Systematic Localization of Common Disease-Associated Variation in Regulatory DNA

Maurano, Matthew T.; Humbert, Richard; Rynes, Eric; Thurman, Robert E.; Haugen, Eric; Wang, Hao; Reynolds, Alex; Sandstrom, Richard; Qu, Hongzhu; Brody, Jennifer A.; Shafer, Anthony; Neri, Fidencio; Lee, Kristen; Kutyavin, Tanya; Stehling-Sun, Sandra; Johnson, Audra; Canfield, Theresa K.; Giste, Erika; Diegel, Morgan; Bates, Daniel; Hansen, R. Scott; Neph, Shane; Sabo, Peter J.; Heimfeld, Shelly; Raubitschek, Antony; Ziegler, Steven F.; Cotsapas, Chris; Sotoodehnia, Nona; Glass, Ian A.; Sunyaev, Shamil; Kaul, Rajinder; Stamatoyannopoulos, J

doi:10.1126/science.1222794

Cited by 3,184 publications

(2,883 citation statements)

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“…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. MS GWAS loci are also enriched for expression quantitative trait loci (eQTL) in multiple tissues,46, 57 supporting the idea that much risk is due to changes to gene regulation.…”

Section: Identifying Causal Variants and Pathogenic Genesmentioning

confidence: 99%

“…These are genomic regions of 150–400 base pairs where chromatin has been relaxed in some cell types in order to allow DNA‐binding protein interaction and is thus sensitive to cleavage by DNase I. These DNase I hypersensitive sites (DHS) usually contain transcription factor binding sites and overlap either promoter or enhancer elements 55. We were able to detect significant enrichment of risk alleles on open chromatin elements in 25/48 MS risk loci and that these were due to 177 DHS, of a total of > 500 000 DHS sites present in all 48 loci.…”

Section: Identifying Causal Variants and Pathogenic Genesmentioning

confidence: 99%

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Genome‐wide association studies of multiple sclerosis

Cotsapas¹,

Mitrovič

2018

Clin & Trans Imm

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Large‐scale genetic studies of multiple sclerosis have identified over 230 risk effects across the human genome, making it a prototypical common disease with complex genetic architecture. Here, after a brief historical background on the discovery and definition of the disease, we summarise the last fifteen years of genetic discoveries and map out the challenges that remain to translate these findings into an aetiological framework and actionable clinical understanding.

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Section: Identifying Causal Variants and Pathogenic Genesmentioning

confidence: 99%

Section: Identifying Causal Variants and Pathogenic Genesmentioning

confidence: 99%

Genome‐wide association studies of multiple sclerosis

Cotsapas¹,

Mitrovič

2018

Clin & Trans Imm

Self Cite

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“…11 Gene expression levels are known to be heritable. [12][13][14] Genetic variants correlated with nearby transcript levels are defined as cis-expression quantitative trait loci (cis-eQTL).…”

Section: Introductionmentioning

confidence: 99%

Variants at IRX4 as prostate cancer expression quantitative trait loci

Hussain

Vijai

et al. 2013

Eur J Hum Genet

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Genome-wide association studies (GWAS) have identified numerous prostate cancer-associated risk loci. Some variants at these loci may be regulatory and influence expression of nearby genes. Such loci are known as cis-expression quantitative trait loci (cis-eQTL). As cis-eQTLs are highly tissue-specific, we asked if GWAS-identified prostate cancer risk loci are cis-eQTLs in human prostate tumor tissues. We investigated 50 prostate cancer samples for their genotype at 59 prostate cancer riskassociated single-nucleotide polymorphisms (SNPs) and performed cis-eQTL analysis of transcripts from paired primary tumors within two megabase windows. We tested 586 transcript-genotype associations, of which 27 were significant (false discovery rate r10%). An equivalent eQTL analysis of the same prostate cancer risk loci in lymphoblastoid cell lines did not result in any significant associations. The top-ranked cis-eQTL involved the IRX4 (Iroquois homeobox protein 4) transcript and rs12653946, tagged by rs10866528 in our study (P ¼ 4.91 Â 10 À5 ). Replication studies, linkage disequilibrium, and imputation analyses highlight population specificity at this locus. We independently validated IRX4 as a potential prostate cancer risk gene through cis-eQTL analysis of prostate cancer risk variants. Cis-eQTL analysis in relevant tissues, even with a small sample size, can be a powerful method to expedite functional follow-up of GWAS.

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“…Likewise, while genome‐wide association studies have identified potential disease‐associated variation in regulatory regions, the genes they affect remain largely unknown116 (mutation impact modelled in Figure 9). FOXP3 itself may actively drive intrachromosomal interactions in Treg.…”

Section: Enhancer–promoter Interactions Shape the Function Of Tregmentioning

confidence: 99%

Unravelling the molecular basis for regulatory T‐cell plasticity and loss of function in disease

et al. 2018

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Regulatory T cells (Treg) are critical for preventing autoimmunity and curtailing responses of conventional effector T cells (Tconv). The reprogramming of T‐cell fate and function to generate Treg requires switching on and off of key gene regulatory networks, which may be initiated by a subtle shift in expression levels of specific genes. This can be achieved by intermediary regulatory processes that include microRNA and long noncoding RNA‐based regulation of gene expression. There are well‐documented microRNA profiles in Treg and Tconv, and these can operate to either reinforce or reduce expression of a specific set of target genes, including FOXP3 itself. This type of feedforward/feedback regulatory loop is normally stable in the steady state, but can alter in response to local cues or genetic risk. This may go some way to explaining T‐cell plasticity. In addition, in chronic inflammation or autoimmunity, altered Treg/Tconv function may be influenced by changes in enhancer–promoter interactions, which are highly cell type‐specific. These interactions are impacted by genetic risk based on genome‐wide association studies and may cause subtle alterations to the gene regulatory networks controlled by or controlling FOXP3 and its target genes. Recent insights into the 3D organisation of chromatin and the mapping of noncoding regulatory regions to the genes they control are shedding new light on the direct impact of genetic risk on T‐cell function and susceptibility to inflammatory and autoimmune conditions.

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Systematic Localization of Common Disease-Associated Variation in Regulatory DNA

Cited by 3,184 publications

References 45 publications

Genome‐wide association studies of multiple sclerosis

Genome‐wide association studies of multiple sclerosis

Variants at IRX4 as prostate cancer expression quantitative trait loci

Unravelling the molecular basis for regulatory T‐cell plasticity and loss of function in disease

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