Asthma is a common chronic inflammatory disease of the airways in which the interplay between genetic factors and environmental exposures controls disease susceptibility and progression. Currently there is an epidemic of asthma that disproportionately affects underrepresented minorities in the US and creates a major public health burden. Asthmatics of African descent tend to have higher Immunoglobulin E (IgE) levels and more severe clinical symptoms than their non-African counterparts. Despite the fact that much progress has been made in our understanding of genetic and environmental influence on asthma, the molecular underpinnings of the disease etiology, and the interactions between genetic and environmental factors, the reasons behind the differences in disease prevalence and severity among populations remain poorly understood. Recent candidate gene studies on patients have demonstrated significant associations between asthma severity and DNA methylation. A comprehensive genome-wide methylation association study on asthma is sorely needed to detect novel associations between asthma and DNA methylation. In this application, we propose to use newly-developed and cost-effective array-based technology to conduct a genome-wide methylation profiling study on Asthma patients of African descent residing in North America. These samples are part of the NHLBI-supported Consortium on Asthma among African-ancestry Populations in the Americas (CAAPA) cohort whose entire genotype profile has been obtained using whole genome deep sequencing. By combining methylation data to be generated here with existing whole-genome sequencing data, we will have the unique opportunity to conduct both epigenome-wide association study (EWAS) and methylation QTL analysis (meQTL) on these samples. We believe that the successful completion of the proposed study will help us to better understand the disease etiology of asthma, and why the disease is more severe in patients of African descent.
The goal of this project is to conduct an epigenome-wide association study and a methylation quantitative trait locus study of asthma in populations of African descent. By combining methylation profile data to be generated here with whole genome sequencing data that are already available on these samples, we will have a great opportunity to better understand the genetics and epigenetics factors that influence the onset of asthma traits among patients of African descent.
|Hu, Yi-Juan; Schmidt, Amand F; Dudbridge, Frank et al. (2017) Impact of Selection Bias on Estimation of Subsequent Event Risk. Circ Cardiovasc Genet 10:|
|Hu, Yi-Juan; Liao, Peizhou; Johnston, H Richard et al. (2016) Testing Rare-Variant Association without Calling Genotypes Allows for Systematic Differences in Sequencing between Cases and Controls. PLoS Genet 12:e1006040|
|Sun, Yan V; Hu, Yi-Juan (2016) Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases. Adv Genet 93:147-90|