Metabolite signatures associated with microRNA miR-143-3p serve as drivers of poor lung function trajectories in childhood asthma

BACKGROUND

Lung function trajectories (LFTs) have been shown to be an important measure of long-term health in asthma. While there is a growing body of metabolomic studies on asthma status and other phenotypes, there are no prospective studies of the relationship between metabolomics and LFTs or their genomic determinants.

METHODS

We utilized ordinal logistic regression to identify plasma metabolite principal components associated with four previously-published LFTs in children from the Childhood Asthma Management Program (CAMP) (n = 660). The top significant metabolite principal component (PCLF) was evaluated in an independent cross-sectional child cohort, the Genetic Epidemiology of Asthma in Costa Rica Study (GACRS) (n = 1151) and evaluated for association with spirometric measures. Using meta-analysis of CAMP and GACRS, we identified associations between PCLF and microRNA, and SNPs in their target genes. Statistical significance was determined using an false discovery rate-adjusted Q-value.

FINDINGS

The top metabolite principal component, PCLF, was significantly associated with better LFTs after multiple-testing correction (Q-value = 0.03). PCLF is composed of the urea cycle, caffeine, corticosteroid, carnitine, and potential microbial (secondary bile acid, tryptophan, linoleate, histidine metabolism) metabolites. Higher levels of PCLF were also associated with increases in lung function measures and decreased circulating neutrophil percentage in both CAMP and GACRS. PCLF was also significantly associated with microRNA miR-143-3p, and SNPs in three miR-143-3p target genes; CCZ1 (P-value = 2.6 × 10-5), SLC8A1 (P-value = 3.9 × 10-5); and TENM4 (P-value = 4.9 × 10-5).

INTERPRETATION

This study reveals associations between metabolites, miR-143-3p and LFTs in children with asthma, offering insights into asthma physiology and possible interventions to enhance lung function and long-term health.

FUNDING

Molecular data for CAMP and GACRS via the Trans-Omics in Precision Medicine (TOPMed) program was supported by the National Heart, Lung, and Blood Institute (NHLBI).

GSDMB/ORMDL3 Rare/Common Variants Are Associated with Inhaled Corticosteroid Response among Children with Asthma

Inhaled corticosteroids (ICS) are efficacious in the treatment of asthma, which affects more than 300 million people in the world. While genome-wide association studies have identified genes involved in differential treatment responses to ICS in asthma, few studies have evaluated the effects of combined rare and common variants on ICS response among children with asthma. Among children with asthma treated with ICS with whole exome sequencing (WES) data in the PrecisionLink Biobank (91 White and 20 Black children), we examined the effect and contribution of rare and common variants with hospitalizations or emergency department visits. For 12 regions previously associated with asthma and ICS response (DPP10, FBXL7, NDFIP1, TBXT, GLCCI1, HDAC9, TBXAS1, STAT6, GSDMB/ORMDL3, CRHR1, GNGT2, FCER2), we used the combined sum test for the sequence kernel association test (SKAT) adjusting for age, sex, and BMI and stratified by race. Validation was conducted in the Biorepository and Integrative Genomics (BIG) Initiative (83 White and 134 Black children). Using a Bonferroni threshold for the 12 regions tested (i.e., 0.05/12 = 0.004), GSDMB/ORMDL3 was significantly associated with ICS response for the combined effect of rare and common variants (p-value = 0.003) among White children in the PrecisionLink Biobank and replicated in the BIG Initiative (p-value = 0.02). Using WES data, the combined effect of rare and common variants for GSDMB/ORMDL3 was associated with ICS response among asthmatic children in the PrecisionLink Biobank and replicated in the BIG Initiative. This proof-of-concept study demonstrates the power of biobanks of pediatric real-life populations in asthma genomic investigations.

Novel genetic variants associated with inhaled corticosteroid treatment response in older adults with asthma

INTRODUCTION

Older adults have the greatest burden of asthma and poorest outcomes. The pharmacogenetics of inhaled corticosteroid (ICS) treatment response is not well studied in older adults.

METHODS

A genome-wide association study of ICS response was performed in asthmatics of European ancestry in Genetic Epidemiology Research on Adult Health and Aging (GERA) by fitting Cox proportional hazards regression models, followed by validation in the Mass General Brigham (MGB) Biobank and Rotterdam Study. ICS response was measured using two definitions in asthmatics on ICS treatment: (1) absence of oral corticosteroid (OCS) bursts using prescription records and (2) absence of asthma-related exacerbations using diagnosis codes. A fixed-effect meta-analysis was performed for each outcome. The validated single-nucleotide polymorphisms (SNPs) were functionally annotated to standard databases.

RESULTS

In 5710 subjects in GERA, 676 subjects in MGB Biobank, and 465 subjects in the Rotterdam Study, four novel SNPs on chromosome six near PTCHD4 validated across all cohorts and met genome-wide significance on meta-analysis for the OCS burst outcome. In 4541 subjects in GERA and 505 subjects in MGB Biobank, 152 SNPs with p<5 × 10-5 were validated across these two cohorts for the asthma-related exacerbation outcome. The validated SNPs included methylation and expression quantitative trait loci for CPED1, CRADD and DST for the OCS burst outcome and GM2A, SNW1, CACNA1C, DPH1, and RPS10 for the asthma-related exacerbation outcome.

CONCLUSIONS

Multiple novel SNPs associated with ICS response were identified in older adult asthmatics. Several SNPs annotated to genes previously associated with asthma and other airway or allergic diseases, including PTCHD4.

Metabolomic profiling reveals extensive adrenal suppression due to inhaled corticosteroid therapy in asthma

The application of large-scale metabolomic profiling provides new opportunities for realizing the potential of omics-based precision medicine for asthma. By leveraging data from over 14,000 individuals in four distinct cohorts, this study identifies and independently replicates 17 steroid metabolites whose levels were significantly reduced in individuals with prevalent asthma. Although steroid levels were reduced among all asthma cases regardless of medication use, the largest reductions were associated with inhaled corticosteroid (ICS) treatment, as confirmed in a 4-year low-dose ICS clinical trial. Effects of ICS treatment on steroid levels were dose dependent; however, significant reductions also occurred with low-dose ICS treatment. Using information from electronic medical records, we found that cortisol levels were substantially reduced throughout the entire 24-hour daily period in patients with asthma who were treated with ICS compared to those who were untreated and to patients without asthma. Moreover, patients with asthma who were treated with ICS showed significant increases in fatigue and anemia as compared to those without ICS treatment. Adrenal suppression in patients with asthma treated with ICS might, therefore, represent a larger public health problem than previously recognized. Regular cortisol monitoring of patients with asthma treated with ICS is needed to provide the optimal balance between minimizing adverse effects of adrenal suppression while capitalizing on the established benefits of ICS treatment.

A polygenic risk score for asthma in a large racially diverse population

BACKGROUND

Polygenic risk scores (PRSs) will have important utility for asthma and other chronic diseases as a tool for predicting disease incidence and subphenotypes.

OBJECTIVE

We utilized findings from a large multiancestry GWAS of asthma to compute a PRS for asthma with relevance for racially diverse populations.

METHODS

We derived two PRSs for asthma using a standard approach (based on genome-wide significant variants) and a lasso sum regression approach (allowing all genetic variants to potentially contribute). We used data from the racially diverse Kaiser Permanente GERA cohort (68 638 non-Hispanic Whites, 5874 Hispanics, 6870 Asians and 2760 Blacks). Race was self-reported by questionnaire.

RESULTS

For the standard PRS, non-Hispanic Whites showed the highest odds ratio for a standard deviation increase in PRS for asthma (OR = 1.16 (95% CI 1.14-1.18)). The standard PRS was also associated with asthma in Hispanic (OR = 1.12 (95% CI 1.05-1.19)) and Asian (OR = 1.10 (95% CI 1.04-1.17)) subjects, with a trend towards increased risk in Blacks (OR = 1.05 (95% CI 0.97-1.15)). We detected an interaction by sex, with men showing a higher risk of asthma with an increase in PRS as compared to women. The lasso sum regression-derived PRS showed stronger associations with asthma in non-Hispanic White subjects (OR = 1.20 (95% CI 1.18-1.23)), Hispanics (OR = 1.17 (95% 1.10-1.26)), Asians (OR = 1.18 (95% CI 1.10-1.27)) and Blacks (OR = 1.10 (95% CI 0.99-1.22)).

CONCLUSION

Polygenic risk scores across multiple racial/ethnic groups were associated with increased asthma risk, suggesting that PRSs have potential as a tool for predicting disease development.

Commercially Available Blocking Oligonucleotides Effectively Suppress Unwanted Hemolysis-Related miRNAs in a Large Whole-Blood RNA Cohort

When sequencing small RNA libraries derived from whole blood, the most abundant microRNAs (miRs) detected are often miR-486-5p, miR-451a, and miR-92a-3p. These highly expressed erythropoietic miRs are released into the sample from red blood cell hemolysis. Next-generation sequencing of these unwanted miRs leads to a waste in sequencing cost and diminished detection of lowly expressed miRNAs, including many potential miRNA biomarkers. Previous work has developed a method to reduce targeted miRNAs using oligonucleotides that bind their target miRNA and prevent its ligation during library construction, although the extent to which oligonucleotides can be multiplexed and their effect on larger cohorts has not been thoroughly explored. We present a method for suppressing detection of three highly abundant heme miRs in a single multiplexed blocking oligonucleotide reaction. In a small paired-sample pilot (n = 8) and a large cohort of samples (n = 901), multiplexed oligos reduced detection of their target miRNAs by approximately 70%, allowing for an approximately 10-fold increase in reads mapping to nonheme miRs and increased detection of very lowly expressed miRs, with minimal off-target effects. By removing all three highly expressed erythropoietic miRNAs from next-generational sequencing libraries, this commercially available multiplexed blocking oligonucleotide method allows for greater detection of lowly expressed biomarkers, improving the efficacy, cost-efficiency, and sensitivity of biomarker studies and diagnostic tests.

The Role of SNP Interactions when Determining Independence of Novel Signals in Genetic Association Studies-An Application to ARG1 and Bronchodilator Response

Genome-wide association studies (GWAS) play a critical role in identifying many loci for common diseases and traits. There has been a rapid increase in the number of GWAS over the past decade. As additional GWAS are being conducted, it is unclear whether a novel signal associated with the trait of interest is independent of single nucleotide polymorphisms (SNPs) in the same region that has been previously associated with the trait of interest. The general approach to determining whether the novel association is independent of previous signals is to examine the association of the novel SNP with the trait of interest conditional on the previously identified SNP and/or calculate linkage disequilibrium (LD) between the two SNPs. However, the role of epistasis and SNP by SNP interactions are rarely considered. Through simulation studies, we examined the role of SNP by SNP interactions when determining the independence of two genetic association signals. We have created an R package on Github called gxgRC to generate these simulation studies based on user input. In genetic association studies of asthma, we considered the role of SNP by SNP interactions when determining independence of signals for SNPs in the ARG1 gene and bronchodilator response.

Age by Single Nucleotide Polymorphism Interactions on Bronchodilator Response in Asthmatics

An unaddressed and important issue is the role age plays in modulating response to short acting β2-agonists in individuals with asthma. The objective of this study was to identify whether age modifies genetic associations of single nucleotide polymorphisms (SNPs) with bronchodilator response (BDR) to β2-agonists. Using three cohorts with a total of 892 subjects, we ran a genome wide interaction study (GWIS) for each cohort to examine SNP by age interactions with BDR. A fixed effect meta-analysis was used to combine the results. In order to determine if previously identified BDR SNPs had an age interaction, we also examined 16 polymorphisms in candidate genes from two published genome wide association studies (GWAS) of BDR. There were no significant SNP by age interactions on BDR using the genome wide significance level of 5 × 10⁻⁸. Using a suggestive significance level of 5 × 10⁻⁶, three interactions, including one for a SNP within PRAG1 (rs4840337), were significant and replicated at the significance level of 0.05. Considering candidate genes from two previous GWAS of BDR, three SNPs (rs10476900 (near ADRB2) [p-value = 0.009], rs10827492 (CREM) [p-value = 0.02], and rs72646209 (NCOA3) [p-value = 0.02]) had a marginally significant interaction with age on BDR (p < 0.05). Our results suggest age may be an important modifier of genetic associations for BDR in asthma.

Genome-wide interaction study reveals age-dependent determinants of responsiveness to inhaled corticosteroids in individuals with asthma

While genome-wide association studies have identified genes involved in differential treatment responses to inhaled corticosteroids (ICS) in asthma, few studies have evaluated the potential effects of age in this context. A significant proportion of asthmatics experience exacerbations (hospitalizations and emergency department visits) during ICS treatment. We evaluated the interaction of genetic variation and age on ICS response (measured by the occurrence of exacerbations) through a genome-wide interaction study (GWIS) of 1,321 adult and child asthmatic patients of European ancestry. We identified 107 genome-wide suggestive (P<10-05) age-by-genotype interactions, two of which also met genome-wide significance (P<5x10-08) (rs34631960 [OR 2.3±1.6-3.3] in thrombospondin type 1 domain-containing protein 4 (THSD4) and rs2328386 [OR 0.5±0.3-0.7] in human immunodeficiency virus type I enhancer binding protein 2 (HIVEP2)) by joint analysis of GWIS results from discovery and replication populations. In addition to THSD4 and HIVEP2, age-by-genotype interactions also prioritized genes previously identified as asthma candidate genes, including DPP10, HDAC9, TBXAS1, FBXL7, and GSDMB/ORMDL3, as pharmacogenomic loci as well. This study is the first to link these genes to a pharmacogenetic trait for asthma.

Integration of Metabolomic and Other Omics Data in Population-Based Study Designs: An Epidemiological Perspective

It is not controversial that study design considerations and challenges must be addressed when investigating the linkage between single omic measurements and human phenotypes. It follows that such considerations are just as critical, if not more so, in the context of multi-omic studies. In this review, we discuss (1) epidemiologic principles of study design, including selection of biospecimen source(s) and the implications of the timing of sample collection, in the context of a multi-omic investigation, and (2) the strengths and limitations of various techniques of data integration across multi-omic data types that may arise in population-based studies utilizing metabolomic data.

Longitudinal analysis of bronchodilator response in asthmatics and effect modification of age-related trends by genotype

BACKGROUND AND OBJECTIVES

Genome Wide Association Studies (GWAS) have identified genetic polymorphisms associated with bronchodilator response (BDR), but it is unknown how these associations change across life stages. We examined the impact of genetic variants on BDR from childhood to adulthood in asthmatics to uncover potential effect modification by age.

METHODS

We searched the National Human Genome Research Institute (NHGRI) catalog of published GWAS to obtain a list of genetic associations with BDR, and tested them for effect modification by age in 604 subjects from the Childhood Asthma Management Program (CAMP), a clinical trial with longitudinal measures of BDR (age range 5-30 years). We performed longitudinal analyses using linear mixed models and visualized longitudinal changes in BDR using generalized additive models with repeated measures, adjusting for treatment group, sex, and main effects of age and additive genotype.

RESULTS

Increasing age was associated with decreased BDR (-0.24% per year). Polymorphisms rs295137 (T allele) near SPATS2L and rs2626393 (C allele) near ASB3 demonstrated their strongest associations with BDR in early childhood through adolescence, with a large decrease in their magnitude of effect from adolescence onward. The effect estimate for % BDR associated with rs295137 genotype (Beta = 1.3; 95%CI 0.6-2.1) was diminished by age (interaction term = -0.06, P = 0.004). The effect estimate for rs2626393 (Beta = -0.92 (95%CI -1.7 to -0.2) was also modified by age (interaction term = 0.05, P = 0.0004).

CONCLUSIONS

Polymorphisms associated with BDR in childhood may not be relevant for predicting adolescent and adult BDR, which could reflect age-related changes in asthma phenotypes.

Vitamin D prenatal programming of childhood metabolomics profiles at age 3 y

Background: Vitamin D deficiency is implicated in a range of common complex diseases that may be prevented by gestational vitamin D repletion. Understanding the metabolic mechanisms related to in utero vitamin D exposure may therefore shed light on complex disease susceptibility.Objective: The goal was to analyze the programming role of in utero vitamin D exposure on children's metabolomics profiles.Design: First, unsupervised clustering was done with plasma metabolomics profiles from a case-control subset of 245 children aged 3 y with and without asthma from the Vitamin D Antenatal Asthma Reduction Trial (VDAART), in which pregnant women were randomly assigned to vitamin D supplementation or placebo. Thereafter, we analyzed the influence of maternal pre- and postsupplement vitamin D concentrations on cluster membership. Finally, we used the metabolites driving the clustering of children to identify the dominant metabolic pathways that were influential in each cluster.Results: We identified 3 clusters of children characterized by 1) high concentrations of fatty acids and amines and low maternal postsupplement vitamin D (mean ± SD; 27.5 ± 11.0 ng/mL), 2) high concentrations of amines, moderate concentrations of fatty acids, and normal maternal postsupplement vitamin D (34.0 ± 14.1 ng/mL), and 3) low concentrations of fatty acids, amines, and normal maternal postsupplement vitamin D (35.2 ± 15.9 ng/mL). Adjusting for sample storage time, maternal age and education, and both child asthma and vitamin D concentration at age 3 y did not modify the association between maternal postsupplement vitamin D and cluster membership (P = 0.0014). Maternal presupplement vitamin D did not influence cluster membership, whereas the combination of pre- and postsupplement concentrations did (P = 0.03).Conclusions: Young children can be clustered into distinct biologically meaningful groups by their metabolomics profiles. The clusters differed in concentrations of inflammatory mediators, and cluster membership was influenced by in utero vitamin D exposure, suggesting a prenatal programming role of vitamin D on the child's metabolome. This trial was registered at clinicaltrials.gov as NCT00920621.

Genome-wide expression profiles identify potential targets for gene-environment interactions in asthma severity

BACKGROUND

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.

OBJECTIVE

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.

METHODS

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.

RESULTS

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.

CONCLUSIONS

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.

Genomic architecture of pharmacological efficacy and adverse events

The pharmacokinetic and pharmacodynamic disciplines address pharmacological traits, including efficacy and adverse events. Pharmacogenomics studies have identified pervasive genetic effects on treatment outcomes, resulting in the development of genetic biomarkers for optimization of drug therapy. Pharmacogenomics-based tests are already being applied in clinical decision making. However, despite substantial progress in identifying the genetic etiology of pharmacological response, current biomarker panels still largely rely on single gene tests with a large portion of the genetic effects remaining to be discovered. Future research must account for the combined effects of multiple genetic variants, incorporate pathway-based approaches, explore gene-gene interactions and nonprotein coding functional genetic variants, extend studies across ancestral populations, and prioritize laboratory characterization of molecular mechanisms. Because genetic factors can play a key role in drug response, accurate biomarker tests capturing the main genetic factors determining treatment outcomes have substantial potential for improving individual clinical care.

Phenotype prediction by integrative network analysis of SNP and gene expression microarrays

A long-term goal of biomedical research is to decipher how genetic processes influence disease formation. Ubiquitous and advancing microarray technology can measure millions of DNA structural variants (single-nucleotide polymorphisms, or SNPs) and thousands of gene transcripts (RNA expression microarrays) in cells. Both of these information modalities can be brought to bear on disease etiology. This paper develops a Bayesian network-based approach to integrate SNP and expression microarray data. The network models SNP-gene interactions using a phenotype-centric network. Inferring the network consists of two steps: variable selection and network learning. The learned network illustrates how functionally dependent SNPs and genes influence each other, and also serves as a predictor of the phenotype. The application of the proposed method to a pediatric acute lymphoblastic leukemia dataset demonstrates the feasibility of our approach and its impact on biological investigation and clinical practice.

The Local Geometry of Multiattribute Tradeoff Preferences

Existing representations for multiattribute ceteris paribus preference statements have provided useful treatments and clear semantics for qualitative comparisons, but have not provided similarly clear representations or semantics for comparisons involving quantitative tradeoffs. We use directional derivatives and other concepts from elementary differential geometry to interpret conditional multiattribute ceteris paribus preference comparisons that state bounds on quantitative tradeoff ratios. This semantics extends the familiar economic notion of marginal rate of substitution to multiple continuous or discrete attributes. The same geometric concepts also provide means for interpreting statements about the relative importance of different attributes.

Mapping transcription mechanisms from multimodal genomic data

BACKGROUND

Identification of expression quantitative trait loci (eQTLs) is an emerging area in genomic study. The task requires an integrated analysis of genome-wide single nucleotide polymorphism (SNP) data and gene expression data, raising a new computational challenge due to the tremendous size of data.

RESULTS

We develop a method to identify eQTLs. The method represents eQTLs as information flux between genetic variants and transcripts. We use information theory to simultaneously interrogate SNP and gene expression data, resulting in a Transcriptional Information Map (TIM) which captures the network of transcriptional information that links genetic variations, gene expression and regulatory mechanisms. These maps are able to identify both cis- and trans- regulating eQTLs. The application on a dataset of leukemia patients identifies eQTLs in the regions of the GART, PCP4, DSCAM, and RIPK4 genes that regulate ADAMTS1, a known leukemia correlate.

CONCLUSIONS

The information theory approach presented in this paper is able to infer the dependence networks between SNPs and transcripts, which in turn can identify cis- and trans-eQTLs. The application of our method to the leukemia study explains how genetic variants and gene expression are linked to leukemia.

Integrative predictive model of coronary artery calcification in atherosclerosis

BACKGROUND

Many different genetic and clinical factors have been identified as causes or contributors to atherosclerosis. We present a model of preclinical atherosclerosis based on genetic and clinical data that predicts the presence of coronary artery calcification in healthy Americans of European descent 45 to 84 years of age in the Multi-Ethnic Study of Atherosclerosis (MESA).

METHODS AND RESULTS

We assessed 712 individuals for the presence or absence of coronary artery calcification and assessed their genotypes for 2882 single-nucleotide polymorphisms. With the use of these single-nucleotide polymorphisms and relevant clinical data, a Bayesian network that predicts the presence of coronary calcification was constructed. The model contained 13 single-nucleotide polymorphisms (from genes AGTR1, ALOX15, INSR, PRKAB1, IL1R2, ESR2, KCNK1, FBLN5, PPARA, VEGFA, PON1, TDRD6, PLA2G7, and 1 ancestry informative marker) and 5 clinical variables (sex, age, weight, smoking, and diabetes mellitus) and achieved 85% predictive accuracy, as measured by area under the receiver operating characteristic curve. This is a significant (P<0.001) improvement on models that use just the single-nucleotide polymorphism data or just the clinical variables.

CONCLUSIONS

We present an investigation of joint genetic and clinical factors associated with atherosclerosis that shows predictive results for both cases, as well as enhanced performance for their combination.