Biology subjects

Elhawary, J. R.

Publications and source records attributed to Elhawary, J. R..

4 recordsLinked to original sources

bioRxiv2021

Gene expression in African Americans and Latinos reveals ancestry-specific patterns of genetic architecture

We analyzed whole genome and RNA sequencing data from 2,733 African American and Hispanic/Latino children to explore ancestry- and heterozygosity-related differences in the genetic architecture of whole blood gene expression. We found that heritability of gene expression significantly increases with greater proportion of African genetic ancestry and decreases with higher levels of Indigenous American ancestry, consistent with a relationship between heterozygosity and genetic variance. Among heritable protein-coding genes, the prevalence of statistically significant ancestry-specific expression quantitative trait loci (anc-eQTLs) was 30% in African ancestry and 8% for Indigenous American ancestry segments. Most of the anc-eQTLs (89%) were driven by population differences in allele frequency, demonstrating the importance of measuring gene expression across multiple populations. Transcriptome-wide association analyses of multi-ancestry summary statistics for 28 traits identified 79% more gene-trait pairs using models trained in our admixed population than models trained in GTEx. Our study highlights the importance of large and ancestrally diverse genomic studies for enabling new discoveries of complex trait architecture and reducing disparities.

genetics↗

bioRxiv2020

Integrative genomic analysis in African American children with asthma finds 3 novel loci associated with lung function

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 (FEV1, FVC, and FEV1/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.

genetics↗

bioRxiv2020

Pairwise and Higher-Order Epistatic Interactions Have a Significant Impact on Bronchodilator Drug Response in African American Youth with Asthma

BackgroundAsthma 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. ResultsWe 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. ConclusionsOur 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.

bioinformatics↗

bioRxiv2019

On the cross-population portability of gene expression prediction models

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. Author summaryAdvances 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 amount of shared genotype predictors 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.

genomics↗