Dataset Information

The EVE Asthma Genetics Consortium: Building Upon GWAS

ABSTRACT:

The EVE Asthma Genetics Consortium comprises U.S. investigators who have conducted genome-wide association studies (GWAS) of asthma; the main objective is to combine results of individual studies to increase the overall power to identify loci for asthma and asthma-associated traits. The consortium includes investigators at 9 U.S. institutions with GWAS results for >10,000 individuals representing European American, African American, African Caribbean, U.S. Hispanic, and Mexican populations and includes the following studies:

The Study of Asthma Phenotypes and Pharmacogenomic Interactions by Race-Ethnicity (SAPPHIRE)
The Genetic Research on Asthma in the African Diaspora (GRAAD) Study
The Genetics of Asthma in Latino Americans (GALA 1) Study
The Childhood Asthma Management Program (CAMP)
The Childhood Asthma Research and Education (CARE) Network
The Children's Health Study (CHS)
Mexico City Childhood Asthma Study (MCCAS)
The Chicago Asthma Genetics Study (CAG)*
The National Heart, Lung, and Blood Institute Collaborative Studies of the Genetics of Asthma (CSGA)*
The Severe Asthma Research Program (SARP)*

(*CAG, CSGA, and SARP are part of the NHLBI-supported SNP Typing for Association with Multiple Phenotypes from Existing Epidemiologic Data (STAMPEED) consortium.)

For the GWAS, we developed a common set of >1 million genotyped and imputed SNPs from the EVE Asthma Genetics Consortium to be tested for association with asthma and associated phenotypes in all samples and combined p-values for a grand meta-analysis for asthma gene discovery. A subset of 296 individuals representing African American, European American, and Latino ancestry were selected for whole genome sequencing.

The broad goals of this project were to characterize the genetic architecture of asthma and associated quantitative phenotypes (e.g., lung function, total serum IgE) in ethnically diverse populations from the U.S., Mexico, Puerto Rico, and Barbados.

PROVIDER: phs001156 | dbGaP |

SECONDARY ACCESSION(S): PRJNA326563PRJNA326562

REPOSITORIES: dbGaP

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			Action	DRS
	README.txt	Txt
	phs001156.EVE_Asthma.analysis-PI.MULTI.tar.gz	Other
	phs001156.pha004519.txt	Txt
	phs001156.pha004520.txt	Txt
	phs001156.pha004521.txt	Txt

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Project description:Background: Asthma is common chronic inflammatory disease of the airways with a heterogenous clinical presentation. Individual differences in asthma susceptibility remain poorly understood, although genetics is thought to play a major role. Aim: To build a polygenic risk score (PRS) for asthma and determine whether predictive genetic variants can be epigenomically linked to specific pathophysiological mechanisms. Methods: PRSs were constructed using data from genome-wide association studies and performance was validated using data generated in the Rotterdam Study, a Dutch prospective cohort of 14,926 individuals. Outcomes used were asthma, childhood-onset asthma, adulthood-onset asthma, eosinophilic asthma and exacerbations. Chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-Seq) data from 14 primary cell types, including lung epithelial cells and T lymphocytes was used for epigenomic PRS partitioning. Results: All PRSs successfully predicted risk to develop asthma and related outcomes, with the strongest predictive power (2.42 odds ratios per PRS standard deviation, area under the curve of 0.736) achieved for childhood-onset asthma. PRSs allowed for stratification of the Rotterdam Study cohort into groups at low or high risk to develop asthma. PRS partitioning using genome-wide epigenomic profiles identified 5 clusters of variants within gene regulatory regions linked to specific asthma-relevant cells, genes and biological pathways. Conclusions: PRSs can predict whether individuals in a Dutch cohort developed asthma and asthma-related phenotypes, which is most effective for childhood-onset asthma. Importantly, we show that PRS partitioning based on epigenomics data dissects a genetic risk score into blocks of regulatory variants with differential predictive power, which likely represent distinct genetically driven disease pathways. These findings have potential implications for personalized risk mitigation and treatment strategies.

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The EVE Asthma Genetics Consortium: Building Upon GWAS

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