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1.  Expression analysis of asthma candidate genes during human and murine lung development 
Respiratory Research  2011;12(1):86.
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.
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.
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.
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.
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.
PMCID: PMC3141421  PMID: 21699702
Asthma; Development; Expression; Genetics; Lung
2.  A graphical model approach for inferring large-scale networks integrating gene expression and genetic polymorphism 
BMC Systems Biology  2009;3:55.
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.
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.
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.
PMCID: PMC2694152  PMID: 19473523
3.  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.
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.
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.
PMCID: PMC1361234  PMID: 16336546
Asthma care; randomized controlled trial; chronic care model; physician behavior change

Results 1-3 (3)