Search

Background: Smooth muscle cells (SMC) play a critical role in atherosclerosis. The Aryl hydrocarbon receptor (AHR) is an environment-sensing transcription factor that contributes to vascular development, and has been implicated in coronary artery disease risk. We hypothesized that AHR can affect atherosclerosis by regulating phenotypic modulation of SMC.Methods: We combined RNA-sequencing, chromatin immunoprecipitation followed by sequencing, assay for transposase-accessible chromatin using sequencing, and in vitro assays in human coronary artery SMCs, with single-cell RNA-sequencing, histology, and RNAscope in an SMC-specific lineage-tracing Ahr knockout mouse model of atherosclerosis to better understand the role of AHR in vascular disease.Results: Genomic studies coupled with functional assays in cultured human coronary artery SMCs revealed that AHR modulates the human coronary artery SMC phenotype and suppresses ossification in these cells. Lineage-tracing and activity-tracing studies in the mouse aortic sinus showed that the Ahr pathway is active in modulated SMCs in the atherosclerotic lesion cap. Furthermore, single-cell RNA-sequencing studies of the SMC-specific Ahr knockout mice showed a significant increase in the proportion of modulated SMCs expressing chondrocyte markers such as Col2a1 and Alpl, which localized to the lesion neointima. These cells, which we term "chondromyocytes," were also identified in the neointima of human coronary arteries. In histological analyses, these changes manifested as larger lesion size, increased lineage-traced SMC participation in the lesion, decreased lineage-traced SMCs in the lesion cap, and increased alkaline phosphatase activity in lesions in the Ahr knockout in comparison with wild-type mice. We propose that AHR is likely protective based on these data and inference from human genetic analyses.Conclusions: Overall, we conclude that AHR promotes the maintenance of lesion cap integrity and diminishes the disease-related SMC-to-chondromyocyte transition in atherosclerotic tissues. [ABSTRACT FROM AUTHOR]

Sizes at birth of parents and their children are known to be correlated, reflecting in part the influence of fetal and maternal genes. Sociodemographic factors, regarded as aspects of the shared environment across generations, would also be expected to contribute, but evidence is limited. In the present study, the authors aimed to quantify the role of the shared environment in explaining intergenerational correlations in birth weight and length by using data across 3 consecutive generations from the Uppsala Birth Cohort Multigenerational Study in Uppsala, Sweden. That study included birth and sociodemographic data on 7,657 singletons born in Uppsala in 1915–1929 (generation 1) and their grandchildren (generation 3). Standard regression and biometric models were used to study the correlations in size at birth of generation 1-generation 3 pairs. The data showed stronger correlations in maternal pairs than in paternal pairs for birth weight (0.125 vs. 0.096, P = 0.02) but not for birth length (0.097 vs. 0.093, P = 0.77). These correlations were not reduced by adjustment for sociodemographic factors in regression models. In contrast, significant shared-environment contributions to the intergenerational correlations were identified in biometric models, averaging 14% for both birth measures. These models assumed a common latent factor for the sociodemographic variables. The present results show that the shared environment moderately but significantly contributes to intergenerational correlations. [ABSTRACT FROM AUTHOR]

Complex trait variation is likely to be explained by the combined effects of genes, environmental factors, and gene × environment (G × E) interaction. The authors introduce a novel 2-step method for detecting a G × E interaction in a genome-wide association study (GWAS) of case-parent trios. The method utilizes 2 sources of G × E information in a trio sample to construct a screening step and a testing step. Across a wide range of models, this 2-step procedure provides substantially greater power to detect G × E interaction than a standard test of G × E interaction applied genome-wide. For example, for a disease susceptibility locus with minor allele frequency of 15%, a binary exposure variable with 50% prevalence, and a GWAS scan of 1 million markers in 1,000 case-parent trios, the 2-step method provides 87% power to detect a G × E interaction relative risk of 2.3, as compared with only 25% power using a standard G × E test. The method is easily implemented using standard software. This 2-step scan for G × E interaction is independent of any prior scan that may have been conducted for genetic main effects, and thus has the potential to uncover new genes in a GWAS that have not been previously identified. [ABSTRACT FROM PUBLISHER]

Paraoxonase 1 (PON1) protects against oxidative modification of low density lipoproteins. The PON1 gene has 3 polymorphisms considered strong determinants of PON1 levels: Q192R and L55M in the coding region and C-108T in the promoter region. PON1 levels are also influenced by smoking. The authors hypothesized that PON1 variants could increase the risk of vascular thrombosis, leading in turn to placental insufficiency and small-for-gestational-age (SGA) birth. The author compared PON1 variants and haplotypes between 448 newborn SGA cases and 431 newborn controls from Montréal, Québec, Canada (1998–2000) and studied the effects of interaction with maternal smoking. Transmission of the variants in case-parent trios was used as validation of the case-control results; the authors combined case-control and family data to analyze the associations of variants with SGA birth. In the case-control analysis, the T allele from C-108T increased the risk of SGA birth (additive odds ratio = 1.30, 95% confidence interval (CI): 1.06, 1.59), and the TRL haplotype (T from C-108T, R from Q192R, and L from L55M) was associated with an odds ratio of 1.51 (95% CI: 1.07, 2.15). Among smokers, the CRL haplotype was protective (odds ratio = 0.48, 95% CI: 0.28, 0.82). Case-parent trio results were compatible with case-control results. [ABSTRACT FROM PUBLISHER]

Genetic effects for common variants affecting complex disease risk are subtle. Single genome-wide association (GWA) studies are typically underpowered to detect these effects, and combination of several GWA data sets is needed to enhance discovery. The authors investigated the properties of the discovery process in simulated cumulative meta-analyses of GWA study-derived signals allowing for potential genetic model misspecification and between-study heterogeneity. Variants with null effects on average (but also between-data set heterogeneity) could yield false-positive associations with seemingly homogeneous effects. Random effects had higher than appropriate false-positive rates when there were few data sets. The log-additive model had the lowest false-positive rate. Under heterogeneity, random-effects meta-analyses of 2–10 data sets averaging 1,000 cases/1,000 controls each did not increase power, or the meta-analysis was even less powerful than a single study (power desert). Upward bias in effect estimates and underestimation of between-study heterogeneity were common. Fixed-effects calculations avoided power deserts and maximized discovery of association signals at the expense of much higher false-positive rates. Therefore, random- and fixed-effects models are preferable for different purposes (fixed effects for initial screenings, random effects for generalizability applications). These results may have broader implications for the design and interpretation of large-scale multiteam collaborative studies discovering common gene variants. [ABSTRACT FROM PUBLISHER]

For most associations of common single nucleotide polymorphisms (SNPs) with common diseases, the genetic model of inheritance is unknown. The authors extended and applied a Bayesian meta-analysis approach to data from 19 studies on 17 replicated associations with type 2 diabetes. For 13 SNPs, the data fitted very well to an additive model of inheritance for the diabetes risk allele; for 4 SNPs, the data were consistent with either an additive model or a dominant model; and for 2 SNPs, the data were consistent with an additive or recessive model. Results were robust to the use of different priors and after exclusion of data for which index SNPs had been examined indirectly through proxy markers. The Bayesian meta-analysis model yielded point estimates for the genetic effects that were very similar to those previously reported based on fixed- or random-effects models, but uncertainty about several of the effects was substantially larger. The authors also examined the extent of between-study heterogeneity in the genetic model and found generally small between-study deviation values for the genetic model parameter. Heterosis could not be excluded for 4 SNPs. Information on the genetic model of robustly replicated association signals derived from genome-wide association studies may be useful for predictive modeling and for designing biologic and functional experiments. [ABSTRACT FROM PUBLISHER]

In genetic association studies, investigators compare allele or genotype frequencies in unrelated case and control subjects or examine preferential allele transmissions from parents to affected offspring. In many genetic case-control studies, the collection of DNA material extends to relatives such as parents of cases. Thus, case-control and case-parent trio association analyses are possible. Whereas the goal of collecting genetic information from family members in a study initially designed as a case-control study is to enrich the genetic analysis, increase power, or address concern about population structure bias, methods of combining genetic data from unrelated case and control subjects with genetic trio data from the same study population are not well known. A number of hybrid approaches have been developed that utilize such data together. In this paper, the authors describe key features of genetic case-control and case-parent trio studies and review commonly used methods of genetic analysis for case-parent trio designs. In addition, they provide a pragmatic review of statistical methods and available software for existing hybrid approaches that combine various components of case-control and genetic trio data. The application of all methods is illustrated using a candidate gene study of childhood leukemia that included case-control subjects and their parents. [ABSTRACT FROM PUBLISHER]

If one is interested in dissecting the complex interactions that exist between host and pathogen, the nematode worm Caenorhabditis elegans is perhaps not the first model host that comes to mind. In this review I will introduce ‘the worm’ and try to show how it is, in fact, well suited to the identification of universal virulence factors and holds great promise for the study of conserved mechanisms of innate immunity. [Copyright &y& Elsevier]