Mexican-born children living in the United States have a lower prevalence of asthma than other US children. Although children of Mexican descent near the Arizona (AZ)-Sonora border are genetically similar, differences in environmental exposures might result in differences in asthma prevalence across this region.
Asthma exacerbations are a major cause of morbidity and medical cost.
Stress is associated with asthma morbidity in Puerto Ricans (PRs), who have reduced bronchodilator response (BDR).
Disease-associated loci identified through genome-wide association studies (GWAS) frequently localize to non-coding sequence. We and others have demonstrated strong enrichment of such single nucleotide polymorphisms (SNPs) for expression quantitative trait loci (eQTLs), supporting an important role for regulatory genetic variation in complex disease pathogenesis. Herein we describe our initial efforts to develop a predictive model of disease-associated variants leveraging eQTL information. We first catalogued cis-acting eQTLs (SNPs within 100 kb of target gene transcripts) by meta-analyzing four studies of three blood-derived tissues (n = 586). At a false discovery rate 5%, we mapped eQTLs for 6,535 genes; these were enriched for disease-associated genes (P 10(-04)), particularly those related to immune diseases and metabolic traits. Based on eQTL information and other variant annotations (distance from target gene transcript, minor allele frequency, and chromatin state), we created multivariate logistic regression models to predict SNP membership in reported GWAS. The complete model revealed independent contributions of specific annotations as strong predictors, including evidence for an eQTL (odds ratio (OR) = 1.2-2.0, P 10(-11)) and the chromatin states of active promoters, different classes of strong or weak enhancers, or transcriptionally active regions (OR = 1.5-2.3, P 10(-11)). This complete prediction model including eQTL association information ultimately allowed for better discrimination of SNPs with higher probabilities of GWAS membership (6.3-10.0%, compared to 3.5% for a random SNP) than the other two models excluding eQTL information. This eQTL-based prediction model of disease relevance can help systematically prioritize non-coding GWAS SNPs for further functional characterization.
Identifying geographic areas with increased incidence of disease may elucidate community-level risk factors for intervention development. Lower respiratory illnesses (LRIs) are the leading cause of death in children and are associated with other morbidities. We assessed geographic clustering of LRIs and evaluated if these spatial patterns and associated risk factors differed by phenotype. Participants enrolled at birth in the Tucson Children's Respiratory Study were followed through age three for physician diagnosed LRIs. Spatial clustering analysis, based upon each participant's birth address, was performed for four LRI phenotypes. We conducted principal component analysis at the census tract level to generate indices for lower socioeconomic status (SES), poorer housing conditions, and increased air pollution. Enrollment addresses were mapped for 812 subjects, of whom 58.4%, 33.5%, 34.2%, and 23.4% had any LRI, a wheezing LRI, a viral LRI, and a respiratory syncytial virus (RSV) LRI, respectively. Patterns of spatial clustering and associated risk factors differed by LRI phenotype. Multivariable regression analyses showed that wheezing LRI clusters were associated with increased air pollution (OR = 1.18, P = 0.01). Being in a viral cluster was associated with poorer housing conditions (OR = 1.28, P = 0.01), while being in a RSV cluster was associated with increased air pollution (OR = 1.14, P = 0.006), poorer housing conditions (OR = 1.54, P = 0.003), and higher SES (OR = 0.77, P = 0.001). Our use of social and environmental indices allowed us to identify broad contextual factors that may contribute to increased incidence of LRIs in specific geographic regions. To reduce LRI incidence, multifaceted interventions should be developed at the community level. Pediatr Pulmonol. © 2015 Wiley Periodicals, Inc.
