Racial differences in length of stay and readmission for asthma in the all of us research program.

BACKGROUND: This study addresses the limited research on racial disparities in asthma hospitalization outcomes, specifically length of stay (LOS) and readmission, across the U.S. METHODS: We analyzed in-patient and emergency department visits from the All of Us Research Program, identifying various risk factors (demographic, comorbid, temporal, and place-based) associated with asthma LOS and 30-day readmission using Bayesian mixed-effects models. RESULTS: Of 17,233 patients (48.0% White, 30.7% Black, 19.7% Hispanic/Latino, 1.3% Asian, and 0.3% Middle Eastern and North African) with 82,188 asthma visits, Black participants had 20% shorter LOS and 12% higher odds of readmission, compared to White participants in multivariate analyses. Public-insured patients had 14% longer LOS and 39% higher readmission odds than commercially insured patients. Weekend admissions resulted in a 12% shorter LOS but 10% higher readmission odds. Asthmatics with chronic diseases had a longer LOS (range: 6-39%) and higher readmission odds (range: 9-32%) except for those with allergic rhinitis, who had a 23% shorter LOS. CONCLUSIONS: A comprehensive understanding of the factors influencing asthma hospitalization, in conjunction with diverse datasets and clinical-community partnerships, can help physicians and policymakers to systematically address racial disparities, healthcare utilization and equitable outcomes in asthma care.

Development and validation of asthma risk prediction models using co-expression gene modules and machine learning methods.

Asthma is a heterogeneous respiratory disease characterized by airway inflammation and obstruction. Despite recent advances, the genetic regulation of asthma pathogenesis is still largely unknown. Gene expression profiling techniques are well suited to study complex diseases including asthma. In this study, differentially expressed genes (DEGs) followed by weighted gene co-expression network analysis (WGCNA) and machine learning techniques using dataset generated from airway epithelial cells (AECs) and nasal epithelial cells (NECs) were used to identify candidate genes and pathways and to develop asthma classification and predictive models. The models were validated using bronchial epithelial cells (BECs), airway smooth muscle (ASM) and whole blood (WB) datasets. DEG and WGCNA followed by least absolute shrinkage and selection operator (LASSO) method identified 30 and 34 gene signatures and these gene signatures with support vector machine (SVM) discriminated asthmatic subjects from controls in AECs (Area under the curve: AUC = 1) and NECs (AUC = 1), respectively. We further validated AECs derived gene-signature in BECs (AUC = 0.72), ASM (AUC = 0.74) and WB (AUC = 0.66). Similarly, NECs derived gene-signature were validated in BECs (AUC = 0.75), ASM (AUC = 0.82) and WB (AUC = 0.69). Both AECs and NECs based gene-signatures showed a strong diagnostic performance with high sensitivity and specificity. Functional annotation of gene-signatures from AECs and NECs were enriched in pathways associated with IL-13, PI3K/AKT and apoptosis signaling. Several asthma related genes were prioritized including SERPINB2 and CTSC genes, which showed functional relevance in multiple tissue/cell types and related to asthma pathogenesis. Taken together, epithelium gene signature-based model could serve as robust surrogate model for hard-to-get tissues including BECs to improve the molecular etiology of asthma.

Multi-omics profiling approach in food allergy.

The prevalence of food allergy (FA) among children is increasing, affecting nearly 8% of children, and FA is the most common cause of anaphylaxis and anaphylaxis-related emergency department visits in children. Importantly, FA is a complex, multi-system, multifactorial disease mediated by food-specific immunoglobulin E (IgE) and type 2 immune responses and involving environmental and genetic factors and gene-environment interactions. Early exposure to external and internal environmental factors largely influences the development of immune responses to allergens. Genetic factors and gene-environment interactions have established roles in the FA pathophysiology. To improve diagnosis and identification of FA therapeutic targets, high-throughput omics approaches have emerged and been applied over the past decades to screen for potential FA biomarkers, such as genes, transcripts, proteins, and metabolites. In this article, we provide an overview of the current status of FA omics studies, namely genomic, transcriptomic, epigenomic, proteomic, exposomic, and metabolomic. The current development of multi-omics integration of FA studies is also briefly discussed. As individual omics technologies only provide limited information on the multi-system biological processes of FA, integration of population-based multi-omics data and clinical data may lead to robust biomarker discovery that could translate into advances in disease management and clinical care and ultimately lead to precision medicine approaches.

Genome-wide admixture and association analysis identifies African ancestry-specific risk loci of eosinophilic esophagitis in African Americans.

BACKGROUND: Eosinophilic esophagitis (EoE), a chronic allergic inflammatory disease, is linked to multiple genetic risk factors, but studies have focused on populations of European ancestry. Few studies have assessed Black or African American (AA) populations for loci involved in EoE susceptibility. OBJECTIVE: We performed admixture mapping (AM) and genome-wide association study (GWAS) of EoE using participants from AA populations. METHODS: We conducted AM and GWAS of EoE using 137 EoE cases and 1465 healthy controls from the AA population. Samples were genotyped using molecular evolutionary genetics analysis (MEGA). Genotype imputation was carried out with the Consortium on Asthma Among African-Ancestry Populations in the Americas (CAAPA) reference panel using the Michigan Imputation Server. Global and local ancestry inference was carried out, followed by fine mapping and RNA sequencing. After quality control filtering, over 6,000,000 variants were tested by logistic regression adjusted for sex, age, and global ancestry. RESULTS: The global African ancestry proportion was found to be significantly lower among cases than controls (0.751 vs 0.786, P = .012). Case-only AM identified 3 significant loci (9p13.3, 12q24.22-23, and 15q11.2) associated with EoE, of which 12q24.22-23 and 9p13.3 were further replicated in the case-control analysis, with associations observed with African ancestry. Fine mapping and multiomic functional annotations prioritized the variants rs11068264 (FBXW8) and rs7307331 (VSIG10) at 12q24.23 and rs2297879 (ARHGEF39) at 9p13.3. GWAS identified 1 genome-wide significant locus at chromosome 1p22.3 (rs17131726, DDAH1) and 10 other suggestive loci. Most GWAS variants were low-frequency African ancestry-specific variants. RNA sequencing revealed that esophageal DDAH1 and VSIG10 were downregulated and ARHGEF39 upregulated among EoE cases. CONCLUSIONS: GWAS and AM for EoE in AA revealed that African ancestry-specific genetic susceptibility loci exist at 1p22.3, 9p13.3, and 12q24.23, providing evidence of ancestry-specific inheritance of EoE. More independent genetic studies of different ancestries for EoE are needed.

Multi-Omics Profiling Approach to Asthma: An Evolving Paradigm.

Asthma is a complex multifactorial and heterogeneous respiratory disease. Although genetics is a strong risk factor of asthma, external and internal exposures and their interactions with genetic factors also play important roles in the pathophysiology of asthma. Over the past decades, the application of high-throughput omics approaches has emerged and been applied to the field of asthma research for screening biomarkers such as genes, transcript, proteins, and metabolites in an unbiased fashion. Leveraging large-scale studies representative of diverse population-based omics data and integrating with clinical data has led to better profiling of asthma risk. Yet, to date, no omic-driven endotypes have been translated into clinical practice and management of asthma. In this article, we provide an overview of the current status of omics studies of asthma, namely, genomics, transcriptomics, epigenomics, proteomics, exposomics, and metabolomics. The current development of the multi-omics integrations of asthma is also briefly discussed. Biomarker discovery following multi-omics profiling could be challenging but useful for better disease phenotyping and endotyping that can translate into advances in asthma management and clinical care, ultimately leading to successful precision medicine approaches.

Unraveling racial disparities in asthma emergency department visits using electronic healthcare records and machine learning.

OBJECTIVE: Hospital emergency department (ED) visits by asthmatics differ based on race and season. The objectives of this study were to investigate season- and race-specific disparities for asthma risk, and to identify environmental exposure variables associated with ED visits among more than 42,000 individuals of African American (AA) and European American (EA) descent identified through electronic health records (EHRs). METHODS: We examined data from 42,375 individuals (AAs = 14,491, EAs = 27,884) identified in EHRs. We considered associated demographic (race, age, gender, insurance), clinical (smoking status, ED visits, FEV1%), and environmental exposures data (mold, pollen, and pollutants). Machine learning techniques, including random forest (RF), extreme gradient boosting (XGB), and decision tree (DT) were used to build and identify race- and -season-specific predictive models for asthma ED visits. RESULTS: Significant differences in ED visits and FEV1% among AAs and EAs were identified. ED visits by AAs was 32.0% higher than EAs and AAs had 6.4% lower FEV1% value than EAs. XGB model was used to accurately classify asthma patients visiting ED into AAs and EAs. Pollen factor and pollution (PM2.5, PM10) were the key variables for asthma in AAs and EAs, respectively. Age and cigarette smoking increase asthma risk independent of seasons. CONCLUSIONS: In this study, we observed racial and season-specific disparities between AAs and EAs asthmatics for ED visit and FEV1% severity, suggesting the need to address asthma disparities through key predictors including socio-economic status, particulate matter, and mold.

Correction to: Comprehensive functional annotation of susceptibility variants associated with asthma.

In the original article published, the "p" value in the Fig. 5 legend is incorrectly presented as *p < 0.50. The correct p value is *p < 0.050.

MI-MAAP: marker informativeness for multi-ancestry admixed populations.

BACKGROUND: Admixed populations arise when two or more previously isolated populations interbreed. A powerful approach to addressing the genetic complexity in admixed populations is to infer ancestry. Ancestry inference including the proportion of an individual's genome coming from each population and its ancestral origin along the chromosome of an admixed population requires the use of ancestry informative markers (AIMs) from reference ancestral populations. AIMs exhibit substantial differences in allele frequency between ancestral populations. Given the huge amount of human genetic variation data available from diverse populations, a computationally feasible and cost-effective approach is becoming increasingly important to extract or filter AIMs with the maximum information content for ancestry inference, admixture mapping, forensic applications, and detecting genomic regions that have been under recent selection. RESULTS: To address this gap, we present MI-MAAP, an easy-to-use web-based bioinformatics tool designed to prioritize informative markers for multi-ancestry admixed populations by utilizing feature selection methods and multiple genomics resources including 1000 Genomes Project and Human Genome Diversity Project. Specifically, this tool implements a novel allele frequency-based feature selection algorithm, Lancaster Estimator of Independence (LEI), as well as other genotype-based methods such as Principal Component Analysis (PCA), Support Vector Machine (SVM), and Random Forest (RF). We demonstrated that MI-MAAP is a useful tool in prioritizing informative markers and accurately classifying ancestral populations. LEI is an efficient feature selection strategy to retrieve ancestry informative variants with different allele frequency/selection pressure among (or between) ancestries without requiring computationally expensive individual-level genotype data. CONCLUSIONS: MI-MAAP has a user-friendly interface which provides researchers an easy and fast way to filter and identify AIMs. MI-MAAP can be accessed at https://research.cchmc.org/mershalab/MI-MAAP/login/.

Comprehensive functional annotation of susceptibility variants associated with asthma.

Genome-wide association studies (GWAS) have identified hundreds of primarily non-coding disease-susceptibility variants that further need functional interpretation to prioritize and discriminate the disease-relevant variants. We present a comprehensive genome-wide non-coding variant prioritization scheme followed by validation using Pyrosequencing and TaqMan assays in asthma. We implemented a composite Functional Annotation Score (cFAS) to investigate over 32,000 variants consisting of 1525 GWAS-lead asthma-susceptibility variants and their LD proxies (r2 ≥ 0.80). Functional annotation pipeline in cFAS revealed 274 variants with significant score at 1% false discovery rate. This study implicates a novel locus 4p16 (SLC26A1) with eQTL variant (rs11936407) and known loci in 17q12-21 and 5q22 which encode ORM1-like protein 3 (ORMDL3, rs406527, and rs12936231) and thymic stromal lymphopoietin (TSLP, rs3806932 and rs10073816) epithelial gene, respectively. Follow-up validation analysis through pyrosequencing of CpG sites in and nearby rs4065275 and rs11936407 showed genotype-dependent hypomethylation on asthma cases compared with healthy controls. Prioritized variants are enriched for asthma-specific histone modification associated with active chromatin (H3K4me1 and H3K27ac) in T cells, B cells, lung, and immune-related interferon gamma signaling pathways. Our findings, together with those from prior studies, suggest that SNPs can affect asthma by regulating enhancer activity, and our comprehensive bioinformatics and functional analysis could lead to biological insights into asthma pathogenesis.Graphic abstract.

LEI: A Novel Allele Frequency-Based Feature Selection Method for Multi-ancestry Admixed Populations.

Next-generation sequencing technologies now make it possible to sequence and genotype hundreds of thousands of genetic markers across the human genome. Selection of informative markers for the comprehensive characterization of individual genomic makeup using a high dimensional genomics dataset has become a common practice in evolutionary biology and human genetics. Although several feature selection approaches exist to determine the ancestry proportion in two-way admixed populations including African Americans, there are limited statistical tools developed for the feature selection approaches in three-way admixed populations (including Latino populations). Herein, we present a new likelihood-based feature selection method called Lancaster Estimator of Independence (LEI) that utilizes allele frequency information to prioritize the most informative features useful to determine ancestry proportion from multiple ancestral populations in admixed individuals. The ability of LEI to leverage summary-level statistics from allele frequency data, thereby avoiding the many restrictions (and big data issues) that can accompany access to individual-level genotype data, is appealing to minimize the computation and time-consuming ancestry inference in an admixed population. We compared our allele-frequency based approach with genotype-based approach in estimating admixed proportions in three-way admixed population scenarios. Our results showed ancestry estimates using the top-ranked features from LEI were comparable with the estimates using features from genotype-based methods in three-way admixed population. We provide an easy-to-use R code to assist researchers in using the LEI tool to develop allele frequency-based informative features to conduct admixture mapping studies from mixed samples of multiple ancestry origin.

PAMAM: Power analysis in multiancestry admixture mapping.

Admixed populations arise when two or more previously isolated populations interbreed. Admixture mapping (AM) methods are used for tracing the ancestral origin of disease-susceptibility genetic loci in the admixed population such as African American and Latinos. AM is different from genome-wide association studies in that ancestry rather than genotypes are tracked in the association process. The power and sample size of AM primarily depend on proportion of admixture and differences in the risk allele frequencies among the ancestral populations. Ensuring sufficient power to detect the effect of ancestry on disease susceptibility is critical for interpretability and reliability of studies using AM approach. However, there is no power and sample size analysis tool existing for AM studies in admixed population. In this study, we developed power analysis of multiancestry AM (PAMAM) to estimate power and sample size for two-way and three-way population admixtures. PAMAM is the first web-based bioinformatics tool developed to calculate power and sample size in admixed population under a variety of genetic and disease phenotype models. It is a valuable resource for investigators to design a cost-efficient study and develop grant application to pursue AM studies. PAMAM is built on JavaScript back-end with HTML front-end. It is accessible through any modern web browser such as Firefox, Internet Explorer, and Google Chrome regardless of operating system. It is a user-friendly tool containing links for support information including user manual and examples, and freely available at https://research.cchmc.org/mershalab/PAMAM/login.html.

Genome-wide analysis revealed sex-specific gene expression in asthmatics.

Global gene-expression analysis has shown remarkable difference between males and females in response to exposure to many diseases. Nevertheless, gene expression studies in asthmatics have so far focused on sex-combined analysis, ignoring inherent variabilities between the sexes, which potentially drive disparities in asthma prevalence. The objectives of this study were to identify (1) sex-specific differentially expressed genes (DEGs), (2) genes that show sex-interaction effects and (3) sex-specific pathways and networks enriched in asthma risk. We analyzed 711 males and 689 females and more than 2.8 million transcripts covering 20 000 genes leveraged from five different tissues and cell types (i.e. epithelial, blood, induced sputum, T cells and lymphoblastoids). Using tissue-specific meta-analysis, we identified 439 male- and 297 female-specific DEGs in all cell types, with 32 genes in common. By linking DEGs to the genome-wide association study (GWAS) catalog and the lung and blood eQTL annotation data from GTEx, we identified four male-specific genes (FBXL7, ITPR3 and RAD51B from epithelial tissue and ALOX15 from blood) and one female-specific gene (HLA-DQA1 from epithelial tissue) that are disregulated during asthma. The hypoxia-inducible factor 1 signaling pathway was enriched only in males, and IL-17 and chemokine signaling pathways were enriched in females. The cytokine-cytokine signaling pathway was enriched in both sexes. The presence of sex-specific genes and pathways demonstrates that sex-combined analysis does not identify genes preferentially expressed in each sex in response to diseases. Linking DEG and molecular eQTLs to GWAS catalog represents an important avenue for identifying biologically and clinically relevant genes.

DiffGRN: differential gene regulatory network analysis.

Identification of differential gene regulators with significant changes under disparate conditions is essential to understand complex biological mechanism in a disease. Differential Network Analysis (DiNA) examines different biological processes based on gene regulatory networks that represent regulatory interactions between genes with a graph model. While most studies in DiNA have considered correlation-based inference to construct gene regulatory networks from gene expression data due to its intuitive representation and simple implementation, the approach lacks in the representation of causal effects and multivariate effects between genes. In this paper, we propose an approach named Differential Gene Regulatory Network (DiffGRN) that infers differential gene regulation between two groups. We infer gene regulatory networks of two groups using Random LASSO, and then we identify differential gene regulations by the proposed significance test. The advantages of DiffGRN are to capture multivariate effects of genes that regulate a gene simultaneously, to identify causality of gene regulations, and to discover differential gene regulators between regression-based gene regulatory networks. We assessed DiffGRN by simulation experiments and showed its outstanding performance than the current state-of-the-art correlation-based method, DINGO. DiffGRN is applied to gene expression data in asthma. The DiNA with asthma data showed a number of gene regulations, such as ADAM12 and RELB, reported in biological literature.

AdmixPower: Statistical Power and Sample Size Estimation for Mapping Genetic Loci in Admixed Populations.

Admixed populations result from recent admixture of two or more ancestral populations with divergent allele frequencies. The genome of each admixed individual is a mosaic of haplotypes inherited from the ancestral populations. Despite the substantial work to assess power and sample size requirements for association mapping in genetically homogeneous populations of European ancestry, power and sample size estimation methods for mapping genes in genetically heterogeneous admixed populations such as African Americans are lacking. Admixture mapping is a method that traces the ancestral origin of disease-susceptibility genetic loci in the admixed population. We developed AdmixPower, a freely available tool set based on the open-source R software, to perform power and sample size analysis for genetically heterogeneous admixed populations considering continuous or dichotomous outcomes with a case-only or case-control study design. AdmixPower can be used to compute the sample size required to achieve investigator-specified statistical power under several key parameters including ancestry odds ratio, genotype risk ratio, parental risk ratio, an underlying genetic risk model, trait type, and admixture model (hybrid-isolation or continuous gene flow model). We demonstrate that differences in the key parameters in the admixed population results in substantial differences in the sample size required to achieve adequate power in admixture mapping studies. Our tool provides a resource for researchers to develop a strategy to minimize cost and maximize the success of identifying disease-susceptibility loci in an admixed population. R code used in the sample size and power analysis is freely available from https://research.cchmc.org/mershalab/Tools.html.