Multiancestral polygenic risk score for pediatric asthma

Namjou, Bahram; Lape, Michael; Małolepsza, Edyta; DeVore, Stanley B.; Weirauch, Matthew T.; Dikilitas, Ozan; Jarvik, Gail P.; Kiryluk, Krzysztof; Kullo, Iftikhar; Liu, Cong; Luo, Yuan; Satterfield, Benjamin A.; Smoller, Jordan W.; Walunas, Theresa L.; Connolly, John J.; Sleiman, Patrick; Mersha, Tesfaye B.; Mentch, Frank; Hákonarson, Hákon; Prows, Cynthia A.; Biagini, Jocelyn M.; Hershey, Gurjit K. Khurana; Martin, Lisa; Kottyan, Leah

doi:10.1016/j.jaci.2022.03.035

Cited by 24 publications

(14 citation statements)

References 65 publications

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“…Our AUCs were in line with other proteomic studies reported for asthma using specific candidate proteins 45,46 or for complex diseases using an earlier SOMAScan array or different proteomic assays 47–49 . Compared to AUCs of 0.51–0.70 from previous large genetic studies for asthma, 50,51 AUCs from our proteomic score prediction models are encouraging. The benefit of adding the proteome score to a covariates‐only prediction model is reflected by the improved performance (gain in AUC) in our data: from 0.64 to 0.77 for ALHS and from 0.57 to 0.72 for ARIC EA.…”

Section: Discussionsupporting

confidence: 87%

Plasma protein signatures of adult asthma

Smilnak,

Lee,

Chattopadhyay

et al. 2024

Allergy

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BackgroundAdult 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.MethodsProtein 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)).ResultsMeta‐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.ConclusionThis 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.

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Section: Discussionsupporting

confidence: 87%

Plasma protein signatures of adult asthma

Smilnak,

Lee,

Chattopadhyay

et al. 2024

Allergy

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“…Recently, multi-ancestral PRS for J Investig Allergol Clin Immunol 2023; Vol. 33 (2) © 2022 Esmon Publicidad doi: 10.18176/jiaci.0878 asthma developed using lasso sum [123] or Bayesian regression [124] have captured the risk of asthma, although other studies have failed to achieve this [125,126]. PRS incorporating DNAm or gene expression data may better capture environmental influences in order to improve risk stratification [127].…”

Section: Discussionmentioning

confidence: 99%

Asthma Exacerbations: The Genes Behind the Scenes

Luis¹,

Forno²,

Celedón³

et al. 2023

J Investig Allergol Clin

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The clinical and socioeconomic burden of asthma exacerbations (AEs) represents a major public health problem. In the last four years, there has been an increase in ethnic diversity in candidate-gene and genome-wide association studies (GWAS) of AEs, which in the latter case has led to the identification of novel genes and underlying pathobiological processes. Pharmacogenomics, admixture mapping analyses, and the combination of multiple “omic” layers have contributed to prioritizing genomic regions of interest and/or understanding the functional consequences of genetic variation. Despite this, the field still lags behind the genomics of asthma, where a vast compendium of genetic approaches has been used (e.g., gene-environment interactions, next-generation sequencing, or polygenic risk scores). Furthermore, the roles of the DNA methylome and histone modifications in AEs have been scarcely investigated, and microRNA findings remain to be validated in independent studies. Likewise, the most recent transcriptomic studies highlight the importance of host-airway microbiome interaction in the modulation of AEs risk. Leveraging -omics and deep-phenotyping data from sub-types or homogenous subgroups of patients will be crucial to overcome the inherent heterogeneity of AEs, and boost the identification of potential therapeutic targets and the implementation of precision medicine in clinical practice for AEs.

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“…Previous studies on asthma PRS in the literature have primarily focused on using PRS to predict asthma in pediatric cohorts, and overall found limited performance of PRS [28][29][30]85 . Most of these studies used the P+T approach, while a recently published paper, Namjou et al (2022) 32 , applied PRS-CS to the TAGC multi-ancestry GWAS and found improved discriminatory power of their PRS (receiver-operating characteristic area under the curve, or AUC, of 0.66-0.70 across two pediatric cohorts) compared to the prior studies that used P+T. Sordillo et al ( 2021) 31 applied another genome-wide approach, lassosum, to the TAGC data, but their PRS evaluated in adult cohorts showed moderate performance (AUC of 0.51-0.57 across cohorts of different ancestries).…”

Section: Discussionmentioning

confidence: 99%

“…For asthma, PRS could ultimately play a role in predicting disease severity and development in the clinical setting and serve as a tool for investigating gene-environment interactions in the research setting. So far, some GWAS have been applied to developing PRS for asthma [28][29][30][31][32] , but these models have had limited predictive ability, likely due to the insufficient sample sizes and diversity of existing datasets of asthma. This underscores the genetic complexity of asthma and highlights the need for more large-scale, genomic studies of asthma.…”

Section: Introductionmentioning

confidence: 99%

Multi-ancestry meta-analysis of asthma identifies novel associations and highlights the value of increased power and diversity

Tsuo

Zhou

Wang

et al. 2021

Preprint

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Asthma is a complex disease that affects millions of people and varies in prevalence by an order of magnitude across geographic regions and populations. However, the extent to which genetic variation contributes to these disparities is unclear, as studies probing the genetics of asthma have been primarily limited to populations of European (EUR) descent. As part of the Global Biobank Meta-analysis Initiative (GBMI), we conducted the largest genome-wide association study of asthma to date (153,763 cases and 1,647,022 controls) via meta-analysis across 18 biobanks spanning multiple countries and ancestries. Altogether, we discovered 180 genome-wide significant loci (p < 5x10-8) associated with asthma, 49 of which are not previously reported. We replicate well-known associations such as IL1RL1 and STAT6, and find that overall the novel associations have smaller effects than previously-discovered loci, highlighting our substantial increase in statistical power. Despite the considerable range in prevalence among biobanks, from 3% to 24%, the genetic effects of associated loci are largely consistent across the biobanks and ancestries. To further investigate the polygenic architecture of asthma, we construct polygenic risk scores (PRS) using a multi-ancestry approach, which yields higher predictive power for asthma in non-EUR populations compared to PRS derived from previous asthma meta-analyses and using other methods. Additionally, we find considerable genetic overlap between asthma and chronic obstructive pulmonary disease (COPD) across ancestries but minimal overlap in enriched biological pathways. Our work underscores the multifactorial nature of asthma development and offers insight into the shared genetic architecture of asthma that may be differentially perturbed by environmental factors and contribute to variation in prevalence.

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Multiancestral polygenic risk score for pediatric asthma

Cited by 24 publications

References 65 publications

Plasma protein signatures of adult asthma

Plasma protein signatures of adult asthma

Asthma Exacerbations: The Genes Behind the Scenes

Multi-ancestry meta-analysis of asthma identifies novel associations and highlights the value of increased power and diversity

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