Gene by Environment Interaction in Asthma

London, Stephanie J.; Romieu, Isabelle

doi:10.1146/annurev.publhealth.031308.100151

Cited by 70 publications

(56 citation statements)

References 93 publications

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“…To our knowledge, no other longitudinal clinical trials of asthma have measured mold levels as an exposure; thus, we do not have other populations to replicate our findings. A recent review article on gene-by-environment interaction in asthma mentioned that the study of gene-environment interaction in relation to asthma is in its infancy (33). Thus, our findings provide support for future studies examining gene-environment interactions in asthma, and should encourage evaluation of environmental exposures in these studies.…”

Section: Discussionsupporting

confidence: 62%

Fungal Exposure Modulates the Effect of Polymorphisms of Chitinases on Emergency Department Visits and Hospitalizations

Lasky‐Su²,

Rogers³

et al. 2010

Am J Respir Crit Care Med

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Rationale: Chitinases are enzymes that cleave chitin, which is present in fungal cells. Two types of human chitinases, chitotriosidase and acidic mammalian chitinase, and the chitinase-like protein, YKL-40, seem to play an important role in asthma. We hypothesized that exposure to environmental fungi may modulate the effect of chitinases in individuals with asthma. Objectives: To explore whether interactions between high fungal exposure and common genetic variants in the two chitinases in humans, CHIT1 and CHIA, and the chitinase 3-like 1 gene, CHI3L1, are associated with severe asthma exacerbations and other asthmarelated outcomes. Methods: Forty-eight single nucleotide polymorphisms (SNPs) in CHIT1, CHIA, and CHI3L1 and one CHIT1 duplication were genotyped in 395 subjects and their parents as part of the Childhood Asthma Management Program. Household levels of mold (an index of fungal exposure) were determined on house dust samples. We conducted family-based association tests with gene-environment interactions. Our outcome was severe exacerbation, defined as emergency department visits and hospitalizations from asthma over a 4-year period, and our secondary outcomes included indices of lung function and allergy-related phenotypes. Measurements and Main Results: Of the 395 subjects who had mold levels at randomization, 24% (95 subjects) had levels that were greater than 25,000 units per gram of house dust (high mold exposure). High mold exposure significantly modified the relation between three SNPs in CHIT1 (rs2486953, rs4950936, and rs1417149) and severe exacerbations (P for interaction 0.0010 for rs2486953, 0.0008 for rs4950936, and 0.0005 for rs1417149). High mold exposure did not significantly modify the relationship between any of the other variants and outcomes. Conclusions: Environmental exposure to fungi, modifies the effect of CHIT1 SNPs on severe asthma exacerbations.

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Section: Discussionsupporting

confidence: 62%

Fungal Exposure Modulates the Effect of Polymorphisms of Chitinases on Emergency Department Visits and Hospitalizations

Lasky‐Su²,

Rogers³

et al. 2010

Am J Respir Crit Care Med

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“…L'identification d'un grand nombre de gènes pourrait indiquer que ceux-ci interagissent ensemble et ont chacun un effet mineur sur le phénotype, plutôt qu'un nombre limité d'entre eux ayant un effet marqué sur le développement de la maladie (30). Différents types d'études génétiques ont été utilisés afin de déterminer l'implication de différents gènes dans l'asthme : les études de liaisons ou d'associations pangénomiques, ainsi que les études de gènes candidats (31).…”

Section: Interaction Gènes-environnement Dans L'asthmeunclassified

“…value threshold for each gene considering only tagSNPs (30). Meff = Effective number of independent marker loci.…”

Section: Conflict Of Interestmentioning

confidence: 99%

“…Definition of abbreviations: T = transmitted minor allele count, U = untransmitted allele count, NQO1 = NAD(P)H dehydrogenase quinone 1, CAT= catalase, EPHX1 = epoxide hydrolase 1, microsomal, CHISQ = chi-square statistic; a Significant results after correction; b Corrected;? value considering the number of independent tagSNPs [30]. Meff = Effective number of independent marker loci.…”

Section: Conflict Of Interestmentioning

confidence: 99%

See 1 more Smart Citation

Étude d'association entre l'asthme et les gènes associés à ce phénotype et à la pollution de l'air, dans un échantillon d'asthme provenant d'un environnement régional caractérisé par différentes industries /

Morin¹

2012

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“…Numerous studies have identified genomic loci and transcripts that exhibit GEIs for various phenotypes including psychological conditions [92][93][94][95][96][97], nicotine dependence [88,98], asthma [99][100][101], immune response [72,102,103] and drugs [104]. Genomic loci exhibiting GEIs are often referred to as context-specific Quantitative Trait Loci (QTLs) or, in the case of genetical genomics, eQTLs [79] The environmental conditions in humans are much harder to control than for model organisms.…”

Section: Gene-by-environment Interactionsmentioning

confidence: 99%

Characterization of the genetic and environmental factors driving gene expression variability

Goldinger¹

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Gene expression variation is a quantitative trait that drives phenotypic diversity across populations.On a cellular level, gene expression is an intermediate phenotype between stored genetic information and the functional utilization of this information within the cell. Through Genome Wide Association Studies (GWAS), thousands of genetic polymorphisms associated with numerous diseases have been identified. These have provided many novel insights into the disrupted biological processes that drive the etiology of various health conditions.expression Quantitative Trait Loci (eQTLs) provide an additional layer of biological information about the physiological impact of common genetic variants.Therefore, the study of the genetic regulation of gene expression (eQTL studies) has been useful both in the validation and functional characterisation of GWAS polymorphisms. This has contributed to a better understanding of the precise molecular processes that contribute to the development of disease.Global transcriptomic analyses have provided as greater insight into the level of complexity that drives biological systems. Transcriptomic data are often comprised of gene regulatory and co-expression networks, an emergent property of transcriptomic and other omic data. These networks within each omics fields interact with each other to further add layers of complexity that drive biological systems.Variation contained with gene expression datasets can, therefore, provide detail into the flow of information through these biological systems and how these can be influenced by genetic polymorphisms.Transcriptome variation is highly influenced by genetic and environmental factors. Genetic regulation of gene expression represents, with some exceptions, fixed regulatory points that strictly control the expression of genes. Variance attributed to environmental effects, on the other hand, are often biological responses to specific stimuli. The dissection of the genetic and environmental influences on expression levels will help to form a baseline upon which network models can be built to disseminate the biological flow of information in healthy, latent or disease groups. Booth, "The low EOMES/TBX21 molecular phenotype in multiple sclerosis reflects CD56+ cell dysregulation and is affected by immunomodulatory therapies

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Gene by Environment Interaction in Asthma

Cited by 70 publications

References 93 publications

Fungal Exposure Modulates the Effect of Polymorphisms of Chitinases on Emergency Department Visits and Hospitalizations

Fungal Exposure Modulates the Effect of Polymorphisms of Chitinases on Emergency Department Visits and Hospitalizations

Étude d'association entre l'asthme et les gènes associés à ce phénotype et à la pollution de l'air, dans un échantillon d'asthme provenant d'un environnement régional caractérisé par différentes industries /

Characterization of the genetic and environmental factors driving gene expression variability

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