ArticlePloS one2013
Predicting cell types and genetic variations contributing to disease by combining GWAS and epigenetic data.
Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed, 41 citations in OpenAlex.
- Article
- chromMAGMA: regulatory element-centric interrogation of risk variants.Life science alliance · 2022Article
- Multi-omics colocalization with genome-wide association studies reveals a context-specific genetic mechanism at a childhood onset asthma risk locus.Genome medicine · 2021Article
- Variant to Gene Mapping to Discover New Targets for Immune Tolerance.Frontiers in immunology · 2021Article
- Comprehensive functional annotation of susceptibility variants associated with asthma.Human genetics · 2020Article
- Interactions between environmental pollutants and genetic susceptibility in asthma risk.Current opinion in immunology · 2019Review
- Optimising the identification of causal variants across varying genetic architectures in crops.Plant biotechnology journal · 2019Article
- Integrated Functional Genomic Analysis Enables Annotation of Kidney Genome-Wide Association Study Loci.Journal of the American Society of Nephrology : JASN · 2019Article
- Adult Neural Stem Cells: Basic Research and Production Strategies for Neurorestorative Therapy.Stem cells international · 2018Review
- Histone modifications and their role in epigenetics of atopy and allergic diseases.Allergy, asthma, and clinical immunology : official journal of the Canadian Society of Allergy and Clinical Immunology · 2018Review
- 17q21 asthma-risk variants switch CTCF binding and regulate IL-2 production by T cells.Nature communications · 2016Article
- CD4+ T-cell subsets in inflammatory diseases: beyond the Th1/Th2 paradigm.International immunology · 2016Review
- NRSF and BDNF polymorphisms as biomarkers of cognitive dysfunction in adults with newly diagnosed epilepsy.Epilepsy & behavior : E&B · 2016Article
- Article
- Decoding the non-coding genome: elucidating genetic risk outside the coding genome.Genes, brain, and behavior · 2016Review
- Disentangling the Effects of Colocalizing Genomic Annotations to Functionally Prioritize Non-coding Variants within Complex-Trait Loci.American journal of human genetics · 2015Article
- Epigenome-modifying tools in asthma.Epigenomics · 2015Review
- Genetic-epigenetic dysregulation of thymic TSH receptor gene expression triggers thyroid autoimmunity.Proceedings of the National Academy of Sciences of the United States of America · 2014Article
- Epigenomic analysis of primary human T cells reveals enhancers associated with TH2 memory cell differentiation and asthma susceptibility.Nature immunology · 2014Article
- On the identification of potential regulatory variants within genome wide association candidate SNP sets.BMC medical genomics · 2014Article
Corrections and comments
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
Abstract
Genome-wide association studies (GWASs) identify single nucleotide polymorphisms (SNPs) that are enriched in individuals suffering from a given disease. Most disease-associated SNPs fall into non-coding regions, so that it is not straightforward to infer phenotype or function; moreover, many SNPs are in tight genetic linkage, so that a SNP identified as associated with a particular disease may not itself be causal, but rather signify the presence of a linked SNP that is functionally relevant to disease pathogenesis. Here, we present an analysis method that takes advantage of the recent rapid accumulation of epigenomics data to address these problems for some SNPs. Using asthma as a prototypic example; we show that non-coding disease-associated SNPs are enriched in genomic regions that function as regulators of transcription, such as enhancers and promoters. Identifying enhancers based on the presence of the histone modification marks such as H3K4me1 in different cell types, we show that the location of enhancers is highly cell-type specific. We use these findings to predict which SNPs are likely to be directly contributing to disease based on their presence in regulatory regions, and in which cell types their effect is expected to be detectable. Moreover, we can also predict which cell types contribute to a disease based on overlap of the disease-associated SNPs with the locations of enhancers present in a given cell type. Finally, we suggest that it will be possible to re-analyze GWAS studies with much higher power by limiting the SNPs considered to those in coding or regulatory regions of cell types relevant to a given disease.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.