While progress has been made in identifying the genes and exposures related to lung function and asthma, the common susceptibility factors and critical pathways underlying these pathological processes are not well understood. For example, traffic-related pollutants have adverse effects on airway hyperreactivity (AHR), lung development, and asthma, and both genome-wide association study (GWAS) and candidate gene approaches have identified genetic variants associated with asthma-related phenotypes. However, there has been limited success in identifying genes that modulate susceptibility to environmental exposures. We propose to use an innovative genetics approach in mice and humans to identify novel variants that interact with traffic-related pollutant exposures to affect lung function phenotypes and the risk of childhood asthma.
In Specific Aim 1, we will characterize ~150 inbred and recombinant inbred mouse strains from the Hybrid Mouse Diversity Panel (HMDP) to determine their response to diesel exhaust particles (DEP) exposure, a model traffic-related pollutant. Lung function and AHR will be assessed using state-of-the-art plethysmography. In preliminary studies, we observe substantial variability among 8 HMDP strains characterized by this protocol, which is most likely attributable to naturally occurring genetic differences amongst these strains.
In Specific Aim 2, we will use the phenotype data from the HMDP and publicly available genotypes of ~130,000 single nucleotide polymorphisms to carry out a gene-environment (GxE) GWAS and identify loci exhibiting evidence for an interaction with DEP on lung function. Positional """"""""exposure-responsive"""""""" candidate genes and regions of interest will be prioritized for further evaluation using functional and bioinformatics experiments.
Specific Aim 3 will build on the mouse studies and use synteny mapping to identify loci that exhibit GxE interactions to affect asthma-related phenotypes in humans. In preliminary studies, we have carried out a GWAS and a GxE GWAS for traffic exposure and asthma in ~4000 subjects from the Children's Health Study (CHS). Leveraging these results and newly developed and innovative spatial temporal exposure models, we will use a two-stage design, with replication in an independent sample, to investigate whether loci of interest identified in th CHS GxE GWAS are supported by the results of the HMDP GxE GWAS and vice versa.
In Specific Aim 4, we will confirm the GxE interactions through in vivo studies. For each of two validated susceptibility loci in humans, we will use genotype data to recall 120 CHS subjects not part of the GWAS discovery sample who live in high (n=30) and low (n=30) areas of traffic-related pollution exposure levels and who are homozygous for the non- effect allele (n=30) and who carry at least one effect allele (n=30). These subjects will be phenotyped for AHR and lung function using standard spirometry protocols and assessed for clinical asthma status. The results of this integrative genomics approach in mice and humans will lead to a better understanding of how genes and environmental exposures interact to affect lung function phenotypes, which could have important clinical, epidemiological, and translational implications for the treatment of asthma.

Public Health Relevance

The overall goals of this project are to understand the relationship between asthma, genetic factors, and traffic- related pollution using a combination of mouse models and human populations. The results of these integrative genetics studies will lead to a better understanding of how genes and environmental exposures interact to affect lung function and asthma susceptibility, which could have important clinical, epidemiological, and translational implications for the development of novel treatment strategies.

Agency
National Institute of Health (NIH)
Institute
National Institute of Environmental Health Sciences (NIEHS)
Type
Research Project (R01)
Project #
1R01ES021801-01
Application #
8346305
Study Section
Infectious Diseases, Reproductive Health, Asthma and Pulmonary Conditions Study Section (IRAP)
Program Officer
Mcallister, Kimberly A
Project Start
2012-08-01
Project End
2017-04-30
Budget Start
2012-08-01
Budget End
2013-04-30
Support Year
1
Fiscal Year
2012
Total Cost
$529,788
Indirect Cost
$196,728
Name
University of Southern California
Department
Public Health & Prev Medicine
Type
Schools of Medicine
DUNS #
072933393
City
Los Angeles
State
CA
Country
United States
Zip Code
90089
Hormozdiari, Farhad; van de Bunt, Martijn; Segrè, Ayellet V et al. (2016) Colocalization of GWAS and eQTL Signals Detects Target Genes. Am J Hum Genet 99:1245-1260
Rigas, Diamanda; Lewis, Gavin; Aron, Jennifer L et al. (2016) Type 2 innate lymphoid cell suppression by regulatory T cells attenuates airway hyperreactivity and requires inducible T-cell costimulator-inducible T-cell costimulator ligand. J Allergy Clin Immunol :
Duong, Dat; Zou, Jennifer; Hormozdiari, Farhad et al. (2016) Using genomic annotations increases statistical power to detect eGenes. Bioinformatics 32:i156-i163
Smallwood, Tangi; Allayee, Hooman; Bennett, Brian J (2016) Choline metabolites: gene by diet interactions. Curr Opin Lipidol 27:33-9
Suzuki, Yuzo; Maazi, Hadi; Sankaranarayanan, Ishwarya et al. (2016) Lack of autophagy induces steroid-resistant airway inflammation. J Allergy Clin Immunol 137:1382-1389.e9
Hartiala, Jaana A; Tang, W H Wilson; Wang, Zeneng et al. (2016) Genome-wide association study and targeted metabolomics identifies sex-specific association of CPS1 with coronary artery disease. Nat Commun 7:10558
Zhang, Pingye; Lewinger, Juan Pablo; Conti, David et al. (2016) Detecting Gene-Environment Interactions for a Quantitative Trait in a Genome-Wide Association Study. Genet Epidemiol 40:394-403
Lavinsky, Joel; Ge, Marshall; Crow, Amanda L et al. (2016) The Genetic Architecture of Noise-Induced Hearing Loss: Evidence for a Gene-by-Environment Interaction. G3 (Bethesda) 6:3219-3228
Hormozdiari, Farhad; Kang, Eun Yong; Bilow, Michael et al. (2016) Imputing Phenotypes for Genome-wide Association Studies. Am J Hum Genet 99:89-103
Kang, Eun Yong; Martin, Lisa; Mangul, Serghei et al. (2016) Discovering SNPs Regulating Human Gene Expression Using Allele Specific Expression from RNA-Seq Data. Genetics :

Showing the most recent 10 out of 29 publications