Characterization of Bronchodilator Response in Children With Bronchiolitis Using Phenotypic and Genotypic Features
Phase: PHASE3
Conditions: Bronchiolitis, Bronchodilator Agents
Sponsor: Nemours Children's Clinic

 
 
 
 
 
 
 
 
 
 
Characterization of Bronchodilator Response in Children With Bronchiolitis Using Phenotypic and 
Genotypic Features 
 
 
 
 
 
NCT number (not available) 
 
 
 
 
 
 
April 18, 2025 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

4. RESEARCH STRATEGY 
4.1. Significance.  
4.1.a. Bronchiolitis Heterogeneity and the Need for Individualized Treatment. Bronchiolitis is a leading 
cause of pediatric morbidity and healthcare costs among children less than 2 years of age.15–20 Bronchodilator 
treatments with beta-agonists, such as albuterol, are commonly used in conditions like asthma. Their efficacy in 
bronchiolitis remains controversial, yet use is common practice due to the heterogeneity in patient response.21–
35 Published evidence suggests that bronchodilators do not improve outcomes in all children with 
bronchiolitis;22,33,34 however, meta-analyses can obscure the heterogeneity of treatment effects.36 While no prior 
studies have evaluated the genetics of bronchodilator response in bronchiolitis, variations in bronchodilator 
effectiveness for asthma often depend on genomic factors.49,50 Furthermore, studies have identified bronchiolitis 
phenotypes based on a child’s clinical presentation,37,56,57 environmental factors,4,38–40 and molecular 
pathobiology,37,41,45 suggesting the existence of treatment response heterogeneity, and supporting the 
identification of specific phenotypes that may guide the selection of bronchodilator treatment in children with 
bronchiolitis.37,41,45  
Hence, the association between bronchiolitis phenotypes and their heterogeneous treatment response merits 
further investigation. Clinical features associated with a potential bronchodilator-responsive bronchiolitis 
phenotype include older age,34,43,58–60 wheezing at presentation,44,57 atopic conditions like atopic dermatitis or 
eczema,38,39 and asthma in a first-degree relative.44,61,56 The effective management of children with bronchiolitis 
is critical. Yet, bronchodilator administration is based on non-specific examination findings like wheezing or 
retractions with past medical and family history.12 Importantly, our preliminary findings show these clinical 
evaluations are not reliably associated with a bronchodilator response. The heterogeneity of bronchodilator 
response in bronchiolitis highlights the complexity of this condition and underscores the need for individualized 
patient assessment and treatment decisions. This work is clinically meaningful because it will provide a targeted, 
rational approach to bronchodilator use by identifying a bronchodilator-responsive phenotype. 
An evidence-based tool is needed to identify bronchodilator-responsive children with bronchiolitis 
because current practice leads to inefficient clinical decisions,46 provider variation,28 unnecessary 
treatments,21 and increased resource use.18   
4.1.b. Scoring Tools in Bronchiolitis. The American Academy of Pediatrics (AAP) recognizes the need for 
more research on scoring tools to set clinical standards and assess bronchiolitis severity and management.21 
Bronchiolitis scores have been developed to assist in decisions to initiate respiratory support.62,63 However, no 
current studies define airway response to treatment in children with bronchiolitis (Aim 1), differentiate statistically 
distinct from clinically significant respiratory changes (Aim 1), or determine the association between genetic and 
clinical variables on treatment response (Aim 2a/2b).  
4.1.c. Bronchodilator Use in Bronchiolitis. The use of bronchodilator therapy in the emergency department 
(ED) and hospital setting for bronchiolitis is not associated with reduced frequency of hospital admission, 
intensive care unit admission, return ED visits after ED discharge, invasive ventilation, or noninvasive 
ventilation.33 However, bronchodilators remain widely used to treat bronchiolitis in the ED.17–25 Bronchiolitis 
treatment has a diverse response pattern, partly due to short-term clinical improvement,22 perceived 
responsiveness,22 and the recognition of bronchiolitis as a heterogeneous disease.64 This suggests that tailored 
bronchodilator therapies may offer advantages based on specific bronchiolitis phenotypes.37,41,45,58 Establishing 
short-term responsiveness in children with bronchiolitis is challenging because no single treatment intervention 
reliably changes respiratory status in bronchiolitis,21,33 unlike bronchodilator use in children with asthma. 
Provider-driven practice patterns lack an objective measure of a positive response to bronchodilator 
administration in bronchiolitis, and we will address this gap in the proposed research plan. 
4.1.d. Genetic Associations with Bronchodilator Response. No studies have evaluated the genetics of 
bronchodilator response in bronchiolitis. However, genetic variants are associated with bronchodilator response 
and clinical outcomes in childhood asthma,51–54,65–67 suggesting a bronchodilator-responsive genotype. 
Investigators who implemented genotype-directed prescribing of bronchodilators in children with asthma found 
that patients had a better quality of life and improved patient outcomes.55 The identification of genetic variants 
or single nucleotide polymorphisms (SNPs) through Genome-Wide Association Studies (GWAS) allows for a 
targeted treatment approach in asthma, and this proposal will extend this paradigm to bronchiolitis (Aims 2a). 
Our proposed research will combine clinical characteristics with GWAS to characterize bronchiolitis phenotypes 
with bronchodilator-responsive genotypes. Using GWAS data integrated with clinical characteristics to aid 
clinicians in real-time decision-making for bronchiolitis treatment is novel. This study will stratify patient 
information, provide reliable measures of disease severity, and predict bronchodilator response outcomes 
founded on patient-specific genetic information in children with bronchiolitis (Aim 2b).  
 
Based on my prior research, we hypothesize that children with bronchiolitis who exhibit atopic history 

and specific physical findings (e.g., retractions, wheezing) have SNPs linked to bronchodilator response. 
4.2. Innovation. The proposed research is innovative, representing the first effort in bronchiolitis research to: (1) 
define a minimum clinically important difference for bronchodilator response using the validated modified Tal 
score (MTS) in children with bronchiolitis (Aim 1); (2) explore associations between candidate SNPs and 
bronchodilator response to uncover genetic predictors in bronchiolitis (Aim 2a); and (3) develop a predictive 
model that integrates changes in the MTS with phenotypic and genotypic characteristics (Aim 2b). Data from 
Aims 1 and 2a will inform but not depend on Aim 2b. If Aim 2a identifies no SNPs, Aim 2b will still build a clinically 
relevant predictive model using Aim 1 data. By addressing these critical gaps, this study establishes the 
foundation for identifying predictive characteristics and creating a clinical prediction rule to enable rapid, 
evidence-based decision-making in the ED. These 
actions represent a transformative step towards 
precision treatment for children with bronchiolitis, 
offering a first-in-field approach to improving 
outcomes in pediatric bronchiolitis care.  
4.3. Preliminary Results. During the PI’s K12, we 
conducted a double-blind, randomized, placebo-
controlled trial of a single albuterol dose in children 
(3 to 24 months) with bronchiolitis to identify factors 
associated with bronchodilator response. Preliminary data 
from 82 participants (July 2022 to December 2023) showed 
that ≥25% of children treated with albuterol experienced at 
least a 2-point reduction in symptom severity by MTS, 
indicating a high responder group. However, historical and 
physical findings (before or after treatment) (Table 3) could 
not reliably predict bronchodilator response. GWAS analysis 
revealed candidate SNPs (Table 4) associated with 
bronchodilator response, including coding and non-coding 
SNPs, highlighting the need for further investigation into the 
genetic architecture of bronchodilator response. We will 
expand enrollment and biospecimen using the PI’s K12 study 
infrastructure (Section 4.4.1.h.).  
4.4. Approach. This proposal aims to characterize phenotypic and genotypic variations of children with 
bronchiolitis and their association with bronchodilator response.  
4.4.1. Specific Aim 1. Define airway responsiveness to bronchodilator treatment in children with 
bronchiolitis using the change in respiratory score. 
4.4.1.a. Rationale for modified Tal Score (MTS). There is no standard definition of airway response nor 
consensus on the best respiratory scoring tool for assessing treatment response in bronchiolitis.68–70 The 
validated MTS is the most appropriate clinical scoring tool71–78 for this proposal based on clinical applicability, 
ease of use,72 practicality, and high inter-rater agreement,79 even among physicians with varying experience and 
training levels71,80 (Table 5). The MTS includes respiratory parameters documented as part of the ED standard 
of care. The MTS score ranges from 0 to 12 points, with higher scores indicating greater respiratory distress. A 
limitation in all respiratory scoring tools, including the MTS, is that there is no well-established correlation 
between 
statistically 
significant 
changes 
in 
respiratory 
scores 
and 
clinically 
meaningful 
differences. We will identify the clinically relevant 
change in bronchodilator response by determining 
the minimum clinically important difference (MCID) in 
the MTS. Identifying the MCID helps standardize the 
evaluation 
of 
treatment 
efficacy, 
ensuring 
interventions are guided by clinically meaningful 
outcomes rather than statistically significant but 
subclinical changes. The MCID represents the 
smallest 
change 
in 
a 
clinical 
(respiratory) 
measurement that parents/caregivers or clinicians 
perceive as beneficial, meaningfully influencing 
clinical decision-making or having a tangible impact on the patient’s symptoms.  
4.4.1.b. Hypothesis. Bronchodilator response can be defined by establishing a threshold for airway response 

using the validated MTS. To accomplish this aim, we will establish the MCID and apply it to the validated MTS 
using anchor-based (subjective) and distribution-based (objective) methods.81 We will use this result to identify 
high and low responders to bronchodilator therapy. Our preliminary data suggest that ≥25% of children with 
bronchiolitis will be classified as high responders to bronchodilators using a dichotomous outcome. The MCID 
responses will identify characteristics that indicate which patients will benefit from bronchodilator therapy. 
4.4.1.c. Study Design. We will conduct a prospective, double-blind, randomized, placebo-controlled trial (RCT) 
of a single albuterol dose in children ages 3 to 24 months who present with bronchiolitis to the Nemours 
Children’s Health-Florida (NCH-FL) ED. Albuterol is a bronchodilator commonly used to treat children with 
bronchiolitis and asthma.82 This study will identify predictors of bronchodilator responsiveness among children 
with bronchiolitis. An RCT provides the best design for an unbiased collection of patient information and 
respiratory assessment outcomes. We will use the infrastructure from the previous K12 led by the PI to collect 
clinical data and biospecimens (Section 4.3).  
4.4.1.d. Study Population. Eligible patients will include infants (3 to 24 months) with bronchiolitis. Bronchiolitis 
is a clinical syndrome involving lower respiratory tract symptoms21 as identified by the treating provider(s). We 
will include children who either have no history of prematurity or have a history of prematurity but without 
associated co-morbidities (see exclusion criteria). We will exclude patients previously enrolled in the PI’s K12 
study, patients with a documented history of asthma or reactive airway disease, co-morbidities affecting airway 
response (e.g., chronic lung disease, bronchopulmonary dysplasia, bronchiectasis, congenital heart disease, 
immunodeficiency, neurologic condition), and diagnosis of pneumonia by chest radiography. Other exclusion 
criteria include inhaled, nebulized, or oral corticosteroid use within 72 hours of 
ED evaluation and inhaled, nebulized, or oral bronchodilator administration within 
4 hours of ED arrival.  
4.4.1.e. Patient Recruitment, Data Collection, and Management. Enrollment 
will not delay clinical management. After identifying each potential participant, a 
research staff member (RSM) will confirm eligibility, obtain informed consent, and 
initiate data collection procedures (Table 6). Data variables are summarized in 
Table 3. A clinically trained RSM will perform an examination and compute the 
MTS (pre-intervention score). Twenty to 30 minutes after nebulization of albuterol 
or placebo is complete, the patient will be re-evaluated, and the MTS will be 
computed (post-intervention score). The MTS delta (D) respiratory score is the 
numerical change observed between the pre-intervention and the post-
intervention score. The parent/caregiver and treating provider will be asked the 
global transition question (GTQ):83 “Since treatment, has there been any change 
in the child's respiratory function?.” DNA buccal swab specimens (for genotype 
characterization), and blood, urine, and nasopharyngeal swabs (for future 
epigenetic studies) will be collected. Data collected for this study, including the 
results from the MTS Drespiratory score, will not be shared with the treating 
provider or used for medical decision-making. The treating provider will direct 
continued patient care after treatment with the blinded intervention drug, and 
study participation will be concluded. The expected duration for the study participation is 30-60 minutes. As in 
the K12 workflow, enrollment will be performed by a research staff member on weekdays from 7a-11p. The PI 
will be on-call for overnight and weekend enrollment.   
4.4.1.f. Sample Strategy, Randomization, and 
Rationale for Saline Placebo. Patients will be 
randomized to receive either a bronchodilator 
(albuterol sulfate 2.5mg, 3mL inhalation solution) or 
normal saline (NS) as a placebo (sodium chloride 
0.9%, 3mL inhalation solution) (Figure 1). Nebulized 
NS helps thin mucus and aids in airway 
clearance.21,84 It acts as a placebo, replicating the 
NS component of the albuterol sulfate in the 
intervention group.82 NS and albuterol are commonly 
used in ED settings for bronchiolitis treatment. The 
parent/caregiver, treating provider, and RSM will be blinded to intervention allocation. The bedside nurse will be 
unblinded to ensure clinical safety.  
4.4.1.g. Specimen Handling and Storage. Within 2 hours of collection, urine, and serum prepared from whole 
blood are aliquoted and stored frozen at -80°C until subsequent analysis. The nasopharyngeal swabs are placed  

in a viral transport medium, RNA-seq Lysis Buffer, and MagMAX Lysis Buffer. After processing, samples are 
frozen at -80°C in the NCH Clinical Research Center (CRC) and stored until ready for analysis. The buccal swab 
for DNA (Oragene OGD-575) (DNA Genotek, Inc., Stittsville, Canada) will remain stored in nucleic acid 
stabilization media at room temperature until subsequent analysis. The PI actively participates in biospecimen 
collection and prepares samples for future epigenetic studies. 
4.4.1.h. Current and Projected Enrollment. In the PI’s 
K12 study, we enrolled 126 children with bronchiolitis 
(July 2022 through January 2025) and collected over 
300 biospecimens (buccal DNA, nasopharynx, serum, 
and urine). In the proposed study, we are expanding 
patient enrollment, data collection, and biobanking of 
specimens. Based on accrual projections accounting for 
seasonality and bronchiolitis prevalence, we anticipate 
a cumulative enrollment of 400 children by the end of 
Q4 in Year 4 of the K23 award (Table 7).  
4.4.1.i. Methods.  
Outcome Measures. The overall goal is to identify the MCID in the MTS in children with bronchiolitis from 
parents/caregivers and treating providers’ perceived improvement in respiratory response, which will be used in 
conjunction with patient characteristics to identify children with the likelihood of experiencing the greatest benefit 
from bronchodilator therapy. The primary outcome is to establish the MCID in the MTS using anchor-based 
(subjective) and distribution-based (objective) methods to identify high and low responders to bronchodilator 
therapy. Secondary outcomes will be evaluated in 2 ways: (1) We will determine the optimal cutoff value in the 
MTS to differentiate high versus low bronchodilator responsiveness, and (2) We will compare the Drespiratory 
score for high versus low responses to determine a statistically significant response.  
Statistical Analysis. The MCID85 will be calculated within our cohort using anchor-based and distribution-based 
methods81 (two methods each). The anchor-based MCID will determine the MTS Drespiratory score associated 
with the caregivers’ and treating providers’ perceived improvement in respiratory response, as assessed by the 
GTQ.83 An anchor establishes if the patient is better after treatment as a dichotomous variable (yes/no) and as 
a ranked continuous variable on a 15-point scale (-7 to +7, very much worse to very much improved), as 
assessed by responses from the caregiver(s) and the treating provider(s). A response of +1 to +2 will be 
interpreted as a “small” perceived improvement in respiratory function. The MCID will be calculated from the 
mean MTS Drespiratory score for caregivers reporting a “small” perceived change in the GTQ. A receiver 
operator curve (ROC)86 will be constructed from the dichotomous outcome of the GTQ as a function of the MTS 
Drespiratory score, and the Youden index from the area under the curve will determine the optimal MCID.87 
The distribution-based method will calculate the MCID as 0.5x the standard deviation (SD)88 of the MTS 
Drespiratory score mean and one standard error of the mean89 of the MTS Drespiratory score. The effect size 
will be expressed as the standardized mean difference and obtained by dividing the difference between the 
means of the pre- and post-intervention scores by the SD of the pre-intervention score.89 
4.4.1.j. Aim 1: Potential Problems and Alternative Approaches.  
Seasonal Variation of Bronchiolitis. Florida’s subtropical climate supports the year-round circulation of respiratory 
viruses that cause bronchiolitis (e.g., respiratory syncytial virus (RSV) and influenza), with a peak from November 
to March.90,91 Bronchiolitis ED visits at NCH-FL have remained steady (>600 cases annually) over the last 2 
years. Monthly accrual projections are adjusted for seasonality and bronchiolitis prevalence to guide enrollment. 
Severity of Bronchiolitis. Bronchiolitis severity may be self-selected, with milder cases seen in primary care and 
moderate-to-severe cases in the ED. We will stratify pre-treatment scores by severity and incorporate severity 
into our predictive modeling, ensuring a comprehensive illness analysis. 
Immunization Against Bronchiolitis-Causing Viruses. The 2023-2024 national immunization rate of children for 
COVID-19 is 14% (95% CI 13-15%),92 influenza is 52% (95% CI 51-53%),92 and RSV is 41%.93 Collected 
variables will include maternal and infant RSV vaccination and monoclonal antibody receipt. In 2024, 476/912 
(~52%) bronchiolitis cases at NCH-FL were non-RSV, highlighting the prevalence of other viral causes and 
underscoring that RSV preventive strategies are unlikely to impact patient enrollment substantively.  
Role of Age and Bronchodilator Responsiveness. Age may play a role in disease severity and bronchodilator 
response. Rather than exclude children of certain ages, we will account for age in statistical modeling, and we 
hypothesize that age will be either an effect modifier or a potential predictor of bronchodilator response.  
Selection Bias. Since the original submission, an additional RSM was included to expand enrollment hours 
(weekdays, 7a-11p). The PI will be on-call for overnight and weekend enrollment. RSMs will identify missed 
eligible children with bronchiolitis who visited the ED and collect their historical and clinical findings from the 

electronic health record. We will compare predictors and outcomes for enrolled participants versus missed 
participants versus candidate subjects whose caregivers declined participation to assess for selection bias. 
Sampling Bias. We will use ethnically and racially diverse research coordinators and assistants to encourage 
enrollment of children of all races and ethnicities. Patient enrollment and data collection will be performed in 
English or Spanish based on parent/caregiver preference, ensuring linguistic accessibility and cultural relevance. 
Missing Data. Missing variables will be verified through medical chart review and communication with the family 
as needed. Multiple imputation and sensitivity analyses will be performed to quantify the effect of the missing 
outcome data and assess the robustness of the study findings.  
Drug Shortage. Despite a national shortage of albuterol, the PI’s K12 maintained adequate supplies of albuterol.  
4.4.2. Specific Aim 2. Identify the associations between candidate genetic variants and bronchodilator 
response among children with bronchiolitis. 
4.4.2.a. Rationale for Genome-Wide Association Studies (GWAS). No genetic studies have evaluated 
bronchodilator response in bronchiolitis. Several genetic studies have explored children’s predisposition to 
developing bronchiolitis94–96 and its relationship with the later development of childhood asthma.59,97–102 Studies 
of childhood asthma have shown that genes associated with bronchodilator response are familial,103,104 
suggesting that a significant component of the response to beta-adrenergic agonists (bronchodilators) is 
heritable.54,105 The proposed GWAS will assess known SNPs (such as in ADRB2) and explore additional genetic 
factors, including those beyond beta-adrenergic pathways, to inform the development of a clinically applicable 
predictive algorithm based on genotype-phenotype correlations. We hypothesize that, as in asthma, genetic 
predictors associated with bronchodilator response in bronchiolitis are associated with specific patient 
characteristics identified as clinical, historical, and physical examination factors (Tables 3-6).  
4.4.2.b. Hypothesis. SNP alleles will be associated with a bronchodilator-responsive phenotype in bronchiolitis.  
4.4.2.c. Methods.  
Outcome Measures. The primary outcome is to identify candidate SNPs, rank-ordered by the strength of their 
association with the MTS Drespiratory score based on an odds ratio (dichotomous response) or a beta coefficient 
(continuous response) and filtered by adjusted p-value. A secondary outcome will be the identification of 
candidate SNPs and their association with the disaggregated components of the pre- and post-intervention MTS.  
Processing and Analysis of Genetic Samples. Genomic DNA will be purified from saliva collected in Oragene 
OGD-575106 and quantitated using a fluorescent dye-binding assay107 (Quant-iT PicoGreen dsDNA Assay, 
Thermo Fisher Scientific, Waltham, MA). DNA samples will be diluted to 10 ng/µL in deionized, sterile water, and 
20 µL are aliquoted into a 2.2 mL Eppendorf DeepWell 96-well plate (Eppendorf Biotech, Hamburg, Germany) 
and sealed with an adhesive-backed, foil microplate sealing film.  Analyses will contain 5% duplicate samples, 
two positive controls, and a designated blank to assess reproducibility and background contamination. The Gene 
Expression & Genotyping group of the University of Florida’s Interdisciplinary Center for Biotechnology Research 
(UF-ICBR) will genotype the specimens using the Precision Medicine Diversity Array108 on the Applied 
Biosystems Gene Titan platform (Thermo Fisher, Waltham, MA). The instrument’s Axiom Analysis Suite 
software109 assesses overall array quality from the Dish Quality Control (DQC). DQC is based on quantile 
normalized intensities of probe sequences for non-polymorphic genome locations and is a measure of signal-to-
noise ratio for specific hybridization of both A/T and G/C probe sets for each of the 96 arrays on the plate. 
Samples with a DQC value less than the default DQC threshold are excluded. Additional filtering is performed 
based on the sample call rate threshold (typically ≥97%). Plates with an average quality control (QC) call rate of 
passing samples <98.5% (>1 failing sample on a 96-well plate) are considered non-passing plates. The average 
QC call rate of passing samples on the plate represents the mean of the QC call rates of samples passing DQC 
and the 97% QC call rate thresholds. We anticipate an average QC call rate of ≥93% (saliva specimen). The 
best-performing probe set for each SNP will be identified based on 17 SNP-level QC metrics. The software 
reports SNP genotype calls, insertions/deletions (indels), multi-allelic variants, and copy number variants, and 
data is exported in variant call format. We anticipate a concordance between duplicate samples >99.8%. 
Additional genotype information will be imputed using published methods.110 Imputation accuracy will be 
assessed by comparing the discordance rate (% of discordance between imputed and masked genotypes) with 
the missing rate (% of no calls made for masked genotypes) on different imputation genotype posterior 
probabilities111 thresholds. Sensitivity and false positive rates will be calculated. We expect the imputation 
accuracy (mean R2) to be >0.9 for SNPs with minor allele frequency (MAF) >0.05. 
Covariates. Demographics (i.e., age, sex, race, ethnicity), ED vital sign abnormalities (i.e., tachycardia, 
tachypnea, hypoxia), intervention drug, disease severity, and ED physical examination findings. 
Statistical Analysis. Genetic Predictors of Bronchodilator Response in Bronchiolitis. Variants associated with 
MTS Drespiratory scores will be identified using multivariate linear regression models in PLINK 2.0.112,113 The 
PLINK 2.0 PHENO and COV files will be built by combining outcomes data (including MTS Drespiratory scores), 

other covariates, and the first 5 dimensions of the principal components analysis (PCA). The initial QC steps will 
be conducted in PLINK 2.0114,115 and include; (1) the removal of individuals with ≥10 % SNP missingness and 
SNPs with <95% call rate; (2) SNPs with a MAF <5% (very low power); (3) SNPs with deviations from Hardy–
Weinberg equilibrium in the control group (potential genotyping issues); (4) SNPs with a difference in 
heterozygosity between sexes >7% or absolute difference in call rate between sexes >2.5%; (5) linkage 
disequilibrium (LD) pruning (100Kb window, r2 threshold 0.8) to keep one SNP with the highest MAF that is a 
representative of a cluster of SNPs in LD; (6) relatedness; and (7) correcting for population stratification by PCA.  
The analysis will include SNPs associated with bronchodilator response in children with asthma.52,53,116 The 
association between the MTS Drespiratory scores and SNPs coded using an additive model (i.e., the effect of 
each additional minor allele) will be evaluated to perform a linear regression analysis for each SNP. Regression 
coefficients and 95% confidence intervals will determine the difference in mean treatment response 
(bronchodilator versus placebo) corresponding to each additional minor allele. Data transformations of MTS 
Drespiratory scores will be considered to address distributional skewness. A Bonferroni correction for multiple 
testing will be utilized.117 All statistical tests will be two-sided.  
4.5. Sample Size. We will conduct a sub-analysis of all SNPs interrogated genome-wide. Candidate SNP tests 
will be restricted to ≤20 (Bonferroni correction ≥0.0025) of the top replicated candidate SNPs identified from 
previous studies.51–54,66,116,118,119 Assuming an additive genetic model, a sample size of 200 patients with 
bronchiolitis in each randomly assigned group (total cohort N= 400) and a Cohen’s d effect size of 0.8 for 
bronchodilator response in SNP carriers relative to noncarriers and a response rate of 0.25, with an alpha of 
0.0025, we will have 80% power at a MAF of 7.6% to significantly associate bronchodilator response and SNP(s).   
4.7. Research Timeline. Table 8 outlines the 
anticipated timeline for the 5-year research plan.  
4.8. Future Directions. Data generated from this 
study will inform a future NIH R01 grant, for which I will 
apply during Year 5 of the K23 award, focused on the 
derivation and external validation of an evidence-
based prediction rule. We will also have developed a 
large biorepository in children with bronchiolitis to 
facilitate the future discovery of additional candidate 
SNPs and biomarkers for bronchodilator response.