Adhesion molecules, endothelin-1 and lung function in seven population-based cohorts.

CONTEXT: Endothelial function is abnormal in chronic obstructive pulmonary disease (COPD); whether endothelial dysfunction causes COPD is unknown. OBJECTIVE: Test associations of endothelial biomarkers with FEV1 using instrumental variables. METHODS: Among 26 907 participants with spirometry, ICAM-1, P-selectin, E-selectin and endothelin-1 were measured in subsets. RESULTS: ICAM-1 and P-selectin were inversely associated with FEV1 among European-Americans (-29 mL and -34 mL per standard deviation of log-transformed biomarker, p < 0.001), as was endothelin-1 among African-Americans (-22 mL, p = 0.008). Genetically-estimated ICAM-1 and P-selectin were not significantly associated with FEV1. The instrumental variable for endothelin-1 was non-informative. CONCLUSION: Although ICAM-1, P-selectin and endothelin-1 were inversely associated with FEV1, associations for ICAM-1 and P-selectin do not appear causal.

Common genetic variation near the connexin-43 gene is associated with resting heart rate in African Americans: a genome-wide association study of 13,372 participants.

BACKGROUND: Genome-wide association studies have identified several genetic loci associated with variation in resting heart rate in European and Asian populations. No study has evaluated genetic variants associated with heart rate in African Americans. OBJECTIVE: To identify novel genetic variants associated with resting heart rate in African Americans. METHODS: Ten cohort studies participating in the Candidate-gene Association Resource and Continental Origins and Genetic Epidemiology Network consortia performed genome-wide genotyping of single nucleotide polymorphisms (SNPs) and imputed 2,954,965 SNPs using HapMap YRI and CEU panels in 13,372 participants of African ancestry. Each study measured the RR interval (ms) from 10-second resting 12-lead electrocardiograms and estimated RR-SNP associations using covariate-adjusted linear regression. Random-effects meta-analysis was used to combine cohort-specific measures of association and identify genome-wide significant loci (P≤2.5×10(-8)). RESULTS: Fourteen SNPs on chromosome 6q22 exceeded the genome-wide significance threshold. The most significant association was for rs9320841 (+13 ms per minor allele; P = 4.98×10(-15)). This SNP was approximately 350 kb downstream of GJA1, a locus previously identified as harboring SNPs associated with heart rate in Europeans. Adjustment for rs9320841 also attenuated the association between the remaining 13 SNPs in this region and heart rate. In addition, SNPs in MYH6, which have been identified in European genome-wide association study, were associated with similar changes in the resting heart rate as this population of African Americans. CONCLUSIONS: An intergenic region downstream of GJA1 (the gene encoding connexin 43, the major protein of the human myocardial gap junction) and an intragenic region within MYH6 are associated with variation in resting heart rate in African Americans as well as in populations of European and Asian origin.

Genome-wide meta-analyses of smoking behaviors in African Americans.

The identification and exploration of genetic loci that influence smoking behaviors have been conducted primarily in populations of the European ancestry. Here we report results of the first genome-wide association study meta-analysis of smoking behavior in African Americans in the Study of Tobacco in Minority Populations Genetics Consortium (n = 32,389). We identified one non-coding single-nucleotide polymorphism (SNP; rs2036527[A]) on chromosome 15q25.1 associated with smoking quantity (cigarettes per day), which exceeded genome-wide significance (ß = 0.040, s.e. = 0.007, P = 1.84 × 10(-8)). This variant is present in the 5'-distal enhancer region of the CHRNA5 gene and defines the primary index signal reported in studies of the European ancestry. No other SNP reached genome-wide significance for smoking initiation (SI, ever vs never smoking), age of SI, or smoking cessation (SC, former vs current smoking). Informative associations that approached genome-wide significance included three modestly correlated variants, at 15q25.1 within PSMA4, CHRNA5 and CHRNA3 for smoking quantity, which are associated with a second signal previously reported in studies in European ancestry populations, and a signal represented by three SNPs in the SPOCK2 gene on chr10q22.1. The association at 15q25.1 confirms this region as an important susceptibility locus for smoking quantity in men and women of African ancestry. Larger studies will be needed to validate the suggestive loci that did not reach genome-wide significance and further elucidate the contribution of genetic variation to disparities in cigarette consumption, SC and smoking-attributable disease between African Americans and European Americans.

A quantitative-trait analysis of human plasma-dopamine beta-hydroxylase activity: evidence for a major functional polymorphism at the DBH locus.

Dopamine-beta-hydroxylase (D beta H) catalyzes the conversion of dopamine to norepinephrine and is released from sympathetic neurons into the circulation. Plasma-D beta H activity varies widely between individuals, and a subgroup of the population has very low activity levels. Mounting evidence suggests that the DBH structural gene is itself the major quantitative-trait locus (QTL) for plasma-D beta H activity, and a single unidentified polymorphism may account for a majority of the variation in activity levels. Through use of both sequencing-based mutational analysis of extreme phenotypes and genotype/phenotype correlations in samples from African American, European American (EA), and Japanese populations, we have identified a novel polymorphism (--1021C-->T), in the 5' flanking region of the DBH gene, that accounts for 35%--52% of the variation in plasma-D beta H activity in these populations. In EAs, homozygosity at the T allele predicted the very low D beta H-activity trait, and activity values in heterozygotes formed an intermediate distribution, indicating codominant inheritance. Our findings demonstrate that --1021C-->T is a major genetic marker for plasma-D beta H activity and provide new tools for investigation of the role of both D beta H and the DBH gene in human disease.

Predicting quantitative trait levels by modeling SNP interaction.

Predicting phenotype from genotype is difficult when the phenotype is affected by a gene with numerous weakly penetrant alleles that differ only in the pattern of their single nucleotide polymorphisms (SNPs). While it is probable that SNP interactions affect phenotype, to our knowledge no one has determined the most effective way of evaluating whether SNPs interact and of modeling the interaction. Therefore, to explore this issue, we investigate here three methods of modeling SNP interaction using data from Genetic Analysis Workshop 12. Since major gene 5 (MG5) has sequence information and explains 37% of the variation in quantitative trait 5 (Q5), we focus on using SNPs within MG5 to predict Q5 among individuals who married into the pedigree. As a preliminary screening step, we reduced the number of SNPs from 269 to 34 based on their association with Q5. In our first models we assumed that SNPs affected Q5 in a simple additive manner. These models explained 34% and 15% of the variation in Q5 in women and men, respectively. Our second model was a linear model, which used individual SNPs and simple interaction terms as predictors. These models explained 36% and 16% of the variation in Q5 levels for women and men, respectively. Our last model was a "hit"-based model which was motivated by the hypothesis that disequilibrium between SNPs may reflect the fact that SNPs affect phenotype by acting in concert with other SNPs within their "disequilibrium set." Thus, the number of hits within the disequilibrium sets were used as predictors. These models explained 35% and 19% of the variation in Q5 for women and men, respectively. Our results suggest that phenotype can be predicted from complex patterns of weakly penetrant SNPs using relatively simple models. We concluded that SNP interaction either was not included in the simulation model, or had only a weak impact on Q5 levels.