These data are from the Asthma BioRepository for Integrative Genomic Exploration (Asthma BRIDGE), which is publicly accessible and includes 1,542 individuals with asthma and controls with comprehensive phenotype and genomic data. Using case-control data, we applied a novel gene-centric approach to test for interactions between multiple SNPs and CpG sites simultaneously in the neighborhood of each gene across the whole genome. In addition, pairwise approaches may not optimally leverage information from dependencies between SNPs and CpG sites across contiguous genomic regions.

For example, in a study with 1 million SNPs and 450,000 CpG sites, 450 billion tests would be required. These pairwise approaches suffer from a multiple testing challenge, which is particularly severe if applied on a genome-wide scale. Methods for detecting statistical interactions in population studies often involve linear or logistic models that include a product term to represent the interaction effect. In fact, interactions between SNPs and DNA methylation in the genomic regions of T-helper 2 pathway genes IL4R and others were found to affect asthma risk, possibly through expression of these genes. There is evidence to suggest that genetic and epigenetic variation interact synergistically to affect gene expression.

Epigenomic alterations, while heritable, may also occur as a response to the endogenous and exogenous environment, and contribute to asthma pathogenesis. Epigenetic mechanisms, another source of disease variation, may explain a portion of this missing heritability. However, only a fraction of this variation has been explained by specific causal variants, such as those near Chr17q12вЂ“21. Numerous genome-wide association studies have shown a substantial genomic contribution to the etiology of asthma, with heritability estimates varying between 35 and 95%. In the United States, more than 23 million people were diagnosed with asthma as of August 2015, including over 6 million children. Asthma prevalence has increased in recent years, afflicting 300 million worldwide, and is highly variable across population centers, ranging from 1 to 18%. Statistical methods that simultaneously account for multiple variations across chromosomal regions may be needed to detect these types of effects on a genome-wide scale.ДЄsthma is a chronic inflammatory disorder of the airways, characterized by airway hyperresponsiveness and airflow limitation. Conclusions: Joint effects of genetic and epigenetic variation may play an important role in asthma pathogenesis. Follow-up analysis revealed statistically significant pairwise SNP-CpG interactions for several of these genes, including SCARNA18, LHX6, and LOC101928523 ( p = 1.33EвЂ“04, 8.21EвЂ“04, 1.11EвЂ“03, respectively). Of these, three have previously been implicated in asthma risk ( PF4, ATF3, and TPRA1). Results: Twelve genes, PF4, ATF3, TPRA1, HOPX, SCARNA18, STC1, OR10K1, UPK1B, LOC101928523, LHX6, CHMP4B, and LANCL1, exhibited statistically significant SNP-CpG interactions (false discovery rate = 0.05).

Methods: Using asthma case-control data, we applied a novel nonparametric gene-centric approach to test for interactions between multiple SNPs and CpG sites simultaneously in the vicinities of 18,178 genes across the genome.

The aim of this studies was to identify genes with genetic-epigenetic interactions associated with asthma. However, little research has been done to identify such interactions on a genome-wide scale. Objectives: There is evidence to suggest that asthma pathogenesis is affected by both genetic and epigenetic variation independently, and there is some evidence to suggest that genetic-epigenetic interactions affect risk of asthma.

Mediator 9 epigenetics