Multiancestral polygenic risk score for pediatric asthma

Dissecting cross-population polygenic heterogeneity across respiratory and cardiometabolic diseases

Biological mechanisms underlying multimorbidity remain elusive. To dissect the polygenic heterogeneity of multimorbidity in twelve complex traits across populations, we leveraged biobank resources of genome-wide association studies (GWAS) for 232,987 East Asian individuals (the 1st and 2nd cohorts of BioBank Japan) and 751,051 European individuals (UK Biobank and FinnGen). Cross-trait analyses of respiratory and cardiometabolic diseases, rheumatoid arthritis, and smoking identified negative genetic correlations between respiratory and cardiometabolic diseases in East Asian individuals, opposite from the positive associations in European individuals. Associating genome-wide polygenic risk scores (PRS) with 325 blood metabolome and 2917 proteome biomarkers supported the negative cross-trait genetic correlations in East Asian individuals. Bayesian pathway PRS analysis revealed a negative association between asthma and dyslipidemia in a gene set of peroxisome proliferator-activated receptors. The pathway suggested heterogeneity of cell type specificity in the enrichment analysis of the lung single-cell RNA-sequencing dataset. Our study highlights the heterogeneous pleiotropy of immunometabolic dysfunction in multimorbidity.

Sequencing and health data resource of children of African ancestry

Purpose

Individuals who self-report as Black or African American are historically underrepresented in genome-wide studies of disease risk, a disparity particularly evident in pediatric disease research. To address this gap, Cincinnati Children's Hospital Medical Center (CCHMC) established a biorepository and developed a comprehensive DNA sequencing resource including 15,684 individuals who self-identified as African American or Black and received care at CCHMC.

Methods

Participants were enrolled through the CCHMC Discover Together Biobank and sequenced. Admixture analyses confirmed the genetic ancestry of the cohort, which was then linked to electronic medical records.

Results

High-quality genome-wide genotypes from common variants accompanied by medical recordsourced data are available through the Genomic Information Commons. This dataset performs well in genetic studies. Specifically, we replicated known associations in sickle cell disease (HBB, p = 4.05 × 10-1), anxiety (PLAA3, p = 6.93 × 10-), and asthma (PCDH15, p = 5.6 × 10-1), while also identifying novel loci associated with asthma severity.

Conclusion

We present the acquisition and quality of genetic and disease-associated data and present an analytical framework for using this resource. In partnership with a community advisory council, we have co-developed a valuable framework for data use and future research.

Update on the genetics of allergic diseases

The field of genetic etiology of allergic diseases has advanced significantly in recent years. Shared risk loci reflect the contribution of genetic factors to the sequential development of allergic conditions across the atopic march, while unique risk loci provide opportunities to understand tissue specific manifestations of allergic disease. Most identified risk variants are noncoding, indicating that they likely influence gene expression through gene regulatory mechanisms. Despite recent advances, challenges persist, particularly regarding the need for increased ancestral diversity in research populations. Further, while polygenic risk scores show promise for identifying individuals at higher genetic risk for allergic diseases, their predictive accuracy varies across different ancestries and can be difficult to translate to an individual's absolute risk of developing a disease. Methodologies, including "nearest gene," 3D chromatin interaction analysis, expression quantitative trait locus analysis, experimental screens, and integrative bioinformatic models, have established connections between genetic variants and their regulatory targets, enhancing our understanding of disease risk and phenotypic variability. In this review, we focus on the state of knowledge of allergic sensitization and 5 allergic diseases: asthma, atopic dermatitis, allergic rhinitis, food allergy, and eosinophilic esophagitis. We summarize recent progress and highlight opportunities for advancing our understanding of their genetic etiology.

A polygenic score for height identifies an unmeasured genetic predisposition among pediatric patients with idiopathic short stature

BACKGROUND

A subset of children with short stature do not have an identified clinical explanation after extensive diagnostic evaluation. We hypothesized that a polygenic score for height (PGSheight) could identify children with non-familial idiopathic short stature (ISS-NF) who carry a polygenic predisposition to shorter height that is not accounted for by existing measures.

METHODS

We studied 534 pediatric participants in an electronic health record (EHR)-linked DNA biobank (BioVU) who had been evaluated for short stature by an endocrinologist. Participants were classified as having one of five short stature subtypes: primary growth disorders, secondary growth disorders, idiopathic short stature (ISS), which was sub-classified into familial (ISS-F) and non-familial (ISS-NF), and constitutional delay of puberty (ISS-DP). Differences in polygenic predisposition between subtypes were analyzed using a validated PGSheight which was standardized to a standard deviation score (SDS). Adult height predictions were generated using the PGSheight and mid-parental height (MPH). Within-child differences in height predictions were compared across subtypes. Logistic regression models and AUC analyses were used to test the ability of the PGSheight to differentiate ISS-NF from growth disorders. The incremental improvement (ΔAUC) of adding the PGSheight to prediction models with MPH was also estimated.

RESULTS

Among the 534 participants, 29.0% had secondary growth disorders, 24.9% had ISS-F, 20.2% had ISS-NF, 17.2% had ISS-DP, and 8.6% had primary growth disorders. Participants with ISS-NF had similar PGSheight values to those with ISS-F (difference [Δ] in PGSheight SDS [95% CI] = 0.19 [- 0.31 to 0.70], p = 0.75). Predicted heights generated by the PGSheight were lower than the MPH estimate for children with ISS-NF (Δ[PGSheight - MPH] =  - 0.37 SDS; p = 3.2 × 10-9) but not for children with ISS-F (Δ =  - 0.07; p = 0.56). Children with ISS-NF also had lower PGSheight than children with primary growth disorders (ΔPGSheight =  - 0.53 [- 1.03 to - 0.04], p = 0.03) and secondary growth disorders (Δ =  - 0.45 [- 0.80 to - 0.10], p = 0.005). The PGSheight improved model discrimination between ISS-NF and children with primary (ΔAUC, + 0.07 [95% CI, 0.02 to 0.17]) and secondary growth disorders (ΔAUC, + 0.03 [95% CI, 0.01 to 0.10]).

CONCLUSIONS

Some children with ISS-NF have an unrecognized polygenic predisposition to shorter height, similar to children with ISS-F and greater than those with growth disorders. A PGSheight could aid clinicians in identifying children with a benign, polygenic predisposition to shorter height.

Blood-Based EWAS of Asthma Polygenic Burden in The Netherlands Twin Register

Asthma, a chronic respiratory condition characterized by airway inflammation, affects millions of individuals worldwide. Challenges remain in asthma prediction and diagnosis from its complex etiology involving genetic and environmental factors. Here, we investigated the relationship between genome-wide DNA methylation and genetic risk for asthma quantified via polygenic scores in two cohorts from the Netherlands Twin Register; one enriched with asthmatic families measured on the Illumina EPIC array (n = 526) and a general population cohort measured on the Illumina HM450K array (n = 2680). We performed epigenome-wide association studies of asthma polygenic scores in each cohort with results combined through meta-analysis (total samples = 3206). The EWAS meta-analysis identified 63 significantly associated CpGs, (following Bonferroni correction, α = 0.05/358,316). An investigation of previous mQTL associations identified 48 mQTL associations between 24 unique CpGs and 48 SNPs, of which two SNPs have previous associations with asthma. Enrichment analysis using the 63 significant CpGs highlighted previous associations with ancestry, smoking, and air pollution. A dizygotic twin within-pair analysis of the 63 CpGs revealed similar directional effects between the two cohorts in 33 of the 63 CpGs. These findings further characterize the intricate relationship between DNA methylation and genetics relative to asthma.

The clinical value and most informative threshold of polygenic risk score in the Quebec City Case-Control Asthma Cohort

Genome-wide association studies (GWAS) have identified genetic variants robustly associated with asthma. A potential near-term clinical application is to calculate polygenic risk score (PRS) to improve disease risk prediction. The value of PRS, as part of numerous multi-source variables used to define asthma, remains unclear. This study aims to evaluate PRS and define most informative thresholds in relation to conventional clinical and physiological criteria of asthma using a multivariate statistical method. Clinical and genome-wide genotyping data were obtained from the Quebec City Case-Control Asthma Cohort (QCCCAC), which is an independent cohort from previous GWAS. PRS was derived using LDpred2 and integrated with other asthma phenotypes by means of Principal Component Analysis with Optimal Scaling (PCAOS). PRS was considered using 'ordinal level of scaling' to account for non-linear information. In two dimensional PCAOS space, the first component delineated individuals with and without asthma, whereas the severity of asthma was discerned on the second component. The positioning of high vs. low PRS in this space matched the presence and absence of airway hyperresponsiveness, showing that PRS delineated cases and controls at the same extent as a positive bronchial challenge test. The top 10% and the bottom 5% of the PRS were the most informative thresholds to define individuals at high and low genetic risk of asthma in this cohort. PRS used in a multivariate method offers a decision-making space similar to hyperresponsiveness in this cohort and highlights the most informative and asymmetrical thresholds to define high and low genetic risk of asthma.

Polygenic scores and their applications in kidney disease

Genome-wide association studies (GWAS) have uncovered thousands of risk variants that individually have small effects on the risk of human diseases, including chronic kidney disease, type 2 diabetes, heart diseases and inflammatory disorders, but cumulatively explain a substantial fraction of disease risk, underscoring the complexity and pervasive polygenicity of common disorders. This complexity poses unique challenges to the clinical translation of GWAS findings. Polygenic scores combine small effects of individual GWAS risk variants across the genome to improve personalized risk prediction. Several polygenic scores have now been developed that exhibit sufficiently large effects to be considered clinically actionable. However, their clinical use is limited by their partial transferability across ancestries and a lack of validated models that combine polygenic, monogenic, family history and clinical risk factors. Moreover, prospective studies are still needed to demonstrate the clinical utility and cost-effectiveness of polygenic scores in clinical practice. Here, we discuss evolving methods for developing polygenic scores, best practices for validating and reporting their performance, and the study designs that will empower their clinical implementation. We specifically focus on the polygenic scores relevant to nephrology and other chronic, complex diseases and review their key limitations, necessary refinements and potential clinical applications.

Evaluation of Polygenic Risk Score for Prediction of Childhood Onset and Severity of Asthma

Asthma is a common complex disease with susceptibility defined through an interplay of genetic and environmental factors. Responsiveness to asthma treatment varies between individuals and is largely determined by genetic variability. The polygenic score (PGS) approach enables an individual risk of asthma and respective response to drug therapy. PGS models could help to predict the individual risk of asthma using 26 SNPs of drug pathway genes involved in the metabolism of glucocorticosteroids (GCS), and beta-2-agonists, antihistamines, and antileukotriene drugs associated with the response to asthma treatment within GWAS were built. For PGS, summary statistics from the Trans-National Asthma Genetic Consortium GWAS meta-analysis, and genotype data for 882 individuals with asthma/controls from the Volga-Ural region, were used. The study group was comprised of Russian, Tatar, Bashkir, and mixed ethnicity individuals with asthma (N = 378) aged 2-18 years. and individuals without features of atopic disease (N = 504) aged 4-67 years from the Volga-Ural region. The DNA samples for the study were collected from 2000 to 2021. The drug pathway genes' PGS revealed a higher odds for childhood asthma risk (p = 2.41 × 10-12). The receiver operating characteristic (ROC) analysis showed an Area Under the Curve, AUC = 0.63. The AUC of average significance for moderate-to-severe and severe asthma was observed (p = 5.7 × 10-9, AUC = 0.64). Asthma drug response pathway gene variant PGS models may contribute to the development of modern approaches to optimise asthma diagnostics and treatment.

Omics in allergy and asthma

This review explores the transformative impact of omics technologies on allergy and asthma research in recent years, focusing on advancements in high-throughput technologies related to genomics and transcriptomics. In particular, the rapid spread of single-cell RNA sequencing has markedly advanced our understanding of the molecular pathology of allergic diseases. Furthermore, high-throughput genome sequencing has accelerated the discovery of monogenic disorders that were previously overlooked as ordinary intractable allergic diseases. We also introduce microbiomics, proteomics, lipidomics, and metabolomics, which are quickly growing areas of research interest, although many of their current findings remain inconclusive as solid evidence. By integrating these omics data, we will gain deeper insights into disease mechanisms, leading to the development of precision medicine approaches that promise to enhance treatment outcomes.

A polygenic score for height identifies an unmeasured genetic predisposition among pediatric patients with idiopathic short stature

Background

A subset of children with short stature do not have an identified clinical explanation and are assigned a diagnosis of idiopathic short stature (ISS). We hypothesized that a polygenic score for height (PGSheight) could identify children with ISS who have an unrecognized heritable predisposition to shorter height.

Methods

We examined 534 pediatric participants in an EHR-linked DNA biobank (BioVU) who had undergone an evaluation for short stature by an endocrinologist. We used a previously validated PGSheight and standardized it to a standard deviation (SDS) of 1. PGSheight differences between short stature subtypes was estimated using Tukey's HSD. The PGSheight and mid-parental height (MPH) were then used to predict adult heights for each participant and these predictions were compared using Cohen's d stratifying by short stature subtype. The ability of the PGSheight to discriminate between ISS and short stature due to underlying disease was evaluated using logistic regression models with area under the ROC curve (AUC) analyses and testing the incremental benefit (ΔAUC) of adding the PGSheight to prediction models.

Results

Among the 534 participants, 22.1% had ISS (median [IQR] PGSheight SDS = -1.31 [-2.15 to -0.47]), 6.6% had familial (genetic) short stature (FSS) (-1.62 [-2.13 to -0.54]), and 45.1% had short stature due to underlying pathology (-0.74 [-1.23 to -0.19]). Children with ISS had similar PGSheight values as those with FSS (ΔPGSheight [95% CI] = 0.19 [-0.31 to 0.70], p = 0.75), but predicted heights generated by the PGSheight were lower than the MPH estimate for children with ISS (d = -0.64; p = 4.0×10-18) but not FSS (d = 0.05; p = 0.46), suggesting that MPH underestimates height in the ISS group. Children with ISS had lower PGSheight values than children with pathology (ΔPGSheight = -0.60 SDS [-0.89 to -0.31], p < 0.001), suggesting children with ISS have a larger predisposition to shorter height. In addition, the PGSheight improved model discrimination between ISS and pathologic short stature (ΔAUC, + 0.07 [95% CI, 0.01 to 0.11]).

Conclusions

Some children with ISS have a clinically unrecognized polygenic predisposition to shorter height that is comparable to children with FSS and larger than those with underlying pathology. A PGSheight could help clinicians identify children who have a benign predisposition to shorter height.

Asthma-Genomic Advances Toward Risk Prediction

Asthma is a common complex airway disease whose prediction of disease risk and most severe outcomes is crucial in clinical practice for adequate clinical management. This review discusses the latest findings in asthma genomics and current obstacles faced in moving forward to translational medicine. While genome-wide association studies have provided valuable insights into the genetic basis of asthma, there are challenges that must be addressed to improve disease prediction, such as the need for diverse representation, the functional characterization of genetic variants identified, variant selection for genetic testing, and refining prediction models using polygenic risk scores.

Epigenomic partitioning of a polygenic risk score for asthma reveals distinct genetically driven disease pathways

BACKGROUND

Individual differences in susceptibility to developing asthma, a heterogeneous chronic inflammatory lung disease, are poorly understood. Whether genetics can predict asthma risk and how genetic variants modulate the complex pathophysiology of asthma are still debated.

AIM

To build polygenic risk scores for asthma risk prediction and epigenomically link predictive genetic variants to pathophysiological mechanisms.

METHODS

Restricted polygenic risk scores were constructed using single nucleotide variants derived from genome-wide association studies and validated using data generated in the Rotterdam Study, a Dutch prospective cohort of 14 926 individuals. Outcomes used were asthma, childhood-onset asthma, adulthood-onset asthma, eosinophilic asthma and asthma exacerbations. Genome-wide chromatin analysis data from 19 disease-relevant cell types were used for epigenomic polygenic risk score partitioning.

RESULTS

The polygenic risk scores obtained predicted asthma and related outcomes, with the strongest associations observed for childhood-onset asthma (2.55 odds ratios per polygenic risk score standard deviation, area under the curve of 0.760). Polygenic risk scores allowed for the classification of individuals into high-risk and low-risk groups. Polygenic risk score partitioning using epigenomic profiles identified five clusters of variants within putative gene regulatory regions linked to specific asthma-relevant cells, genes and biological pathways.

CONCLUSIONS

Polygenic risk scores were associated with asthma(-related traits) in a Dutch prospective cohort, with substantially higher predictive power observed for childhood-onset than adult-onset asthma. Importantly, polygenic risk score variants could be epigenomically partitioned into clusters of regulatory variants with different pathophysiological association patterns and effect estimates, which likely represent distinct genetically driven disease pathways. Our findings have potential implications for personalised risk mitigation and treatment strategies.

Unraveling the Complexity of Asthma: Insights from Omics Approaches

Asthma is a heterogeneous respiratory disease that represents a substantial social and economic burden [...].

A Comprehensive Look at the Development of Asthma in Children

Asthma, a prevalent chronic respiratory condition affecting millions of children globally, presents a significant health challenge. This review critically examines the developmental pathways of asthma in children, focusing on genetic, environmental, and early-life determinants. Specifically, we explore the impact of prenatal and postnatal factors such as maternal smoking, nutrition, respiratory infections, and allergen exposure on asthma development. Our analysis highlights the intricate interplay of these influences and their contribution to childhood asthma. Moreover, we emphasize targeted strategies and interventions to mitigate its burden, including genetic counseling for at-risk families, environmental modifications to reduce triggers, and early-life immunomodulation. By delving into these preventive measures and interventions, our review aims to provide actionable insights for healthcare professionals in developing tailored strategies to address the complexities of childhood asthma. In summary, this article offers a detailed examination of asthma development in children, aiming to enhance understanding and inform efforts to reduce its burden through targeted interventions.

Update on asthma biology

This is an exciting time to be conducting asthma research. The recent development of targeted asthma biologics has validated the power of basic research to discover new molecules amenable to therapeutic intervention. Advances in high-throughput sequencing are providing a wealth of "omics" data about genetic and epigenetic underpinnings of asthma, as well as about new cellular interacting networks and potential endotypes in asthma. Airway epithelial cells have emerged not only as key sensors of the outside environment but also as central drivers of dysregulated mucosal immune responses in asthma. Emerging data suggest that the airway epithelium in asthma remembers prior encounters with environmental exposures, resulting in potentially long-lasting changes in structure and metabolism that render asthmatic individuals susceptible to subsequent exposures. Here we summarize recent insights into asthma biology, focusing on studies using human cells or tissue that were published in the past 2 years. The studies are organized thematically into 6 content areas to draw connections and spur future research (on genetics and epigenetics, prenatal and early-life origins, microbiome, immune and inflammatory pathways, asthma endotypes and biomarkers, and lung structural alterations). We highlight recent studies of airway epithelial dysfunction and response to viral infections and conclude with a framework for considering how bidirectional interactions between alterations in airway structure and mucosal immunity can lead to sustained lung dysfunction in asthma.

Building a regional pediatric asthma learning health system in support of optimal, equitable outcomes

Introduction

Asthma is characterized by preventable morbidity, cost, and inequity. We sought to build an Asthma Learning Health System (ALHS) to coordinate regional pediatric asthma improvement activities.

Methods

We generated quantitative and qualitative insights pertinent to a better, more equitable care delivery system. We used electronic health record data to calculate asthma hospitalization rates for youth in our region. We completed an "environmental scan" to catalog the breadth of asthma-related efforts occurring in our children's hospital and across the region. We supplemented the scan with group-level assessments and focus groups with parents, clinicians, and community partners. We used insights from this descriptive epidemiology to inform the definition of shared aims, drivers, measures, and prototype interventions.

Results

Greater Cincinnati's youth are hospitalized for asthma at a rate three times greater than the U.S. average. Black youth are hospitalized at a rate five times greater than non-Black youth. Certain neighborhoods bear the disproportionate burden of asthma morbidity. Across Cincinnati, there are many asthma-relevant activities that seek to confront this morbidity; however, efforts are largely disconnected. Qualitative insights highlighted the importance of cross-sector coordination, evidence-based acute and preventive care, healthy homes and neighborhoods, and accountability. These insights also led to a shared, regional aim: to equitably reduce asthma-related hospitalizations. Early interventions have included population-level pattern recognition, multidisciplinary asthma action huddles, and enhanced social needs screening and response.

Conclusion

Learning health system methods are uniquely suited to asthma's complexity. Our nascent ALHS provides a scaffold atop which we can pursue better, more equitable regional asthma outcomes.

Omics approaches in asthma research: Challenges and opportunities

Asthma, a chronic respiratory disease with a global prevalence of approximately 300 million individuals, presents a significant societal and economic burden. This multifaceted syndrome exhibits diverse clinical phenotypes and pathogenic endotypes influenced by various factors. The advent of omics technologies has revolutionized asthma research by delving into the molecular foundation of the disease to unravel its underlying mechanisms. Omics technologies are employed to systematically screen for potential biomarkers, encompassing genes, transcripts, methylation sites, proteins, and even the microbiome components. This review provides an insightful overview of omics applications in asthma research, with a special emphasis on genetics, transcriptomics, epigenomics, and the microbiome. We explore the cutting-edge methods, discoveries, challenges, and potential future directions in the realm of asthma omics research. By integrating multi-omics and non-omics data through advanced statistical techniques, we aspire to advance precision medicine in asthma, guiding diagnosis, risk assessment, and personalized treatment strategies for this heterogeneous condition.

Plasma protein signatures of adult asthma

BACKGROUND

Adult asthma is complex and incompletely understood. Plasma proteomics is an evolving technique that can both generate biomarkers and provide insights into disease mechanisms. We aimed to identify plasma proteomic signatures of adult asthma.

METHODS

Protein abundance in plasma was measured in individuals from the Agricultural Lung Health Study (ALHS) (761 asthma, 1095 non-case) and the Atherosclerosis Risk in Communities study (470 asthma, 10,669 non-case) using the SOMAScan 5K array. Associations with asthma were estimated using covariate adjusted logistic regression and meta-analyzed using inverse-variance weighting. Additionally, in ALHS, we examined phenotypes based on both asthma and seroatopy (asthma with atopy (n = 207), asthma without atopy (n = 554), atopy without asthma (n = 147), compared to neither (n = 948)).

RESULTS

Meta-analysis of 4860 proteins identified 115 significantly (FDR<0.05) associated with asthma. Multiple signaling pathways related to airway inflammation and pulmonary injury were enriched (FDR<0.05) among these proteins. A proteomic score generated using machine learning provided predictive value for asthma (AUC = 0.77, 95% CI = 0.75-0.79 in training set; AUC = 0.72, 95% CI = 0.69-0.75 in validation set). Twenty proteins are targeted by approved or investigational drugs for asthma or other conditions, suggesting potential drug repurposing. The combined asthma-atopy phenotype showed significant associations with 20 proteins, including five not identified in the overall asthma analysis.

CONCLUSION

This first large-scale proteomics study identified over 100 plasma proteins associated with current asthma in adults. In addition to validating previous associations, we identified many novel proteins that could inform development of diagnostic biomarkers and therapeutic targets in asthma management.

Ancestry-specific polygenic risk scores are risk enhancers for clinical cardiovascular disease assessments

Clinical implementation of new prediction models requires evaluation of their utility in a broad range of intended use populations. Here we develop and validate ancestry-specific Polygenic Risk Scores (PRSs) for Coronary Artery Disease (CAD) using 29,389 individuals from diverse cohorts and genetic ancestry groups. The CAD PRSs outperform published scores with an average Odds Ratio per Standard Deviation of 1.57 (SD = 0.14) and identify between 12% and 24% of individuals with high genetic risk. Using this risk factor to reclassify borderline or intermediate 10 year Atherosclerotic Cardiovascular Disease (ASCVD) risk improves assessments for both CAD (Net Reclassification Improvement (NRI) = 13.14% (95% CI 9.23-17.06%)) and ASCVD (NRI = 10.70 (95% CI 7.35-14.05)) in an independent cohort of 9,691 individuals. Our analyses demonstrate that using PRSs as Risk Enhancers improves ASCVD risk assessments outlining an approach for guiding ASCVD prevention with genetic information.

Association between severe bronchiolitis in infancy and age 6-year lung function

BACKGROUND AND OBJECTIVES

Understanding early life risk factors for decreased lung function could guide prevention efforts and improve lung health throughout the lifespan. Our objective was to investigate the association between history of severe (hospitalized) bronchiolitis in infancy and age 6-year lung function.

METHODS

We analyzed data from two prospective cohort studies: infants hospitalized with bronchiolitis and a parallel cohort of healthy infants. Children were followed longitudinally, and spirometry was performed at age 6 years. To examine the relationship between history of severe bronchiolitis and primary outcomes - FEV1% predicted (pp) and FEV1/FVCpp - we used multivariable linear regression models adjusted for insurance status, perterm birth, secondhand smoke exposure, breastfeeding status, traffic-related air pollution and polygenic risk score. Secondary outcomes included FVCpp and bronchodilator responsiveness (BDR).

RESULTS

Age 6-year spirometry was available for 425 children with history of severe bronchiolitis in infancy and 48 controls. Unadjusted analysis revealed that while most children had normal range lung function, children with a history of severe bronchiolitis had lower FEV1pp and FEV1/FVCpp. In adjusted analyses, the same findings were observed: FEV1pp was 8% lower (p = 0.004) and FEV1/FVCpp was 4% lower (p = 0.007) in children with history of severe bronchiolitis versus controls. FVC and BDR did not differ between groups.

CONCLUSIONS

Children with severe bronchiolitis in infancy have decreased FEV1 and FEV1/FVC at age 6 years, compared to controls. These children may be at increased risk for chronic respiratory illness later in life.

Revealing polygenic pleiotropy using genetic risk scores for asthma

In this study we examined how genetic risk for asthma associates with different features of the disease and with other medical conditions and traits. Using summary statistics from two multi-ancestry genome-wide association studies of asthma, we modeled polygenic risk scores (PRSs) and validated their predictive performance in the UK Biobank. We then performed phenome-wide association studies of the asthma PRSs with 371 heritable traits in the UK Biobank. We identified 228 total significant associations across a variety of organ systems, including associations that varied by PRS model, sex, age of asthma onset, ancestry, and human leukocyte antigen region alleles. Our results highlight pervasive pleiotropy between asthma and numerous other traits and conditions and elucidate pathways that contribute to asthma and its comorbidities.

Precision medicine in complex diseases-Molecular subgrouping for improved prediction and treatment stratification

Complex diseases are caused by a combination of genetic, lifestyle, and environmental factors and comprise common noncommunicable diseases, including allergies, cardiovascular disease, and psychiatric and metabolic disorders. More than 25% of Europeans suffer from a complex disease, and together these diseases account for 70% of all deaths. The use of genomic, molecular, or imaging data to develop accurate diagnostic tools for treatment recommendations and preventive strategies, and for disease prognosis and prediction, is an important step toward precision medicine. However, for complex diseases, precision medicine is associated with several challenges. There is a significant heterogeneity between patients of a specific disease-both with regards to symptoms and underlying causal mechanisms-and the number of underlying genetic and nongenetic risk factors is often high. Here, we summarize precision medicine approaches for complex diseases and highlight the current breakthroughs as well as the challenges. We conclude that genomic-based precision medicine has been used mainly for patients with highly penetrant monogenic disease forms, such as cardiomyopathies. However, for most complex diseases-including psychiatric disorders and allergies-available polygenic risk scores are more probabilistic than deterministic and have not yet been validated for clinical utility. However, subclassifying patients of a specific disease into discrete homogenous subtypes based on molecular or phenotypic data is a promising strategy for improving diagnosis, prediction, treatment, prevention, and prognosis. The availability of high-throughput molecular technologies, together with large collections of health data and novel data-driven approaches, offers promise toward improved individual health through precision medicine.

Genetics in Pediatric Practice: From Baby Steps to Running Fast

In the last few decades, medical genetics has undergone a revolution because of the development of technologies and informatics approaches that can generate and analyze large amounts of genomic data. Pediatricians have been hugely affected by these changes. The early age of presentation for birth defects and neurocognitive disorders, together with a shortage of trained genetics professionals, has increased consultations for conditions with a genetic cause, not only in pediatric practice but also in other subspecialties. In the future, genetic testing in childhood is likely to include pediatricians, who can initiate testing in partnership with trained genetics professionals.

Recent progress in the genetic and epigenetic underpinnings of atopy

In the past 2 years, there continue to be advances in our understanding of the genetic and epigenetic underpinnings of atopy pertaining to disease risk and disease severity. The joint role of genetics and the environment has been emphasized in multiple studies. Combining genetics with family history, biomarkers, and comorbidities is further refining our ability to predict the development of individual atopic diseases as well as the advancement of the atopic march. Polygenic risk scores will be an important next step for the field moving toward clinical translation of the genetic findings thus far. A systems biology approach, as illustrated by studies of the microbiome and epigenome, will be necessary to fully understand disease development and to develop increasingly targeted therapeutics.