Stratified genetic analysis reveals sex differences in MPO-ANCA-associated vasculitis.

OBJECTIVE: To identify and genetically characterize subgroups of patients with ANCA-associated vasculitides (AAV) based on sex and ANCA subtype. METHODS: A previously established SNP dataset derived from DNA sequencing of 1853 genes and genotyping of 1088 Scandinavian cases with AAV and 1589 controls was stratified for sex and ANCA subtype and analysed for association with five top AAV SNPs. rs9274619, a lead variant at the HLA-DQB1/HLA-DQA2 locus previously associated with AAV positive for myeloperoxidase (MPO)-ANCA, was analysed for association with the cumulative disease involvement of ten different organ systems. RESULTS: rs9274619 showed a significantly stronger association to MPO-ANCA-positive females than males [P = 2.0 × 10-4, OR = 2.3 (95% CI 1.5, 3.5)], whereas proteinase 3 (PR3)-ANCA-associated variants rs1042335, rs9277341 (HLA-DPB1/A1) and rs28929474 (SERPINA1) were equally associated with females and males with PR3-ANCA. In MPO-ANCA-positive cases, carriers of the rs9274619 risk allele were more prone to disease engagement of eyes [P = 0.021, OR = 11 (95% CI 2.2, 205)] but less prone to pulmonary involvement [P = 0.026, OR = 0.52 (95% CI 0.30, 0.92)]. Moreover, AAV with both MPO-ANCA and PR3-ANCA was associated with the PR3-ANCA lead SNP rs1042335 [P = 0.0015, OR = 0.091 (95% CI 0.0022, 0.55)] but not with rs9274619. CONCLUSIONS: Females and males with MPO-ANCA-positive AAV differ in genetic predisposition to disease, suggesting at least partially distinct disease mechanisms between the sexes. Double ANCA-positive AAV cases are genetically similar to PR3-ANCA-positive cases, providing clues to the clinical follow-up and treatment of these patients.

Identification and functional characterization of a novel susceptibility locus for small vessel vasculitis with MPO-ANCA.

OBJECTIVE: To identify and characterize genetic loci associated with the risk of developing ANCA-associated vasculitides (AAV). METHODS: Genetic association analyses were performed after Illumina sequencing of 1853 genes and subsequent replication with genotyping of selected single nucleotide polymorphisms in a total cohort of 1110 Scandinavian cases with granulomatosis with polyangiitis or microscopic polyangiitis, and 1589 controls. A novel AAV-associated single nucleotide polymorphism was analysed for allele-specific effects on gene expression using luciferase reporter assay. RESULTS: PR3-ANCA+ AAV was significantly associated with two independent loci in the HLA-DPB1/HLA-DPA1 region [rs1042335, P = 6.3 × 10-61, odds ratio (OR) 0.10; rs9277341, P = 1.5 × 10-44, OR 0.22] and with rs28929474 in the SERPINA1 gene (P = 2.7 × 10-10, OR 2.9). MPO-ANCA+ AAV was significantly associated with the HLA-DQB1/HLA-DQA2 locus (rs9274619, P = 5.4 × 10-25, OR 3.7) and with a rare variant in the BACH2 gene (rs78275221, P = 7.9 × 10-7, OR 3.0), the latter a novel susceptibility locus for MPO-ANCA+ granulomatosis with polyangiitis/microscopic polyangiitis. The rs78275221-A risk allele reduced luciferase gene expression in endothelial cells, specifically, as compared with the non-risk allele. CONCLUSION: We identified a novel susceptibility locus for MPO-ANCA+ AAV and propose that the associated variant is of mechanistic importance, exerting a regulatory function on gene expression in specific cell types.

Analysis of the genetic variants associated with circulating levels of sgp130. Results from the IMPROVE study.

The genes regulating circulating levels of soluble gp130 (sgp130), the antagonist of the inflammatory response in atherosclerosis driven by interleukin 6, are largely unknown. Aims of the present study were to identify genetic loci associated with circulating sgp130 and to explore the potential association between variants associated with sgp130 and markers of subclinical atherosclerosis. The study is based on IMPROVE (n = 3703), a cardiovascular multicentre study designed to investigate the determinants of carotid intima media thickness, a measure of subclinical atherosclerosis. Genomic DNA was genotyped by the CardioMetaboChip and ImmunoChip. About 360,842 SNPs were tested for association with log-transformed sgp130, using linear regression adjusted for age, gender, and population stratification using PLINK v1.07. A p value of 1 × 10-5 was chosen as threshold for significance value. In an exploratory analysis, SNPs associated with sgp130 were tested for association with c-IMT measures. We identified two SNPs significantly associated with sgp130 levels and 24 showing suggestive association with sgp130 levels. One SNP (rs17688225) on chromosome 14 was positively associated with sgp130 serum levels (ß = 0.03 SE = 0.007, p = 4.77 × 10-5) and inversely associated with c-IMT (c-IMTmean-max ß = -0.001 SE = 0.005, p = 0.0342). Our data indicate that multiple loci regulate sgp130 levels and suggest a possible common pathway between sgp130 and c-IMT measures.

Mapping of 79 loci for 83 plasma protein biomarkers in cardiovascular disease.

Recent advances in highly multiplexed immunoassays have allowed systematic large-scale measurement of hundreds of plasma proteins in large cohort studies. In combination with genotyping, such studies offer the prospect to 1) identify mechanisms involved with regulation of protein expression in plasma, and 2) determine whether the plasma proteins are likely to be causally implicated in disease. We report here the results of genome-wide association (GWA) studies of 83 proteins considered relevant to cardiovascular disease (CVD), measured in 3,394 individuals with multiple CVD risk factors. We identified 79 genome-wide significant (p<5e-8) association signals, 55 of which replicated at P<0.0007 in separate validation studies (n = 2,639 individuals). Using automated text mining, manual curation, and network-based methods incorporating information on expression quantitative trait loci (eQTL), we propose plausible causal mechanisms for 25 trans-acting loci, including a potential post-translational regulation of stem cell factor by matrix metalloproteinase 9 and receptor-ligand pairs such as RANK-RANK ligand. Using public GWA study data, we further evaluate all 79 loci for their causal effect on coronary artery disease, and highlight several potentially causal associations. Overall, a majority of the plasma proteins studied showed evidence of regulation at the genetic level. Our results enable future studies of the causal architecture of human disease, which in turn should aid discovery of new drug targets.

H19 Induces Abdominal Aortic Aneurysm Development and Progression.

BACKGROUND: Long noncoding RNAs have emerged as critical molecular regulators in various biological processes and diseases. Here we sought to identify and functionally characterize long noncoding RNAs as potential mediators in abdominal aortic aneurysm development. METHODS: We profiled RNA transcript expression in 2 murine abdominal aortic aneurysm models, Angiotensin II (ANGII) infusion in apolipoprotein E-deficient ( ApoE-/-) mice (n=8) and porcine pancreatic elastase instillation in C57BL/6 wild-type mice (n=12). The long noncoding RNA H19 was identified as 1 of the most highly upregulated transcripts in both mouse aneurysm models compared with sham-operated controls. This was confirmed by quantitative reverse transcription-polymerase chain reaction and in situ hybridization. RESULTS: Experimental knock-down of H19, utilizing site-specific antisense oligonucleotides (LNA-GapmeRs) in vivo, significantly limited aneurysm growth in both models. Upregulated H19 correlated with smooth muscle cell (SMC) content and SMC apoptosis in progressing aneurysms. Importantly, a similar pattern could be observed in human abdominal aortic aneurysm tissue samples, and in a novel preclinical LDLR-/- (low-density lipoprotein receptor) Yucatan mini-pig aneurysm model. In vitro knock-down of H19 markedly decreased apoptotic rates of cultured human aortic SMCs, whereas overexpression of H19 had the opposite effect. Notably, H19-dependent apoptosis mechanisms in SMCs appeared to be independent of miR-675, which is embedded in the first exon of the H19 gene. A customized transcription factor array identified hypoxia-inducible factor 1α as the main downstream effector. Increased SMC apoptosis was associated with cytoplasmic interaction between H19 and hypoxia-inducible factor 1α and sequential p53 stabilization. Additionally, H19 induced transcription of hypoxia-inducible factor 1α via recruiting the transcription factor specificity protein 1 to the promoter region. CONCLUSIONS: The long noncoding RNA H19 is a novel regulator of SMC survival in abdominal aortic aneurysm development and progression. Inhibition of H19 expression might serve as a novel molecular therapeutic target for aortic aneurysm disease.

Genome-wide association study with additional genetic and post-transcriptional analyses reveals novel regulators of plasma factor XI levels.

Coagulation factor XI (FXI) has become increasingly interesting for its role in pathogenesis of thrombosis. While elevated plasma levels of FXI have been associated with venous thromboembolism and ischemic stroke, its deficiency is associated with mild bleeding. We aimed to determine novel genetic and post-transcriptional plasma FXI regulators.We performed a genome-wide association study (GWAS) for plasma FXI levels, using novel data imputed to the 1000 Genomes reference panel. Individual GWAS analyses, including a total of 16,169 European individuals from the ARIC, GHS, MARTHA and PROCARDIS studies, were meta-analysed and further replicated in 2,045 individuals from the F5L family, GAIT2 and MEGA studies. Additional association with activated partial thromboplastin time (aPTT) was tested for the top SNPs. In addition, a study on the effect of miRNA on FXI regulation was performed using in silico prediction tools and in vitro luciferase assays.Three loci showed robust, replicating association with circulating FXI levels: KNG1 (rs710446, P-value = 2.07 × 10-302), F11 (rs4253417, P-value = 2.86 × 10-193), and a novel association in GCKR (rs780094, P-value = 3.56 ×10-09), here for the first time implicated in FXI regulation. The two first SNPs (rs710446 and rs4253417) also associated with aPTT. Conditional and haplotype analyses demonstrated a complex association signal, with additional novel SNPs modulating plasma FXI levels in both the F11 and KNG1 loci. Finally, eight miRNAs were predicted to bind F11 mRNA. Over-expression of either miR-145 or miR-181 significantly reduced the luciferase activity in cells transfected with a plasmid containing FXI-3'UTR.These results should open the door to new therapeutic targets for thrombosis prevention.

Analysis with the exome array identifies multiple new independent variants in lipid loci.

It has been hypothesized that low frequency (1-5% minor allele frequency (MAF)) and rare (<1% MAF) variants with large effect sizes may contribute to the missing heritability in complex traits. Here, we report an association analysis of lipid traits (total cholesterol, LDL-cholesterol, HDL-cholesterol triglycerides) in up to 27 312 individuals with a comprehensive set of low frequency coding variants (ExomeChip), combined with conditional analysis in the known lipid loci. No new locus reached genome-wide significance. However, we found a new lead variant in 26 known lipid association regions of which 16 were >1000-fold more significant than the previous sentinel variant and not in close LD (six had MAF <5%). Furthermore, conditional analysis revealed multiple independent signals (ranging from 1 to 5) in a third of the 98 lipid loci tested, including rare variants. Addition of our novel associations resulted in between 1.5- and 2.5-fold increase in the proportion of heritability explained for the different lipid traits. Our findings suggest that rare coding variants contribute to the genetic architecture of lipid traits.

Fast and general tests of genetic interaction for genome-wide association studies.

A complex disease has, by definition, multiple genetic causes. In theory, these causes could be identified individually, but their identification will likely benefit from informed use of anticipated interactions between causes. In addition, characterizing and understanding interactions must be considered key to revealing the etiology of any complex disease. Large-scale collaborative efforts are now paving the way for comprehensive studies of interaction. As a consequence, there is a need for methods with a computational efficiency sufficient for modern data sets as well as for improvements of statistical accuracy and power. Another issue is that, currently, the relation between different methods for interaction inference is in many cases not transparent, complicating the comparison and interpretation of results between different interaction studies. In this paper we present computationally efficient tests of interaction for the complete family of generalized linear models (GLMs). The tests can be applied for inference of single or multiple interaction parameters, but we show, by simulation, that jointly testing the full set of interaction parameters yields superior power and control of false positive rate. Based on these tests we also describe how to combine results from multiple independent studies of interaction in a meta-analysis. We investigate the impact of several assumptions commonly made when modeling interactions. We also show that, across the important class of models with a full set of interaction parameters, jointly testing the interaction parameters yields identical results. Further, we apply our method to genetic data for cardiovascular disease. This allowed us to identify a putative interaction involved in Lp(a) plasma levels between two 'tag' variants in the LPA locus (p = 2.42 ⋅ 10-09) as well as replicate the interaction (p = 6.97 ⋅ 10-07). Finally, our meta-analysis method is used in a small (N = 16,181) study of interactions in myocardial infarction.

Discovering Genetic Interactions in Large-Scale Association Studies by Stage-wise Likelihood Ratio Tests.

Despite the success of genome-wide association studies in medical genetics, the underlying genetics of many complex diseases remains enigmatic. One plausible reason for this could be the failure to account for the presence of genetic interactions in current analyses. Exhaustive investigations of interactions are typically infeasible because the vast number of possible interactions impose hard statistical and computational challenges. There is, therefore, a need for computationally efficient methods that build on models appropriately capturing interaction. We introduce a new methodology where we augment the interaction hypothesis with a set of simpler hypotheses that are tested, in order of their complexity, against a saturated alternative hypothesis representing interaction. This sequential testing provides an efficient way to reduce the number of non-interacting variant pairs before the final interaction test. We devise two different methods, one that relies on a priori estimated numbers of marginally associated variants to correct for multiple tests, and a second that does this adaptively. We show that our methodology in general has an improved statistical power in comparison to seven other methods, and, using the idea of closed testing, that it controls the family-wise error rate. We apply our methodology to genetic data from the PROCARDIS coronary artery disease case/control cohort and discover three distinct interactions. While analyses on simulated data suggest that the statistical power may suffice for an exhaustive search of all variant pairs in ideal cases, we explore strategies for a priori selecting subsets of variant pairs to test. Our new methodology facilitates identification of new disease-relevant interactions from existing and future genome-wide association data, which may involve genes with previously unknown association to the disease. Moreover, it enables construction of interaction networks that provide a systems biology view of complex diseases, serving as a basis for more comprehensive understanding of disease pathophysiology and its clinical consequences.

Rare and low-frequency variants and their association with plasma levels of fibrinogen, FVII, FVIII, and vWF.

Fibrinogen, coagulation factor VII (FVII), and factor VIII (FVIII) and its carrier von Willebrand factor (vWF) play key roles in hemostasis. Previously identified common variants explain only a small fraction of the trait heritabilities, and additional variations may be explained by associations with rarer variants with larger effects. The aim of this study was to identify low-frequency (minor allele frequency [MAF] ≥0.01 and <0.05) and rare (MAF <0.01) variants that influence plasma concentrations of these 4 hemostatic factors by meta-analyzing exome chip data from up to 76,000 participants of 4 ancestries. We identified 12 novel associations of low-frequency (n = 2) and rare (n = 10) variants across the fibrinogen, FVII, FVIII, and vWF traits that were independent of previously identified associations. Novel loci were found within previously reported genes and had effect sizes much larger than and independent of previously identified common variants. In addition, associations at KCNT1, HID1, and KATNB1 identified new candidate genes related to hemostasis for follow-up replication and functional genomic analysis. Newly identified low-frequency and rare-variant associations accounted for modest amounts of trait variance and therefore are unlikely to increase predicted trait heritability but provide new information for understanding individual variation in hemostasis pathways.

HMG-coenzyme A reductase inhibition, type 2 diabetes, and bodyweight: evidence from genetic analysis and randomised trials.

BACKGROUND: Statins increase the risk of new-onset type 2 diabetes mellitus. We aimed to assess whether this increase in risk is a consequence of inhibition of 3-hydroxy-3-methylglutaryl-CoA reductase (HMGCR), the intended drug target. METHODS: We used single nucleotide polymorphisms in the HMGCR gene, rs17238484 (for the main analysis) and rs12916 (for a subsidiary analysis) as proxies for HMGCR inhibition by statins. We examined associations of these variants with plasma lipid, glucose, and insulin concentrations; bodyweight; waist circumference; and prevalent and incident type 2 diabetes. Study-specific effect estimates per copy of each LDL-lowering allele were pooled by meta-analysis. These findings were compared with a meta-analysis of new-onset type 2 diabetes and bodyweight change data from randomised trials of statin drugs. The effects of statins in each randomised trial were assessed using meta-analysis. FINDINGS: Data were available for up to 223 463 individuals from 43 genetic studies. Each additional rs17238484-G allele was associated with a mean 0·06 mmol/L (95% CI 0·05-0·07) lower LDL cholesterol and higher body weight (0·30 kg, 0·18-0·43), waist circumference (0·32 cm, 0·16-0·47), plasma insulin concentration (1·62%, 0·53-2·72), and plasma glucose concentration (0·23%, 0·02-0·44). The rs12916 SNP had similar effects on LDL cholesterol, bodyweight, and waist circumference. The rs17238484-G allele seemed to be associated with higher risk of type 2 diabetes (odds ratio [OR] per allele 1·02, 95% CI 1·00-1·05); the rs12916-T allele association was consistent (1·06, 1·03-1·09). In 129 170 individuals in randomised trials, statins lowered LDL cholesterol by 0·92 mmol/L (95% CI 0·18-1·67) at 1-year of follow-up, increased bodyweight by 0·24 kg (95% CI 0·10-0·38 in all trials; 0·33 kg, 95% CI 0·24-0·42 in placebo or standard care controlled trials and -0·15 kg, 95% CI -0·39 to 0·08 in intensive-dose vs moderate-dose trials) at a mean of 4·2 years (range 1·9-6·7) of follow-up, and increased the odds of new-onset type 2 diabetes (OR 1·12, 95% CI 1·06-1·18 in all trials; 1·11, 95% CI 1·03-1·20 in placebo or standard care controlled trials and 1·12, 95% CI 1·04-1·22 in intensive-dose vs moderate dose trials). INTERPRETATION: The increased risk of type 2 diabetes noted with statins is at least partially explained by HMGCR inhibition. FUNDING: The funding sources are cited at the end of the paper.

Genetic loci on chromosome 5 are associated with circulating levels of interleukin-5 and eosinophil count in a European population with high risk for cardiovascular disease.

IL-5 is a Th2 cytokine which activates eosinophils and is suggested to have an atheroprotective role. Genetic variants in the IL5 locus have been associated with increased risk of CAD and ischemic stroke. In this study we aimed to identify genetic variants associated with IL-5 concentrations and apply a Mendelian randomisation approach to assess IL-5 levels for causal effect on intima-media thickness in a European population at high risk of coronary artery disease. We analysed SNPs within robustly associated candidate loci for immune, inflammatory, metabolic and cardiovascular traits. We identified 2 genetic loci for IL-5 levels (chromosome 5, rs56183820, BETA=0.11, P=6.73E(-5) and chromosome 14, rs4902762, BETA=0.12, P=5.76E(-6)) and one for eosinophil count (rs72797327, BETA=-0.10, P=1.41E(-6)). Both chromosome 5 loci were in the vicinity of the IL5 gene, however the association with IL-5 levels failed to replicate in a meta-analysis of 2 independent cohorts (rs56183820, BETA=0.04, P=0.2763, I(2)=24, I(2)-P=0.2516). No significant associations were observed between SNPs associated with IL-5 levels or eosinophil count and IMT measures. Expression quantitative trait analyses indicate effects of the IL-5 and eosinophil-associated SNPs on RAD50 mRNA expression levels (rs12652920 (r2=0.93 with rs56183820) BETA=-0.10, P=8.64E(-6) and rs11739623 (r2=0.96 with rs72797327) BETA=-0.23, P=1.74E(-29), respectively). Our data do not support a role for IL-5 levels and eosinophil count in intima-media thickness, however SNPs associated with IL-5 and eosinophils might influence stability of the atherosclerotic plaque via modulation of RAD50 levels.

Integrating Sequence Evolution into Probabilistic Orthology Analysis.

Orthology analysis, that is, finding out whether a pair of homologous genes are orthologs - stemming from a speciation - or paralogs - stemming from a gene duplication - is of central importance in computational biology, genome annotation, and phylogenetic inference. In particular, an orthologous relationship makes functional equivalence of the two genes highly likely. A major approach to orthology analysis is to reconcile a gene tree to the corresponding species tree, (most commonly performed using the most parsimonious reconciliation, MPR). However, most such phylogenetic orthology methods infer the gene tree without considering the constraints implied by the species tree and, perhaps even more importantly, only allow the gene sequences to influence the orthology analysis through the a priori reconstructed gene tree. We propose a sound, comprehensive Bayesian Markov chain Monte Carlo-based method, DLRSOrthology, to compute orthology probabilities. It efficiently sums over the possible gene trees and jointly takes into account the current gene tree, all possible reconciliations to the species tree, and the, typically strong, signal conveyed by the sequences. We compare our method with PrIME-GEM, a probabilistic orthology approach built on a probabilistic duplication-loss model, and MrBayesMPR, a probabilistic orthology approach that is based on conventional Bayesian inference coupled with MPR. We find that DLRSOrthology outperforms these competing approaches on synthetic data as well as on biological data sets and is robust to incomplete taxon sampling artifacts.

Gene-pseudogene evolution: a probabilistic approach.

Over the last decade, methods have been developed for the reconstruction of gene trees that take into account the species tree. Many of these methods have been based on the probabilistic duplication-loss model, which describes how a gene-tree evolves over a species-tree with respect to duplication and losses, as well as extension of this model, e.g., the DLRS (Duplication, Loss, Rate and Sequence evolution) model that also includes sequence evolution under relaxed molecular clock. A disjoint, almost as recent, and very important line of research has been focused on non protein-coding, but yet, functional DNA. For instance, DNA sequences being pseudogenes in the sense that they are not translated, may still be transcribed and the thereby produced RNA may be functional.

A Bayesian method for analyzing lateral gene transfer.

Lateral gene transfer (LGT)--which transfers DNA between two non-vertically related individuals belonging to the same or different species--is recognized as a major force in prokaryotic evolution, and evidence of its impact on eukaryotic evolution is ever increasing. LGT has attracted much public attention for its potential to transfer pathogenic elements and antibiotic resistance in bacteria, and to transfer pesticide resistance from genetically modified crops to other plants. In a wider perspective, there is a growing body of studies highlighting the role of LGT in enabling organisms to occupy new niches or adapt to environmental changes. The challenge LGT poses to the standard tree-based conception of evolution is also being debated. Studies of LGT have, however, been severely limited by a lack of computational tools. The best currently available LGT algorithms are parsimony-based phylogenetic methods, which require a pre-computed gene tree and cannot choose between sometimes wildly differing most parsimonious solutions. Moreover, in many studies, simple heuristics are applied that can only handle putative orthologs and completely disregard gene duplications (GDs). Consequently, proposed LGT among specific gene families, and the rate of LGT in general, remain debated. We present a Bayesian Markov-chain Monte Carlo-based method that integrates GD, gene loss, LGT, and sequence evolution, and apply the method in a genome-wide analysis of two groups of bacteria: Mollicutes and Cyanobacteria. Our analyses show that although the LGT rate between distant species is high, the net combined rate of duplication and close-species LGT is on average higher. We also show that the common practice of disregarding reconcilability in gene tree inference overestimates the number of LGT and duplication events.

Genome-wide association study for circulating levels of PAI-1 provides novel insights into its regulation.

We conducted a genome-wide association study to identify novel associations between genetic variants and circulating plasminogen activator inhibitor-1 (PAI-1) concentration, and examined functional implications of variants and genes that were discovered. A discovery meta-analysis was performed in 19 599 subjects, followed by replication analysis of genome-wide significant (P < 5 × 10(-8)) single nucleotide polymorphisms (SNPs) in 10 796 independent samples. We further examined associations with type 2 diabetes and coronary artery disease, assessed the functional significance of the SNPs for gene expression in human tissues, and conducted RNA-silencing experiments for one novel association. We confirmed the association of the 4G/5G proxy SNP rs2227631 in the promoter region of SERPINE1 (7q22.1) and discovered genome-wide significant associations at 3 additional loci: chromosome 7q22.1 close to SERPINE1 (rs6976053, discovery P = 3.4 × 10(-10)); chromosome 11p15.2 within ARNTL (rs6486122, discovery P = 3.0 × 10(-8)); and chromosome 3p25.2 within PPARG (rs11128603, discovery P = 2.9 × 10(-8)). Replication was achieved for the 7q22.1 and 11p15.2 loci. There was nominal association with type 2 diabetes and coronary artery disease at ARNTL (P < .05). Functional studies identified MUC3 as a candidate gene for the second association signal on 7q22.1. In summary, SNPs in SERPINE1 and ARNTL and an SNP associated with the expression of MUC3 were robustly associated with circulating levels of PAI-1.

Serum 25-hydroxyvitamin D concentration in subclinical carotid atherosclerosis.

OBJECTIVE: Vitamin D deficiency has been implicated in cardiovascular disease and is associated with multiple cardiovascular risk factors. We investigated the serum 25-hydroxyvitamin D (25(OH)D) concentration in relation to latitude, baseline carotid intima-media thickness (IMT), and IMT progression, the carotid IMT measures being surrogate markers of subclinical atherosclerosis and cardiovascular disease risk. APPROACH AND RESULTS: Serum 25(OH)D concentration was related to high-resolution carotid IMT measures in 3430 middle-aged and elderly subjects with high cardiovascular risk but no prevalent disease, who were recruited at 7 centers in Finland, Sweden, The Netherlands, France, and Italy. Participants underwent carotid ultrasound examination at baseline and at months 15 and 30 after entry into the study, whereas blood samples, clinical data, and information about lifestyle were collected at baseline. Serum 25(OH)D levels were positively associated with latitude (Jonckheere-Terpstra χ=166.643; P<0.001) and, as previously reported, associated with a range of cardiovascular risk factors. There were no independent relationships between 25(OH)D and segment-specific or composite IMT measures in the entire cohort. In analyses stratified by sex, diabetes mellitus, and statin treatment, weak associations with some baseline and progression measures of carotid IMT were observed in males, diabetics, and nonstatin-treated individuals. CONCLUSIONS: Levels of 25(OH)D differed across Europe, were highest in the North, showed multiple associations with established and emerging cardiovascular risk factors but were not consistently, independently related to measures of carotid IMT. This argues against a protective role of vitamin D against subclinical atherosclerosis in high-risk individuals.

Towards high-throughput parallel imaging and single-cell transcriptomics of microbial eukaryotic plankton.

Single-cell transcriptomics has the potential to provide novel insights into poorly studied microbial eukaryotes. Although several such technologies are available and benchmarked on mammalian cells, few have been tested on protists. Here, we applied a microarray single-cell sequencing (MASC-seq) technology, that generates microscope images of cells in parallel with capturing their transcriptomes, on three species representing important plankton groups with different cell structures; the ciliate Tetrahymena thermophila, the diatom Phaeodactylum tricornutum, and the dinoflagellate Heterocapsa sp. Both the cell fixation and permeabilization steps were adjusted. For the ciliate and dinoflagellate, the number of transcripts of microarray spots with single cells were significantly higher than for background spots, and the overall expression patterns were correlated with that of bulk RNA, while for the much smaller diatom cells, it was not possible to separate single-cell transcripts from background. The MASC-seq method holds promise for investigating "microbial dark matter", although further optimizations are necessary to increase the signal-to-noise ratio.

GenPhyloData: realistic simulation of gene family evolution.

BACKGROUND: PrIME-GenPhyloData is a suite of tools for creating realistic simulated phylogenetic trees, in particular for families of homologous genes. It supports generation of trees based on a birth-death process and--perhaps more interestingly--also supports generation of gene family trees guided by a known (synthetic or biological) species tree while accounting for events such as gene duplication, gene loss, and lateral gene transfer (LGT). The suite also supports a wide range of branch rate models enabling relaxation of the molecular clock. RESULT: Simulated data created with PrIME-GenPhyloData can be used for benchmarking phylogenetic approaches, or for characterizing models or model parameters with respect to biological data. CONCLUSION: The concept of tree-in-tree evolution can also be used to model, for instance, biogeography or host-parasite co-evolution.

Genome-wide probabilistic reconciliation analysis across vertebrates.

Gene duplication is considered to be a major driving force in evolution that enables the genome of a species to acquire new functions. A reconciliation--a mapping of gene tree vertices to the edges or vertices of a species tree--explains where gene duplications have occurred on the species tree. In this study, we sample reconciliations from a posterior over reconciliations, gene trees, edge lengths and other parameters, given a species tree and gene sequences. We employ a Bayesian analysis tool, based on the probabilistic model DLRS that integrates gene duplication, gene loss and sequence evolution under a relaxed molecular clock for substitution rates, to obtain this posterior.