Springer Science and Business Media LLC

β2-Adrenergic receptor (β2AR) gene polymorphisms have been reported to be associated with various asthma-related traits in different racial/ethnic populations. However, it is unknown whether β 2 AR genetic variants are associated with asthma in African Americans. In this study, we have examined whether there is association between β 2 AR genetic variants and asthma in African Americans. We have recruited 264 African American asthmatic subjects and 176 matched healthy controls participating in the Study of African Americans, Asthma, Genes and Environments (SAGE). We genotyped seven known and recently identified β 2 AR SNP variants, then tested genotype and haplotype association of asthma-related traits with the β 2 AR SNPs in our African American cohort with adjustment of confounding effect due to admixture background and environmental risk factors. We found a significant association of the SNP −47 (Arg-19Cys) polymorphism with ΔFEF25–75, a measure of bronchodilator drug responsiveness, in African American asthmatics after correction for multiple testing (P=0.001). We did not observe association of the SNP +46 (Arg16Gly) variant with asthma disease diagnosis and asthma-related phenotypes. In contrast to previous results between the Arg16Gly variant and traits related to bronchodilator responsiveness, our results indicate that the Arg-19Cys polymorphism in β upstream peptide may play an important role in bronchodilator drug responsiveness in African American subjects. Our findings highlight the importance of investigating genetic risk factors for asthma in different 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.

Pathogens have played a substantial role in human evolution, with past infections shaping genetic variation at loci influencing immune function. We selected 168 genes known to be involved in the immune response, genotyped common single nucleotide polymorphisms across each gene in three population samples (CEPH Europeans from Utah, Han Chinese from Guangxi, and Yoruba Nigerians from Southwest Nigeria) and searched for evidence of selection based on four tests for non-neutral evolution: minor allele frequency (MAF), derived allele frequency (DAF), Fst versus heterozygosity and extended haplotype homozygosity (EHH). Six of the 168 genes show some evidence for non-neutral evolution in this initial screen, with two showing similar signals in independent data from the International HapMap Project. These analyses identify two loci involved in immune function that are candidates for having been subject to evolutionary selection, and highlight a number of analytical challenges in searching for selection in genome-wide polymorphism data.

Older Puerto Ricans living in the continental U.S. suffer from higher rates of diabetes, obesity, cardiovascular disease and depression compared to non-Hispanic White populations. Complex diseases, such as these, are likely due to multiple, potentially interacting, genetic, environmental and social risk factors. Presumably, many of these environmental and genetic risk factors are contextual. We reasoned that racial background may modify some of these risk factors and be associated with health disparities among Puerto Ricans. The contemporary Puerto Rican population is genetically heterogeneous and originated from three ancestral populations: European settlers, native Taíno Indians, and West Africans. This rich-mixed ancestry of Puerto Ricans provides the intrinsic variability needed to untangle complex gene–environment interactions in disease susceptibility and severity. Herein, we determined whether a specific ancestral background was associated with either of four major disease outcomes (diabetes, obesity, cardiovascular disease, and depression). We estimated the genetic ancestry of 1,129 subjects from the Boston Puerto Rican Health Study based on genotypes of 100 ancestry informative markers (AIMs). We examined the effects of ancestry on tests of association between single AIMs and disease traits. The ancestral composition of this population was 57.2% European, 27.4% African, and 15.4% Native American. African ancestry was negatively associated with type 2 diabetes and cardiovascular disease, and positively correlated with hypertension. It is likely that the high prevalence rate of diabetes in Africans, Hispanics, and Native Americans is not due to genetic variation alone, but to the combined effects of genetic variation interacting with environmental and social factors.

Close genetic linkage has been shown between the DNA sequence G8 (locus D4S10) and 16 British families with Huntington disease using the HindIII, EcoR1, Nci1, and Pst1 polymorphisms detected by G8, and by combining all the polymorphisms to give a combined haplotype. Two recombinants have been detected in these families giving a maximum lod score of 17.60 at a Θ of 0.02. These results confirm the originally reported linkage between the loci and provide evidence against significant multilocus heterogeneity for Huntington disease.

Population stratification may confound the results of genetic association studies among unrelated individuals from admixed populations. Several methods have been proposed to estimate the ancestral information in admixed populations and used to adjust the population stratification in genetic association tests. We evaluate the performances of three different methods: maximum likelihood estimation, ADMIXMAP and Structure through various simulated data sets and real data from Latino subjects participating in a genetic study of asthma. All three methods provide similar information on the accuracy of ancestral estimates and control type I error rate at an approximately similar rate. The most important factor in determining accuracy of the ancestry estimate and in minimizing type I error rate is the number of markers used to estimate ancestry. We demonstrate that approximately 100 ancestry informative markers (AIMs) are required to obtain estimates of ancestry that correlate with correlation coefficients more than 0.9 with the true individual ancestral proportions. In addition, after accounting for the ancestry information in association tests, the excess of type I error rate is controlled at the 5% level when 100 markers are used to estimate ancestry. However, since the effect of admixture on the type I error rate worsens with sample size, the accuracy of ancestry estimates also needs to increase to make the appropriate correction. Using data from the Latino subjects, we also apply these methods to an association study between body mass index and 44 AIMs. These simulations are meant to provide some practical guidelines for investigators conducting association studies in admixed populations.