A genome-wide association and admixture mapping study of bronchodilator drug response in African Americans with asthma

Abstract

Short-acting β-adrenergic receptor agonists (SABAs) are the most commonly prescribed asthma medications worldwide. Response to SABAs is measured as bronchodilator drug response (BDR), which varies among racial/ethnic groups in the United States. However, the genetic variation that contributes to BDR is largely undefined in African Americans with asthma. To identify genetic variants that may contribute to differences in BDR in African Americans with asthma, we performed a genome-wide association study (GWAS) of BDR in 949 African-American children with asthma, genotyped with the Axiom World Array 4 (Affymetrix, Santa Clara, CA) followed by imputation using 1000 Genomes phase III genotypes. We used linear regression models adjusting for age, sex, body mass index (BMI) and genetic ancestry to test for an association between BDR and genotype at single-nucleotide polymorphisms (SNPs). To increase power and distinguish between shared vs. population-specific associations with BDR in children with asthma, we performed a meta-analysis across 949 African Americans and 1830 Latinos (total = 2779). Finally, we performed genome-wide admixture mapping to identify regions whereby local African or European ancestry is associated with BDR in African Americans. We identified a population-specific association with an intergenic SNP on chromosome 9q21 that was significantly associated with BDR (rs73650726, p = 7.69 × 10). A trans-ethnic meta-analysis across African Americans and Latinos identified three additional SNPs within the intron of PRKG1 that were significantly associated with BDR (rs7903366, rs7070958 and rs7081864, p ≤ 5 × 10). Our results failed to replicate in three additional populations of 416 Latinos and 1615 African Americans. Our findings indicate that both population-specific and shared genetic variation contributes to differences in BDR in minority children with asthma, and that the genetic underpinnings of BDR may differ between racial/ethnic groups.

Population genetic simulation study of power in association testing across genetic architectures and study designs

“…We simulated three different sampling strategies: randomly sampling cases and controls proportional to the trait prevalence; sampling half of your study size from cases and half from your controls; and sampling individuals from the extreme tails of a quantitative distribution. Our results show that choosing from the tails of an underlying quantitative distribution produces the best power (such as sequencing individuals with the highest/lowest high-density lipoprotein cholesterol; Cohen, 2004, or bronchodilator response;Spear et al, 2018). This means for any case/ control association study, spending some time to find the extreme tails of an underlying quantitative distribution for a trait will likely produce the best possible RVAT power (as previously argued using more constrained simulations; Barnett, Lee, & Lin, 2013).…”

“…Each point represents a different simulated genetic architecture where we vary the number of causal bins (10 or 100), heritability (0.2 or 0.8), sampling strategy, (⍴, τ) for the underlying phenotype distribution, and the number of simulated case/control individuals in the study choosing from the tails of an underlying quantitative distribution produces the best power (such as sequencing individuals with the highest/lowest high-density lipoprotein cholesterol; Cohen, 2004, or bronchodilator response;Spear et al, 2018). We simulated three different sampling strategies: randomly sampling cases and controls proportional to the trait prevalence; sampling half of your study size from cases and half from your controls; and sampling individuals from the extreme tails of a quantitative distribution.…”

Population genetic simulation study of power in association testing across genetic architectures and study designs

“…To run a case/control association study, the first step is to determine which individuals to select for your study, and how to acquire their genetic data. We simulated three different sampling strategies: randomly sampling cases and controls proportional to the trait prevalence; sampling half of your study size from cases and half from your controls; and sampling individuals from the extreme tails of a quantitative distribution [or a proxy underlying the trait such as bronchodilator response (Spear et al, 2018), for example]. Our results show that choosing from the tails of an underlying quantitative distribution produces the best power.…”

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

“…34 We excluded two participants who were statistical outliers for BDR (raw values) as previously described. 39…”