Asthma is a common disease with a complex risk architecture including both genetic and environmental factors. We performed a meta-analysis of North American genome-wide association studies (GWAS) of asthma in 5,416 asthma cases representing European Americans, African Americans/African Caribbeans, and Latinos, and replicated five regions among the most significant signals in 12,649 individuals from the same ethnic groups. Four were at previously reported loci on 17q21, and near the IL1RL1, TSLP, and IL33, genes, but we report for the first time that these loci are associated with asthma risk in three ethnic groups. In addition, we identified a novel association with asthma in the PYHIN1, gene that was specific to individuals of African descent (p=3.9×10−9). These results suggest that some asthma susceptibility loci are robust to differences in ancestry when sufficiently large samples sizes are investigated, and that ancestry-specific associations also contribute to the complex genetic architecture of asthma.

BACKGROUND:

The effects of in utero tobacco smoke exposure on childhood respiratory health have been investigated, and outcomes have been inconsistent.

OBJECTIVE:

To determine if in utero tobacco smoke exposure is associated with childhood persistent asthma in Mexican, Puerto Rican, and black children.

PATIENTS AND METHODS:

There were 295 Mexican, Puerto Rican, and black asthmatic children, aged 8 to 16 years, who underwent spirometry, and clinical data were collected from the parents during a standardized interview. The effect of in utero tobacco smoke exposure on the development of persistent asthma and related clinical outcomes was evaluated by logistic regression.

RESULTS:

Children with persistent asthma had a higher odds of exposure to in utero tobacco smoke, but not current tobacco smoke, than did children with intermittent asthma (odds ratio [OR]: 3.57; P = .029). Tobacco smoke exposure from parents in the first 2 years of life did not alter this association. Furthermore, there were higher odds of in utero tobacco smoke exposure in children experiencing nocturnal symptoms (OR: 2.77; P = .048), daily asthma symptoms (OR: 2.73; P = .046), and emergency department visits (OR: 3.85; P = .015) within the year.

CONCLUSIONS:

Exposure to tobacco smoke in utero was significantly associated with persistent asthma among Mexican, Puerto Rican, and black children compared with those with intermittent asthma. These results suggest that smoking cessation during pregnancy may lead to a decrease in the incidence of persistent asthma in these populations.

Background

Understanding the effects of interactions between multiple genes and asthma medications may aid in the understanding of the heterogeneous response to asthma therapies.

Objective

To identify modulating effects of ALOX5AP and LTA4H gene polymorphisms on the drug-drug interaction between leukotriene modifiers and albuterol in Mexicans and Puerto Ricans.

Methods

In a cross-sectional study of 293 Mexicans and 356 Puerto Ricans with asthma, ALOX5AP and LTA4H genes were sequenced, and interactions between gene polymorphisms and bronchodilator responsiveness to albuterol was analyzed between leukotriene modifier users and non-users.

Results

In heterozygotes and homozygotes for the minor allele at LTA4H SNP rs2540491 and heterozygotes for the major allele at LTA4H SNP rs2540487, leukotriene modifier use was associated with a clinically significant increase in percent change in forced expiratory volume in 1 second (FEV1) after albuterol administration of 7.10% (p=0.002), 10.06% (p=0.001), and 10.03% (p<0.001), respectively. Presence of the major allele at ALOX5AP SNP rs10507391 or the minor allele at ALOX5AP SNP rs9551963 augmented this response. When stratified by ethnicity, these findings held true for Puerto Ricans, but not Mexicans.

Conclusions

LTA4H and ALOX5AP gene polymorphisms modify the augmentation of bronchodilator responsiveness by leukotriene modifiers in Puerto Ricans but not Mexicans with asthma.

BACKGROUND

Self-identified race or ethnic group is used to determine normal reference standards in the prediction of pulmonary function. We conducted a study to determine whether the genetically determined percentage of African ancestry is associated with lung function and whether its use could improve predictions of lung function among persons who identified themselves as African American.

METHODS

We assessed the ancestry of 777 participants self-identified as African American in the Coronary Artery Risk Development in Young Adults (CARDIA) study and evaluated the relation between pulmonary function and ancestry by means of linear regression. We performed similar analyses of data for two independent cohorts of subjects identifying themselves as African American: 813 participants in the Health, Aging, and Body Composition (HABC) study and 579 participants in the Cardiovascular Health Study (CHS). We compared the fit of two types of models to lung-function measurements: models based on the covariates used in standard prediction equations and models incorporating ancestry. We also evaluated the effect of the ancestry-based models on the classification of disease severity in two asthma-study populations.

RESULTS

African ancestry was inversely related to forced expiratory volume in 1 second (FEV1) and forced vital capacity in the CARDIA cohort. These relations were also seen in the HABC and CHS cohorts. In predicting lung function, the ancestry-based model fit the data better than standard models. Ancestry-based models resulted in the reclassification of asthma severity (based on the percentage of the predicted FEV1) in 4 to 5% of participants.

CONCLUSIONS

Current predictive equations, which rely on self-identified race alone, may misestimate lung function among subjects who identify themselves as African American. Incorporating ancestry into normative equations may improve lung-function estimates and more accurately categorize disease severity. (Funded by the National Institutes of Health and others.)

Background

Short-acting inhaled β2-agonists such as albuterol are used for bronchodilation and are the mainstay of asthma treatment worldwide. There is significant variation in bronchodilator responsiveness to albuterol not only between individuals but also across racial/ethnic groups. The β2-adrenergic receptor (β2AR) is the target for β2-agonist drugs. The enzyme S-nitrosoglutathione reductase (GSNOR), which regulates levels of the endogenous bronchodilator S-nitrosoglutathione, has been shown to modulate the response to β2-agonists.

Objective

We hypothesized that there are pharmacogenetic interactions between GSNOR and β2AR gene variants which are associated with variable response to albuterol.

Methods

We performed family-based analyses to test for association between GSNOR gene variants and asthma and related phenotypes in 609 Puerto Rican and Mexican families with asthma. In addition, we tested these subjects for pharmacogenetic interaction between GSNOR and β2AR gene variants and responsiveness to albuterol using linear regression. Cell transfection experiments were performed to test the potential effect of the GSNOR gene variants.

Results

Among Puerto Ricans, several GSNOR SNPs and a haplotype in the 3′UTR were significantly associated with increased risk for asthma and lower bronchodilator responsiveness (p = 0.04 to 0.007). The GSNOR risk haplotype affects expression of GSNOR mRNA and protein, suggesting a gain of function. Furthermore, gene-gene interaction analysis provided evidence of pharmacogenetic interaction between GSNOR and β2AR gene variants and the response to albuterol in Puerto Rican (p = 0.03), Mexican (p = 0.15) and combined Puerto Rican and Mexican asthmatics (p = 0.003). Specifically, GSNOR+17059*β2AR+46 genotype combinations (TG+GG*AG and TG+GG*GG) were associated with lower bronchodilator response.

Conclusion

Genotyping of GSNOR and β2AR genes may be a useful in identifying Latino subjects, who might benefit from adjuvant therapy for refractory asthma.

Objective

A recent admixture mapping analysis identified interleukin 6 (IL6) and IL6 receptor (IL6R) as candidate genes for inflammatory diseases. In the airways during allergic inflammation, IL6 signaling controls the production of proinflammatory and anti-inflammatory factors. In addition, albuterol, a commonly prescribed asthma therapy, has been shown to influence IL6 gene expression. Therefore, we reasoned that interactions between the IL6 and IL6R genes might be associated with bronchodilator drug responsiveness to albuterol in asthmatic patients.

Methods

Four functional IL6 single nucleotide polymorphisms (SNPs) and a nonsynonymous IL6R SNP were genotyped in 700 Mexican and Puerto Rican asthma families and in 443 African-American asthma cases and controls. Both family-based association tests and linear regression models were used to assess the association between individual SNPs and haplotypes with bronchodilator response. Gene–gene interactions were tested by using multiple linear regression analyses.

Results

No single SNP was consistently associated with drug response in all the three populations. However, on the gene level, we found a consistent IL6 and IL6R pharmacogenetic interaction in the three populations. This pharmacogenetic gene–gene interaction was contextual and dependent upon ancestry (racial background). This interaction resulted in higher drug response to albuterol in Latinos, but lower drug response in African-Americans. Herein, we show that there is an effect modification by ancestry on bronchodilator responsiveness to albuterol.

Conclusion

Genetic variants in the IL6 and IL6R genes act synergistically to modify the bronchodilator drug responsiveness in asthma and this pharmacogenetic interaction is modified by the genetic ancestry.

Examination of ancestry-informative genetic markers shows that Puerto Rican and Mexican populations have shown strong assortative mating that continues to this day.

Background

While spouse correlations have been documented for numerous traits, no prior studies have assessed assortative mating for genetic ancestry in admixed populations.

Results

Using 104 ancestry informative markers, we examined spouse correlations in genetic ancestry for Mexican spouse pairs recruited from Mexico City and the San Francisco Bay Area, and Puerto Rican spouse pairs recruited from Puerto Rico and New York City. In the Mexican pairs, we found strong spouse correlations for European and Native American ancestry, but no correlation in African ancestry. In the Puerto Rican pairs, we found significant spouse correlations for African ancestry and European ancestry but not Native American ancestry. Correlations were not attributable to variation in socioeconomic status or geographic heterogeneity. Past evidence of spouse correlation was also seen in the strong evidence of linkage disequilibrium between unlinked markers, which was accounted for in regression analysis by ancestral allele frequency difference at the pair of markers (European versus Native American for Mexicans, European versus African for Puerto Ricans). We also observed an excess of homozygosity at individual markers within the spouses, but this provided weaker evidence, as expected, of spouse correlation. Ancestry variance is predicted to decline in each generation, but less so under assortative mating. We used the current observed variances of ancestry to infer even stronger patterns of spouse ancestry correlation in previous generations.

Conclusions

Assortative mating related to genetic ancestry persists in Latino populations to the current day, and has impacted on the genomic structure in these populations.

While the number of success stories for mapping genes associated with complex diseases using genome-wide association approaches is growing, there is still much work to be done in developing methods for such studies when the samples are collected from a population which may not be homogeneous. Here we report the first genome-wide association study to identify genes associated with asthma in an admixed population. We genotyped 96 Puerto Rican moderate to severe asthma cases and 88 controls as well as 109 samples representing Puerto Rico’s founding populations using the Affymetrix GeneChip Human Mapping 100K array sets. The data from samples representing Puerto Rico’s founding populations was used to identify ancestry informative markers for admixture mapping analyses. In addition, a genome-wide association analysis using logistic regression was performed on the data. Although neither admixture mapping nor regression analysis gave any significant association with asthma after correction for multiple testing, an overlap analysis using the top scoring SNPs from different methods suggested chromosomal regions 5q23.3 and 13q13.3 as potential regions harboring genes for asthma in Puerto Ricans. The validation analysis of these two regions in 284 Puerto Rican asthma trios gave significant association for the 5q23.3 region. Our results provide strong evidence that the previously linked 5q23 region is associated with asthma in Puerto Ricans. The detection of causative variants in this region will require fine mapping and functional validation.

Asthma is a common but complex respiratory ailment; current data indicate that interaction of genetic and environmental factors lead to its clinical expression. In the United States, asthma prevalence, morbidity, and mortality vary widely among different Latino ethnic groups. The prevalence of asthma is highest in Puerto Ricans, intermediate in Dominicans and Cubans, and lowest in Mexicans and Central Americans. Independently, known socioeconomic, environmental, and genetic differences do not fully account for this observation. One potential explanation is that there may be unique and ethnic-specific gene–environment interactions that can differentially modify risk for asthma in Latino ethnic groups. These gene–environment interactions can be tested using genetic ancestry as a surrogate for genetic risk factors. Latinos are admixed and share varying proportions of African, Native American, and European ancestry. Most Latinos are unaware of their precise ancestry and report their ancestry based on the national origin of their family and their physical appearance. The unavailability of precise ancestry and the genetic complexity among Latinos may complicate asthma research studies in this population. On the other hand, precisely because of this rich mixture of ancestry, Latinos present a unique opportunity to disentangle the clinical, social, environmental, and genetic underpinnings of population differences in asthma prevalence, severity, and bronchodilator drug responsiveness.

Background: Puerto Ricans, an admixed population of African, European, and Native American ancestries, have the highest asthma prevalence, morbidity, and mortality rates of any United States' population. Although socioeconomic status (SES) is negatively correlated with asthma incidence in most populations, no such relationship has been identified among Puerto Ricans. We hypothesized that, in this admixed population, the association between SES and asthma may interact with genetic ancestry.

Methods: We analyzed 135 Puerto Rican subjects with asthma and 156 control subjects recruited from six different recruitment centers in Puerto Rico. Individual ancestry for each subject was estimated using 44 ancestry informative markers. SES was assigned using the census tracts' median family income. Analyses of SES were based on the SES of the clinic site from which the subjects were recruited and on a subset of individuals on whom home address–based SES was available.

Results: In the two (independent) analyses, we found a significant interaction between SES, ancestry, and asthma disease status. At lower SES, European ancestry was associated with increased risk of asthma, whereas African ancestry was associated with decreased risk. The opposite was true for their higher SES counterparts.

Conclusions: The observed interaction may help to explain the unique pattern of risk for asthma in Puerto Ricans and the lack of association with SES observed in previous studies when not accounting for varying proportions of ancestry.

Existing methods to ascertain small sets of markers for the identification of human population structure require prior knowledge of individual ancestry. Based on Principal Components Analysis (PCA), and recent results in theoretical computer science, we present a novel algorithm that, applied on genomewide data, selects small subsets of SNPs (PCA-correlated SNPs) to reproduce the structure found by PCA on the complete dataset, without use of ancestry information. Evaluating our method on a previously described dataset (10,805 SNPs, 11 populations), we demonstrate that a very small set of PCA-correlated SNPs can be effectively employed to assign individuals to particular continents or populations, using a simple clustering algorithm. We validate our methods on the HapMap populations and achieve perfect intercontinental differentiation with 14 PCA-correlated SNPs. The Chinese and Japanese populations can be easily differentiated using less than 100 PCA-correlated SNPs ascertained after evaluating 1.7 million SNPs from HapMap. We show that, in general, structure informative SNPs are not portable across geographic regions. However, we manage to identify a general set of 50 PCA-correlated SNPs that effectively assigns individuals to one of nine different populations. Compared to analysis with the measure of informativeness, our methods, although unsupervised, achieved similar results. We proceed to demonstrate that our algorithm can be effectively used for the analysis of admixed populations without having to trace the origin of individuals. Analyzing a Puerto Rican dataset (192 individuals, 7,257 SNPs), we show that PCA-correlated SNPs can be used to successfully predict structure and ancestry proportions. We subsequently validate these SNPs for structure identification in an independent Puerto Rican dataset. The algorithm that we introduce runs in seconds and can be easily applied on large genome-wide datasets, facilitating the identification of population substructure, stratification assessment in multi-stage whole-genome association studies, and the study of demographic history in human populations.

Author Summary

Genetic markers can be used to infer population structure, a task that remains a central challenge in many areas of genetics such as population genetics, and the search for susceptibility genes for common disorders. In such settings, it is often desirable to reduce the number of markers needed for structure identification. Existing methods to identify structure informative markers demand prior knowledge of the membership of the studied individuals to predefined populations. In this paper, based on the properties of a powerful dimensionality reduction technique (Principal Components Analysis), we develop a novel algorithm that does not depend on any prior assumptions and can be used to identify a small set of structure informative markers. Our method is very fast even when applied to datasets of hundreds of individuals and millions of markers. We evaluate this method on a large dataset of 11 populations from around the world, as well as data from the HapMap project. We show that, in most cases, we can achieve 99% genotyping savings while at the same time recovering the structure of the studied populations. Finally, we show that our algorithm can also be successfully applied for the identification of structure informative markers when studying populations of complex ancestry.