Background

Cigarette smoking is a major risk factor in the development of age-related chronic obstructive pulmonary disease (COPD). The serotonin transporter (SERT) gene polymorphism has been reported to be associated with COPD, and the degree of cigarette smoking has been shown to be a significant mediator in this relationship. The interrelation between circulating serotonin (5-hydroxytyptamine, 5-HT), cigarette smoking and COPD is however largely unknown. The current study aimed at investigating the mediation effects of plasma 5-HT on cigarette smoking-induced COPD and the relation between plasma 5-HT levels and age.

Methods

The association between plasma 5-HT, age and COPD was analyzed in a total of 62 COPD patients (ever-smokers) and 117 control subjects (healthy non-smokers and ever-smokers). Plasma 5-HT levels were measured by enzyme-linked immuno assay (EIA).

Results

The elevated plasma 5-HT levels were significantly associated with increased odds for COPD (OR = 1.221, 95% CI = 1.123 to 1.319, p<0.0001). The effect remained significant after being adjusted for age and pack-years smoked (OR = 1.271, 95% CI = 1.134 to 1.408, p = 0.0003). Furthermore, plasma 5-HT was found to mediate the relation between pack-years smoked and COPD. A positive correlation (r = 0.303, p = 0.017) was found between plasma 5-HT levels and age in COPD, but not in the control subjects (r = −0.149, p = 0.108).

Conclusion

Our results suggest that cigarette smoke-induced COPD is partially mediated by the plasma levels of 5-HT, and that these become elevated with increased age in COPD. The elevated plasma 5-HT levels in COPD might contribute to the pathogenesis of this disease.

Recently genome-wide association studies (GWAS) have identified numerous susceptibility variants for complex diseases. In this study we proposed several approaches to estimate the total number of variants underlying these diseases. We assume that the variance explained by genetic markers (Vg) follow an exponential distribution, which is justified by previous studies on theories of adaptation. Our aim is to fit the observed distribution of Vg from GWAS to its theoretical distribution. The number of variants is obtained by the heritability divided by the estimated mean of the exponential distribution. In practice, due to limited sample sizes, there is insufficient power to detect variants with small effects. Therefore the power was taken into account in fitting. Besides considering the most significant variants, we also tried to relax the significance threshold, allowing more markers to be fitted. The effects of false positive variants were removed by considering the local false discovery rates. In addition, we developed an alternative approach by directly fitting the z-statistics from GWAS to its theoretical distribution. In all cases, the “winner's curse” effect was corrected analytically. Confidence intervals were also derived. Simulations were performed to compare and verify the performance of different estimators (which incorporates various means of winner's curse correction) and the coverage of the proposed analytic confidence intervals. Our methodology only requires summary statistics and is able to handle both binary and continuous traits. Finally we applied the methods to a few real disease examples (lipid traits, type 2 diabetes and Crohn's disease) and estimated that hundreds to nearly a thousand variants underlie these traits.

Family data are used extensively in quantitative genetic studies to disentangle the genetic and environmental contributions to various diseases. Many family studies based their analysis on population-based registers containing a large number of individuals composed of small family units. For binary trait analyses, exact marginal likelihood is a common approach, but, due to the computational demand of the enormous data sets, it allows only a limited number of effects in the model. This makes it particularly difficult to perform joint estimation of variance components for a binary trait and the potential confounders. We have developed a data-reduction method of ascertaining informative families from population-based family registers. We propose a scheme where the ascertained families match the full cohort with respect to some relevant statistics, such as the risk to relatives of an affected individual. The ascertainment-adjusted analysis, which we implement using a pseudo-likelihood approach, is shown to be efficient relative to the analysis of the whole cohort and robust to mis-specification of the random effect distribution.

Hypertension is caused by the interaction of environmental and genetic factors. The condition which is very common, with about 18% of the adult Hong Kong Chinese population and over 50% of older individuals affected, is responsible for considerable morbidity and mortality. To identify genes influencing hypertension and blood pressure, we conducted a combined linkage and association study using over 500,000 single nucleotide polymorphisms (SNPs) genotyped in 328 individuals comprising 111 hypertensive probands and their siblings. Using a family-based association test, we found an association with SNPs on chromosome 5q31.1 (rs6596140; P<9×10−8) for hypertension. One candidate gene, PDC, was replicated, with rs3817586 on 1q31.1 attaining P = 2.5×10−4 and 2.9×10−5 in the within-family tests for DBP and MAP, respectively. We also identified regions of significant linkage for systolic and diastolic blood pressure on chromosomes 2q22 and 5p13, respectively. Further family-based association analysis of the linkage peak on chromosome 5 yielded a significant association (rs1605685, P<7×10−5) for DBP. This is the first combined linkage and association study of hypertension and its related quantitative traits with Chinese ancestry. The associations reported here account for the action of common variants whereas the discovery of linkage regions may point to novel targets for rare variant screening.