Prevalence of sexually transmitted infections and human papillomavirus in cervical samples from incarcerated women in São Paulo, Brazil: a retrospective single-center study.

Introduction: Sexually transmitted infections (STIs) cause considerable morbidity worldwide and, depending on the specific pathogen, may lead to serious complications in the female reproductive tract. Incarcerated women are particularly vulnerable to health problems with a disproportionate high rate of STIs, including infections with human papillomavirus (HPV). Methods: Here, cervical swab samples collected from 299 women (18 to 64 years) living in one of the women's prisons of São Paulo, Brazil were submitted for liquid-based cytology to determine the prevalence of precancerous lesions. Furthermore, direct detection of 30 genital HPV genotypes (18 high-risk and 12 low-risk types) and 11 additional STIs (Chlamydia trachomatis, Neisseria gonorrhoeae, Herpes simplex virus 1 and 2, Haemophilus ducreyi, Mycoplasma genitalium and hominis, Treponema pallidum, Trichomonas vaginalis, Ureaplasma parvum and urealyticum) were performed by molecular typing using two PCR-based DNA microarray systems, i.e., EUROArray HPV and EUROArray STI (EUROIMMUN), respectively. Results: The overall prevalence of cytological abnormalities was 5.8%, including five women with low-grade and five women with high-grade squamous intraepithelial lesions. The overall prevalence of HPV was 62.2, and 87.1% of the HPV-positive women were infected with oncogenic high-risk (HR) HPV types. HPV types 16 (24.1%), 33 and 52 (both 10.4%) were the most frequently detected. The prevalence of the other STIs was 72.8%. Up to four different pathogens were found in the infected women, the most frequent being Ureaplasma parvum (45.3%), Mycoplasma hominis (36.2%) and Trichomonas vaginalis (24.8%). Conclusion: The high number of HR-HPV infections and other STIs described here highlights the fact that the Brazilian female prison population requires more attention in the country's health policies. The implementation of screening programs and treatment measures might contribute to a decrease in the incidence of STIs and cervical cancer in this vulnerable population. However, for such measures to be effective, further studies are needed to investigate the best practice to get more women to engage in in-prison prevention programs, e.g., through offering further sexual health education and self-sampling.

Transcriptome-Wide Analysis Identifies Novel Associations With Blood Pressure.

Hypertension represents a major cardiovascular risk factor. The pathophysiology of increased blood pressure (BP) is not yet completely understood. Transcriptome profiling offers possibilities to uncover genetics effects on BP. Based on 2 populations including 2549 individuals, a meta-analyses of monocytic transcriptome-wide profiles were performed to identify transcripts associated with BP. Replication was performed in 2 independent studies of whole-blood transcriptome data including 1990 individuals. For identified candidate genes, a direct link between long-term changes in BP and gene expression over time and by treatment with BP-lowering therapy was assessed. The predictive value of protein levels encoded by candidate genes for subsequent cardiovascular disease was investigated. Eight transcripts (CRIP1, MYADM, TIPARP, TSC22D3, CEBPA, F12, LMNA, and TPPP3) were identified jointly accounting for up to 13% (95% confidence interval, 8.7-16.2) of BP variability. Changes in CRIP1, MYADM, TIPARP, LMNA, TSC22D3, CEBPA, and TPPP3 expression associated with BP changes-among these, CRIP1 gene expression was additionally correlated to measures of cardiac hypertrophy. Assessment of circulating CRIP1 (cystein-rich protein 1) levels as biomarkers showed a strong association with increased risk for incident stroke (hazard ratio, 1.06; 95% confidence interval, 1.03-1.09; P=5.0×10-5). Our comprehensive analysis of global gene expression highlights 8 novel transcripts significantly associated with BP, providing a link between gene expression and BP. Translational approaches further established evidence for the potential use of CRIP1 as emerging disease-related biomarker.

Large-scale genome-wide analysis identifies genetic variants associated with cardiac structure and function.

BACKGROUND: Understanding the genetic architecture of cardiac structure and function may help to prevent and treat heart disease. This investigation sought to identify common genetic variations associated with inter-individual variability in cardiac structure and function. METHODS: A GWAS meta-analysis of echocardiographic traits was performed, including 46,533 individuals from 30 studies (EchoGen consortium). The analysis included 16 traits of left ventricular (LV) structure, and systolic and diastolic function. RESULTS: The discovery analysis included 21 cohorts for structural and systolic function traits (n = 32,212) and 17 cohorts for diastolic function traits (n = 21,852). Replication was performed in 5 cohorts (n = 14,321) and 6 cohorts (n = 16,308), respectively. Besides 5 previously reported loci, the combined meta-analysis identified 10 additional genome-wide significant SNPs: rs12541595 near MTSS1 and rs10774625 in ATXN2 for LV end-diastolic internal dimension; rs806322 near KCNRG, rs4765663 in CACNA1C, rs6702619 near PALMD, rs7127129 in TMEM16A, rs11207426 near FGGY, rs17608766 in GOSR2, and rs17696696 in CFDP1 for aortic root diameter; and rs12440869 in IQCH for Doppler transmitral A-wave peak velocity. Findings were in part validated in other cohorts and in GWAS of related disease traits. The genetic loci showed associations with putative signaling pathways, and with gene expression in whole blood, monocytes, and myocardial tissue. CONCLUSION: The additional genetic loci identified in this large meta-analysis of cardiac structure and function provide insights into the underlying genetic architecture of cardiac structure and warrant follow-up in future functional studies. FUNDING: For detailed information per study, see Acknowledgments.

Genetic Analysis Workshop 19: methods and strategies for analyzing human sequence and gene expression data in extended families and unrelated individuals.

Genetic Analysis Workshop 19 provided a platform for developing and evaluating statistical methods to analyze whole-genome sequence and gene expression data from a pedigree-based sample, as well as whole-exome sequence data from a large cohort of unrelated individuals. In this article we present an overview of the data sets, the GAW experience, and summaries of the contributions arranged into nine methodological themes.

Comparison of pre-processing methods for multiplex bead-based immunoassays.

BACKGROUND: High throughput protein expression studies can be performed using bead-based protein immunoassays, such as the Luminex® xMAP® technology. Technical variability is inherent to these experiments and may lead to systematic bias and reduced power. To reduce technical variability, data pre-processing is performed. However, no recommendations exist for the pre-processing of Luminex® xMAP® data. RESULTS: We compared 37 different data pre-processing combinations of transformation and normalization methods in 42 samples on 384 analytes obtained from a multiplex immunoassay based on the Luminex® xMAP® technology. We evaluated the performance of each pre-processing approach with 6 different performance criteria. Three performance criteria were plots. All plots were evaluated by 15 independent and blinded readers. Four different combinations of transformation and normalization methods performed well as pre-processing procedure for this bead-based protein immunoassay. CONCLUSIONS: The following combinations of transformation and normalization were suitable for pre-processing Luminex® xMAP® data in this study: weighted Box-Cox followed by quantile or robust spline normalization (rsn), asinh transformation followed by loess normalization and Box-Cox followed by rsn.

Removing Batch Effects from Longitudinal Gene Expression - Quantile Normalization Plus ComBat as Best Approach for Microarray Transcriptome Data.

Technical variation plays an important role in microarray-based gene expression studies, and batch effects explain a large proportion of this noise. It is therefore mandatory to eliminate technical variation while maintaining biological variability. Several strategies have been proposed for the removal of batch effects, although they have not been evaluated in large-scale longitudinal gene expression data. In this study, we aimed at identifying a suitable method for batch effect removal in a large study of microarray-based longitudinal gene expression. Monocytic gene expression was measured in 1092 participants of the Gutenberg Health Study at baseline and 5-year follow up. Replicates of selected samples were measured at both time points to identify technical variability. Deming regression, Passing-Bablok regression, linear mixed models, non-linear models as well as ReplicateRUV and ComBat were applied to eliminate batch effects between replicates. In a second step, quantile normalization prior to batch effect correction was performed for each method. Technical variation between batches was evaluated by principal component analysis. Associations between body mass index and transcriptomes were calculated before and after batch removal. Results from association analyses were compared to evaluate maintenance of biological variability. Quantile normalization, separately performed in each batch, combined with ComBat successfully reduced batch effects and maintained biological variability. ReplicateRUV performed perfectly in the replicate data subset of the study, but failed when applied to all samples. All other methods did not substantially reduce batch effects in the replicate data subset. Quantile normalization plus ComBat appears to be a valuable approach for batch correction in longitudinal gene expression data.

Comparison of Cox Model Methods in A Low-dimensional Setting with Few Events.

Prognostic models based on survival data frequently make use of the Cox proportional hazards model. Developing reliable Cox models with few events relative to the number of predictors can be challenging, even in low-dimensional datasets, with a much larger number of observations than variables. In such a setting we examined the performance of methods used to estimate a Cox model, including (i) full model using all available predictors and estimated by standard techniques, (ii) backward elimination (BE), (iii) ridge regression, (iv) least absolute shrinkage and selection operator (lasso), and (v) elastic net. Based on a prospective cohort of patients with manifest coronary artery disease (CAD), we performed a simulation study to compare the predictive accuracy, calibration, and discrimination of these approaches. Candidate predictors for incident cardiovascular events we used included clinical variables, biomarkers, and a selection of genetic variants associated with CAD. The penalized methods, i.e., ridge, lasso, and elastic net, showed a comparable performance, in terms of predictive accuracy, calibration, and discrimination, and outperformed BE and the full model. Excessive shrinkage was observed in some cases for the penalized methods, mostly on the simulation scenarios having the lowest ratio of a number of events to the number of variables. We conclude that in similar settings, these three penalized methods can be used interchangeably. The full model and backward elimination are not recommended in rare event scenarios.

Joint analysis of multiple phenotypes: summary of results and discussions from the Genetic Analysis Workshop 19.

For Genetic Analysis Workshop 19, 2 extensive data sets were provided, including whole genome and whole exome sequence data, gene expression data, and longitudinal blood pressure outcomes, together with nongenetic covariates. These data sets gave researchers the chance to investigate different aspects of more complex relationships within the data, and the contributions in our working group focused on statistical methods for the joint analysis of multiple phenotypes, which is part of the research field of data integration. The analysis of data from different sources poses challenges to researchers but provides the opportunity to model the real-life situation more realistically.Our 4 contributions all used the provided real data to identify genetic predictors for blood pressure. In the contributions, novel multivariate rare variant tests, copula models, structural equation models and a sparse matrix representation variable selection approach were applied. Each of these statistical models can be used to investigate specific hypothesized relationships, which are described together with their biological assumptions.The results showed that all methods are ready for application on a genome-wide scale and can be used or extended to include multiple omics data sets. The results provide potentially interesting genetic targets for future investigation and replication. Furthermore, all contributions demonstrated that the analysis of complex data sets could benefit from modeling correlated phenotypes jointly as well as by adding further bioinformatics information.

Analyzing Illumina Gene Expression Microarray Data Obtained From Human Whole Blood Cell and Blood Monocyte Samples.

Microarray profiling of gene expression is widely applied to studies in molecular biology and functional genomics. Experimental and technical variations make not only the statistical analysis of single studies but also meta-analyses of different studies very challenging. Here, we describe the analytical steps required to substantially reduce the variations of gene expression data without affecting true effect sizes. A software pipeline has been established using gene expression data from a total of 3358 whole blood cell and blood monocyte samples, all from three German population-based cohorts, measured on the Illumina HumanHT-12 v3 BeadChip array. In summary, adjustment for a few selected technical factors greatly improved reliability of gene expression analyses. Such adjustments are particularly required for meta-analyses of different studies.

Extensive alterations of the whole-blood transcriptome are associated with body mass index: results of an mRNA profiling study involving two large population-based cohorts.

BACKGROUND: Obesity, defined as pathologically increased body mass index (BMI), is strongly related to an increased risk for numerous common cardiovascular and metabolic diseases. It is particularly associated with insulin resistance, hyperglycemia, and systemic oxidative stress and represents the most important risk factor for type 2 diabetes (T2D). However, the pathophysiological mechanisms underlying these associations are still not completely understood. Therefore, in order to identify potentially disease-relevant BMI-associated gene expression signatures, a transcriptome-wide association study (TWAS) on BMI was performed. METHODS: Whole-blood mRNA levels determined by array-based transcriptional profiling were correlated with BMI in two large independent population-based cohort studies (KORA F4 and SHIP-TREND) comprising a total of 1977 individuals. RESULTS: Extensive alterations of the whole-blood transcriptome were associated with BMI: More than 3500 transcripts exhibited significant positive or negative BMI-correlation. Three major whole-blood gene expression signatures associated with increased BMI were identified. The three signatures suggested: i) a ratio shift from mature erythrocytes towards reticulocytes, ii) decreased expression of several genes essentially involved in the transmission and amplification of the insulin signal, and iii) reduced expression of several key genes involved in the defence against reactive oxygen species (ROS). CONCLUSIONS: Whereas the first signature confirms published results, the other two provide possible mechanistic explanations for well-known epidemiological findings under conditions of increased BMI, namely attenuated insulin signaling and increased oxidative stress. The putatively causative BMI-dependent down-regulation of the expression of numerous genes on the mRNA level represents a novel finding. BMI-associated negative transcriptional regulation of insulin signaling and oxidative stress management provide new insights into the pathogenesis of metabolic syndrome and T2D.

Genome-wide association study identifies new susceptibility loci for cutaneous lupus erythematosus.

Cutaneous lupus erythematosus (CLE) is a chronic autoimmune disease of the skin with typical clinical manifestations. Here, we genotyped 906 600 single nucleotide polymorphisms (SNPs) in 183 CLE cases and 1288 controls of Central European ancestry. Replication was performed for 13 SNPs in 219 case subjects and 262 controls from Finland. Association was particularly pronounced at 4 loci, all with genomewide significance (P < 5 × 10(-8) ): rs2187668 (PGWAS  = 1.4 × 10(-12) ), rs9267531 (PGWAS  = 4.7 × 10(-10) ), rs4410767 (PGWAS  = 1.0 × 10(-9) ) and rs3094084 (PGWAS  = 1.1 × 10(-9) ). All mentioned SNPs are located within the major histocompatibility complex (MHC) region of chromosome 6 and near genes of known immune functions or associations with other autoimmune diseases such as HLA-DQ alpha chain 1 (HLA-DQA1), MICA, MICB, MSH5, TRIM39 and RPP21. For example, TRIM39/RPP21 read through transcript is a known mediator of the interferon response, a central pathway involved in the pathogenesis of CLE and systemic lupus erythematosus (SLE). Taken together, this genomewide analysis of disease association of CLE identified candidate genes and genomic regions that may contribute to pathogenic mechanisms in CLE via dysregulated antigen presentation (HLA-DQA1), apoptosis regulation, RNA processing and interferon response (MICA, MICB, MSH5, TRIM39 and RPP21).

Dysregulation of microRNAs in angioimmunoblastic T-cell lymphoma.

BACKGROUND: Angioimmunoblastic T-cell lymphomas (AITLs) are the second most frequent peripheral T-cell lymphomas in humans worldwide and histomorphologically well characterized. MicroRNAs are a group of small non-coding RNAs that can negatively regulate gene expression on a posttranscriptional level. Their dysregulation has been shown to be of importance in numerous tumour entities. MATERIALS AND METHODS: As a first step towards understanding the possible influence of microRNA-dysregulation in AITL, we analyzed the expression signatures of 760 microRNAs in 30 nodal AITLs in comparison to reactive lymphadenitis with T-zone hyperplasia. RESULTS: We found miR-34a, miR-146a and miR-193b to be up-regulated, as well as miR-140-3p, let-7g, miR-30b and miR-664 to be down-regulated in AITL to a significant level. CONCLUSION: The microRNA-signatures of AITL reveal some overlap to autoimmune diseases, virus-triggered lymphomas and angiogenic factors that, coupled with future studies, will potentially provide better understanding of this disease.

Meta-analysis of Genome-Wide Association Studies Identifies Novel Loci Associated With Optic Disc Morphology.

Primary open-angle glaucoma is the most common optic neuropathy and an important cause of irreversible blindness worldwide. The optic nerve head or optic disc is divided in two parts: a central cup (without nerve fibers) surrounded by the neuroretinal rim (containing axons of the retinal ganglion cells). The International Glaucoma Genetics Consortium conducted a meta-analysis of genome-wide association studies consisting of 17,248 individuals of European ancestry and 6,841 individuals of Asian ancestry. The outcomes of the genome-wide association studies were disc area and cup area. These specific measurements describe optic nerve morphology in another way than the vertical cup-disc ratio, which is a clinically used measurement, and may shed light on new glaucoma mechanisms. We identified 10 new loci associated with disc area (CDC42BPA, F5, DIRC3, RARB, ABI3BP, DCAF4L2, ELP4, TMTC2, NR2F2, and HORMAD2) and another 10 new loci associated with cup area (DHRS3, TRIB2, EFEMP1, FLNB, FAM101, DDHD1, ASB7, KPNB1, BCAS3, and TRIOBP). The new genes participate in a number of pathways and future work is likely to identify more functions related to the pathogenesis of glaucoma.

Genome-wide association study for refractive astigmatism reveals genetic co-determination with spherical equivalent refractive error: the CREAM consortium.

To identify genetic variants associated with refractive astigmatism in the general population, meta-analyses of genome-wide association studies were performed for: White Europeans aged at least 25 years (20 cohorts, N = 31,968); Asian subjects aged at least 25 years (7 cohorts, N = 9,295); White Europeans aged <25 years (4 cohorts, N = 5,640); and all independent individuals from the above three samples combined with a sample of Chinese subjects aged <25 years (N = 45,931). Participants were classified as cases with refractive astigmatism if the average cylinder power in their two eyes was at least 1.00 diopter and as controls otherwise. Genome-wide association analysis was carried out for each cohort separately using logistic regression. Meta-analysis was conducted using a fixed effects model. In the older European group the most strongly associated marker was downstream of the neurexin-1 (NRXN1) gene (rs1401327, P = 3.92E-8). No other region reached genome-wide significance, and association signals were lower for the younger European group and Asian group. In the meta-analysis of all cohorts, no marker reached genome-wide significance: The most strongly associated regions were, NRXN1 (rs1401327, P = 2.93E-07), TOX (rs7823467, P = 3.47E-07) and LINC00340 (rs12212674, P = 1.49E-06). For 34 markers identified in prior GWAS for spherical equivalent refractive error, the beta coefficients for genotype versus spherical equivalent, and genotype versus refractive astigmatism, were highly correlated (r = -0.59, P = 2.10E-04). This work revealed no consistent or strong genetic signals for refractive astigmatism; however, the TOX gene region previously identified in GWAS for spherical equivalent refractive error was the second most strongly associated region. Analysis of additional markers provided evidence supporting widespread genetic co-susceptibility for spherical and astigmatic refractive errors.

MicroRNA profiling of low-grade and transformed nodal marginal zone lymphoma reveals a similar signature pattern distinct from diffuse large B cell lymphoma.

BACKGROUND/AIMS: As critical post-transcriptional regulators of gene expression, microRNAs are involved in several cellular processes of vital impact including cell growth and apoptosis. Many hematologic malignancies exhibit distinct microRNA signatures. MicroRNA implication in the pathogenesis of nodal marginal zone lymphoma (NMZL), however, remains widely elusive. METHODS: Comprehensive morphologic, immunophenotypic and cytogenetic studies were carried out on a cohort of NMZL (n = 30) incorporating indolent as well as transformed MZL. In addition, microRNA signatures were generated, employing a quantitative real-time polymerase chain reaction approach. These were then compared to signatures from cases of diffuse large B cell lymphoma (DLBCL) alongside reactive lymph node controls. RESULTS: While microRNA signatures of low-grade and transformed NMZL did not differ significantly, several microRNAs were differentially expressed between transformed NMZL and DLBCL, hinting at molecularly distinct mechanisms of lymphomagenesis and indicating the biological disparity of transformed NMZL from DLBCL. CONCLUSION: In the light of the unresolved issue regarding the classification of marginal zone-derived transformed B-cell neoplasms, microRNAs may be a valuable aid in discriminating NMZL from DLBCL.

A comparison of two collapsing methods in different approaches.

Sequencing technologies have enabled the investigation of whole genomes of many individuals in parallel. Studies have shown that the joint consideration of multiple rare variants may explain a relevant proportion of the genetic basis for disease so that grouping of rare variants, termed collapsing, can enrich the association signal. Following this assumption, we investigate the type I error and the power of two proposed collapsing methods (combined multivariate and collapsing method and the functional principal component analysis [FPCA]-based statistic) using the case-control data provided for the Genetic Analysis Workshop 18 with knowledge of the true model. Variants with a minor allele frequency (MAF) of 0.05 or less were collapsed per gene for combined multivariate and collapsing. Neither of the methods detected any of the truly associated genes reliably. Although combined multivariate and collapsing identified one gene with a power of 0.66, it had an unacceptably high false-positive rate of 75%. In contrast, FPCA covered the type I error level well but at the cost of low power. A strict filtering of variants by small MAF might lead to a better performance of the collapsing methods. Furthermore, the inclusion of information on functionality of the variants could be helpful.

BiomarCaRE: rationale and design of the European BiomarCaRE project including 300,000 participants from 13 European countries.

Biomarkers are considered as tools to enhance cardiovascular risk estimation. However, the value of biomarkers on risk estimation beyond European risk scores, their comparative impact among different European regions and their role towards personalised medicine remains uncertain. Biomarker for Cardiovascular Risk Assessment in Europe (BiomarCaRE) is an European collaborative research project with the primary objective to assess the value of established and emerging biomarkers for cardiovascular risk prediction. BiomarCaRE integrates clinical and epidemiological biomarker research and commercial enterprises throughout Europe to combine innovation in biomarker discovery for cardiovascular disease prediction with consecutive validation of biomarker effectiveness in large, well-defined primary and secondary prevention cohorts including over 300,000 participants from 13 European countries. Results from this study will contribute to improved cardiovascular risk prediction across different European populations. The present publication describes the rationale and design of the BiomarCaRE project.

Meta-analysis of genome-wide association studies identifies novel loci that influence cupping and the glaucomatous process.

Glaucoma is characterized by irreversible optic nerve degeneration and is the most frequent cause of irreversible blindness worldwide. Here, the International Glaucoma Genetics Consortium conducts a meta-analysis of genome-wide association studies of vertical cup-disc ratio (VCDR), an important disease-related optic nerve parameter. In 21,094 individuals of European ancestry and 6,784 individuals of Asian ancestry, we identify 10 new loci associated with variation in VCDR. In a separate risk-score analysis of five case-control studies, Caucasians in the highest quintile have a 2.5-fold increased risk of primary open-angle glaucoma as compared with those in the lowest quintile. This study has more than doubled the known loci associated with optic disc cupping and will allow greater understanding of mechanisms involved in this common blinding condition.

Genome-wide association study of L-arginine and dimethylarginines reveals novel metabolic pathway for symmetric dimethylarginine.

BACKGROUND: Dimethylarginines (DMA) interfere with nitric oxide formation by inhibiting nitric oxide synthase (asymmetrical DMA [ADMA]) and l-arginine uptake into the cell (ADMA and symmetrical DMA [SDMA]). In prospective clinical studies, ADMA has been characterized as a cardiovascular risk marker, whereas SDMA is a novel marker for renal function and associated with all-cause mortality after ischemic stroke. The aim of the current study was to characterize the environmental and genetic contributions to interindividual variability of these biomarkers. METHODS AND RESULTS: This study comprised a genome-wide association analysis of 3 well-characterized population-based cohorts (Framingham Heart Study [FHS; n=2992], Gutenberg Health Study [GHS; n=4354], and Multinational Monitoring of Trends and Determinants in Cardiovascular Disease Study [MONICA]/Cooperative Health Research in the Augsburg Area, Augsburg, Bavaria, Germany [KORA] F3 [n=581]) and identified replicated loci (DDAH1, MED23, Arg1, and AGXT2) associated with the interindividual variability in ADMA, l-arginine, and SDMA. Experimental in silico and in vitro studies confirmed functional significance of the identified AGXT2 variants. Clinical outcome analysis in 384 patients of the Leeds stroke study demonstrated an association between increased plasma levels of SDMA, AGXT2 variants, and various cardiometabolic risk factors. AGXT2 variants were not associated with poststroke survival in the Leeds study or were they associated with incident stroke in the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium. CONCLUSIONS: These genome-wide association study support the importance of DDAH1 and MED23/Arg1 in regulating ADMA and l-arginine metabolism, respectively, and identify a novel regulatory renal pathway for SDMA by AGXT2. AGXT2 variants might explain part of the pathogenic link between SDMA, renal function, and outcome. An association between AGXT2 variants and stroke is unclear and warrants further investigation.