Celeste Eng

Patagonia represents the final frontier of the human out-of-Africa expansion, where humans have lived for at least 13,500 years, yet the genetic adaptations of its indigenous populations remain understudied. We analyzed genome-wide data from 71 present-day individuals across 7 Patagonian locations. Our analyses reveal widespread European admixture initiating approximately 10 to 12 generations ago, followed by region-specific demographic trajectories. We identified a profound north-south genetic differentiation. Populations from the Chiloé Archipelago and Argentinean Patagonia share ancestry with the Mapuche, whereas individuals from Tierra del Fuego exhibit a unique, deeply rooted genetic component shared with ancient Fueguian groups. To identify prehistoric local adaptations, we performed selection scans on masked Native American genomic segments. We detected robust selection signals predating European contact in genes associated with height and skeletal development (SUPT3H, CADM1, RUNX2), lipid metabolism (MSMO1, LYPLA1, SORT1/PSCR1/CELSR2), and energy homeostasis (ZNF202). Furthermore, we observed a significant enrichment of Native American ancestry within the Major Histocompatibility Complex on chromosome 6. In conclusion, our findings indicate that the current admixed populations in Patagonia exhibit signals of local adaptation that likely originated in their ancient indigenous ancestors. These groups accumulated critical morphological, metabolic, and immunological adaptations to their extreme environment and local pathogens, traits that remained highly beneficial even after European admixture.

Background: Albuterol is the first-line asthma medication used in diverse populations. Although DNA methylation (DNAm) is an epigenetic mechanism involved in asthma and bronchodilator drug response (BDR), no study has assessed whether albuterol could induce changes in the airway epithelial methylome. We aimed to characterize albuterol-induced DNAm changes in airway epithelial cells, and assess potential functional consequences and the influence of genetic variation and asthma-related clinical variables. Results: We followed a discovery and validation study design to characterize albuterol-induced DNAm changes in paired airway epithelial cultures stimulated in vitro with albuterol. In the discovery phase, an epigenome-wide association study using paired nasal epithelial cultures from Puerto Rican children (n = 97) identified 22 CpGs genome-wide associated with repeated-use albuterol treatment (p < 9 × 10^–8). Albuterol predominantly induced a hypomethylation effect on CpGs captured by the EPIC array across the genome (probability of hypomethylation: 76%, p value = 3.3 × 10^–5). DNAm changes on the CpGs cg23032799 (CREB3L1), cg00483640 (MYLK4-LINC01600), and cg05673431 (KSR1) were validated in nasal epithelia from 10 independent donors (false discovery rate [FDR] < 0.05). The effect on the CpG cg23032799 (CREB3L1) was cross-tissue validated in bronchial epithelial cells at nominal level (p = 0.030). DNAm changes in these three CpGs were shown to be influenced by three independent genetic variants (FDR < 0.05). In silico analyses showed these polymorphisms regulated gene expression of nearby genes in lungs and/or fibroblasts including KSR1 and LINC01600 (6.30 × 10^–14 ≤ p ≤ 6.60 × 10^–5). Additionally, hypomethylation at the CpGs cg10290200 (FLNC) and cg05673431 (KSR1) was associated with increased gene expression of the genes where they are located (FDR < 0.05). Furthermore, while the epigenetic effect of albuterol was independent of the asthma status, severity, and use of medication, BDR was nominally associated with the effect on the CpG cg23032799 (CREB3L1) (p = 0.004). Gene-set enrichment analyses revealed that epigenomic modifications of albuterol could participate in asthma-relevant processes (e.g., IL-2, TNF-α, and NF-κB signaling pathways). Finally, nine differentially methylated regions were associated with albuterol treatment, including CREB3L1, MYLK4, and KSR1 (adjusted p value < 0.05). Conclusions: This study revealed evidence of epigenetic modifications induced by albuterol in the mucociliary airway epithelium. The epigenomic response induced by albuterol might have potential clinical implications by affecting biological pathways relevant to asthma.

Asthma is a respiratory disease whose prevalence changes throughout the lifespan and differs by sex, being more prevalent in males during childhood and in females after puberty. In this study, we assessed the influence of sex on the genetic susceptibility to childhood asthma in admixed populations. Sex-interaction and sex-stratified genome-wide association studies (GWAS) were performed in 4291 Latinos and 1730 African Americans separately, and results were meta-analyzed. Genome-wide (p ≤ 9.35 × 10⁻⁸) and suggestive (p ≤ 1.87 × 10⁻⁶) population-specific significance thresholds were calculated based on 1000 Genomes Project data. Additionally, protein quantitative trait locus (pQTL) information was gathered for the suggestively associated variants, and enrichment analyses of the proteins identified were carried out. Four independent loci showed interaction with sex at a suggestive level. The stratified GWAS highlighted the 17q12-21 asthma locus as a contributor to asthma susceptibility in both sexes but reached genome-wide significance only in females (p-females < 9.2 × 10⁻⁸; p-males < 1.25 × 10⁻²). Conversely, genetic variants upstream of ligand-dependent nuclear receptor corepressor-like gene (LCORL), previously involved in height determination and spermatogenesis, were associated with asthma only in males (minimum p = 5.31 × 10⁻⁸ for rs4593128). Enrichment analyses revealed an overrepresentation of processes related to the immune system and highlighted differences between sexes. In conclusion, we identified sex-specific polymorphisms that could contribute to the differences in the prevalence of childhood asthma between males and females.

Type 2 immune development depends on MARCH1-mediated turnover of MHCII and CD86 by lymph node–resident dendritic cells.

BACKGROUND: Variation in genetic ancestry among admixed racial/ethnic groups may influence the fit of guideline-recommended spirometry reference equations, which rely on self-identified race/ethnicity. RESEARCH QUESTION: What is the influence of genetic ancestry on the fit of the guideline-recommended racial/ethnic-based spirometry reference equations in populations of genetically admixed children? STUDY DESIGN AND METHODS: Cross-sectional fit of guideline-recommended racial/ethnic-based spirometry reference equations was evaluated in control subjects from case-control studies of asthma. Anthropometry, blood samples, and spirometric measurements were obtained in 599 healthy admixed children, aged 8 to 21-years. Genetic ancestry was estimated using genome-wide genotype data. Equation fit was determined as a mean z-score between -0.5 and 0.5 and assessed in self-identified African American (N = 275) and Puerto Rican (N = 324) children using the distribution to determine cut points of genetic ancestry. RESULTS: For African American children, African American-derived equations fit for predicting FEV ₁ and FVC in those with an African ancestry above the median (81-100%), whereas composite equations for “other/mixed” populations fit for predicting FEV ₁ and FVC in those with an African ancestry below the median (31-81%). Among Puerto Rican children, White-derived equations fit for predicting FEV ₁ , and the composite equations fit for predicting FVC for those with African ancestry above the median (21-88%). In contrast, in Puerto Rican children with African ancestry below the median (6-21%), only equations derived in Whites provide an adequate fit. INTERPRETATION: Guideline-recommended spirometry reference equations yielded biased estimates of lung function in admixed populations with high variation of African ancestry. Spirometry is due for reference equations that incorporate genetic ancestry, either for more precise application of the current equations or the derivation and utilization of new equations.

The genetic control of gene expression is a core component of human physiology. For the past several years, transcriptome-wide association studies have leveraged large datasets of linked genotype and RNA sequencing information to create a powerful gene-based test of association that has been used in dozens of studies. While numerous discoveries have been made, the populations in the training data are overwhelmingly of European descent, and little is known about the generalizability of these models to other populations. Here, we test for cross-population generalizability of gene expression prediction models using a dataset of African American individuals with RNA-Seq data in whole blood. We find that the default models trained in large datasets such as GTEx and DGN fare poorly in African Americans, with a notable reduction in prediction accuracy when compared to European Americans. We replicate these limitations in cross-population generalizability using the five populations in the GEUVADIS dataset. Via realistic simulations of both populations and gene expression, we show that accurate cross-population generalizability of transcriptome prediction only arises when eQTL architecture is substantially shared across populations. In contrast, models with non-identical eQTLs showed patterns similar to real-world data. Therefore, generating RNA-Seq data in diverse populations is a critical step towards multi-ethnic utility of gene expression prediction. Advances in RNA sequencing technology have reduced the cost of measuring gene expression at a genome-wide level. However, sequencing enough human RNA samples for adequately-powered disease association studies remains prohibitively costly. To this end, modern transcriptome-wide association analysis tools leverage existing paired genotype-expression datasets by creating models to predict gene expression using genotypes. These predictive models enable researchers to perform cost-effective association tests with gene expression in independently genotyped samples. However, most of these models use European reference data, and the extent to which gene expression prediction models work across populations is not fully resolved. We observe that these models predict gene expression worse than expected in a dataset of African-Americans when derived from European-descent individuals. Using simulations, we show that gene expression predictive model performance depends on both the proportion of genetic variants shared between population-specific prediction models as well as the genetic relatedness between populations. Our findings suggest a need to carefully select reference populations for prediction and point to a pressing need for more genetically diverse genotype-expression datasets.

Bronchodilator drugs are commonly prescribed for treatment and management of obstructive lung function present with diseases such as asthma. Administration of bronchodilator medication can partially or fully restore lung function as measured by pulmonary function tests. The genetics of baseline lung function measures taken prior to bronchodilator medication has been extensively studied, and the genetics of the bronchodilator response itself has received some attention. However, few studies have focused on the genetics of post-bronchodilator lung function. To address this gap, we analyzed lung function phenotypes in 1,103 subjects from the Study of African Americans, Asthma, Genes, and Environment (SAGE), a pediatric asthma case-control cohort, using an integrative genomic analysis approach that combined genotype, locus-specific genetic ancestry, and functional annotation information. We integrated genome-wide association study (GWAS) results with an admixture mapping scan of three pulmonary function tests (FEV ₁ , FVC, and FEV ₁ /FVC) taken before and after albuterol bronchodilator administration on the same subjects, yielding six traits. We identified 18 GWAS loci, and 5 additional loci from admixture mapping, spanning several known and novel lung function candidate genes. Most loci identified via admixture mapping exhibited wide variation in minor allele frequency across genotyped global populations. Functional fine-mapping revealed an enrichment of epigenetic annotations from peripheral blood mononuclear cells, fetal lung tissue, and lung fibroblasts. Our results point to three novel potential genetic drivers of pre- and post-bronchodilator lung function: ADAMTS1, RAD54B , and EGLN3 .

Background: Asthma is one of the leading chronic illnesses among children in the United States. Asthma prevalence is higher among African Americans (11.2%) compared to European Americans (7.7%). Bronchodilator medications are part of the first-line therapy, and the rescue medication, for acute asthma symptoms. Bronchodilator drug response (BDR) varies substantially among different racial/ethnic groups. Asthma prevalence in African Americans is only 3.5% higher than that of European Americans, however, asthma mortality among African Americans is four times that of European Americans; variation in BDR may play an important role in explaining this health disparity. To improve our understanding of disparate health outcomes in complex phenotypes such as BDR, it is important to consider interactions between environmental and biological variables. Results: We evaluated the impact of pairwise and three-variable interactions between environmental, social, and biological variables on BDR in 617 African American youth with asthma using Visualization of Statistical Epistasis Networks (ViSEN). ViSEN is a non-parametric entropy-based approach able to identify interaction effects. We performed analyses in the full dataset and in sex-stratified subsets. Analysis in the full dataset identified six significant interactions associated with BDR, the strongest of which was an interaction between prenatal smoke exposure, age, and global African ancestry (IG: 1.09%, p=0.005). Sex-stratified analyses yielded additional significant, but divergent, results for females and males, indicating the presence of sex-specific effects. Conclusions: Our study identified novel interaction effects significantly influencing BDR in African American children with asthma. Notably, we found that the impact of higher-order interactions was greater than that of pairwise or main effects on BDR highlighting the complexity of the network of genetic and environmental factors impacting this phenotype. Several associations uncovered by ViSEN would not have been detected using regression-based methods emphasizing the importance of employing statistical methods optimized to detect both linear and non-linear interaction effects when studying complex phenotypes such as BDR. The information gained in this study increases our understanding and appreciation of the complex nature of the interactions between environmental and health-related factors that influence BDR and will be invaluable to biomedical researchers designing future studies.

Baseline lung function, quantified as forced expiratory volume in the first second of exhalation (FEV ₁ ), is a standard diagnostic criterion used by clinicians to identify and classify lung diseases. Using whole genome sequencing data from the National Heart, Lung, and Blood Institute TOPMed project, we identified a novel genetic association with FEV ₁ on chromosome 12 in 867 African American children with asthma (p = 1.26 × 10 ⁻⁸ , β = 0.302). Conditional analysis within 1 Mb of the tag signal (rs73429450) yielded one major and two other weaker independent signals within this peak. We explored statistical and functional evidence for all variants in linkage disequilibrium with the three independent signals and yielded 9 variants as the most likely candidates responsible for the association with FEV ₁ . Hi-C data and eQTL analysis demonstrated that these variants physically interacted with KITLG (aka SCF) and their minor alleles were associated with increased expression of KITLG gene in nasal epithelial cells. Gene-by-air-pollution interaction analysis found that the candidate variant rs58475486 interacted with past-year SO ₂ exposure (p = 0.003, β = 0.32). This study identified a novel protective genetic association with FEV ₁ , possibly mediated through KITLG , in African American children with asthma.