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1.  Fetal lung and placental methylation is associated with in utero nicotine exposure 
Epigenetics  2014;9(11):1473-1484.
In utero smoke exposure has been shown to have detrimental effects on lung function and to be associated with persistent wheezing and asthma in children. One potential mechanism of IUS effects could be alterations in DNA methylation, which may have life-long implications. The goal of this study was to examine the association between DNA methylation and nicotine exposure in fetal lung and placental tissue in early development; nicotine exposure in this analysis represents a likely surrogate for in-utero smoke. We performed an epigenome-wide analysis of DNA methylation in fetal lung tissue (n = 85, 41 smoke exposed (48%), 44 controls) and the corresponding placental tissue samples (n = 80, 39 smoke exposed (49%), 41 controls) using the Illumina HumanMethylation450 BeadChip array. Differential methylation analyses were conducted to evaluate the variation associated with nicotine exposure. The most significant CpG sites in the fetal lung analysis mapped to the PKP3 (P = 2.94 × 10−03), ANKRD33B (P = 3.12 × 10−03), CNTD2 (P = 4.9 × 10−03) and DPP10 (P = 5.43 × 10−03) genes. In the placental methylome, the most significant CpG sites mapped to the GTF2H2C and GTF2H2D genes (P = 2.87 × 10−06 − 3.48 × 10−05). One hundred and one unique CpG sites with P-values < 0.05 were concordant between lung and placental tissue analyses. Gene Set Enrichment Analysis demonstrated enrichment of specific disorders, such as asthma and immune disorders. Our findings demonstrate an association between in utero nicotine exposure and variable DNA methylation in fetal lung and placental tissues, suggesting a role for DNA methylation variation in the fetal origins of chronic diseases.
doi:10.4161/15592294.2014.971593
PMCID: PMC4623268  PMID: 25482056
asthma; developmental biology; epigenomics; nicotine and DNA methylation; smoking
2.  Expression analysis of asthma candidate genes during human and murine lung development 
Respiratory Research  2011;12(1):86.
Background
Little is known about the role of most asthma susceptibility genes during human lung development. Genetic determinants for normal lung development are not only important early in life, but also for later lung function.
Objective
To investigate the role of expression patterns of well-defined asthma susceptibility genes during human and murine lung development. We hypothesized that genes influencing normal airways development would be over-represented by genes associated with asthma.
Methods
Asthma genes were first identified via comprehensive search of the current literature. Next, we analyzed their expression patterns in the developing human lung during the pseudoglandular (gestational age, 7-16 weeks) and canalicular (17-26 weeks) stages of development, and in the complete developing lung time series of 3 mouse strains: A/J, SW, C57BL6.
Results
In total, 96 genes with association to asthma in at least two human populations were identified in the literature. Overall, there was no significant over-representation of the asthma genes among genes differentially expressed during lung development, although trends were seen in the human (Odds ratio, OR 1.22, confidence interval, CI 0.90-1.62) and C57BL6 mouse (OR 1.41, CI 0.92-2.11) data. However, differential expression of some asthma genes was consistent in both developing human and murine lung, e.g. NOD1, EDN1, CCL5, RORA and HLA-G. Among the asthma genes identified in genome wide association studies, ROBO1, RORA, HLA-DQB1, IL2RB and PDE10A were differentially expressed during human lung development.
Conclusions
Our data provide insight about the role of asthma susceptibility genes during lung development and suggest common mechanisms underlying lung morphogenesis and pathogenesis of respiratory diseases.
doi:10.1186/1465-9921-12-86
PMCID: PMC3141421  PMID: 21699702
Asthma; Development; Expression; Genetics; Lung
3.  A graphical model approach for inferring large-scale networks integrating gene expression and genetic polymorphism 
BMC Systems Biology  2009;3:55.
Background
Graphical models (e.g., Bayesian networks) have been used frequently to describe complex interaction patterns and dependent structures among genes and other phenotypes. Estimation of such networks has been a challenging problem when the genes considered greatly outnumber the samples, and the situation is exacerbated when one wishes to consider the impact of polymorphisms (SNPs) in genes.
Results
Here we describe a multistep approach to infer a gene-SNP network from gene expression and genotyped SNP data. Our approach is based on 1) construction of a graphical Gaussian model (GGM) based on small sample estimation of partial correlation and false-discovery rate multiple testing; 2) extraction of a subnetwork of genes directly linked to a target candidate gene of interest; 3) identification of cis-acting regulatory variants for the genes composing the subnetwork; and 4) evaluating the identified cis-acting variants for trans-acting regulatory effects of the target candidate gene. This approach identifies significant gene-gene and gene-SNP associations not solely on the basis of gene co-expression but rather through whole-network modeling. We demonstrate the method by building two complex gene-SNP networks around Interferon Receptor 12B2 (IL12RB2) and Interleukin 1B (IL1B), two biologic candidates in asthma pathogenesis, using 534,290 genotyped variants and gene expression data on 22,177 genes from total RNA derived from peripheral blood CD4+ lymphocytes from 154 asthmatics.
Conclusion
Our results suggest that graphical models based on integrative genomic data are computationally efficient, work well with small samples, and can describe complex interactions among genes and polymorphisms that could not be identified by pair-wise association testing.
doi:10.1186/1752-0509-3-55
PMCID: PMC2694152  PMID: 19473523
4.  Practice-Level Effects of Interventions to Improve Asthma Care in Primary Care Settings: The Pediatric Asthma Care Patient Outcomes Research Team 
Health Services Research  2005;40(6 Pt 1):1737-1757.
Objective
To assess the practice-level effects of (1) a physician peer leader intervention and (2) peer leaders in combination with the introduction of asthma education nurses to facilitate care improvement. And, to compare findings with previously reported patient-level outcomes of trial enrollees.
Study Setting
Data were included on children 5–17 years old with asthma in 40 primary care practices, affiliated with managed health care plans enrolled in the Pediatric Asthma Care Patient Outcomes Research Team (PORT) randomized trial.
Study Design
Primary care practices were randomly assigned to one of two care improvement arms or to usual care. Automated claims data were analyzed for 12-month periods using a repeated cross-sectional design. The primary outcome was evidence of at least one controller medication dispensed among patients with persistent asthma. Secondary outcomes included controller dispensing among all identified asthmatics, evidence of chronic controller use, and the dispensing of oral steroids. Health service utilization outcomes included numbers of ambulatory visits and hospital-based events.
Principal Findings
The proportion of children with persistent asthma prescribed controllers increased in all study arms. No effect of the interventions on the proportion receiving controllers was detected (peer leader intervention effect 0.01, 95 percent confidence interval [CI]: −0.07, 0.08; planned care intervention effect −0.03, 95 percent CI: −0.09, 0.02). A statistical trend was seen toward an increased number of oral corticosteroid bursts dispensed in intervention practices. Significant adjusted increases in ambulatory visits of 0.08–0.10 visits per child per year were seen in the first intervention year, but only a statistical trend in these outcomes persisted into the second year of follow-up. No differences in hospital-based events were detected.
Conclusions
This analysis showed a slight increase in ambulatory asthma visits as a result of asthma care improvement interventions, using automated data. The absence of detectable impact on medication use at the practice level differs from the positive intervention effect observed in patient self-reported data from trial enrollees. Analysis of automated data on nonenrollees adds information about practice-level impact of care improvement strategies. Benefits of practice-level interventions may accrue disproportionately to the subgroup of trial enrollees. The effect of such interventions may be less apparent at the level of practices or health plans.
doi:10.1111/j.1475-6773.2005.00451.x
PMCID: PMC1361234  PMID: 16336546
Asthma care; randomized controlled trial; chronic care model; physician behavior change

Results 1-4 (4)