Genetic and epigenetic fine mapping of causal autoimmune disease variants
Open accessAbstract
Genome-wide association studies have identified loci underlying human diseases, but the causal nucleotide changes and mechanisms remain largely unknown. Here we developed a fine-mapping algorithm to identify candidate causal variants for 21 autoimmune diseases from genotyping data. We integrated these predictions with transcription and cis-regulatory element annotations, derived by mapping RNA and chromatin in primary immune cells, including resting and stimulated CD4(+) T-cell subsets, regulatory T cells, CD8(+) T cells, B cells, and monocytes. We find that ∼90% of causal variants are non-coding, with ∼60% mapping to immune-cell enhancers, many of which gain histone acetylation and transcribe enhancer-associated RNA upon immune stimulation. Causal variants tend to occur near binding sites for master regulators of immune differentiation and stimulus-dependent gene activation, but only 10-20% directly alter recognizable transcription factor binding motifs. Rather, most non-coding risk variants, including those that alter gene expression, affect non-canonical sequence determinants not well-explained by current gene regulatory models.
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Functional and epigenomic annotation studies suggest that other elements of the immune system may be involved in MS.
“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 [7]
The section “Causal Autoimmunity Variants Map to Immune Cell-Specific Enhancers” (page unavailable) reports enrichment of autoimmune-disease variants in both B-cell and T-cell enhancers, supporting a possible role beyond T cells. The passage reports that result for autoimmune diseases collectively, however, rather than establishing a B-cell or other non-T-cell contribution specifically to MS. (LLM, conf=0.82)
Experimental — accuracy not yet measuredIn the authors' previous examination of ulcerative colitis, they observed enrichment of genetic variants mapping to colon tissue.
“we observed enrichment of genetic variants mapping to colon tissue.(7)”
From Multiple sclerosis genomic map implicates peripheral immune cells and microglia in susceptibility (2019) · cites this paper as [7]
The ‘Cell-Type Specific Signatures of Complex Diseases’ section reports that ulcerative colitis variants mapped to gastrointestinal tract regulatory elements. That supports enrichment in a bowel-related tissue, but the passage does not identify colon tissue specifically. (LLM, conf=0.84)
Experimental — accuracy not yet measuredThe majority of multiple sclerosis risk variants appear to localise to gene regulatory regions, rather than coding sequence, and specifically to enhancer elements active in stimulated immune cell subsets.
“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 [56]
A 6-hour activation time point maximizes expression of interferon-sensitive genes in dendritic cells and T cells.
“a time point we previously found to maximize the expression of interferon-sensitive genes (ISGs) in dendritic cells (DCs) and T cells26, 27.”
From Multiplexed droplet single-cell RNA-sequencing using natural genetic variation (2018) · cites this paper as [27]
The Introduction’s discussion of immune-cell enhancers reports gene induction after T-cell stimulation, but not interferon-sensitive gene expression or its peak time. The Supplementary Methods describe an eight-hour T-cell stimulation; searches for a six-hour ISG maximum and for dendritic-cell results found no passage addressing the claim. (LLM, conf=0.74)
Experimental — accuracy not yet measuredFor 709 genes, estimates of fold change in response to IFN-β in myeloid and CD4+ cells are consistent with estimates in monocyte derived dendritic cells and CD4+ T cells, and correlated with qPCR results of sorted CD4+ T cells.
“For 709 genes, estimates of fold change in response to IFN-β stimulation in myeloid and CD4+ cells are consistent with estimates in monocyte derived dendritic cells28 and CD4+ T cells27, respectively (Supplementary Fig. 17) and correlated with qPCR results of sorted CD4+ T cells (Supplementary Fig. 18).”
From Multiplexed droplet single-cell RNA-sequencing using natural genetic variation (2018) · cites this paper as [27]
The Supplementary Methods (Cell Isolation and Culture; Analysis of ex vivo Stimulation-dependent Enhancers, pages not provided) describe CD4+ T-cell sorting and stimulation with PMA/ionomycin or anti-CD3/CD28, not IFN-β. Searches for “709 genes,” IFN-β fold changes, and the stated comparisons and qPCR correlation found no passage addressing the claim. (LLM, conf=0.76)
Experimental — accuracy not yet measured
These reference epigenomes, combined with other genomics data, have provided functional and causal insights about several human diseases.
“These reference epigenomes, combined with other genomics data, have provided functional and causal insights about several human disease (De Jager et al., 2014; Farh et al., 2015; Yao, Tak, Berman, & Farnham, 2014).”
From Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases (2016) · cites this paper as Farh et al., 2015
For many traits, there is clear convergence between common-variant association signals and genes implicated in monogenic forms of the same disease, as well as enrichment of GWAS signals in regulatory elements active in cell types consistent with known disease biology.
“For many traits, there is clear convergence between common-variant association signals and genes implicated in monogenic forms of the same disease, as well as enrichment of GWAS signals in regulatory elements specifically active in cell types consistent with known disease biology60,61.”
From A brief history of human disease genetics (2020) · cites this paper as [60]
Patterns of genomic overlap between these functional data and GWAS results enable functional inference of risk variants, deliver clues to specific cell types driving disease pathogenesis, and accelerate locus-specific mechanistic insights.
“The patterns of genomic overlap between these data and GWAS results enable the functional inference of risk variants, deliver clues to the specific cell types driving disease pathogenesis60,109 and accelerate locus-specific mechanistic insights.”
From A brief history of human disease genetics (2020) · cites this paper as [60]
The PICS algorithm assigned rs9349379 a greater than 99% probability of being the causal SNP given the local haplotype structure.
“The Probabilistic Identification of Causal SNPs (PICS) algorithm (Farh et al., 2015) also identifies rs9349379 as the causal SNP, given the haplotype structure at the locus, with greater than 99% probability (Table S1).”
From A Genetic Variant Associated with Five Vascular Diseases Is a Distal Regulator of Endothelin-1 Gene Expression (2017) · cites this paper as Farh et al., 2015
The majority of disease-associated non-coding variation occurs in the vicinity of enhancers and thus likely impacts these enhancers' target genes.
“This mystery, which we will call the enhancer-gene specificity conundrum, is important to solve because the majority of disease-associated non-coding variation occurs in the vicinity of enhancers and thus likely impacts these enhancers’ target genes (Ernst et al., 2011; Farh et al., 2015; Hnisz et al., 2013; Maurano et al., 2012).”
From Insulated Neighborhoods: Structural and Functional Units of Mammalian Gene Control (2016) · cites this paper as Farh et al., 2015
Tens of thousands of non-coding genetic variants have been linked with various human diseases and traits in genome-wide association studies (GWAS), and the majority of these variants occur in enhancers.
“Tens of thousands of non-coding genetic variants have been linked with various human diseases and traits in genome-wide association studies (GWAS), and the majority of these variants occur in enhancers (Ernst et al., 2011; Farh et al., 2015; Hnisz et al., 2013; Maurano et al., 2012).”
From Insulated Neighborhoods: Structural and Functional Units of Mammalian Gene Control (2016) · cites this paper as Farh et al., 2015
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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 · see citing works
Self-citationAn atlas of genetic correlations across human diseases and traits
ReproGen Consortium, Bulik-Sullivan, B., Psychiatric Genomics Consortium, Genetic Consortium for Anorexia Nervosa of the Wellcome Trust Case Control Consortium 3, Finucane, H. K., Anttila, V., Gusev, A., Day, F. R., Loh, P.-R., Duncan, L., et al. (2015) · Nature Genetics · doi:10.1038/ng.3406 · Cited by 4,255 · see citing works
Self-citationGenetic effects on gene expression across human tissues
GTEx Consortium (2017) · Nature · doi:10.1038/nature24277 · Cited by 4,036 · see citing works
10 Years of GWAS Discovery: Biology, Function, and Translation
Visscher, P. M., Wray, N. R., Zhang, Q., Sklar, P., McCarthy, M. I., Brown, M. A., & Yang, J. (2017) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2017.06.005 · Cited by 3,719 · see citing works
Alzheimer Disease Genetics Consortium (ADGC), Kunkle, B. W., The European Alzheimer’s Disease Initiative (EADI), Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium (CHARGE), Genetic and Environmental Risk in AD/Defining Genetic, P. A. E. R. F. A. D. C. (., Grenier-Boley, B., Sims, R., Bis, J. C., Damotte, V., Naj, A. C., et al. (2019) · Nature Genetics · doi:10.1038/s41588-019-0358-2 · Cited by 2,946 · see citing works
An Expanded View of Complex Traits: From Polygenic to Omnigenic
Boyle, E. A., Li, Y. I., & Pritchard, J. K. (2017) · Cell · doi:10.1016/j.cell.2017.05.038 · Cited by 2,851 · see citing works
Partitioning heritability by functional annotation using genome-wide association summary statistics
ReproGen Consortium, Finucane, H. K., Schizophrenia Working Group of the Psychiatric Genomics Consortium, The RACI Consortium, Bulik-Sullivan, B., Gusev, A., Trynka, G., Reshef, Y., Loh, P.-R., Anttila, V., et al. (2015) · Nature Genetics · doi:10.1038/ng.3404 · Cited by 2,604 · see citing works
Self-citationPredicting splicing from primary sequence with deep learning
Jaganathan, K., Kyriazopoulou Panagiotopoulou, S., McRae, J. F., Darbandi, S. F., Knowles, D., Li, Y. I., Kosmicki, J. A., Arbelaez, J., Cui, W., Schwartz, G. B., et al. (2019) · Cell · doi:10.1016/j.cell.2018.12.015 · Cited by 2,853 · see citing works
Self-citationMechanisms of Insulin Action and Insulin Resistance
Petersen, M. C., & Shulman, G. I. (2018) · Physiological Reviews · doi:10.1152/physrev.00063.2017 · Cited by 3,028 · see citing works
Expanded encyclopaedias of DNA elements in the human and mouse genomes
The ENCODE Project Consortium, Abascal, F., Acosta, R., Addleman, N. J., Adrian, J., Afzal, V., Ai, R., Aken, B., Akiyama, J. A., Jammal, O. A., et al. (2020) · Nature · doi:10.1038/s41586-020-2493-4 · Cited by 2,378 · see citing works
Self-citationImmunopathology of multiple sclerosis
Dendrou, C. A., Fugger, L., & Friese, M. A. (2015) · Nature Reviews Immunology · doi:10.1038/nri3871 · Cited by 2,215 · see citing works
Multiple sclerosis genomic map implicates peripheral immune cells and microglia in susceptibility
International Multiple Sclerosis Genetics Consortium, Patsopoulos, N. A., Baranzini, S. E., Santaniello, A., Shoostari, P., Cotsapas, C., Wong, G., Beecham, A. H., James, T., Replogle, J., et al. (2019) · Science · doi:10.1126/science.aav7188 · Cited by 1,471 · see citing works
“Although 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).”
BackgroundResultSelf-citationMultiplexed droplet single-cell RNA-sequencing using natural genetic variation
Kang, H. M., Subramaniam, M., Targ, S., Nguyen, M., Maliskova, L., McCarthy, E., Wan, E., Wong, S., Byrnes, L., Lanata, C. M., et al. (2018) · Nature Biotechnology · doi:10.1038/nbt.4042 · Cited by 1,339 · see citing works
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Finucane, H. K., Reshef, Y. A., Anttila, V., Slowikowski, K., Gusev, A., Byrnes, A., Gazal, S., Loh, P.-R., Lareau, C., Shoresh, N., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0081-4 · Cited by 1,097 · see citing works
Self-citationImpact of cytosine methylation on DNA binding specificities of human transcription factors
Yin, Y., Morgunova, E., Jolma, A., Kaasinen, E., Sahu, B., Khund-Sayeed, S., Das, P. K., Kivioja, T., Dave, K., Zhong, F., et al. (2017) · Science · doi:10.1126/science.aaj2239 · Cited by 1,257 · see citing works
The role of regulatory variation in complex traits and disease
Albert, F. W., & Kruglyak, L. (2015) · Nature Reviews Genetics · doi:10.1038/nrg3891 · Cited by 1,047 · see citing works
The epigenetic landscape of T cell exhaustion
Sen, D. R., Kaminski, J., Barnitz, R. A., Kurachi, M., Gerdemann, U., Yates, K. B., Tsao, H.-W., Godec, J., LaFleur, M. W., Brown, F. D., et al. (2016) · Science · doi:10.1126/science.aae0491 · Cited by 986 · see citing works
Satpathy, A. T., Granja, J. M., Yost, K. E., Qi, Y., Meschi, F., McDermott, G. P., Olsen, B. N., Mumbach, M. R., Pierce, S. E., Corces, M. R., et al. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0206-z · Cited by 980 · see citing works
The Post-GWAS Era: From Association to Function
Gallagher, M. D., & Chen-Plotkin, A. S. (2018) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2018.04.002 · Cited by 822 · see citing works
Reprogramming human T cell function and specificity with non-viral genome targeting
Roth, T. L., Puig-Saus, C., Yu, R., Shifrut, E., Carnevale, J., Li, P. J., Hiatt, J., Saco, J., Krystofinski, P., Li, H., et al. (2018) · Nature · doi:10.1038/s41586-018-0326-5 · Cited by 839 · see citing works
Self-citationPrimary sclerosing cholangitis – a comprehensive review
Karlsen, T. H., Folseraas, T., Thorburn, D., & Vesterhus, M. (2017) · Journal of Hepatology · doi:10.1016/j.jhep.2017.07.022 · Cited by 868 · see citing works
A human brain vascular atlas reveals diverse mediators of Alzheimer’s risk
Yang, A. C., Vest, R. T., Kern, F., Lee, D. P., Agam, M., Maat, C. A., Losada, P. M., Chen, M. B., Schaum, N., Khoury, N., et al. (2022) · Nature · doi:10.1038/s41586-021-04369-3 · Cited by 797 · see citing works
A brief history of human disease genetics
Claussnitzer, M., Cho, J. H., Collins, R., Cox, N. J., Dermitzakis, E. T., Hurles, M. E., Kathiresan, S., Kenny, E. E., Lindgren, C. M., MacArthur, D. G., et al. (2020) · Nature · doi:10.1038/s41586-019-1879-7 · Cited by 697 · see citing works
Generation of knock-in primary human T cells using Cas9 ribonucleoproteins
Schumann, K., Lin, S., Boyer, E., Simeonov, D. R., Subramaniam, M., Gate, R. E., Haliburton, G. E., Ye, C. J., Bluestone, J. A., Doudna, J. A., et al. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1512503112 · Cited by 702 · see citing works
Self-citationIntegrative analysis of omics summary data reveals putative mechanisms underlying complex traits
Wu, Y., Zeng, J., Zhang, F., Zhu, Z., Qi, T., Zheng, Z., Lloyd-Jones, L. R., Marioni, R. E., Martin, N. G., Montgomery, G. W., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-03371-0 · Cited by 685 · see citing works
Genetic Drivers of Epigenetic and Transcriptional Variation in Human Immune Cells
Chen, L., Ge, B., Casale, F. P., Vasquez, L., Kwan, T., Garrido-Martín, D., Watt, S., Yan, Y., Kundu, K., Ecker, S., et al. (2016) · Cell · doi:10.1016/j.cell.2016.10.026 · Cited by 679 · see citing works
Common genetic variation drives molecular heterogeneity in human iPSCs
Kilpinen, H., Goncalves, A., Leha, A., Afzal, V., Alasoo, K., Ashford, S., Bala, S., Bensaddek, D., Casale, F. P., Culley, O. J., et al. (2017) · Nature · doi:10.1038/nature22403 · Cited by 586 · see citing works
Comparative genetic architectures of schizophrenia in East Asian and European populations
Schizophrenia Working Group of the Psychiatric Genomics Consortium, Lam, M., Indonesia Schizophrenia Consortium, Genetic REsearch on schizophreniA neTwork-China and the Netherlands (GREAT-CN), Chen, C.-Y., Li, Z., Martin, A. R., Bryois, J., Ma, X., Gaspar, H., et al. (2019) · Nature Genetics · doi:10.1038/s41588-019-0512-x · Cited by 655 · see citing works
Self-citationFine-mapping inflammatory bowel disease loci to single-variant resolution
International Inflammatory Bowel Disease Genetics Consortium, Huang, H., Fang, M., Jostins, L., Umićević Mirkov, M., Boucher, G., Anderson, C. A., Andersen, V., Cleynen, I., Cortes, A., et al. (2017) · Nature · doi:10.1038/nature22969 · Cited by 611 · see citing works
Self-citationShared genetic origin of asthma, hay fever and eczema elucidates allergic disease biology
23andMe Research Team, Ferreira, M. A., collaborators, A., BIOS consortium, Study, L. C., Vonk, J. M., Baurecht, H., Marenholz, I., Tian, C., Hoffman, J. D., et al. (2017) · Nature Genetics · doi:10.1038/ng.3985 · Cited by 550 · see citing works
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Droplet-based combinatorial indexing for massive-scale single-cell chromatin accessibility
Lareau, C. A., Duarte, F. M., Chew, J. G., Kartha, V. K., Burkett, Z. D., Kohlway, A. S., Pokholok, D., Aryee, M. J., Steemers, F. J., Lebofsky, R., et al. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0147-6 · Cited by 444 · see citing works
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Self-citationTewhey, R., Kotliar, D., Park, D. S., Liu, B., Winnicki, S., Reilly, S. K., Andersen, K. G., Mikkelsen, T. S., Lander, E. S., Schaffner, S. F., et al. (2016) · Cell · doi:10.1016/j.cell.2016.04.027 · Cited by 480 · see citing works
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Self-citationSun, Y. V., & Hu, Y.-J. (2016) · Advances in Genetics · doi:10.1016/bs.adgen.2015.11.004 · Cited by 459 · see citing works
Qi, T., Wu, Y., Zeng, J., Zhang, F., Xue, A., Jiang, L., Zhu, Z., Kemper, K., Yengo, L., Zheng, Z., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-04558-1 · Cited by 427 · see citing works
Insulated Neighborhoods: Structural and Functional Units of Mammalian Gene Control
Hnisz, D., Day, D. S., & Young, R. A. (2016) · Cell · doi:10.1016/j.cell.2016.10.024 · Cited by 420 · see citing works
HIPSCI Consortium, Alasoo, K., Rodrigues, J., Mukhopadhyay, S., Knights, A. J., Mann, A. L., Kundu, K., Hale, C., Dougan, G., & Gaffney, D. J. (2018) · Nature Genetics · doi:10.1038/s41588-018-0046-7 · Cited by 338 · see citing works
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Systematic Functional Dissection of Common Genetic Variation Affecting Red Blood Cell Traits
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Where Are the Disease-Associated eQTLs?
Umans, B. D., Battle, A., & Gilad, Y. (2021) · Trends in Genetics · doi:10.1016/j.tig.2020.08.009 · Cited by 296 · see citing works
Viral infections in type 1 diabetes mellitus — why the β cells?
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Discovery of stimulation-responsive immune enhancers with CRISPR activation
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Self-citationGenetics and Pathogenesis of Inflammatory Bowel Disease
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Genomic Instability of iPSCs: Challenges Towards Their Clinical Applications
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Methods for ChIP-seq analysis: A practical workflow and advanced applications
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Extreme Polygenicity of Complex Traits Is Explained by Negative Selection
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Classification of common human diseases derived from shared genetic and environmental determinants
Wang, K., Gaitsch, H., Poon, H., Cox, N. J., & Rzhetsky, A. (2017) · Nature Genetics · doi:10.1038/ng.3931 · Cited by 242 · see citing works
Functional Genetic Variants Revealed by Massively Parallel Precise Genome Editing
Sharon, E., Chen, S.-A. A., Khosla, N. M., Smith, J. D., Pritchard, J. K., & Fraser, H. B. (2018) · Cell · doi:10.1016/j.cell.2018.08.057 · Cited by 225 · see citing works
Functional inference of gene regulation using single-cell multi-omics
Kartha, V. K., Duarte, F. M., Hu, Y., Ma, S., Chew, J. G., Lareau, C. A., Earl, A., Burkett, Z. D., Kohlway, A. S., Lebofsky, R., et al. (2022) · Cell Genomics · doi:10.1016/j.xgen.2022.100166 · Cited by 224 · see citing works
Genetic determinants of co-accessible chromatin regions in activated T cells across humans
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Interrogation of human hematopoiesis at single-cell and single-variant resolution
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Combining SNP-to-gene linking strategies to identify disease genes and assess disease omnigenicity
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An integrative ENCODE resource for cancer genomics
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Genetics of primary sclerosing cholangitis and pathophysiological implications
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Inferring relevant cell types for complex traits by using single-cell gene expression
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High-Resolution Epigenomic Atlas of Human Embryonic Craniofacial Development
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Genetic Variation Determines PPARγ Function and Anti-diabetic Drug Response In Vivo
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Origins of human disease: the chrono-epigenetic perspective
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Using GWAS to identify genetic predisposition in hepatic autoimmunity
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Hold out the genome: a roadmap to solving the cis-regulatory code
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Genome‐wide association studies of multiple sclerosis
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Genomics implicates adaptive and innate immunity in Alzheimer’s and Parkinson’s diseases
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Determinants of transcription factor regulatory range
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Chromosome contacts in activated T cells identify autoimmune disease candidate genes
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Genetics of allergy and allergic sensitization: common variants, rare mutations
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Enhancer RNA: Biogenesis, function, and regulation
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Approaches and advances in the genetic causes of autoimmune disease and their implications
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Single-cell transcriptome landscape of circulating CD4+ T cell populations in autoimmune diseases
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Self-citationA human brain vascular atlas reveals diverse cell mediators of Alzheimer’s disease risk
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- Authors
- Farh, K. K.-H., Marson, A., Zhu, J., Kleinewietfeld, M., Housley, W. J., Beik, S., Shoresh, N., Whitton, H., Ryan, R. J. H., Shishkin, A. A., Hatan, M., Carrasco-Alfonso, M. J., Mayer, D., Luckey, C. J., Patsopoulos, N. A., De Jager, P. L., Kuchroo, V. K., Epstein, C. B., Daly, M. J., Hafler, D. A., & Bernstein, B. E.
- Affiliations
- Broad Institute (US)
- University of California, San Francisco (US)
- Yale University (US)
- Brigham and Women's Hospital (US)
- Corresponding author
- Marson, Alexander
- ORCID
- Venue
- Nature
- Published
- 2014-10-29
- Volume
- 518
- Issue
- 7539
- Pages
- 337-343
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC4336207
- OpenAlex
- W2056198580
- Cited by
- 1,956
- References
- 67
- Citations by year
- 2026: 98 · 2025: 127 · 2024: 171 · 2023: 148 · 2022: 166 · 2021: 172 · 2020: 187 · 2019: 197 · 2018: 186 · 2017: 174 · 2016: 177 · 2015: 143 · 2014: 5 · 2013: 2 · 2012: 1
- FWCI
- 107.93
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- CaltechAUTHORS (California Institute of Technology) (submittedVersion)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- Howard Hughes Medical Institute
- National Multiple Sclerosis Society (CA 1061-A-18)
- Brigham and Women's Hospital
- Multiple Sclerosis Society
- National Institutes of Health (5u54hg004570-04, AI039671, AI045757)
- University of California, San Francisco
- National Human Genome Research Institute (U54HG006991, U54-HG004570, HG004570)
- National Institute of General Medical Sciences (GM093080, RC2GM093080, T32GM007748)
- National Institute of Allergy and Infectious Diseases (U19AI070352, U19AI046130, P01AI045757, AI039671, AI070352, AI045757, AI046130, P01AI039671, T32AI007019)
- National Institute of Diabetes and Digestive and Kidney Diseases (P30DK063720)
- National Institute of Neurological Disorders and Stroke (NS067305, NS 24247, R37NS024247, R01NS024247, R01NS067305)
- National Institute of Environmental Health Sciences (U01ES017155, ES017155)
- Common Fund
- 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
- Health Sciences › Medicine › Pathology and Forensic Medicine › Multiple Sclerosis Research Studies
- Keywords
- autoimmune disease, non-coding variants, enhancers, histone acetylation, T cell stimulation, CD4+ T cells, regulatory T cells, CD8+ T cells, B cells, monocytes, transcription factor binding motifs, gene expression regulation, immune cells, epigenomics
- MeSH terms
- Autoimmune Diseases, Base Sequence, Chromatin, Enhancer Elements, Genetic, Humans, Organ Specificity, T-Lymphocytes, Transcription Factors, Consensus Sequence, Polymorphism, Single Nucleotide, Epigenesis, Genetic, Genome-Wide Association Study, Epigenomics, Nucleotide Motifs
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