Expression Quantitative Trait Methylation Analysis Reveals Methylomic Associations With Gene Expression in Childhood Asthma

Kim, Soyeon; Forno, Erick; Zhang, Rong; Park, Hyun Jung; Xu, Zhongli; Yan, Qi; Boutaoui, Nadia; Acosta‐Pérez, Edna; Canino, Glorisa; Chen, Wei; Celedón, Juan C.

doi:10.1016/j.chest.2020.05.601

Cited by 28 publications

(17 citation statements)

References 38 publications

(34 reference statements)

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“…For example, GWASs do not generally consider tissue-or environment-specific effects, or gene by environment interactions. Moreover, most genome-wide epigenetic studies of exposures (e.g., [14][15][16][17][18]) or of asthma-related traits (e.g., [19][20][21][22][23][24]) have not integrated their findings with GWAS. Only a few studies have formally integrated asthma GWAS and epigenetic studies in airway tissues (e.g., [25][26][27]), but none have considered genotype effects on genome-wide epigenetic and transcriptional responses to RV infection with asthma GWASs.…”

Section: Introductionmentioning

confidence: 99%

“…In vitro functional studies of airway epithelium have been used to characterize gene pathways affected by environmental disease modifiers of asthma (e.g., refs. [14,[40][41][42]), and to identify functional variants that contribute to asthma pathogenesis in ex vivo airway epithelial cells (AECs), including eQTLs [43][44][45][46] and methylation QTLs (meQTLs) [14,19,25]). However, no studies to date have formally integrated molecular QTLs in AECs exposed to different conditions with asthma GWAS results.…”

Section: Introductionmentioning

confidence: 99%

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Multi-omics colocalization with genome-wide association studies reveals a context-specific genetic mechanism at a childhood onset asthma risk locus

et al. 2021

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Background Genome-wide association studies (GWASs) have identified thousands of variants associated with asthma and other complex diseases. However, the functional effects of most of these variants are unknown. Moreover, GWASs do not provide context-specific information on cell types or environmental factors that affect specific disease risks and outcomes. To address these limitations, we used an upper airway epithelial cell (AEC) culture model to assess transcriptional and epigenetic responses to rhinovirus (RV), an asthma-promoting pathogen, and provide context-specific functional annotations to variants discovered in GWASs of asthma. Methods Genome-wide genetic, gene expression, and DNA methylation data in vehicle- and RV-treated upper AECs were collected from 104 individuals who had a diagnosis of airway disease (n=66) or were healthy participants (n=38). We mapped cis expression and methylation quantitative trait loci (cis-eQTLs and cis-meQTLs, respectively) in each treatment condition (RV and vehicle) in AECs from these individuals. A Bayesian test for colocalization between AEC molecular QTLs and adult onset asthma and childhood onset asthma GWAS SNPs, and a multi-ethnic GWAS of asthma, was used to assign the function to variants associated with asthma. We used Mendelian randomization to demonstrate DNA methylation effects on gene expression at asthma colocalized loci. Results Asthma and allergic disease-associated GWAS SNPs were specifically enriched among molecular QTLs in AECs, but not in GWASs from non-immune diseases, and in AEC eQTLs, but not among eQTLs from other tissues. Colocalization analyses of AEC QTLs with asthma GWAS variants revealed potential molecular mechanisms of asthma, including QTLs at the TSLP locus that were common to both the RV and vehicle treatments and to both childhood onset and adult onset asthma, as well as QTLs at the 17q12-21 asthma locus that were specific to RV exposure and childhood onset asthma, consistent with clinical and epidemiological studies of these loci. Conclusions This study provides evidence of functional effects for asthma risk variants in AECs and insight into RV-mediated transcriptional and epigenetic response mechanisms that modulate genetic effects in the airway and risk for asthma.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-omics colocalization with genome-wide association studies reveals a context-specific genetic mechanism at a childhood onset asthma risk locus

et al. 2021

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“…genome-wide studies investigating the associations between DNA methylation levels and gene expression [9,10]. Several eQTM studies have been published in diverse cell types/tissues: whole blood [8,11], monocytes [11–13], lymphoblastoid cell lines, T-cells and fibroblasts derived from umbilical cords [14,15], fibroblasts [16], liver [17], skeletal muscle [18], nasal airway epithelium [19] and placenta [20]. As most of the EWAS are conducted in whole blood [6,21], there is a need for comprehensive eQTM studies in this tissue.…”

Section: Introductionmentioning

confidence: 99%

Identification of autosomal cis expression quantitative trait methylation (cis eQTMs) in children’s blood

Ruiz‐Arenas

Hernández-Ferrer

Vives-Usano

et al. 2020

Preprint

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Background: The identification of expression quantitative trait methylation (eQTMs), defined as correlations between gene expression and DNA methylation levels, might help the biological interpretation of epigenome-wide association studies (EWAS). We aimed to identify autosomal cis-eQTMs in child blood, using data from 832 children of the Human Early Life Exposome (HELIX) project. Methods: Blood DNA methylation and gene expression were measured with the Illumina 450K and the Affymetrix HTA v2 arrays, respectively. The relationship between methylation levels and expression of nearby genes (transcription start site (TSS) within a window of 1 Mb) was assessed by fitting 13.6 M linear regressions adjusting for sex, age, and cohort. Results: We identified 63,831 autosomal cis-eQTMs, representing 35,228 unique CpGs and 11,071 unique transcript clusters (TCs, genes). 74.3% of these cis-eQTMs were located at <250 kb, 60.0% showed an inverse relationship and 23.9% had at least one genetic variant associated with the methylation and expression levels. They were enriched for active blood regulatory regions. Adjusting for cellular composition decreased the number of cis-eQTMs to 37.7%, suggesting that some of them were cell type-specific. The overlap of child blood cis-eQTMs with those described in adults was small, and child and adult shared cis-eQTMs tended to be proximal to the TSS, enriched for genetic variants and with lower cell type specificity. Only half of the cis-eQTMs could be captured through annotation to the closest gene. Conclusions: This catalogue of blood autosomal cis-eQTMs in children can help the biological interpretation of EWAS findings, and is publicly available at https://helixomics.isglobal.org/.

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“…CCL26 , the next predicted gene, is ranked second on the transcriptomic level but fourth on the epigenetic level. It is also associated with specific methylation status in lung- and respiratory-related tissues under various pathological conditions, including lung adenocarcinoma ( Dong et al, 2020 ) and asthma ( Kim et al, 2020 ). CCL26 has been validated to be regulated by specific interleukins, such as IL-13 ( Lyles and Rothenberg, 2019 ), and further studies have validated that the methylation status of CCL26 is greatly altered during the inflammatory responses of ASMCs under either pathological or physical conditions ( Grozdanovic et al, 2019 ).…”

Section: Discussionmentioning

confidence: 99%

Detecting the Multiomics Signatures of Factor-Specific Inflammatory Effects on Airway Smooth Muscles

Zhang

Zeng

et al. 2021

Front. Genet.

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Smooth muscles are a specific muscle subtype that is widely identified in the tissues of internal passageways. This muscle subtype has the capacity for controlled or regulated contraction and relaxation. Airway smooth muscles are a unique type of smooth muscles that constitute the effective, adjustable, and reactive wall that covers most areas of the entire airway from the trachea to lung tissues. Infection with SARS-CoV-2, which caused the world-wide COVID-19 pandemic, involves airway smooth muscles and their surrounding inflammatory environment. Therefore, airway smooth muscles and related inflammatory factors may play an irreplaceable role in the initiation and progression of several severe diseases. Many previous studies have attempted to reveal the potential relationships between interleukins and airway smooth muscle cells only on the omics level, and the continued existence of numerous false-positive optimal genes/transcripts cannot reflect the actual effective biological mechanisms underlying interleukin-based activation effects on airway smooth muscles. Here, on the basis of newly presented machine learning-based computational approaches, we identified specific regulatory factors and a series of rules that contribute to the activation and stimulation of airway smooth muscles by IL-13, IL-17, or the combination of both interleukins on the epigenetic and/or transcriptional levels. The detected discriminative factors (genes) and rules can contribute to the identification of potential regulatory mechanisms linking airway smooth muscle tissues and inflammatory factors and help reveal specific pathological factors for diseases associated with airway smooth muscle inflammation on multiomics levels.

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Expression Quantitative Trait Methylation Analysis Reveals Methylomic Associations With Gene Expression in Childhood Asthma

Cited by 28 publications

References 38 publications

Multi-omics colocalization with genome-wide association studies reveals a context-specific genetic mechanism at a childhood onset asthma risk locus

Multi-omics colocalization with genome-wide association studies reveals a context-specific genetic mechanism at a childhood onset asthma risk locus

Identification of autosomal cis expression quantitative trait methylation (cis eQTMs) in children’s blood

Detecting the Multiomics Signatures of Factor-Specific Inflammatory Effects on Airway Smooth Muscles

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