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
Biological insights from 108 schizophrenia-associated genetic loci
Schizophrenia Working Group of the Psychiatric Genomics Consortium (2014) · Nature · doi:10.1038/nature13595 · Cited by 7,577
Integrative analysis of 111 reference human epigenomes
Roadmap Epigenomics Consortium, Kundaje, A., Meuleman, W., Ernst, J., Bilenky, M., Yen, A., Heravi-Moussavi, A., Kheradpour, P., Zhang, Z., Wang, J., et al. (2015) · Nature · doi:10.1038/nature14248 · Cited by 6,368
Self-citationBuenrostro, J. D., Giresi, P. G., Zaba, L. C., Chang, H. Y., & Greenleaf, W. J. (2013) · Nature Methods · doi:10.1038/nmeth.2688 · Cited by 6,422
The Genotype-Tissue Expression (GTEx) pilot analysis: Multitissue gene regulation in humans
The GTEx Consortium, Ardlie, K. G., Deluca, D. S., Segrè, A. V., Sullivan, T. J., Young, T. R., Gelfand, E. T., Trowbridge, C. A., Maller, J. B., Tukiainen, T., et al. (2015) · Science · doi:10.1126/science.1262110 · Cited by 5,062
Functional mapping and annotation of genetic associations with FUMA
Watanabe, K., Taskesen, E., van Bochoven, A., & Posthuma, D. (2017) · Nature Communications · doi:10.1038/s41467-017-01261-5 · Cited by 4,083
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
