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1.  Universality of Human Microbial Dynamics 
Nature  2016;534(7606):259-262.
The recent realization that human-associated microbial communities play a crucial role in determining our health and well-being1,2 has led to the ongoing development of microbiome-based therapies3 such as fecal microbiota transplantation4,5. Thosemicrobial communities are very complex, dynamic6 and highly personalized ecosystems3,7, exhibiting a high degree of inter-individual variability in both species assemblages8 and abundance profiles9. It is not known whether the underlying ecological dynamics, which can be parameterized by growth rates, intra- and inter-species interactions in population dynamics models10, are largely host-independent (i.e. “universal”) or host-specific. If the inter-individual variability reflects host-specific dynamics due to differences in host lifestyle11, physiology12, or genetics13, then generic microbiome manipulations may have unintended consequences, rendering them ineffectual or even detrimental. Alternatively, microbial ecosystems of different subjects may follow a universal dynamics with the inter-individual variability mainly stemming from differences in the sets of colonizing species7,14. Here we developed a novel computational method to characterize human microbial dynamics. Applying this method to cross-sectional data from two large-scale metagenomic studies, the Human Microbiome Project9,15 and the Student Microbiome Project16, we found that both gut and mouth microbiomes display pronounced universal dynamics, whereas communities associated with certain skin sites are likely shaped by differences in the host environment. Interestingly, the universality of gut microbial dynamics is not observed in subjects with recurrent Clostridium difficile infection17 but is observed in the same set of subjects after fecal microbiota transplantation. These results fundamentally improve our understanding of forces and processes shaping human microbial ecosystems, paving the way to design general microbiome-based therapies18.
PMCID: PMC4902290  PMID: 27279224
2.  Identifying Pediatric Severe Sepsis and Septic Shock: Accuracy of Diagnosis Codes 
The Journal of pediatrics  2015;167(6):1295-1300.e4.
To evaluate accuracy of two established administrative methods of identifying children with sepsis using a medical record review reference standard.
Study design
Multicenter retrospective study at six US children’s hospitals. Subjects were children >60 days and <19 years of age were identified in four groups based on ICD9-CM codes: (1) Severe sepsis/septic shock (Sepsis Codes); (2) Infection plus organ dysfunction (Combination Codes); (3) Subjects without codes for infection, organ dysfunction, or severe sepsis; and (4) Infection but not severe sepsis or organ dysfunction. Combination codes were allowed, but not required within the Sepsis Codes group. We determined the presence of reference standard severe sepsis according to consensus criteria. Logistic regression was performed to determine whether addition of codes for sepsis therapies improved case identification.
130 of 432 subjects met reference standard definition of severe sepsis. Sepsis codes had sensitivity 73% (95% CI 70–86), specificity 92% (95% CI 87–95), and positive predictive value (PPV) 79% (95% CI 70–86). Combination codes had sensitivity 15% (95% CI 9–22), specificity 71% (95% CI 65–76), and PPV 18% (95% CI 11–27). Slight improvements in model characteristics were observed when codes for vasoactive medications and endotracheal intubation were added to sepsis codes (c-statistic 0.83 vs. 0.87, p=0.008).
Sepsis specific ICD9-CM codes identify pediatric patients with severe sepsis in administrative data more accurately than a combination of codes for infection plus organ dysfunction.
PMCID: PMC4662908  PMID: 26470685
Septic Shock; Epidemiology
3.  CTNNA3 and SEMA3D: promising loci for asthma exacerbation identified through multiple GWAS 
Asthma exacerbations are a major cause of morbidity and medical cost.
The objective of this study was to identify genetic predictors of exacerbations in subjects with asthma.
We performed GWAS meta analysis of acute asthma exacerbation in two pediatric clinical trials: Childhood Asthma Management Program (CAMP, n=581) and Childhood Asthma Research and Education (CARE, n=205) network trials. Acute asthma exacerbations was defined as treatment with a five-day course of oral steroids. We obtained a replication cohort from BioVU (n=786), the Vanderbilt University electronic medical record-linked DNA biobank. We used CD4+ lymphocyte genome-wide mRNA expression profiling to identify associations of top SNPs with mRNA abundance of nearby genes.
A locus in CTNNA3 reached genome-wide significance (rs7915695, p=2.19*10-8, mean exacerbations 6.05 for minor alleles vs. 3.71 for homozygous major). Among four top SNPs replicated in BioVU, rs993312 in SEMA3D was significant (p=0.0083), and displayed stronger association among African Americans (p=0.0004 in BioVU, mean exacerbations 3.91 vs. 1.53; p=0.0089 in CAMP, mean exacerbations 6.0 vs. 3.25). CTNNA3 variants did not replicate in BioVU. A regulatory variant in the CTNNA3 locus was associated with CTNNA3 mRNA expression in CD4+ cells from asthmatics (p=0.00079). CTNNA3 appears to be active in immune response, and SEMA3D has a plausible role in airway remodeling. We also provide a replication of a previous association of P2RX7 with asthma exacerbation.
We identified two loci associated with exacerbations through GWAS. CTNNA3 met genome-wide significance thresholds and SEMA3D replicated in a clinical Biobank database.
PMCID: PMC4676949  PMID: 26073756
Asthma; Exacerbation; GWAS; eQTL; CTNNA3; SEMA3D; BioBank; Childhood Asthma
4.  Whole genome prediction and heritability of childhood asthma phenotypes 
While whole genome prediction (WGP) methods have recently demonstrated successes in the prediction of complex genetic diseases, they have not yet been applied to asthma and related phenotypes. Longitudinal patterns of lung function differ between asthmatics, but these phenotypes have not been assessed for heritability or predictive ability. Herein, we assess the heritability and genetic predictability of asthma‐related phenotypes.
We applied several WGP methods to a well‐phenotyped cohort of 832 children with mild‐to‐moderate asthma from CAMP. We assessed narrow‐sense heritability and predictability for airway hyperresponsiveness, serum immunoglobulin E, blood eosinophil count, pre‐ and post‐bronchodilator forced expiratory volume in 1 sec (FEV1), bronchodilator response, steroid responsiveness, and longitudinal patterns of lung function (normal growth, reduced growth, early decline, and their combinations). Prediction accuracy was evaluated using a training/testing set split of the cohort.
We found that longitudinal lung function phenotypes demonstrated significant narrow‐sense heritability (reduced growth, 95%; normal growth with early decline, 55%). These same phenotypes also showed significant polygenic prediction (areas under the curve [AUCs] 56% to 62%). Including additional demographic covariates in the models increased prediction 4–8%, with reduced growth increasing from 62% to 66% AUC. We found that prediction with a genomic relatedness matrix was improved by filtering available SNPs based on chromatin evidence, and this result extended across cohorts.
Longitudinal reduced lung function growth displayed extremely high heritability. All phenotypes with significant heritability showed significant polygenic prediction. Using SNP‐prioritization increased prediction across cohorts. WGP methods show promise in predicting asthma‐related heritable traits.
PMCID: PMC5134727  PMID: 27980782
Childhood asthma; heritability; longitudinal lung function patterns; polygenic prediction; whole‐genome prediction
5.  Comparison of Two Sepsis Recognition Methods in a Pediatric Emergency Department 
To compare the effectiveness of physician judgment and an electronic algorithmic alert to identify pediatric patients with severe sepsis/septic shock in a pediatric emergency department (ED).
This was an observational cohort study of patients older than 56 days with fever or hypothermia. All patients were evaluated for potential sepsis in real time by the ED clinical team. An electronic algorithmic alert was retrospectively applied to identify patients with potential sepsis independent of physician judgment. The primary outcome was the proportion of patients correctly identified with severe sepsis/septic shock defined by consensus criteria. Test characteristics were determined and receiver operating characteristic (ROC) curves were compared.
Of 19,524 eligible patient visits, 88 patients developed consensus-confirmed severe sepsis or septic shock. Physician judgment identified 159, and the algorithmic alert identified 3,301 patients with potential sepsis. Physician judgment had sensitivity of 72.7% (95% CI = 72.1% to 73.4%) and specificity 99.5% (95% CI = 99.4% to 99.6%); the algorithmic alert had sensitivity 92.1% (95% CI = 91.7% to 92.4%), and specificity 83.4% (95% CI = 82.9% to 83.9%) for severe sepsis/septic shock. There was no significant difference in the area under the ROC curve for physician judgment (0.86, 95% CI = 0.81 to 0.91) or the algorithm (0.88, 95% CI = 0.85 to 0.91; p = 0.54). A combination method using either positive physician judgment or an algorithmic alert improved sensitivity to 96.6% and specificity to 83.3%. A sequential approach, in which positive identification by the algorithmic alert was then confirmed by physician judgment, achieved 68.2% sensitivity and 99.6% specificity. Positive and negative predictive values for physician judgment vs. algorithmic alert were 40.3% vs. 2.5% and 99.88 % vs. 99.96%, respectively.
The electronic algorithmic alert was more sensitive but less specific than physician judgment for recognition of pediatric severe sepsis and septic shock. These findings can help to guide institutions in selecting pediatric sepsis recognition methods based on institutional needs and priorities.
PMCID: PMC4639443  PMID: 26474032
6.  Vitamin D Binding Protein Haplotype is Associated with Hospitalization for RSV Bronchiolitis 
Between 75,000–125,000 U.S. infants are hospitalized for respiratory syncytial virus (RSV) bronchiolitis each year. Up to half will be diagnosed with asthma in later childhood. Vitamin D deficiency has been associated with susceptibility to asthma and respiratory infections. Measured vitamin D is largely bound to vitamin D binding protein (VDBP); VDBP levels are influenced by its gene (GC) haplotype.
We assessed the relationship between polymorphisms rs7041 and rs4588, which define haplotypes GC1s, GC1f, and GC2, and RSV bronchiolitis susceptibility and subsequent asthma.
We retrospectively recruited 198 otherwise healthy children (93% White) hospitalized for severe RSV bronchiolitis in Boston and 333 parents into a follow up study to assess asthma diagnosis. Data were analyzed using family-based genetic association tests. We independently validated our results in 465 White children hospitalized with RSV bronchiolitis and 930 White population controls from the Netherlands.
The rs7041_C allele (denoting haplotype GC1s) was overtransmitted (P=0.02, additive model) in the entire Boston cohort, and in Whites (P=0.03), and in those subsequently diagnosed with asthma (P=0.006). The GC1f haplotype was undertransmitted in the White and asthma subgroups (both P=0.05). The rs7041_C allele was also more frequent in the RSV bronchiolitis group compared to controls (OR 1.12, 95% CI 1.02, 1.4, P=0.03) in the Netherlands; especially in mechanically ventilated patients (P=0.009).
Conclusion and Clinical Relevance
GC1s haplotype carriage may increase the risk of RSV bronchiolitis in infancy and subsequent asthma development. The GC1s haplotype is associated with higher VDBP levels, resulting in less freely-available vitamin D.
PMCID: PMC5062603  PMID: 24447085
RSV; bronchiolitis; asthma; children; infants; polymorphisms; vitamin D binding protein; haplotype; GC; vitamin D
7.  The Role of Vitamin D in the Transcriptional Program of Human Pregnancy 
PLoS ONE  2016;11(10):e0163832.
Patterns of gene expression of human pregnancy are poorly understood. In a trial of vitamin D supplementation in pregnant women, peripheral blood transcriptomes were measured longitudinally on 30 women and used to characterize gene co-expression networks.
Studies suggest that increased maternal Vitamin D levels may reduce the risk of asthma in early life, yet the underlying mechanisms have not been examined. In this study, we used a network-based approach to examine changes in gene expression profiles during the course of normal pregnancy and evaluated their association with maternal Vitamin D levels.
The VDAART study is a randomized clinical trial of vitamin D supplementation in pregnancy for reduction of pediatric asthma risk. The trial enrolled 881 women at 10–18 weeks of gestation. Longitudinal gene expression measures were obtained on thirty pregnant women, using RNA isolated from peripheral blood samples obtained in the first and third trimesters. Differentially expressed genes were identified using significance of analysis of microarrays (SAM), and clustered using a weighted gene co-expression network analysis (WGCNA). Gene-set enrichment was performed to identify major biological pathways.
Comparison of transcriptional profiles between first and third trimesters of pregnancy identified 5839 significantly differentially expressed genes (FDR<0.05). Weighted gene co-expression network analysis clustered these transcripts into 14 co-expression modules of which two showed significant correlation with maternal vitamin D levels. Pathway analysis of these two modules revealed genes enriched in immune defense pathways and extracellular matrix reorganization as well as genes enriched in notch signaling and transcription factor networks.
Our data show that gene expression profiles of healthy pregnant women change during the course of pregnancy and suggest that maternal Vitamin D levels influence transcriptional profiles. These alterations of the maternal transcriptome may contribute to fetal immune imprinting and reduce allergic sensitization in early life.
Trial Registration NCT00920621
PMCID: PMC5053446  PMID: 27711190
9.  Implementing Personalized Medicine in the Academic Health Center 
PMCID: PMC5040964  PMID: 27657137
10.  Pharmacogenomics: Challenges and Opportunities 
Annals of internal medicine  2006;145(10):749-757.
The outcome of drug therapy is often unpredictable, ranging from beneficial effects to lack of efficacy to serious adverse effects. Variations in single genes are 1 well-recognized cause of such unpredictability, defining the field of pharmacogenetics (see Glossary). Such variations may involve genes controlling drug metabolism, drug transport, disease susceptibility, or drug targets. The sequencing of the human genome and the cataloguing of variants across human genomes are the enabling resources for the nascent field of pharmacogenomics (see Glossary), which tests the idea that genomic variability underlies variability in drug responses. However, there are many challenges that must be overcome to apply rapidly accumulating genomic information to understand variable drug responses, including defining candidate genes and pathways; relating disease genes to drug response genes; precisely defining drug response phenotypes; and addressing analytic, ethical, and technological issues involved in generation and management of large drug response data sets. Overcoming these challenges holds the promise of improving new drug development and ultimately individualizing the selection of appropriate drugs and dosages for individual patients.
PMCID: PMC5006954  PMID: 17116919
11.  Transcriptomic variation of pharmacogenes in multiple human tissues and lymphoblastoid cell lines 
The pharmacogenomics journal  2016;10.1038/tpj.2015.93.
Variation in the expression level and activity of genes involved in drug disposition and action (“pharmacogenes”) can affect drug response and toxicity, especially when in tissues of pharmacological importance. Previous studies have relied primarily on microarrays to understand gene expression differences, or have focused on a single tissue or small number of samples. The goal of this study was to use RNA-seq to determine the expression levels and alternative splicing of 389 PGRN pharmacogenes across four tissues (liver, kidney, heart and adipose) and lymphoblastoid cell lines (LCLs), which are used widely in pharmacogenomics studies. Analysis of RNA-seq data from 139 different individuals across the 5 tissues (20–45 individuals per tissue type) revealed substantial variation in both expression levels and splicing across samples and tissue types. This in-depth exploration also revealed 183 splicing events in pharmacogenes that were previously not annotated. Overall, this study serves as a rich resource for the research community to inform biomarker and drug discovery and use.
PMCID: PMC4980276  PMID: 26856248
RNA-seq; Pharmacogenes; Splicing; Biomarkers; JuncBASE; Pharmacogenomics of Research Network
12.  Hyperferritinemic Sepsis: An Opportunity for Earlier Diagnosis and Intervention? 
We describe a case of an infant with HSV meningitis and septic shock who demonstrated a remarkably high serum ferritin level. Aggressive pediatric intensive care and the administration of high-dose glucocorticoids were not able to reverse the multiple organ dysfunctions. Subsequent autopsy identified the presence of hemophagocytosis, thus the patient fulfilled hemophagocytic lymphohistiocytosis (HLH) criteria post-mortem. This case highlights that serum ferritin may be an important early indicator of mortality in sepsis due to a cytokine storm similar to macrophage activation syndrome and HLH.
PMCID: PMC4969285  PMID: 27532033
sepsis; hemophagocytic lymphohistiocytosis; macrophage activation syndrome; HSV; ferritin; cytokine storm
13.  Measuring the Corticosteroid Responsiveness Endophenotype in Asthma 
Inhaled corticosteroids are the most commonly used controller therapies for asthma, producing treatment responses in six clinical phenotypes; lung function, bronchodilator response, airway responsiveness, symptoms, need for oral steroids and frequency of emergency department visits and hospitalizations. We hypothesize that treatment response in all of these phenotypes is modulated by a single, quantative corticosteroid responsiveness endophenotype.
To develop a composite phenotype that combines multiple clinical phenotypes to measure corticosteroid responsiveness with high accuracy, high stability across populations, and high robustness to missing data.
We employed principal component analysis (PCA) to determine a composite corticosteroid responsiveness phenotype that we tested in four replication populations. We evaluated the relative accuracy with which the composite and clinical phenotypes measure the endophenotype using treatment effect area under the receiver operating characteristic curve (AUC).
In the study population, the composite phenotype measured the endophenotype with an AUC of 0.74, significantly exceeding the AUCs of the six individual clinical phenotypes, which ranged from 0.56 (p-value <.001) to 0.67 (p-value 0.015). In four replication populations with a total of 22 clinical phenotypes available, the composite phenotype AUC ranged from 0.69 to 0.73, significantly exceeded the AUCs of 14 phenotypes, and was not significantly exceeded by any single phenotype.
The composite phenotype measured the endophenotype with higher accuracy, higher stability across populations, and higher robustness to missing data than any clinical phenotype. This should provide the capability to model corticosteroid pharmacologic response and resistance with increased accuracy and reproducibility.
PMCID: PMC4530065  PMID: 25951964
asthma; corticosteroids; drug therapy; endophenotype; pharmacogenetics; pharmacologic response
14.  A Multibiomarker-Based Model for Estimating the Risk of Septic Acute Kidney Injury 
Critical care medicine  2015;43(8):1646-1653.
The development of acute kidney injury in patients with sepsis is associated with worse outcomes. Identifying those at risk for septic acute kidney injury could help to inform clinical decision making. We derived and tested a multibiomarker-based model to estimate the risk of septic acute kidney injury in children with septic shock.
Candidate serum protein septic acute kidney injury biomarkers were identified from previous transcriptomic studies. Model derivation involved measuring these biomarkers in serum samples from 241 subjects with septic shock obtained during the first 24 hours of admission and then using a Classification and Regression Tree approach to estimate the probability of septic acute kidney injury 3 days after the onset of septic shock, defined as at least two-fold increase from baseline serum creatinine. The model was then tested in a separate cohort of 200 subjects.
Multiple PICUs in the United States.
None other than standard care.
Measurements and Main Results
The decision tree included a first-level decision node based on day 1 septic acute kidney injury status and five subsequent biomarker-based decision nodes. The area under the curve for the tree was 0.95 (CI95, 0.91–0.99), with a sensitivity of 93% and a specificity of 88%. The tree was superior to day 1 septic acute kidney injury status alone for estimating day 3 septic acute kidney injury risk. In the test cohort, the tree had an area under the curve of 0.83 (0.72–0.95), with a sensitivity of 85% and a specificity of 77% and was also superior to day 1 septic acute kidney injury status alone for estimating day 3 septic acute kidney injury risk.
We have derived and tested a model to estimate the risk of septic acute kidney injury on day 3 of septic shock using a novel panel of biomarkers. The model had very good performance in a test cohort and has test characteristics supporting clinical utility and further prospective evaluation.
PMCID: PMC4667777  PMID: 25962083
biomarkers; decision tree; inflammation; kidney injury; modeling; sepsis
15.  Stress and Bronchodilator Response in Children with Asthma 
Rationale: Stress is associated with asthma morbidity in Puerto Ricans (PRs), who have reduced bronchodilator response (BDR).
Objectives: To examine whether stress and/or a gene regulating anxiety (ADCYAP1R1) is associated with BDR in PR and non-PR children with asthma.
Methods: This was a cross-sectional study of stress and BDR (percent change in FEV1 after BD) in 234 PRs ages 9–14 years with asthma. We assessed child stress using the Checklist of Children’s Distress Symptoms, and maternal stress using the Perceived Stress Scale. Replication analyses were conducted in two cohorts. Polymorphisms in ADCYAP1R1 were genotyped in our study and six replication studies. Multivariable models of stress and BDR were adjusted for age, sex, income, environmental tobacco smoke, and use of inhaled corticosteroids.
Measurements and Main Results: High child stress was associated with reduced BDR in three cohorts. PR children who were highly stressed (upper quartile, Checklist of Children’s Distress Symptoms) and whose mothers had high stress (upper quartile, Perceived Stress Scale) had a BDR that was 10.2% (95% confidence interval, 6.1–14.2%) lower than children who had neither high stress nor a highly stressed mother. A polymorphism in ADCYAP1R1 (rs34548976) was associated with reduced BDR. This single-nucleotide polymorphism is associated with reduced expression of the gene for the β2-adrenergic receptor (ADRB2) in CD4+ lymphocytes of subjects with asthma, and it affects brain connectivity of the amygdala and the insula (a biomarker of anxiety).
Conclusions: High child stress and an ADCYAP1R1 single-nucleotide polymorphism are associated with reduced BDR in children with asthma. This is likely caused by down-regulation of ADRB2 in highly stressed children.
PMCID: PMC4511425  PMID: 25918834
asthma; Puerto Ricans; bronchodilator response; stress
16.  Circulating MicroRNAs: Association with Lung Function in Asthma 
PLoS ONE  2016;11(6):e0157998.
MicroRNAs are key transcriptional and network regulators previously associated with asthma susceptibility. However, their role in relation to asthma severity has not been delineated.
We hypothesized that circulating microRNAs could serve as biomarkers of changes in lung function in asthma patients.
We isolated microRNAs from serum samples obtained at randomization for 160 participants of the Childhood Asthma Management Program. Using a TaqMan microRNA array containing 754 microRNA primers, we tested for the presence of known asthma microRNAs, and assessed the association of the individual microRNAs with lung function as measured by FEV1/FVC, FEV1% and FVC%. We further tested the subset of FEV1/FVC microRNAs for sex-specific and lung developmental associations.
Of the 108 well-detected circulating microRNAs, 74 (68.5%) had previously been linked to asthma susceptibility. We found 22 (20.3%), 4 (3.7%) and 8 (7.4%) microRNAs to be associated with FEV1/FVC, FEV1% and FVC%, respectively. 8 (of 22) FEV1/FVC, 3 (of 4) FEV1% and 1 (of 8) FVC% microRNAs had functionally validated target genes that have been linked via genome wide association studies to asthma and FEV1 change. Among the 22 FEV1/FVC microRNAs, 9 (40.9%) remain associated with FEV1/FVC in boys alone in a sex-stratified analysis (compared with 3 FEV1/FVC microRNAs in girls alone), 7 (31.8%) were associated with fetal lung development, and 3 (13.6%) in both. Ontology analyses revealed enrichment for pathways integral to asthma, including PPAR signaling, G-protein coupled signaling, actin and myosin binding, and respiratory system development.
Circulating microRNAs reflect asthma biology and are associated with lung function differences in asthmatics. They may represent biomarkers of asthma severity.
PMCID: PMC4928864  PMID: 27362794
17.  Contribution of common non-synonymous variants in PCSK1 to body mass index variation and risk of obesity: a systematic review and meta-analysis with evidence from up to 331 175 individuals 
Human Molecular Genetics  2015;24(12):3582-3594.
Polymorphisms rs6232 and rs6234/rs6235 in PCSK1 have been associated with extreme obesity [e.g. body mass index (BMI) ≥ 40 kg/m2], but their contribution to common obesity (BMI ≥ 30 kg/m2) and BMI variation in a multi-ethnic context is unclear. To fill this gap, we collected phenotypic and genetic data in up to 331 175 individuals from diverse ethnic groups. This process involved a systematic review of the literature in PubMed, Web of Science, Embase and the NIH GWAS catalog complemented by data extraction from pre-existing GWAS or custom-arrays in consortia and single studies. We employed recently developed global meta-analytic random-effects methods to calculate summary odds ratios (OR) and 95% confidence intervals (CIs) or beta estimates and standard errors (SE) for the obesity status and BMI analyses, respectively. Significant associations were found with binary obesity status for rs6232 (OR = 1.15, 95% CI 1.06–1.24, P = 6.08 × 10−6) and rs6234/rs6235 (OR = 1.07, 95% CI 1.04–1.10, P = 3.00 × 10−7). Similarly, significant associations were found with continuous BMI for rs6232 (β = 0.03, 95% CI 0.00–0.07; P = 0.047) and rs6234/rs6235 (β = 0.02, 95% CI 0.00–0.03; P = 5.57 × 10−4). Ethnicity, age and study ascertainment significantly modulated the association of PCSK1 polymorphisms with obesity. In summary, we demonstrate evidence that common gene variation in PCSK1 contributes to BMI variation and susceptibility to common obesity in the largest known meta-analysis published to date in genetic epidemiology.
PMCID: PMC4498155  PMID: 25784503
18.  A disease module in the interactome explains disease heterogeneity, drug response and captures novel pathways and genes in asthma 
Human Molecular Genetics  2015;24(11):3005-3020.
Recent advances in genetics have spurred rapid progress towards the systematic identification of genes involved in complex diseases. Still, the detailed understanding of the molecular and physiological mechanisms through which these genes affect disease phenotypes remains a major challenge. Here, we identify the asthma disease module, i.e. the local neighborhood of the interactome whose perturbation is associated with asthma, and validate it for functional and pathophysiological relevance, using both computational and experimental approaches. We find that the asthma disease module is enriched with modest GWAS P-values against the background of random variation, and with differentially expressed genes from normal and asthmatic fibroblast cells treated with an asthma-specific drug. The asthma module also contains immune response mechanisms that are shared with other immune-related disease modules. Further, using diverse omics (genomics, gene-expression, drug response) data, we identify the GAB1 signaling pathway as an important novel modulator in asthma. The wiring diagram of the uncovered asthma module suggests a relatively close link between GAB1 and glucocorticoids (GCs), which we experimentally validate, observing an increase in the level of GAB1 after GC treatment in BEAS-2B bronchial epithelial cells. The siRNA knockdown of GAB1 in the BEAS-2B cell line resulted in a decrease in the NFkB level, suggesting a novel regulatory path of the pro-inflammatory factor NFkB by GAB1 in asthma.
PMCID: PMC4447811  PMID: 25586491
19.  Genome-wide Association Study and Admixture Mapping Reveal New Loci Associated with Total IgE Levels in Latinos 
Immunoglobulin E (IgE) is a key mediator of allergic inflammation and is frequently elevated in allergic disorders.
To identify genetic variants associated with IgE levels in Latinos.
We performed a genome-wide association study (GWAS) and admixture mapping of total IgE levels in 3,334 Latinos from the Genes-environments & Admixture in Latino Americans (GALA II) study. Replication was evaluated in 454 Latinos, 1,564 European Americans, and 3,187 African Americans from independent studies.
We confirmed associations of six genes identified by previous GWAS and identified a novel genome-wide significant association of a polymorphism in ZNF365 with total IgE (rs200076616, p=2.3x10−8). We next identified four admixture mapping peaks (6p21.32-p22.1, 13p22-31, 14q23.2, and 22q13.1) where local African, European, and/or Native American ancestry was significantly associated with IgE levels. The most significant peak was 6p21.32-p22.1, where Native American ancestry was associated with lower levels of IgE (p=4.95x10−8). All but 22q13.1 were replicated in an independent sample of Latinos, and two of the peaks were replicated in African Americans (6p21.32-p22.1 and 14q23.2). Fine mapping of 6p21.32-p22.1 identified six genome-wide significant single nucleotide polymorphisms in Latinos, two of which replicated in European Americans. Another SNP was peak-wide significant within 14q23.2 in African Americans (rs1741099, p=3.7x10−6), and replicated in non-African American samples (p=0.011).
We confirmed genetic associations at six genes, and identified novel associations within ZNF365, HLA-DQA1, and 14q23.2. Our results highlight the importance of studying diverse, multi-ethnic populations to uncover novel loci associated with total IgE levels.
PMCID: PMC4458233  PMID: 25488688
immunoglobulin E; genome-wide association study; admixture mapping; allergy; asthma; next-generation sequencing; Latinos; Hispanics; minority populations
21.  Implementation of Electronic Consent at a Biobank: An Opportunity for Precision Medicine Research 
The purpose of this study is to characterize the potential benefits and challenges of electronic informed consent (eIC) as a strategy for rapidly expanding the reach of large biobanks while reducing costs and potentially enhancing participant engagement. The Partners HealthCare Biobank (Partners Biobank) implemented eIC tools and processes to complement traditional recruitment strategies in June 2014. Since then, the Partners Biobank has rigorously collected and tracked a variety of metrics relating to this novel recruitment method. From June 2014 through January 2016, the Partners Biobank sent email invitations to 184,387 patients at Massachusetts General Hospital and Brigham and Women’s Hospital. During the same time period, 7078 patients provided their consent via eIC. The rate of consent of emailed patients was 3.5%, and the rate of consent of patients who log into the eIC website at Partners Biobank was 30%. Banking of biospecimens linked to electronic health records has become a critical element of genomic research and a foundation for the NIH’s Precision Medicine Initiative (PMI). eIC is a feasible and potentially game-changing strategy for these large research studies that depend on patient recruitment.
PMCID: PMC4932464  PMID: 27294961
biobank; electronic consent; precision medicine; informed consent
22.  Multi-ethnic genome-wide association study of 21,000 cases and 95,000 controls identifies new risk loci for atopic dermatitis 
Paternoster, Lavinia | Standl, Marie | Waage, Johannes | Baurecht, Hansjörg | Hotze, Melanie | Strachan, David P | Curtin, John A | Bønnelykke, Klaus | Tian, Chao | Takahashi, Atsushi | Esparza-Gordillo, Jorge | Alves, Alexessander Couto | Thyssen, Jacob P | den Dekker, Herman T | Ferreira, Manuel A | Altmaier, Elisabeth | Sleiman, Patrick MA | Xiao, Feng Li | Gonzalez, Juan R | Marenholz, Ingo | Kalb, Birgit | Yanes, Maria Pino | Xu, Cheng-Jian | Carstensen, Lisbeth | Groen-Blokhuis, Maria M | Venturini, Cristina | Pennell, Craig E | Barton, Sheila J | Levin, Albert M | Curjuric, Ivan | Bustamante, Mariona | Kreiner-Møller, Eskil | Lockett, Gabrielle A | Bacelis, Jonas | Bunyavanich, Supinda | Myers, Rachel A | Matanovic, Anja | Kumar, Ashish | Tung, Joyce Y | Hirota, Tomomitsu | Kubo, Michiaki | McArdle, Wendy L | Henderson, A J | Kemp, John P | Zheng, Jie | Smith, George Davey | Rüschendorf, Franz | Bauerfeind, Anja | Lee-Kirsch, Min Ae | Arnold, Andreas | Homuth, Georg | Schmidt, Carsten O | Mangold, Elisabeth | Cichon, Sven | Keil, Thomas | Rodríguez, Elke | Peters, Annette | Franke, Andre | Lieb, Wolfgang | Novak, Natalija | Fölster-Holst, Regina | Horikoshi, Momoko | Pekkanen, Juha | Sebert, Sylvain | Husemoen, Lise L | Grarup, Niels | de Jongste, Johan C | Rivadeneira, Fernando | Hofman, Albert | Jaddoe, Vincent WV | Pasmans, Suzanne GMA | Elbert, Niels J | Uitterlinden, André G | Marks, Guy B | Thompson, Philip J | Matheson, Melanie C | Robertson, Colin F | Ried, Janina S | Li, Jin | Zuo, Xian Bo | Zheng, Xiao Dong | Yin, Xian Yong | Sun, Liang Dan | McAleer, Maeve A | O'Regan, Grainne M | Fahy, Caoimhe MR | Campbell, Linda E | Macek, Milan | Kurek, Michael | Hu, Donglei | Eng, Celeste | Postma, Dirkje S | Feenstra, Bjarke | Geller, Frank | Hottenga, Jouke Jan | Middeldorp, Christel M | Hysi, Pirro | Bataille, Veronique | Spector, Tim | Tiesler, Carla MT | Thiering, Elisabeth | Pahukasahasram, Badri | Yang, James J | Imboden, Medea | Huntsman, Scott | Vilor-Tejedor, Natàlia | Relton, Caroline L | Myhre, Ronny | Nystad, Wenche | Custovic, Adnan | Weiss, Scott T | Meyers, Deborah A | Söderhäll, Cilla | Melén, Erik | Ober, Carole | Raby, Benjamin A | Simpson, Angela | Jacobsson, Bo | Holloway, John W | Bisgaard, Hans | Sunyer, Jordi | Hensch, Nicole M Probst | Williams, L Keoki | Godfrey, Keith M | Wang, Carol A | Boomsma, Dorret I | Melbye, Mads | Koppelman, Gerard H | Jarvis, Deborah | McLean, WH Irwin | Irvine, Alan D | Zhang, Xue Jun | Hakonarson, Hakon | Gieger, Christian | Burchard, Esteban G | Martin, Nicholas G | Duijts, Liesbeth | Linneberg, Allan | Jarvelin, Marjo-Riitta | Noethen, Markus M | Lau, Susanne | Hübner, Norbert | Lee, Young-Ae | Tamari, Mayumi | Hinds, David A | Glass, Daniel | Brown, Sara J | Heinrich, Joachim | Evans, David M | Weidinger, Stephan
Nature genetics  2015;47(12):1449-1456.
Genetic association studies have identified 21 loci associated with atopic dermatitis risk predominantly in populations of European ancestry. To identify further susceptibility loci for this common complex skin disease, we performed a meta-analysis of >15 million genetic variants in 21,399 cases and 95,464 controls from populations of European, African, Japanese and Latino ancestry, followed by replication in 32,059 cases and 228,628 controls from 18 studies. We identified 10 novel risk loci, bringing the total number of known atopic dermatitis risk loci to 31 (with novel secondary signals at 4 of these). Notably, the new loci include candidate genes with roles in regulation of innate host defenses and T-cell function, underscoring the important contribution of (auto-)immune mechanisms to atopic dermatitis pathogenesis.
PMCID: PMC4753676  PMID: 26482879
23.  Noninvasive Analysis of the Sputum Transcriptome Discriminates Clinical Phenotypes of Asthma 
Rationale: The airway transcriptome includes genes that contribute to the pathophysiologic heterogeneity seen in individuals with asthma.
Objectives: We analyzed sputum gene expression for transcriptomic endotypes of asthma (TEA), gene signatures that discriminate phenotypes of disease.
Methods: Gene expression in the sputum and blood of patients with asthma was measured using Affymetrix microarrays. Unsupervised clustering analysis based on pathways from the Kyoto Encyclopedia of Genes and Genomes was used to identify TEA clusters. Logistic regression analysis of matched blood samples defined an expression profile in the circulation to determine the TEA cluster assignment in a cohort of children with asthma to replicate clinical phenotypes.
Measurements and Main Results: Three TEA clusters were identified. TEA cluster 1 had the most subjects with a history of intubation (P = 0.05), a lower prebronchodilator FEV1 (P = 0.006), a higher bronchodilator response (P = 0.03), and higher exhaled nitric oxide levels (P = 0.04) compared with the other TEA clusters. TEA cluster 2, the smallest cluster, had the most subjects that were hospitalized for asthma (P = 0.04). TEA cluster 3, the largest cluster, had normal lung function, low exhaled nitric oxide levels, and lower inhaled steroid requirements. Evaluation of TEA clusters in children confirmed that TEA clusters 1 and 2 are associated with a history of intubation (P = 5.58 × 10−6) and hospitalization (P = 0.01), respectively.
Conclusions: There are common patterns of gene expression in the sputum and blood of children and adults that are associated with near-fatal, severe, and milder asthma.
PMCID: PMC4451618  PMID: 25763605
molecular endotyping; genomic; RNA; severe asthma; pathway analysis
25.  Glucocorticoid Genes and the Developmental Origins of Asthma Susceptibility and Treatment Response 
Antenatal corticosteroids enhance lung maturation. However, the importance of glucocorticoid genes on early lung development, asthma susceptibility, and treatment response remains unknown. We investigated whether glucocorticoid genes are important during lung development and their role in asthma susceptibility and treatment response. We identified genes that were differentially expressed by corticosteroids in two of three genomic datasets: lymphoblastoid cell lines of participants in the Childhood Asthma Management Program, a glucocorticoid chromatin immunoprecipitation/RNA sequencing experiment, or a murine model; these genes made up the glucocorticoid gene set (GCGS). Using gene expression profiles from 38 human fetal lungs and C57BL/6J murine fetal lungs, we identified developmental genes that were in the top 5% of genes contributing to the top three principal components (PCs) most highly associated with post-conceptional age. Glucocorticoid genes that were enriched in this set of developmental genes were then included in the developmental glucocorticoid gene set (DGGS). We then investigated whether glucocorticoid genes are important during lung development, and their role in asthma susceptibility and treatment response. A total of 232 genes were included in the GCGS. Analysis of gene expression demonstrated that glucocorticoid genes were enriched in lung development (P = 7.02 × 10−26). The developmental GCGS was enriched for genes that were differentially expressed between subjects with asthma and control subjects (P = 4.26 × 10−3) and were enriched after treatment of subjects with asthma with inhaled corticosteroids (P < 2.72 × 10−4). Our results show that glucocorticoid genes are overrepresented among genes implicated in fetal lung development. These genes influence asthma susceptibility and treatment response, suggesting their involvement in the early ontogeny of asthma.
PMCID: PMC4491138  PMID: 25192440
glucocorticoid genes; lung development; asthma; asthma treatment

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