Rahu, I., Kolberg, P., Raudvere, U., Kuzmin, I., Chowdhary, A., Vija, A., Teras, H., et al. (2023) · PLoS Genetics · doi:10.1371/journal.pgen.1010932 · Cited by 57
Ochiai, H., Miyamoto, T., Kanai, A., Hosoba, K., Sakuma, T., Kudo, Y., Asami, K., Ogawa, A., Watanabe, A., Kajii, T., et al. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1317008111 · Cited by 60
Yu, J., Wu, H., Wen, Y., Liu, Y., Zhou, T., Ni, B., Lin, Y., Dong, J., Zhou, Z., Hu, Z., et al. (2014) · Human Molecular Genetics · doi:10.1093/hmg/ddu557 · Cited by 61
Wilken, M. S., Brzezinski, J. A., Torre, A. L., Siebenthall, K. T., Thurman, R. E., Sabo, P. J., Sandstrom, R. S., Vierstra, J. D., Canfield, T. K., Hansen, R. S., et al. (2015) · Epigenetics & Chromatin · doi:10.1186/1756-8935-8-8 · Cited by 58
Self-citationBabu, D., & Fullwood, M. J. (2015) · Nucleus · doi:10.1080/19491034.2015.1106676 · Cited by 59
Mammalian Synthetic Biology: Engineering Biological Systems
Black, J. B., Pérez‐Piñera, P., & Gersbach, C. A. (2017) · Annual Review of Biomedical Engineering · doi:10.1146/annurev-bioeng-071516-044649 · Cited by 60
BRCA1-associated R-loop affects transcription and differentiation in breast luminal epithelial cells
Chiang, H.-C., Zhang, X., Li, J., Zhao, X., Chen, J., Wang, H. T.-H., Jatoi, I., Brenner, A. J., Hu, Y., & Li, R. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz262 · Cited by 60
Self-citationStrengthening Causal Inference for Complex Disease Using Molecular Quantitative Trait Loci
Neumeyer, S., Hemani, G., & Zeggini, E. (2019) · Trends in Molecular Medicine · doi:10.1016/j.molmed.2019.10.004 · Cited by 55
Li, K., Zhang, Y., Liu, X., Liu, Y., Gu, Z., Cao, H., Dickerson, K. E., Chen, M., Chen, W., Shao, Z., et al. (2020) · Cancer Discovery · doi:10.1158/2159-8290.cd-19-1128 · Cited by 61
Athie, A., Marchese, F. P., González, J., Lozano, T., Raimondi, I., Juvvuna, P. K., Abad, A., Marín-Béjar, O., Serizay, J., Martínez, D., et al. (2020) · The Journal of Cell Biology · doi:10.1083/jcb.201908078 · Cited by 58
Enhancer-gene specificity in development and disease
Pachano, T., Haro, E., & Rada-Iglesias, Á. (2022) · Development · doi:10.1242/dev.186536 · Cited by 58
Peña-Martínez, E. G., & Rodríguez‐Martínez, J. A. (2024) · Frontiers in Bioscience-Scholar · doi:10.31083/j.fbs1601004 · Cited by 63
Ganz, J. (2017) · Developmental Dynamics · doi:10.1002/dvdy.24597 · Cited by 59
The effect of genetic variation on promoter usage and enhancer activity
Garieri, M., Delaneau, O., Santoni, F. A., Fish, R. J., Mull, D., Carninci, P., Dermitzakis, E. T., Antonarakis, S. E., & Fort, A. (2017) · Nature Communications · doi:10.1038/s41467-017-01467-7 · Cited by 61
Nutritional Genomics and Direct-to-Consumer Genetic Testing: An Overview
Guasch‐Ferré, M., Dashti, H. S., & Merino, J. (2018) · Advances in Nutrition · doi:10.1093/advances/nmy001 · Cited by 64
The MHC in the era of next-generation sequencing: Implications for bridging structure with function
Petersdorf, E. W., & Ó’hUigín, C. (2018) · Human Immunology · doi:10.1016/j.humimm.2018.10.002 · Cited by 61
Iyer, D., Zhao, Q., Wirka, R. C., Naravane, A. V., Nguyen, T., Liu, B., Nagao, M., Cheng, P. P., Miller, C. L., Kim, J. B., et al. (2018) · PLoS Genetics · doi:10.1371/journal.pgen.1007681 · Cited by 60
Current perspectives in assessing humoral immunity after measles vaccination
Haralambieva, I. H., Kennedy, R. B., Ovsyannikova, I. G., Schaid, D. J., & Poland, G. A. (2018) · Expert Review of Vaccines · doi:10.1080/14760584.2019.1559063 · Cited by 60
Long Noncoding RNA in Myeloid and Lymphoid Cell Differentiation, Polarization and Function
Ahmad, I., Valverde, A. M., Ahmad, F., & Naqvi, A. R. (2020) · Cells · doi:10.3390/cells9020269 · Cited by 59
Sensitive detection of chromatin-altering polymorphisms reveals autoimmune disease mechanisms
del Rosario, R. C.-H., Poschmann, J., Rouam, S. L., Png, E., Khor, C. C., Hibberd, M. L., & Prabhakar, S. (2015) · Nature Methods · doi:10.1038/nmeth.3326 · Cited by 58
Perspectives of International Human Epigenome Consortium
Bae, J. (2013) · Genomics & Informatics · doi:10.5808/gi.2013.11.1.7 · Cited by 58
Qian, C., Li, L.-J., Huang, H., Yang, H., & Wu, D. (2020) · Cancer Cell International · doi:10.1186/s12935-020-1158-6 · Cited by 59
Lu, Z., Gopalan, S., Dong, Y., Conti, D. V., Paşaniuc, B., Gusev, A., & Mancuso, N. (2022) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2022.07.002 · Cited by 59
Environmental stress and transposon transcription in the mammalian brain
Hunter, R. G., McEwen, B. S., & Pfaff, D. W. (2013) · Mobile Genetic Elements · doi:10.4161/mge.24555 · Cited by 58
Schierding, W. S., Cutfield, W. S., & O'Sullivan, J. M. (2014) · Frontiers in Genetics · doi:10.3389/fgene.2014.00039 · Cited by 60
Next-Generation Sequencing and Epigenomics Research: A Hammer in Search of Nails
Sarda, S., & Hannenhalli, S. S. (2014) · Genomics & Informatics · doi:10.5808/gi.2014.12.1.2 · Cited by 58
Bendesky, A., Pitts, J., Rockman, M. V., Chen, W., Tan, M., Kruglyak, L., & Bargmann, C. I. (2012) · PLoS Genetics · doi:10.1371/journal.pgen.1003157 · Cited by 56
A CRISPR Path to Engineering New Genetic Mouse Models for Cardiovascular Research
Miano, J. M., Zhu, Q. M., & Lowenstein, C. J. (2016) · Arteriosclerosis Thrombosis and Vascular Biology · doi:10.1161/atvbaha.116.304790 · Cited by 58
Genome-Wide Association Study of the Genetic Determinants of Emphysema Distribution
Boueiz, A. R., Lutz, S. M., Cho, M. H., Hersh, C. P., Bowler, R. P., Washko, G. R., Halper‐Stromberg, E., Bakke, P. S., Gulsvik, A., Laird, N. M., et al. (2016) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.201605-0997oc · Cited by 56
Platelet function is modified by common sequence variation in megakaryocyte super enhancers
Petersen, R., Lambourne, J. J., Javierre, B. M., Grassi, L., Kreuzhuber, R., Ruklisa, D., Rosa, I. M., Tomé, A. R., Elding, H., van Geffen, J. P., et al. (2017) · Nature Communications · doi:10.1038/ncomms16058 · Cited by 56
Characterization of cross-tissue genetic-epigenetic effects and their patterns in schizophrenia
Lin, D., Chen, J., Perrone‐Bizzozero, N. I., Bustillo, J., Du, Y., Calhoun, V. D., & Liu, J. (2018) · Genome Medicine · doi:10.1186/s13073-018-0519-4 · Cited by 58
Sex differences in human adipose tissue gene expression and genetic regulation involve adipogenesisPreprint
Anderson, W. D., Soh, J. Y., Innis, S. E., Dimanche, A., Ma, L., Langefeld, C. D., Comeau, M. E., Das, S. K., Schadt, E. E., Björkegren, J., et al. (2020) · Genome Research · doi:10.1101/gr.264614.120 · Cited by 60
Hong, T., Parameswaran, S., Donmez, O. A., Miller, D., Forney, C. R., Lape, M., Ribeiro, M. S. J., Liang, J., Edsall, L., Magnusen, A. F., et al. (2021) · Genome Research · doi:10.1101/gr.264705.120 · Cited by 57
Crossed wires: 3D genome misfolding in human disease
Norton, H. K., & Phillips-Cremins, J. E. (2017) · Journal of Cell Biology · doi:10.1083/jcb.201611001 · Cited by 57
Watt, S., Vasquez, L., Walter, K., Mann, A. L., Kundu, K., Chen, L., Sims, Y., Ecker, S., Burden, F., Farrow, S., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-22548-8 · Cited by 63
Determination of local chromatin interactions using a combined CRISPR and peroxidase APEX2 system
Qiu, W., Xu, Z., Zhang, M., Zhang, D., Fan, H., Li, T., Wang, Q., Liu, P., Zhu, Z., Du, D., et al. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz134 · Cited by 56
Missing heritability in Parkinson’s disease: the emerging role of non-coding genetic variation
Ohnmacht, J., May, P., Sinkkonen, L., & Krüger, R. (2020) · Journal of Neural Transmission · doi:10.1007/s00702-020-02184-0 · Cited by 60
Genetic approaches for increasing fitness in endangered species
Kosch, T. A., Waddle, A. W., Cooper, C. A., Zenger, K. R., Garrick, D. J., Berger, L., & Skerratt, L. F. (2022) · Trends in Ecology & Evolution · doi:10.1016/j.tree.2021.12.003 · Cited by 58
The missing diversity in human epigenomic studies
Breeze, C. E., Beck, S., Berndt, S. I., & Franceschini, N. (2022) · Nature Genetics · doi:10.1038/s41588-022-01081-4 · Cited by 57
Genetic and epigenetic variation in the lineage specification of regulatory T cells
Arvey, A. J., van der Veeken, J., Plitas, G., Rich, S. S., CONCANNON, P. J., & Rudensky, A. Y. (2015) · eLife · doi:10.7554/elife.07571 · Cited by 58
Koufariotis, L. T., Chen, Y. P., Bolormaa, S., & Hayes, B. J. (2014) · BMC Genomics · doi:10.1186/1471-2164-15-436 · Cited by 59
Population Genetics of Rare Variants and Complex Diseases
Maher, M. C., Uricchio, L. H., Torgerson, D. G., & Hernandez, R. D. (2012) · Human Heredity · doi:10.1159/000346826 · Cited by 63
Genes with High Network Connectivity Are Enriched for Disease Heritability
Kim, S. S., Dai, C. L., Hormozdiari, F., van de Geijn, B., Gazal, S., Park, Y., O’Connor, L. J., Amariuta, T., Loh, P., Finucane, H. K., et al. (2019) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2019.03.020 · Cited by 55
INFERNO: inferring the molecular mechanisms of noncoding genetic variants
Amlie‐Wolf, A., Tang, M., Mlynarski, E. E., Kuksa, P. P., Valladares, O., Katanić, Ž., Tsuang, D. W., Brown, C. D., Schellenberg, G. D., & Wang, L. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky686 · Cited by 56
Orchard, P., Manickam, N., Varshney, A., Vadlamudi, S., Burant, C., Parker, S. C. J., Kaplan, J., Lalancette, C., Mohlke, K. L., Gallagher, K., et al. (2021) · Genome Research · doi:10.1101/gr.268482.120 · Cited by 58
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