Rationale Few studies have systematically assessed the influence of gut microbiota on cardiovascular disease (CVD) risk. Objective To examine the association between gut microbiota and lifetime CVD risk profile among 55 Bogalusa Heart Study (BHS) participants with the highest and 57 with the lowest lifetime burdens of CVD risk factors. Methods and Results 16S rRNA sequencing was conducted on microbial DNA extracted from stool samples of the BHS participants. Alpha diversity, including measures of richness and evenness, and individual genera were tested for associations with lifetime CVD risk profile. Multivariable regression techniques were employed to adjust for age, gender, and race (Model 1), along with body mass index (BMI) (Model 2) and both BMI and diet (Model 3). In Model 1, odds ratios (95% confidence intervals) for each standard deviation increase in richness, measured by the number of observed operational taxonomic units, Chao 1 index, and abundance-based coverage estimator, were 0.62 (0.39, 0.99), 0.61 (0.38, 0.98), and 0.63 (0.39, 0.99), respectively. Associations were consistent in Models 2 and 3. Four genera were enriched among those with high versus low CVD risk profile in all models. Model 1 p-values were: 2.12×10−3, 7.95×10−5, 4.39×10−4, and 1.51×10−4 for Prevotella 2, Prevotella 7, Tyzzerella and Tyzzerella 4, respectively. Two genera were depleted among those with high versus low CVD risk profile in all models. Model 1 P-values were: 2.96×10−6 and 1.82×10−4 for Alloprevotella and Catenibacterium, respectively. Conclusions The current study identified associations of overall microbial richness and six microbial genera with lifetime CVD risk.
Despite the great success of genome-wide association studies (GWAS) in identification of the common genetic variants associated with complex diseases, the current GWAS have focused on single-SNP analysis. However, single-SNP analysis often identifies only a few of the most significant SNPs that account for a small proportion of the genetic variants and offers only a limited understanding of complex diseases. To overcome these limitations, we propose gene and pathway-based association analysis as a new paradigm for GWAS. As a proof of concept, we performed a comprehensive gene and pathway-based association analysis of 13 published GWAS. Our results showed that the proposed new paradigm for GWAS not only identified the genes that include significant SNPs found by single-SNP analysis, but also detected new genes in which each single SNP conferred a small disease risk; however, their joint actions were implicated in the development of diseases. The results also showed that the new paradigm for GWAS was able to identify biologically meaningful pathways associated with the diseases, which were confirmed by a gene-set-rich analysis using gene expression data.
Despite the growing consensus on the importance of testing gene-gene interactions in genetic studies of complex diseases, the effect of gene-gene interactions has often been defined as a deviance from genetic additive effects, which is essentially treated as a residual term in genetic analysis and leads to low power in detecting the presence of interacting effects. To what extent the definition of gene-gene interaction at population level reflects the genes' biochemical or physiological interaction remains a mystery. In this article, we introduce a novel definition and a new measure of gene-gene interaction between two unlinked loci (or genes). We developed a general theory for studying linkage disequilibrium (LD) patterns in disease population under two-locus disease models. The properties of using the LD measure in a disease population as a function of the measure of gene-gene interaction between two unlinked loci were also investigated. We examined how interaction between two loci creates LD in a disease population and showed that the mathematical formulation of the new definition for gene-gene interaction between two loci was similar to that of the LD between two loci. This finding motived us to develop an LD-based statistic to detect gene-gene interaction between two unlinked loci. The null distribution and type I error rates of the LD-based statistic for testing gene-gene interaction were validated using extensive simulation studies. We found that the new test statistic was more powerful than the traditional logistic regression under three two-locus disease models and demonstrated that the power of the test statistic depends on the measure of gene-gene interaction. We also investigated the impact of using tagging SNPs for testing interaction on the power to detect interaction between two unlinked loci. Finally, to evaluate the performance of our new method, we applied the LD-based statistic to two published data sets. Our results showed that the P values of the LD-based statistic were smaller than those obtained by other approaches, including logistic regression models.
Gene expression studies bridge the gap between DNA information and trait information by dissecting biochemical pathways into intermediate components between genotype and phenotype. These studies open new avenues for identifying complex disease genes and biomarkers for disease diagnosis and for assessing drug efficacy and toxicity. However, the majority of analytical methods applied to gene expression data are not efficient for biomarker identification and disease diagnosis. In this paper, we propose a general framework to incorporate feature (gene) selection into pattern recognition in the process to identify biomarkers. Using this framework, we develop three feature wrappers that search through the space of feature subsets using the classification error as measure of goodness for a particular feature subset being "wrapped around": linear discriminant analysis, logistic regression, and support vector machines. To effectively carry out this computationally intensive search process, we employ sequential forward search and sequential forward floating search algorithms. To evaluate the performance of feature selection for biomarker identification we have applied the proposed methods to three data sets. The preliminary results demonstrate that very high classification accuracy can be attained by identified composite classifiers with several biomarkers.
Recent progress in the development of single-nucleotide polymorphism (SNP) maps within genes and across the genome provides a valuable tool for fine-mapping and has led to the suggestion of genomewide association studies to search for susceptibility loci for complex traits. Test statistics for genome association studies that consider a single marker at a time, ignoring the linkage disequilibrium between markers, are inefficient. In this study, we present a generalized T2 statistic for association studies of complex traits, which can utilize multiple SNP markers simultaneously and considers the effects of multiple disease-susceptibility loci. This generalized T2 statistic is a corollary to that originally developed for multivariate analysis and has a close relationship to discriminant analysis and common measure of genetic distance. We evaluate the power of the generalized T2 statistic and show that power to be greater than or equal to those of the traditional chi2 test of association and a similar haplotype-test statistic. Finally, examples are given to evaluate the performance of the proposed T2 statistic for association studies using simulated and real data.
Insulin resistance (IR), the hallmark of type 2 diabetes, may be under epigenetic control. This study examines the association between global DNA methylation and IR using 84 monozygotic twin pairs. IR was estimated using homeostasis model assessment (HOMA). Global DNA methylation of Alu repeats in peripheral blood leukocytes was quantified by bisulfite pyrosequencing. The association between global DNA methylation and IR was examined using generalized estimating equation (GEE) and within–twin pair analyses, adjusting for potential confounders. Results show that methylation levels at all four CpG sites were individually associated with IR by GEE (all false discovery rate–adjusted P values ≤0.026). A 10% increase in mean Alu methylation was associated with an increase of 4.55 units (95% CI 2.38–6.73) in HOMA. Intrapair difference in IR was significantly associated with intrapair difference in global methylation level. A 10% increase in the difference in mean Alu methylation was associated with an increase of 4.54 units (0.34–8.71; P = 0.036) in the difference in HOMA. Confirmation of the results by intrapair analyses suggests that genetic factors do not confound the association between global DNA methylation and IR. Exclusion of twins taking diabetes medication (n = 17) did not change our results.
Telomeres play a central role in cellular aging, and shorter telomere length has been associated with age-related disorders including diabetes. However, a causal link between telomere shortening and diabetes risk has not been established. In a well-characterized longitudinal cohort of American Indians participating in the Strong Heart Family Study, we examined whether leukocyte telomere length (LTL) at baseline predicts incident diabetes independent of known diabetes risk factors. Among 2,328 participants free of diabetes at baseline, 292 subjects developed diabetes during an average 5.5 years of follow-up. Compared with subjects in the highest quartile (longest) of LTL, those in the lowest quartile (shortest) had an almost twofold increased risk of incident diabetes (hazard ratio [HR] 1.83 [95% CI 1.26–2.66]), whereas the risk for those in the second (HR 0.87 [95% CI 0.59–1.29]) and the third (HR 0.95 [95% CI 0.65–1.38]) quartiles was statistically nonsignificant. These findings suggest a nonlinear association between LTL and incident diabetes and indicate that LTL could serve as a predictive marker for diabetes development in American Indians, who suffer from disproportionately high rates of diabetes.
Objective Epigenetic mechanisms have been implicated in the pathogenesis of psychiatric disorders. The serotonin transporter gene (SLC6A4) is a key candidate gene for depression. We examined the association between SLC6A4 promoter methylation variation and depressive symptoms using 84 monozygotic twin pairs. Methods DNA methylation level in the SLC6A4 promoter region was quantified by bisulfite pyrosequencing using genomic DNA isolated from peripheral blood leukocytes. The number of current depressive symptoms was assessed using the Beck Depressive Inventory II (BDI-II). The association between methylation variation and depressive symptoms was examined using matched twin-pair analyses, adjusting for body mass index, smoking, physical activity, and alcohol consumption. Multiple testing was controlled by adjusted false discovery rate (q value). Results Intrapair difference in DNA methylation variation at 10 of the 20 studied CpG sites is significantly correlated with intrapair difference in BDI scores. Linear regression using intrapair differences demonstrates that intrapair difference in BDI score was significantly associated with intrapair differences in DNA methylation variation after adjusting for potential confounders and correction for multiple testing. On average, a 10% increase in the difference in mean DNA methylation level was associated with 4.4 increase in the difference in BDI score (95% confidence interval = 0.9–7.9, p = .01). Conclusions This study provides evidence that variation in methylation level within the promoter region of the serotonin transporter gene is associated with variation in depressive symptoms in a large sample of monozygotic twin pairs. This relationship is not confounded by genetic and shared environment. The 5-HTTLPR genotype also does not modulate this association.
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