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Objective To characterize longitudinal changes in blood biomarkers, leukocyte composition, and gene expression following laparoscopic sleeve gastrectomy (LSG). Background LSG is an effective treatment for obesity, leading to sustainable weight loss and improvements in obesity-related comorbidities and inflammatory profiles. However, the effects of LSG on immune function and metabolism remain uncertain. Methods Prospective data were collected from 23 enrolled human subjects from a single institution. Parameters of weight, comorbidities, and trends in blood biomarkers and leukocyte subsets were observed from preoperative baseline to 1 year postsurgery in 3-month follow-up intervals. RNA sequencing was performed on pairs of whole blood samples from the first 6 subjects of the study (baseline and 3 months postsurgery) to identify genome-wide gene expression changes associated with undergoing LSG. Results LSG led to a significant decrease in mean total body weight loss (18.1%) at 3 months and among diabetic subjects a reduction in hemoglobin A1c. Improvements in clinical inflammatory and hormonal biomarkers were demonstrated as early as 3 months after LSG. A reduction in neutrophil-lymphocyte ratio was observed, driven by a reduction in absolute neutrophil counts. Gene set enrichment analyses of differential whole blood gene expression demonstrated that after 3 months LSG induced transcriptomic changes not only in inflammatory cytokine pathways but also in several key metabolic pathways related to energy metabolism. Conclusions LSG induces significant changes in the composition and metabolism of immune cells as early as 3 months postoperatively. Further evaluation is required of bariatric surgery’s effects on immunometabolism and the consequences for host defense and metabolic disease.

The genome-wide association study (GWAS)-identified asthma susceptibility risk alleles on chromosome 17q21 increase the expression of ORMDL3 (ORMDL sphingolipid biosynthesis regulator 3) in lung tissue. Given the importance of epithelial integrity in asthma, we hypothesized that ORMDL3 directly impacted bronchial epithelial function. To determine whether and how ORMDL3 expression impacts the bronchial epithelium, in studies using both primary human bronchial epithelial cells and human bronchial epithelial cell line, 16HBE (16HBE14o-), we assessed the impact of ORMDL3 on autophagy. Studies included: autophagosome detection by electron microscopy, RFP-GFP-LC3B to assess autophagic activity, and Western blot analysis of autophagy-related proteins. Mechanistic assessments included immunoprecipitation assays, intracellular calcium mobilization assessments, and cell viability assays. Coexpression of ORMDL3 and autophagy-related genes was measured in primary human bronchial epithelial cells derived from 44 subjects. Overexpressing ORMDL3 demonstrated increased numbers of autophagosomes and increased levels of autophagy-related proteins LC3B, ATG3, ATG7, and ATG16L1. ORMDL3 overexpression promotes autophagy and subsequent cell death by impairing intracellular calcium mobilization through interacting with SERCA2. Strong correlation was observed between expression of ORMDL3 and autophagy-related genes in patient-derived bronchial epithelial cells. Increased ORMDL3 expression induces autophagy, possibly through interacting with SERCA2, thereby inhibiting intracellular calcium influx, and induces cell death, impairing bronchial epithelial function in asthma.

Background Global gene expression levels are known to be highly dependent upon gross demographic features including age, yet identification of age-related genomic indicators has yet to be comprehensively undertaken in a disease and treatment-specific context. Methods We used gene expression data from CD4+ lymphocytes in the Asthma BioRepository for Integrative Genomic Exploration (Asthma BRIDGE), an open-access collection of subjects participating in genetic studies of asthma with available gene expression data. Replication population participants were Puerto Rico islanders recruited as part of the ongoing Genes environments & Admixture in Latino Americans (GALA II), who provided nasal brushings for transcript sequencing. The main outcome measure was chronic asthma control as derived by questionnaires. Genomic associations were performed using regression of chronic asthma control score on gene expression with age in years as a covariate, including a multiplicative interaction term for gene expression times age. Results The SMARCD1 gene (SWI/SNF-related matrix-associated actin-dependent regulator of chromatin subfamily D member 1) interacted with age to influence chronic asthma control on inhaled corticosteroids, with a doubling of expression leading to an increase of 1.3 units of chronic asthma control per year (95% CI [0.86, 1.74], p = 6 × 10− 9), suggesting worsening asthma control with increasing age. This result replicated in GALA II (p = 3.8 × 10− 8). Cellular assays confirmed the role of SMARCD1 in glucocorticoid response in airway epithelial cells. Conclusion Focusing on age-dependent factors may help identify novel indicators of asthma medication response. Age appears to modulate the effect of SMARCD1 on asthma control with inhaled corticosteroids.

Objectives We aimed to investigate the role of genetics in the respiratory response of asthmatic children to air pollution, with a genome-wide level analysis of gene by nitrogen dioxide (NO2) and carbon monoxide (CO) interaction on lung function and to identify biological pathways involved.Methods We used a two-step method for fast linear mixed model computations for genome-wide association studies, exploring whether variants modify the longitudinal relationship between 4-month average pollution and post-bronchodilator FEV1 in 522 Caucasian and 88 African-American asthmatic children. Top hits were confirmed with classic linear mixed-effect models. We used the improved gene set enrichment analysis for GWAS (i-GSEA4GWAS) to identify plausible pathways.Results Two SNPs near the EPHA3 (rs13090972 and rs958144) and one in TXNDC8 (rs7041938) showed significant interactions with NO2 in Caucasians but we did not replicate this locus in African-Americans. SNP-CO interactions did not reach genome-wide significance. The i-GSEA4GWAS showed a pathway linked to the HO-1/CO system to be associated with CO-related FEV1 changes. For NO2-related FEV1 responses, we identified pathways involved in cellular adhesion, oxidative stress, inflammation, and metabolic responses.Conclusion The host lung function response to long-term exposure to pollution is linked to genes involved in cellular adhesion, oxidative stress, inflammatory, and metabolic pathways.

ObjectiveTo characterize longitudinal changes in blood biomarkers, leukocyte composition, and gene expression following laparoscopic sleeve gastrectomy (LSG).BackgroundLSG is an effective treatment for obesity, leading to sustainable weight loss and improvements in obesity-related co-morbidities and inflammatory profiles. However, the effects of LSG on immune function and metabolism remain uncertain.MethodsProspective data was collected from 23 enrolled human subjects from a single institution. Parameters of weight, co-morbidities, and trends in blood biomarkers and leukocyte subsets were observed from pre-operative baseline to one year in three-month follow-up intervals. RNA-sequencing was performed on pairs of whole blood samples from the first six subjects of the study (baseline and three months post-surgery) to identify genome-wide gene expression changes associated with undergoing LSG.ResultsLSG led to a significant decrease in mean total body weight loss (18.1%) at three months and among diabetic subjects a reduction in HbA1c. Improvements in clinical inflammatory and hormonal biomarkers were demonstrated as early as three months after LSG. A reduction in neutrophil-lymphocyte ratio was observed, driven by a reduction in absolute neutrophil counts. Gene set enrichment analyses of differential whole blood gene expression demonstrated that after three months, LSG induced transcriptomic changes not only in inflammatory cytokine pathways but also in several key metabolic pathways related to energy metabolism.ConclusionsLSG induces significant changes in the composition and metabolism of immune cells as early as three months post-operatively. Further evaluation is required of bariatric surgery’s effects on immunometabolism and consequences for host defense and metabolic disease.

More than one in three adults worldwide is either overweight or obese. Epidemiological studies indicate that the location and distribution of excess fat, rather than general adiposity, are more informative for predicting risk of obesity sequelae, including cardiometabolic disease and cancer. We performed a genome-wide association study meta-analysis of body fat distribution, measured by waist-to-hip ratio (WHR) adjusted for body mass index (WHRadjBMI), and identified 463 signals in 346 loci. Heritability and variant effects were generally stronger in women than men, and we found approximately one-third of all signals to be sexually dimorphic. The 5% of individuals carrying the most WHRadjBMI-increasing alleles were 1.62 times more likely than the bottom 5% to have a WHR above the thresholds used for metabolic syndrome. These data, made publicly available, will inform the biology of body fat distribution and its relationship with disease.

Introduction 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. Methods 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. Results 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. Conclusions 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.

Genetic variation may differentially modify drug and placebo treatment effects in randomized clinical trials. In asthma, although lung function and asthma control improvements are commonplace with placebo, pharmacogenomics of placebo vs. drug response remains unexamined. In a genomewide association study of subjective and objective outcomes with placebo treatment in Childhood Asthma Management Program of nedocromil/budesonide vs. placebo (N = 604), effect estimates for lead single nucleotide polymorphisms (SNPs) were compared across arms. The coughing/wheezing lead SNP, rs2392165 (β = 0.94; P = 1.10E-07) mapped to BBS9, a gene implicated in lung development that contains a lung function expression quantitative trait locus. The effect was attenuated with budesonide (Pinteraction = 1.48E-07), but not nedocromil (Pinteraction = 0.06). The lead forced vital capacity SNP, rs12930749 (β = -5.80; P = 1.47E-06), mapped to KIAA0556, a locus genomewide associated with respiratory diseases. The rs12930749 effect was attenuated with budesonide (Pinteraction = 1.32E-02) and nedocromil (Pinteraction = 1.09E-02). Pharmacogenomic analysis revealed differential effects with placebo and drug treatment that could potentially guide precision drug development in asthma.

Understanding the connectivity patterns of genes in a localized disease neighborhood or disease module in a molecular interaction network (interactome) is a key step toward advancing the knowledge about molecular mechanisms underlying a complex disease. In this work, we introduce a framework that detects peripheral and core regions of a disease in the human interactome. We leverage gene expression data on 104 diseases and analyze the connectivity of differentially expressed genes (quantified by a p-value < 0.05) and their topological membership in the network to distinguish between peripheral and core genes. Per definition, peripheral and core genes are topologically different and we show that they also differ biologically. Core genes are more enriched for Genome-wide association study (GWAS) and Online Mendelian Inheritance in Man (OMIM) data, whereas peripheral genes are more shared across different disease states and their overlap helps predict disease proximity in the human interactome. Based on this observation, we propose a flower model to explain the organization of genes in the human interactome, with core genes of different diseases as the petals and the peripheral genes as the (shared) stem. We show that this network model is an important step towards finding novel drug targets and improving disease classification. Overall, we were able to demonstrate how perturbations percolate through the human interactome and contribute to peripheral and core regions, an important novel feature of the omnigenic model.

BACKGROUND: Tracking longitudinal measurements of growth and decline in lung function in patients with persistent childhood asthma may reveal links between asthma and subsequent chronic airflow obstruction.METHODS: We classified children with asthma according to four characteristic patterns of lung-function growth and decline on the basis of graphs showing forced expiratory volume in 1 second (FEV1), representing spirometric measurements performed from childhood into adulthood. Risk factors associated with abnormal patterns were also examined. To define normal values, we used FEV1 values from participants in the National Health and Nutrition Examination Survey who did not have asthma.RESULTS: Of the 684 study participants, 170 (25%) had a normal pattern of lung-function growth without early decline, and 514 (75%) had abnormal patterns: 176 (26%) had reduced growth and an early decline, 160 (23%) had reduced growth only, and 178 (26%) had normal growth and an early decline. Lower baseline values for FEV1, smaller bronchodilator response, airway hyperresponsiveness at baseline, and male sex were associated with reduced growth (PCONCLUSIONS: Childhood impairment of lung function and male sex were the most significant predictors of abnormal longitudinal patterns of lung-function growth and decline. Children with persistent asthma and reduced growth of lung function are at increased risk for fixed airflow obstruction and possibly COPD in early adulthood. (Funded by the Parker B. Francis Foundation and others; ClinicalTrials.gov number, NCT00000575.).

BackgroundAsthmatic children who develop obesity have poorer outcomes compared to those that do not, including poorer control, more severe symptoms, and greater resistance to standard treatment. Gene expression networks are powerful statistical tools for characterizing the underpinnings of human disease that leverage the putative co-regulatory relationships of genes to infer biological pathways altered in disease states.ObjectiveThe aim of this study was to characterize the biology of childhood asthma complicated by adult obesity.MethodsWe performed weighted gene co-expression network analysis (WGCNA) of gene expression data in whole blood from 514 adult subjects from the Childhood Asthma Management Program (CAMP). We then performed module preservation and association replication analyses in 418 subjects from two independent asthma cohorts (one pediatric and one adult).ResultsWe identified a multivariate model in which four gene co-expression network modules were associated with incident obesity in CAMP (eachP< 0.05). The module memberships were enriched for genes in pathways related to platelets, integrins, extracellular matrix, smooth muscle, NF-κB signaling, and Hedgehog signaling. The network structures of each of the four obese asthma modules were significantly preserved in both replication cohorts (permutationP= 9.999E-05). The corresponding module gene sets were significantly enriched for differential expression in obese subjects in both replication cohorts (eachP< 0.05).ConclusionsOur gene co-expression network profiles thus implicate multiple interrelated pathways in the biology of an important endotype of obese asthma.Key MessagesWe hypothesized that individuals with asthma complicated by obesity had distinct blood gene expression signatures.Gene co-expression network analysis implicated several inflammatory biological pathways in one form of obese asthma.Capsule SummaryThis work addresses a knowledge gap about the molecular relationship between asthma and obesity, suggesting that an endotype of obese asthma, known as asthma complicated by obesity, is underpinned by coherent biological mechanisms.AbbreviationsCAMPChildhood Asthma Management ProgramWGCNAweighted gene co-expression network analysisAsthma BRIDGEAsthma BioRepository for Integrative Genomic ExplorationGACRSGenetics of Asthma in Costa Rica StudyCHSSouthern California Children’s Health StudyBMIbody mass indexBICBayes Information CriterionHUGOHuman Genome OrganisationPCprincipal componentGSEAgene set enrichment analysisIL-1interleukin-1Hh signalingHedgehog signaling

Background Single omic analyses have provided some insight into the basis of lung function in children with asthma, but the underlying biologic pathways are still poorly understood. Methods Weighted gene coexpression network analysis (WGCNA) was used to identify modules of coregulated gene transcripts and metabolites in blood among 325 children with asthma from the Genetic Epidemiology of Asthma in Costa Rica study. The biology of modules associated with lung function as measured by FEV1, the FEV1/FVC ratio, bronchodilator response, and airway responsiveness to methacholine was explored. Significantly correlated gene-metabolite module pairs were then identified, and their constituent features were analyzed for biologic pathway enrichments. Results WGCNA clustered 25,060 gene probes and 8,185 metabolite features into eight gene modules and eight metabolite modules, where four and six, respectively, were associated with lung function (P ≤ .05). The gene modules were enriched for immune, mitotic, and metabolic processes and asthma-associated microRNA targets. The metabolite modules were enriched for lipid and amino acid metabolism. Integration of correlated gene-metabolite modules expanded the single omic findings, linking the FEV1/FVC ratio with ORMDL3 and dysregulated lipid metabolism. This finding was replicated in an independent population. Conclusions The results of this hypothesis-generating study suggest a mechanistic basis for multiple asthma genes, including ORMDL3, and a role for lipid metabolism. They demonstrate that integrating multiple omic technologies may provide a more informative picture of asthmatic lung function biology than single omic analyses.

We aimed to investigate the role of genetics in the respiratory response of asthmatic children to air pollution, with a genome-wide level analysis of gene by nitrogen dioxide (NOWe used a two-step method for fast linear mixed model computations for genome-wide association studies, exploring whether variants modify the longitudinal relationship between 4-month average pollution and post-bronchodilator FEVTwo SNPs near the EPHA3 (rs13090972 and rs958144) and one in TXNDC8 (rs7041938) showed significant interactions with NOThe host lung function response to long-term exposure to pollution is linked to genes involved in cellular adhesion, oxidative stress, inflammatory, and metabolic pathways.

One in four adults worldwide are either overweight or obese. Epidemiological studies indicate that the location and distribution of excess fat, rather than general adiposity, is most informative for predicting risk of obesity sequellae, including cardiometabolic disease and cancer. We performed a genome-wide association study meta-analysis of body fat distribution, measured by waist-to-hip ratio adjusted for BMI (WHRadjBMI), and identified 463 signals in 346 loci. Heritability and variant effects were generally stronger in women than men, and we found approximately one-third of all signals to be sexually dimorphic. The 5% of individuals carrying the most WHRadjBMI-increasing alleles were 1.62 times more likely than the bottom 5% to have a WHR above the thresholds used for metabolic syndrome. These data, made publicly available, will inform the biology of body fat distribution and its relationship with disease.

RationaleStress is associated with asthma morbidity in Puerto Ricans (PRs), who have reduced bronchodilator response (BDR).ObjectivesTo examine whether stress and/or a gene regulating anxiety (ADCYAP1R1) is associated with BDR in PR and non-PR children with asthma.MethodsThis 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 resultsHigh 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).ConclusionsHigh 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.

Short-acting β agonists (e.g., albuterol) are the most commonly used medications for asthma, a disease that affects over 300 million people in the world. Metabolomic profiling of asthmatics taking β agonists presents a new and promising resource for identifying the molecular determinants of asthma control. The objective is to identify novel genetic and biochemical predictors of asthma control using an integrative “omics” approach. We generated lipidomic data by liquid chromatography tandem mass spectrometry (LC-MS), ­ using plasma samples from 20 individuals with asthma. The outcome of interest was a binary indicator of asthma control defined by the use of albuterol inhalers in the preceding week. We integrated metabolomic data with genome-wide genotype, gene expression, and methylation data of this cohort to identify genomic and molecular indicators of asthma control. A Conditional Gaussian Bayesian Network (CGBN) was generated using the strongest predictors from each of these analyses. Integrative and metabolic pathway over-representation analyses (ORA) identified enrichment of known biological pathways within the strongest molecular determinants. Of the 64 metabolites measured, 32 had known identities. The CGBN model based on four SNPs (rs9522789, rs7147228, rs2701423, rs759582) and two metabolites—monoHETE_0863 and sphingosine-1-phosphate (S1P) could predict asthma control with an AUC of 95%. Integrative ORA identified 17 significantly enriched pathways related to cellular immune response, interferon signaling, and cytokine-related signaling, for which arachidonic acid, PGE2 and S1P, in addition to six genes (CHN1, PRKCE, GNA12, OASL, OAS1, and IFIT3) appeared to drive the pathway results. Of these predictors, S1P, GNA12, and PRKCE were enriched in the results from integrative and metabolic ORAs. Through an integrative analysis of metabolomic, genomic, and methylation data from a small cohort of asthmatics, we implicate altered metabolic pathways, related to sphingolipid metabolism, in asthma control. These results provide insight into the pathophysiology of asthma control.

Background Asthma represents a significant public health burden; however, novel biological therapies targeting immunoglobulin E (IgE)-mediated pathways have widened clinical treatment options for the disease. Objective In this study, we sought to identify gene transcripts and gene networks involved in the determination of serum IgE levels in people with asthma that can help inform the development of novel therapeutic agents. Methods We analysed gene expression data from a cross-sectional study of 326 Costa Rican children with asthma, aged 6 to 12 years, from the Genetics of Asthma in Costa Rica Study and 610 young adults with asthma, aged 16 to 25 years, from the Childhood Asthma Management Program trial. We utilized differential gene expression analysis and performed weighted gene coexpression network analysis on 25 060 genes, to identify gene transcripts and network modules associated with total IgE, adjusting for age and gender. We used pathway enrichment analyses to identify key biological pathways underlying significant modules. We compared findings that replicated between both populations. Results We identified 31 transcripts associated with total IgE that replicated between the two study cohorts. These results were notable for increased eosinophil-related transcripts (including IL5RA, CLC, SMPD3, CCL23 and CEBPE). Pathway enrichment identified the regulation of T cell tolerance as important in the determination of total IgE levels, supporting a key role for IDO1. Conclusions and clinical relevance These results provide robust evidence that biologically meaningful gene expression profiles (relating to eosinophilic and regulatory T cell pathways in particular) associated with total IgE levels can be identified in individuals diagnosed with asthma during childhood. These profiles and their constituent genes may represent novel therapeutic targets.

Maintaining optimal symptom control remains the primary objective of asthma treatment. Better understanding of the biologic underpinnings of asthma control may lead to the development of improved clinical and pharmaceutical approaches.To identify molecular pathways and interrelated genes whose differential expression was associated with asthma control.We performed gene set enrichment analyses of asthma control in 1,170 adults with asthma, each with gene expression data derived from either whole blood (WB) or unstimulated CD4In the WB discovery cohort, we found significant overrepresentation of genes associated with asthma control in 1,106 gene sets from the Molecular Signatures Database (false discovery rate,5%). Of these, 583 (53%) replicated in at least one replication cohort (false discovery rate,25%). Suboptimal control was associated with signatures of eosinophilic and granulocytic inflammatory signals, whereas optimal control signatures were enriched for immature lymphocytic patterns. These signatures included two related biologic processes related to activation by TREM-1 (triggering receptor expressed on myeloid cells 1) and lipopolysaccharide.Together, these results demonstrate the existence of specific, reproducible transcriptomic components in blood that vary with degree of asthma control and implicate a novel biologic target (TREM-1).

Asthma is a complex disease affecting over 300 million individuals in the developed world. Most asthma cases, including in adults, have their origins in childhood. Asthma is a clinical syndrome characterized by reversible episodes of airflow obstruction, airway hyperresponsiveness, and a chronic inflammatory process of the airways of which many different cells play a role. Modern estimates of heritability of asthma due to additive genetic effects from common variants are between 61.5%=/−16%. Candidate gene and genome-wide association studies have identified over 30 reproducible loci associated with the disease. Gene expression profiling of asthma in blood and bronchial epithelium implicates highly relevant biology related to systemic inflammation. Despite the availability of several classes of therapeutic agents for asthma, it has been estimated that as many as one half of all patients do not respond to treatment with b2 agonists, leukotriene antagonists, or inhaled corticosteroids. Over 20 reproducible genetic loci have been identified for these three treatment pathways. Asthma genomics is in its early stages and has the potential to have a great clinical impact in the next 5–10 years through the development of genomic predictors of disease occurrence, response to asthma medication, and exacerbations.

Preeclampsia is a leading cause of maternal and fetal mortality worldwide, yet its exact pathogenesis remains elusive. This study, nested within the Vitamin D Antenatal Asthma Reduction Trial (VDAART), aimed to develop integrated omics models of preeclampsia that have utility in both prediction and in the elucidation of underlying biological mechanisms. Metabolomic profiling was performed on first trimester plasma samples of 47 pregnant women from VDAART who subsequently developed preeclampsia and 62 controls with healthy pregnancies, using liquid-chromatography tandem mass-spectrometry. Metabolomic profiles were generated based on logistic regression models and assessed using Received Operator Characteristic Curve analysis. These profiles were compared to profiles from generated using third trimester samples. The first trimester metabolite profile was then integrated with a pre-existing transcriptomic profile using network methods. In total, 72 (0.9%) metabolite features were associated (p

Most autoimmune-disease-risk effects identified by genome-wide association studies (GWAS) localize to open chromatin with gene-regulatory activity. GWAS loci are also enriched in expression quantitative trait loci (eQTLs), thus suggesting that most risk variants alter gene expression. However, because causal variants are difficult to identify, and cis-eQTLs occur frequently, it remains challenging to identify specific instances of disease-relevant changes to gene regulation. Here, we used a novel joint likelihood framework with higher resolution than that of previous methods to identify loci where autoimmune-disease risk and an eQTL are driven by a single shared genetic effect. Using eQTLs from three major immune subpopulations, we found shared effects in only ∼25% of the loci examined. Thus, we show that a fraction of gene-regulatory changes suggest strong mechanistic hypotheses for disease risk, but we conclude that most risk mechanisms are not likely to involve changes in basal gene expression.

The majority of autoimmune disease risk effects identified by genome-wide association studies (GWAS) localize to open chromatin with gene regulatory activity. GWAS loci are also enriched for expression quantitative trait loci (eQTLs), suggesting that most disease risk variants exert their pathological effects by altering gene expression. However, because causal variants are difficult to identify and cis-eQTLs occur frequently, it remains challenging to translate this bulk observation into specific instances of a disease risk variant driving changes to gene regulation. Here, we use a novel joint likelihood framework with higher resolution than previous methods to identify loci where disease risk and an eQTL are driven by a single, shared genetic effect as opposed to distinct effects in close proximity. We find that 22 approximately 25% of autoimmune disease loci harbor an eQTL driven by the same genetic effect, but the majority of loci do not. Thus, we uncover a fraction of gene regulatory changes as strong mechanistic hypotheses for disease risk.

Genome-wide association studies (GWASs) have been successful in discovering replicable SNP-trait associations for many quantitative traits and common diseases in humans. Typically the effect sizes of SNP alleles are very small and this has led to large genome-wide association meta-analyses (GWAMA) to maximize statistical power. A trend towards ever-larger GWAMA is likely to continue, yet dealing with summary statistics from hundreds of cohorts increases logistical and quality control problems, including unknown sample overlap, and these can lead to both false positive and false negative findings. In this study we propose a new set of metrics and visualization tools for GWAMA, using summary statistics from cohort-level GWASs. We proposed a pair of methods in examining the concordance between demographic information and summary statistics. In method I, we use the population genetics Fststatistic to verify the genetic origin of each cohort and their geographic location, and demonstrate using GWAMA data from the GIANT Consortium that geographic locations of cohorts can be recovered and outlier cohorts can be detected. In method II, we conduct principal component analysis based on reported allele frequencies, and is able to recover the ancestral information for each cohort. In addition, we propose a new statistic that uses the reported allelic effect sizes and their standard errors to identify significant sample overlap or heterogeneity between pairs of cohorts. Finally, to quantify unknown sample overlap across all pairs of cohorts we propose a method that uses randomly generated genetic predictors that does not require the sharing of individual-level genotype data and does not breach individual privacy.

Short-acting β agonists (e.g., albuterol) are the most commonly used medications for asthma, a disease that affects over 300 million people in the world. Metabolomic profiling of asthmatics taking β agonists presents a new and promising resource for identifying the molecular determinants of asthma control. The objective is to identify novel genetic and biochemical predictors of asthma control using an integrative “omics” approach. We generated lipidomic data by liquid chromatography tandem mass spectrometry (LC-MS), ­ using plasma samples from 20 individuals with asthma. The outcome of interest was a binary indicator of asthma control defined by the use of albuterol inhalers in the preceding week. We integrated metabolomic data with genome-wide genotype, gene expression, and methylation data of this cohort to identify genomic and molecular indicators of asthma control. A Conditional Gaussian Bayesian Network (CGBN) was generated using the strongest predictors from each of these analyses. Integrative and metabolic pathway over-representation analyses (ORA) identified enrichment of known biological pathways within the strongest molecular determinants. Of the 64 metabolites measured, 32 had known identities. The CGBN model based on four SNPs (rs9522789, rs7147228, rs2701423, rs759582) and two metabolites—monoHETE_0863 and sphingosine-1-phosphate (S1P) could predict asthma control with an AUC of 95%. Integrative ORA identified 17 significantly enriched pathways related to cellular immune response, interferon signaling, and cytokine-related signaling, for which arachidonic acid, PGE2 and S1P, in addition to six genes (CHN1, PRKCE, GNA12, OASL, OAS1, and IFIT3) appeared to drive the pathway results. Of these predictors, S1P, GNA12, and PRKCE were enriched in the results from integrative and metabolic ORAs. Through an integrative analysis of metabolomic, genomic, and methylation data from a small cohort of asthmatics, we implicate altered metabolic pathways, related to sphingolipid metabolism, in asthma control. These results provide insight into the pathophysiology of asthma control.

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.

Gene-environment interaction studies using genome-wide association study data are often underpowered after adjustment for multiple comparisons. Differential gene expression in response to the exposure of interest can capture the most biologically relevant genes at the genome-wide level.We used differential genome-wide expression profiles from the Epidemiology of Home Allergens and Asthma birth cohort in response to Der f 1 allergen (sensitized vs nonsensitized) to inform a gene-environment study of dust mite exposure and asthma severity.Polymorphisms in differentially expressed genes were identified in genome-wide association study data from the Childhood Asthma Management Program, a clinical trial in childhood asthmatic patients. Home dust mite allergen levels (10 or ≥10 μg/g dust) were assessed at baseline, and (≥1) severe asthma exacerbation (emergency department visit or hospitalization for asthma in the first trial year) served as the disease severity outcome. The Genetics of Asthma in Costa Rica Study and a Puerto Rico/Connecticut asthma cohort were used for replication.IL9, IL5, and proteoglycan 2 expression (PRG2) was upregulated in Der f 1-stimulated PBMCs from dust mite-sensitized patients (adjusted P.04). IL9 polymorphisms (rs11741137, rs2069885, and rs1859430) showed evidence for interaction with dust mite in the Childhood Asthma Management Program (P = .02 to .03), with replication in the Genetics of Asthma in Costa Rica Study (P = .04). Subjects with the dominant genotype for these IL9 polymorphisms were more likely to report a severe asthma exacerbation if exposed to increased dust mite levels.Genome-wide differential gene expression in response to dust mite allergen identified IL9, a biologically plausible gene target that might interact with environmental dust mite to increase severe asthma exacerbations in children.