The RD-Connect Genome-Phenome Analysis Platform: Accelerating diagnosis, research, and gene discovery for rare diseases.

Rare disease patients are more likely to receive a rapid molecular diagnosis nowadays thanks to the wide adoption of next-generation sequencing. However, many cases remain undiagnosed even after exome or genome analysis, because the methods used missed the molecular cause in a known gene, or a novel causative gene could not be identified and/or confirmed. To address these challenges, the RD-Connect Genome-Phenome Analysis Platform (GPAP) facilitates the collation, discovery, sharing, and analysis of standardized genome-phenome data within a collaborative environment. Authorized clinicians and researchers submit pseudonymised phenotypic profiles encoded using the Human Phenotype Ontology, and raw genomic data which is processed through a standardized pipeline. After an optional embargo period, the data are shared with other platform users, with the objective that similar cases in the system and queries from peers may help diagnose the case. Additionally, the platform enables bidirectional discovery of similar cases in other databases from the Matchmaker Exchange network. To facilitate genome-phenome analysis and interpretation by clinical researchers, the RD-Connect GPAP provides a powerful user-friendly interface and leverages tens of information sources. As a result, the resource has already helped diagnose hundreds of rare disease patients and discover new disease causing genes.

WEScover: selection between clinical whole exome sequencing and gene panel testing.

BACKGROUND: Whole exome sequencing (WES) is widely adopted in clinical and research settings; however, one of the practical concerns is the potential false negatives due to incomplete breadth and depth of coverage for several exons in clinically implicated genes. In some cases, a targeted gene panel testing may be a dependable option to ascertain true negatives for genomic variants in known disease-associated genes. We developed a web-based tool to quickly gauge whether all genes of interest would be reliably covered by WES or whether targeted gene panel testing should be considered instead to minimize false negatives in candidate genes. RESULTS: WEScover is a novel web application that provides an intuitive user interface for discovering breadth and depth of coverage across population-scale WES datasets, searching either by phenotype, by targeted gene panel(s) or by gene(s). Moreover, the application shows metrics from the Genome Aggregation Database to provide gene-centric view on breadth of coverage. CONCLUSIONS: WEScover allows users to efficiently query genes and phenotypes for the coverage of associated exons by WES and recommends use of panel tests for the genes with potential incomplete coverage by WES.

Variability of multi-omics profiles in a population-based child cohort.

BACKGROUND: Multiple omics technologies are increasingly applied to detect early, subtle molecular responses to environmental stressors for future disease risk prevention. However, there is an urgent need for further evaluation of stability and variability of omics profiles in healthy individuals, especially during childhood. METHODS: We aimed to estimate intra-, inter-individual and cohort variability of multi-omics profiles (blood DNA methylation, gene expression, miRNA, proteins and serum and urine metabolites) measured 6 months apart in 156 healthy children from five European countries. We further performed a multi-omics network analysis to establish clusters of co-varying omics features and assessed the contribution of key variables (including biological traits and sample collection parameters) to omics variability. RESULTS: All omics displayed a large range of intra- and inter-individual variability depending on each omics feature, although all presented a highest median intra-individual variability. DNA methylation was the most stable profile (median 37.6% inter-individual variability) while gene expression was the least stable (6.6%). Among the least stable features, we identified 1% cross-omics co-variation between CpGs and metabolites (e.g. glucose and CpGs related to obesity and type 2 diabetes). Explanatory variables, including age and body mass index (BMI), explained up to 9% of serum metabolite variability. CONCLUSIONS: Methylation and targeted serum metabolomics are the most reliable omics to implement in single time-point measurements in large cross-sectional studies. In the case of metabolomics, sample collection and individual traits (e.g. BMI) are important parameters to control for improved comparability, at the study design or analysis stage. This study will be valuable for the design and interpretation of epidemiological studies that aim to link omics signatures to disease, environmental exposures, or both.

The Clinical Genome and Ancestry Report: An interactive web application for prioritizing clinically implicated variants from genome sequencing data with ancestry composition.

Genome sequencing is positioned as a routine clinical work-up for diverse clinical conditions. A commonly used approach to highlight candidate variants with potential clinical implication is to search over locus- and gene-centric knowledge databases. Most web-based applications allow a federated query across diverse databases for a single variant; however, sifting through a large number of genomic variants with combination of filtering criteria is a substantial challenge. Here we describe the Clinical Genome and Ancestry Report (CGAR), an interactive web application developed to follow clinical interpretation workflows by organizing variants into seven categories: (1) reported disease-associated variants, (2) rare- and high-impact variants in putative disease-associated genes, (3) secondary findings that the American College of Medical Genetics and Genomics recommends reporting back to patients, (4) actionable pharmacogenomic variants, (5) focused reports for candidate genes, (6) de novo variant candidates for trio analysis, and (7) germline and somatic variants implicated in cancer risk, diagnosis, treatment and prognosis. For each variant, a comprehensive list of external links to variant-centric and phenotype databases are provided. Furthermore, genotype-derived ancestral composition is used to highlight allele frequencies from a matched population since some disease-associated variants show a wide variation between populations. CGAR is an open-source software and is available at https://tom.tch.harvard.edu/apps/cgar/.

In utero and childhood exposure to tobacco smoke and multi-layer molecular signatures in children.

BACKGROUND: The adverse health effects of early life exposure to tobacco smoking have been widely reported. In spite of this, the underlying molecular mechanisms of in utero and postnatal exposure to tobacco smoke are only partially understood. Here, we aimed to identify multi-layer molecular signatures associated with exposure to tobacco smoke in these two exposure windows. METHODS: We investigated the associations of maternal smoking during pregnancy and childhood secondhand smoke (SHS) exposure with molecular features measured in 1203 European children (mean age 8.1 years) from the Human Early Life Exposome (HELIX) project. Molecular features, covering 4 layers, included blood DNA methylation and gene and miRNA transcription, plasma proteins, and sera and urinary metabolites. RESULTS: Maternal smoking during pregnancy was associated with DNA methylation changes at 18 loci in child blood. DNA methylation at 5 of these loci was related to expression of the nearby genes. However, the expression of these genes themselves was only weakly associated with maternal smoking. Conversely, childhood SHS was not associated with blood DNA methylation or transcription patterns, but with reduced levels of several serum metabolites and with increased plasma PAI1 (plasminogen activator inhibitor-1), a protein that inhibits fibrinolysis. Some of the in utero and childhood smoking-related molecular marks showed dose-response trends, with stronger effects with higher dose or longer duration of the exposure. CONCLUSION: In this first study covering multi-layer molecular features, pregnancy and childhood exposure to tobacco smoke were associated with distinct molecular phenotypes in children. The persistent and dose-dependent changes in the methylome make CpGs good candidates to develop biomarkers of past exposure. Moreover, compared to methylation, the weak association of maternal smoking in pregnancy with gene expression suggests different reversal rates and a methylation-based memory to past exposures. Finally, certain metabolites and protein markers evidenced potential early biological effects of postnatal SHS, such as fibrinolysis.

Comprehensive study of the exposome and omic data using rexposome Bioconductor Packages.

SUMMARY: Genomics has dramatically improved our understanding of the molecular origins of certain human diseases. Nonetheless, our health is also influenced by the cumulative impact of exposures experienced across the life course (termed 'exposome'). The study of the high-dimensional exposome offers a new paradigm for investigating environmental contributions to disease etiology. However, there is a lack of bioinformatics tools for managing, visualizing and analyzing the exposome. The analysis data should include both association with health outcomes and integration with omic layers. We provide a generic framework called rexposome project, developed in the R/Bioconductor architecture that includes object-oriented classes and methods to leverage high-dimensional exposome data in disease association studies including its integration with a variety of high-throughput data types. The usefulness of the package is illustrated by analyzing a real dataset including exposome data, three health outcomes related to respiratory diseases and its integration with the transcriptome and methylome. AVAILABILITY AND IMPLEMENTATION: rexposome project is available at https://isglobal-brge.github.io/rexposome/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Sensing solutions for improving the performance, health and wellbeing of small ruminants.

Diversity of production systems and specific socio-economic barriers are key reasons explaining why the implementation of new technologies in small ruminants, despite being needed and beneficial for farmers, is harder than in other livestock species. There are, however, helpful peculiarities where small ruminants are concerned: the compulsory use of electronic identification created a unique scenario in Europe in which all small ruminant breeding stock became searchable by appropriate sensing solutions, and the largest small ruminant population in the world is located in Asia, close to the areas producing new technologies. Notwithstanding, only a few research initiatives and literature reviews have addressed the development of new technologies in small ruminants. This Research Reflection focuses on small ruminants (with emphasis on dairy goats and sheep) and reviews in a non-exhaustive way the basic concepts, the currently available sensor solutions and the structure and elements needed for the implementation of sensor-based husbandry decision support. Finally, some examples of results obtained using several sensor solutions adapted from large animals or newly developed for small ruminants are discussed. Significant room for improvement is recognized and a large number of multiple-sensor solutions are expected to be developed in the relatively near future.

CTDquerier: a bioconductor R package for Comparative Toxicogenomics DatabaseTM data extraction, visualization and enrichment of environmental and toxicological studies.

Summary: Biomedical studies currently include a large volume of genomic and environmental factors for studying the etiology of human diseases. R/Bioconductor projects provide several tools for performing enrichment analysis at gene-pathway level, allowing researchers to develop novel hypotheses. However, there is a need to perform similar analyses at the chemicals-genes or chemicals-diseases levels to provide complementary knowledge of the causal path between chemicals and diseases. While the Comparative Toxicogenomics DatabaseTM (CTD) provides information about these relationships, there is no software for integrating it into R/Bioconductor analysis pipelines. CTDquerier helps users to easily download CTD data and integrate it in the R/Bioconductor framework. The package also contains functions for visualizing CTD data and performing enrichment analyses. We illustrate how to use the package with a real data analysis of asthma-related genes. CTDquerier is a flexible and easy-to-use Bioconductor package that provides novel hypothesis about the relationships between chemicals and diseases. Availability and implementation: CTDquerier R package is available through Bioconductor and its development version at https://github.com/isglobal-brge/CTDquerier. Supplementary information: Supplementary data are available at Bioinformatics online.

psygenet2r: a R/Bioconductor package for the analysis of psychiatric disease genes.

MOTIVATION: Psychiatric disorders have a great impact on morbidity and mortality. Genotype-phenotype resources for psychiatric diseases are key to enable the translation of research findings to a better care of patients. PsyGeNET is a knowledge resource on psychiatric diseases and their genes, developed by text mining and curated by domain experts. RESULTS: We present psygenet2r, an R package that contains a variety of functions for leveraging PsyGeNET database and facilitating its analysis and interpretation. The package offers different types of queries to the database along with variety of analysis and visualization tools, including the study of the anatomical structures in which the genes are expressed and gaining insight of gene's molecular function. Psygenet2r is especially suited for network medicine analysis of psychiatric disorders. AVAILABILITY AND IMPLEMENTATION: The package is implemented in R and is available under MIT license from Bioconductor (http://bioconductor.org/packages/release/bioc/html/psygenet2r.html). CONTACT: juanr.gonzalez@isglobal.org or laura.furlong@upf.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MultiDataSet: an R package for encapsulating multiple data sets with application to omic data integration.

BACKGROUND: Reduction in the cost of genomic assays has generated large amounts of biomedical-related data. As a result, current studies perform multiple experiments in the same subjects. While Bioconductor's methods and classes implemented in different packages manage individual experiments, there is not a standard class to properly manage different omic datasets from the same subjects. In addition, most R/Bioconductor packages that have been designed to integrate and visualize biological data often use basic data structures with no clear general methods, such as subsetting or selecting samples. RESULTS: To cover this need, we have developed MultiDataSet, a new R class based on Bioconductor standards, designed to encapsulate multiple data sets. MultiDataSet deals with the usual difficulties of managing multiple and non-complete data sets while offering a simple and general way of subsetting features and selecting samples. We illustrate the use of MultiDataSet in three common situations: 1) performing integration analysis with third party packages; 2) creating new methods and functions for omic data integration; 3) encapsulating new unimplemented data from any biological experiment. CONCLUSIONS: MultiDataSet is a suitable class for data integration under R and Bioconductor framework.

The acute effects of ultraviolet radiation on the blood transcriptome are independent of plasma 25OHD3.

The molecular basis of many health outcomes attributed to solar ultraviolet radiation (UVR) is unknown. We tested the hypothesis that they may originate from transcriptional changes in blood cells. This was determined by assessing the effect of fluorescent solar simulated radiation (FSSR) on the transcriptional profile of peripheral blood pre- and 6h, 24h and 48h post-exposure in nine healthy volunteers. Expression of 20 genes was down-regulated and one was up-regulated at 6h after FSSR. All recovered to baseline expression at 24h or 48h. These genes have been associated with immune regulation, cancer and blood pressure; health effects attributed to vitamin D via solar UVR exposure. Plasma 25-hydroxyvitamin D3 [25OHD3] levels increased over time after FSSR and were maximal at 48h. The increase was more pronounced in participants with low basal 25OHD3 levels. Mediation analyses suggested that changes in gene expression due to FSSR were independent of 25OHD3 and blood cell subpopulations.

affy2sv: an R package to pre-process Affymetrix CytoScan HD and 750K arrays for SNP, CNV, inversion and mosaicism calling.

BACKGROUND: The well-known Genome-Wide Association Studies (GWAS) had led to many scientific discoveries using SNP data. Even so, they were not able to explain the full heritability of complex diseases. Now, other structural variants like copy number variants or DNA inversions, either germ-line or in mosaicism events, are being studies. We present the R package affy2sv to pre-process Affymetrix CytoScan HD/750k array (also for Genome-Wide SNP 5.0/6.0 and Axiom) in structural variant studies. RESULTS: We illustrate the capabilities of affy2sv using two different complete pipelines on real data. The first one performing a GWAS and a mosaic alterations detection study, and the other detecting CNVs and performing an inversion calling. CONCLUSION: Both examples presented in the article show up how affy2sv can be used as part of more complex pipelines aimed to analyze Affymetrix SNP arrays data in genetic association studies, where different types of structural variants are considered.

The Pregnancy Exposome: Multiple Environmental Exposures in the INMA-Sabadell Birth Cohort.

The "exposome" is defined as "the totality of human environmental exposures from conception onward, complementing the genome" and its holistic approach may advance understanding of disease etiology. We aimed to describe the correlation structure of the exposome during pregnancy to better understand the relationships between and within families of exposure and to develop analytical tools appropriate to exposome data. Estimates on 81 environmental exposures of current health concern were obtained for 728 women enrolled in The INMA (INfancia y Medio Ambiente) birth cohort, in Sabadell, Spain, using biomonitoring, geospatial modeling, remote sensors, and questionnaires. Pair-wise Pearson's and polychoric correlations were calculated and principal components were derived. The median absolute correlation across all exposures was 0.06 (5th-95th centiles, 0.01-0.54). There were strong levels of correlation within families of exposure (median = 0.45, 5th-95th centiles, 0.07-0.85). Nine exposures (11%) had a correlation higher than 0.5 with at least one exposure outside their exposure family. Effectively all the variance in the data set (99.5%) was explained by 40 principal components. Future exposome studies should interpret exposure effects in light of their correlations to other exposures. The weak to moderate correlation observed between exposure families will permit adjustment for confounding in future exposome studies.

A comparison between different error modeling of MEMS applied to GPS/INS integrated systems.

Advances in the development of micro-electromechanical systems (MEMS) have made possible the fabrication of cheap and small dimension accelerometers and gyroscopes, which are being used in many applications where the global positioning system (GPS) and the inertial navigation system (INS) integration is carried out, i.e., identifying track defects, terrestrial and pedestrian navigation, unmanned aerial vehicles (UAVs), stabilization of many platforms, etc. Although these MEMS sensors are low-cost, they present different errors, which degrade the accuracy of the navigation systems in a short period of time. Therefore, a suitable modeling of these errors is necessary in order to minimize them and, consequently, improve the system performance. In this work, the most used techniques currently to analyze the stochastic errors that affect these sensors are shown and compared: we examine in detail the autocorrelation, the Allan variance (AV) and the power spectral density (PSD) techniques. Subsequently, an analysis and modeling of the inertial sensors, which combines autoregressive (AR) filters and wavelet de-noising, is also achieved. Since a low-cost INS (MEMS grade) presents error sources with short-term (high-frequency) and long-term (low-frequency) components, we introduce a method that compensates for these error terms by doing a complete analysis of Allan variance, wavelet de-nosing and the selection of the level of decomposition for a suitable combination between these techniques. Eventually, in order to assess the stochastic models obtained with these techniques, the Extended Kalman Filter (EKF) of a loosely-coupled GPS/INS integration strategy is augmented with different states. Results show a comparison between the proposed method and the traditional sensor error models under GPS signal blockages using real data collected in urban roadways.

Patterns of Heterochromatin Transitions Linked to Changes in the Expression of Plasmodium falciparum Clonally Variant Genes.

The survival of malaria parasites in the changing human blood environment largely depends on their ability to alter gene expression by epigenetic mechanisms. The active state of Plasmodium falciparum clonally variant genes (CVGs) is associated with euchromatin characterized by the histone mark H3K9ac, whereas the silenced state is characterized by H3K9me3-based heterochromatin. Expression switches are linked to euchromatin-heterochromatin transitions, but these transitions have not been characterized for the majority of CVGs. To define the heterochromatin distribution patterns associated with the alternative transcriptional states of CVGs, we compared H3K9me3 occupancy at a genome-wide level among several parasite subclones of the same genetic background that differed in the transcriptional state of many CVGs. We found that de novo heterochromatin formation or the complete disruption of a heterochromatin domain is a relatively rare event, and for the majority of CVGs, expression switches can be explained by the expansion or retraction of heterochromatin domains. We identified different modalities of heterochromatin changes linked to transcriptional differences, but despite this complexity, heterochromatin distribution patterns generally enable the prediction of the transcriptional state of specific CVGs. We also found that in some subclones, several var genes were simultaneously in an active state. Furthermore, the heterochromatin levels in the putative regulatory region of the gdv1 antisense noncoding RNA, a regulator of sexual commitment, varied between parasite lines with different sexual conversion rates. IMPORTANCE The malaria parasite P. falciparum is responsible for more than half a million deaths every year. P. falciparum clonally variant genes (CVGs) mediate fundamental host-parasite interactions and play a key role in parasite adaptation to fluctuations in the conditions of the human host. The expression of CVGs is regulated at the epigenetic level by changes in the distribution of a type of chromatin called heterochromatin. Here, we describe at a genome-wide level the changes in the heterochromatin distribution associated with the different transcriptional states of CVGs. Our results also reveal a likely role for heterochromatin at a particular locus in determining the parasite investment in transmission to mosquitoes. Additionally, this data set will enable the prediction of the transcriptional state of CVGs from epigenomic data, which is important for the study of parasite adaptation to the conditions of the host in natural malaria infections.

Epimutation detection in the clinical context: guidelines and a use case from a new Bioconductor package.

Epimutations are rare alterations of the normal DNA methylation pattern at specific loci, which can lead to rare diseases. Methylation microarrays enable genome-wide epimutation detection, but technical limitations prevent their use in clinical settings: methods applied to rare diseases' data cannot be easily incorporated to standard analyses pipelines, while epimutation methods implemented in R packages (ramr) have not been validated for rare diseases. We have developed epimutacions, a Bioconductor package (https://bioconductor.org/packages/release/bioc/html/epimutacions.html). epimutacions implements two previously reported methods and four new statistical approaches to detect epimutations, along with functions to annotate and visualize epimutations. Additionally, we have developed an user-friendly Shiny app to facilitate epimutations detection (https://github.com/isglobal-brge/epimutacionsShiny) to non-bioinformatician users. We first compared the performance of epimutacions and ramr packages using three public datasets with experimentally validated epimutations. Methods in epimutacions had a high performance at low sample sizes and outperformed methods in ramr. Second, we used two general population children cohorts (INMA and HELIX) to determine the technical and biological factors that affect epimutations detection, providing guidelines on how designing the experiments or preprocessing the data. In these cohorts, most epimutations did not correlate with detectable regional gene expression changes. Finally, we exemplified how epimutacions can be used in a clinical context. We run epimutacions in a cohort of children with autism disorder and identified novel recurrent epimutations in candidate genes for autism. Overall, we present epimutacions a new Bioconductor package for incorporating epimutations detection to rare disease diagnosis and provide guidelines for the design and data analyses.

Identification of autosomal cis expression quantitative trait methylation (cis eQTMs) in children's blood.

Background: The identification of expression quantitative trait methylation (eQTMs), defined as associations between DNA methylation levels and gene expression, might help the biological interpretation of epigenome-wide association studies (EWAS). We aimed to identify autosomal cis eQTMs in children's blood, using data from 832 children of the Human Early Life Exposome (HELIX) project. Methods: Blood DNA methylation and gene expression were measured with the Illumina 450K and the Affymetrix HTA v2 arrays, respectively. The relationship between methylation levels and expression of nearby genes (1 Mb window centered at the transcription start site, TSS) was assessed by fitting 13.6 M linear regressions adjusting for sex, age, cohort, and blood cell composition. Results: We identified 39,749 blood autosomal cis eQTMs, representing 21,966 unique CpGs (eCpGs, 5.7% of total CpGs) and 8,886 unique transcript clusters (eGenes, 15.3% of total transcript clusters, equivalent to genes). In 87.9% of these cis eQTMs, the eCpG was located at <250 kb from eGene's TSS; and 58.8% of all eQTMs showed an inverse relationship between the methylation and expression levels. Only around half of the autosomal cis-eQTMs eGenes could be captured through annotation of the eCpG to the closest gene. eCpGs had less measurement error and were enriched for active blood regulatory regions and for CpGs reported to be associated with environmental exposures or phenotypic traits. In 40.4% of the eQTMs, the CpG and the eGene were both associated with at least one genetic variant. The overlap of autosomal cis eQTMs in children's blood with those described in adults was small (13.8%), and age-shared cis eQTMs tended to be proximal to the TSS and enriched for genetic variants. Conclusions: This catalogue of autosomal cis eQTMs in children's blood can help the biological interpretation of EWAS findings and is publicly available at https://helixomics.isglobal.org/ and at Dryad (doi:10.5061/dryad.fxpnvx0t0). Funding: The study has received funding from the European Community's Seventh Framework Programme (FP7/2007-206) under grant agreement no 308333 (HELIX project); the H2020-EU.3.1.2. - Preventing Disease Programme under grant agreement no 874583 (ATHLETE project); from the European Union's Horizon 2020 research and innovation programme under grant agreement no 733206 (LIFECYCLE project), and from the European Joint Programming Initiative "A Healthy Diet for a Healthy Life" (JPI HDHL and Instituto de Salud Carlos III) under the grant agreement no AC18/00006 (NutriPROGRAM project). The genotyping was supported by the projects PI17/01225 and PI17/01935, funded by the Instituto de Salud Carlos III and co-funded by European Union (ERDF, "A way to make Europe") and the Centro Nacional de Genotipado-CEGEN (PRB2-ISCIII). BiB received core infrastructure funding from the Wellcome Trust (WT101597MA) and a joint grant from the UK Medical Research Council (MRC) and Economic and Social Science Research Council (ESRC) (MR/N024397/1). INMA data collections were supported by grants from the Instituto de Salud Carlos III, CIBERESP, and the Generalitat de Catalunya-CIRIT. KANC was funded by the grant of the Lithuanian Agency for Science Innovation and Technology (6-04-2014_31V-66). The Norwegian Mother, Father and Child Cohort Study is supported by the Norwegian Ministry of Health and Care Services and the Ministry of Education and Research. The Rhea project was financially supported by European projects (EU FP6-2003-Food-3-NewGeneris, EU FP6. STREP Hiwate, EU FP7 ENV.2007.1.2.2.2. Project No 211250 Escape, EU FP7-2008-ENV-1.2.1.4 Envirogenomarkers, EU FP7-HEALTH-2009- single stage CHICOS, EU FP7 ENV.2008.1.2.1.6. Proposal No 226285 ENRIECO, EU- FP7- HEALTH-2012 Proposal No 308333 HELIX), and the Greek Ministry of Health (Program of Prevention of obesity and neurodevelopmental disorders in preschool children, in Heraklion district, Crete, Greece: 2011-2014; "Rhea Plus": Primary Prevention Program of Environmental Risk Factors for Reproductive Health, and Child Health: 2012-15). We acknowledge support from the Spanish Ministry of Science and Innovation through the "Centro de Excelencia Severo Ochoa 2019-2023" Program (CEX2018-000806-S), and support from the Generalitat de Catalunya through the CERCA Program. MV-U and CR-A were supported by a FI fellowship from the Catalan Government (FI-DGR 2015 and #016FI_B 00272). MC received funding from Instituto Carlos III (Ministry of Economy and Competitiveness) (CD12/00563 and MS16/00128).

Cells can fine-tune which genes they activate, when and at which levels using a range of chemical marks on the DNA and certain proteins that help to organise the genome. One well-known example of such 'epigenetic tags' is DNA methylation, whereby a methyl group is added onto particular positions in the genome. Many factors ­ including environmental effects such as diet ­ control DNA methylation, allowing an organism to adapt to ever-changing conditions. An expression quantitative trait methylation (eQTM) is a specific position of the genome whose DNA methylation status regulates the activity of a given gene. A catalogue of eQTMs would be useful in helping to reveal how the environment and disease impacts the way cells work. Yet, currently, the relationships between most epigenetic tags and gene activity remains unclear, especially in children. To fill this gap, Ruiz-Arenas et al. studied DNA methylation in blood samples from over 800 healthy children across Europe. Amongst all tested DNA methylation sites, 22,000 (5.7% of total) were associated with the expression of a gene ­ and therefore were eQTMs; reciprocally, 9,000 genes (15.3% of all tested genes) were linked to at least one methylation site, leading to a total of 40,000 pairs of DNA methylation sites and genes. Most often, eQTMs regulated the expression of nearby genes ­ but only half controlled the gene that was the closest to them. Age and the genetic background of the individuals influenced the nature of eQTMs. This catalogue is a useful resource for the scientific community to start understanding the relationship between epigenetics and gene activity. Similar studies are now needed for other tissues and age ranges. Overall, extending our knowledge of eQTMs may help reveal how life events lead to illness, and could inform prevention efforts.

Multi-omics signatures of the human early life exposome.

Environmental exposures during early life play a critical role in life-course health, yet the molecular phenotypes underlying environmental effects on health are poorly understood. In the Human Early Life Exposome (HELIX) project, a multi-centre cohort of 1301 mother-child pairs, we associate individual exposomes consisting of >100 chemical, outdoor, social and lifestyle exposures assessed in pregnancy and childhood, with multi-omics profiles (methylome, transcriptome, proteins and metabolites) in childhood. We identify 1170 associations, 249 in pregnancy and 921 in childhood, which reveal potential biological responses and sources of exposure. Pregnancy exposures, including maternal smoking, cadmium and molybdenum, are predominantly associated with child DNA methylation changes. In contrast, childhood exposures are associated with features across all omics layers, most frequently the serum metabolome, revealing signatures for diet, toxic chemical compounds, essential trace elements, and weather conditions, among others. Our comprehensive and unique resource of all associations ( https://helixomics.isglobal.org/ ) will serve to guide future investigation into the biological imprints of the early life exposome.

Evaluation of a novel rapid genomic test including polygenic risk scores for the diagnosis and management of familial hypercholesterolaemia.

Introduction: Familial hypercholesterolaemia (FH) is a common autosomal dominant genetic condition, characterised by elevated LDL cholesterol (LDL-C), leading to premature cardiovascular disease (CVD). Early and accurate diagnosis, with implementation of preventative therapies, has a major impact on reducing premature CVD, morbidity and mortality. Genetic testing is recommended to confirm clinical diagnosis in the proband and enable cascade testing in relatives. There is growing evidence that the risk of CVD conferred by hypercholesterolaemia depends not only on monogenic causes but also on polygenic factors. GENinCode has developed a novel genomic testing system (Lipid inCode®) which we have assessed against an accredited National Health Service (NHS UK) genetic screening service in order to validate its diagnostic and clinical utility. Methods: DNA samples from 40 index cases who had been referred for FH testing in an ISO15189-accredited NHS genetic screening service, were retrospectively tested using the Lipid inCode® assay. The results were compared with those from NHS testing. Results: There was absolute concordance in variant detection between both diagnostic tests for monogenic and polygenic FH, the only difference being in the interpretation and classification of DNA variants based on ACMG guidelines, which did not differ by more than one classification class.  The Lipid inCode® test was equivalent to the NHS test in providing comprehensive genetic analysis that included the assessment of both monogenic (FH) and polygenic determinants of blood cholesterol and including a pharmacogenomic assessment of predisposition to statin-related myopathy. Conclusion: The Lipid inCode® diagnostic test can be undertaken with rapid turnaround and gave the same results as those reported by standard NHS genetic laboratory testing. In addition to assessment of monogenic FH, the Lipid inCode® assay provides additional genetic data, such as polygenic factors contributing to hypercholesterolaemia, a polygenic risk score (PRS) for coronary artery disease (CAD), pharmacogenomic testing for statin myopathy, and genetic predisposition to raised Lp(a).

Thirty-day outcomes in frail older patients discharged home from the emergency department with acute heart failure: effects of high-risk criteria identified by the DEED FRAIL-AHF trial. / Resultados a 30 días en los pacientes mayores frágiles con insuficiencia cardiaca aguda dados de alta desde urgencias o sus unidades vinculadas que cumplen los criterios de alto riesgo del estudio DEED FRAIL-AHF.

OBJECTIVES: To study the effect of high-risk criteria on 30-day outcomes in frail older patients with acute heart failure (AHF) discharged from an emergency department (ED) or an ED's observation and short-stay areas. MATERIAL AND METHODS: Secondary analysis of discharge records in the Older AHF Key Data registry. We selected frail patients (aged > 70 years) discharged with AHF from EDs. Risk factors were categorized as modifiable or nonmodifiable. The outcomes were a composite endpoint for a cardiovascular event (revisits for AHF, hospitalization for AHF, or cardiovascular death) and the number of days alive out-of-hospital (DAOH) within 30 days of discharge. RESULTS: We included 380 patients with a mean (SD) age of 86 (5.5) years (61.2% women). Modifiable risk factors were identified in 65.1%, nonmodifiable ones in 47.8%, and both types in 81.6%. The 30-day cardiovascular composite endpoint occurred in 83 patients (21.8%). The mean 30-day DAOH observed was 27.6 (6.1) days. Highrisk factors were present more often in patients who developed the cardiovascular event composite endpoint: the rates for patients with modifiable, nonmodifiable, or both types of risk were, respectively, as follows in comparison with patients not at high risk: 25.0% vs 17.2%, P = .092; 27.6% vs 16.7%, P = .010; and 24.7% vs 15.2%, P = .098). The 30-day DAOH outcome was also lower for at-risk patients, according to type of risk factor present: modifiable, 26.9 (7.0) vs 28.4 (4.4) days, P = .011; nonmodifiable, 27.1 (7.0) vs 28.0 (5.0) days, P = .127; and both, 27.1 (6.7) vs 28.8 (3.4) days, P = .005). After multivariate analysis, modifiable risk remained independently associated with fewer days alive (adjusted absolute difference in 30-day DAOH, -1.3 days (95% CI, -2.7 to -0.1 days). Nonmodifiable factors were associated with increased risk for the 30-day cardiovascular composite endpoint (adjusted absolute difference, 10.4%; 95% CI, -2.1% to 18.7%). CONCLUSION: Risk factors are common in frail elderly patients with AHF discharged home from hospital ED areas. Their presence is associated with a worse 30-day prognosis.

OBJETIVO: Estudiar el efecto a 30 días de los criterios de alto riesgo (CAR) en los mayores frágiles con insuficiencia cardiaca aguda (ICA) dados de alta desde urgencias o unidades vinculadas (URG_UV). METODO: Análisis secundario del registro OAK-Discharge. Se seleccionaron pacientes frágiles 70 años con ICA dados de alta desde URG_UV. Los CAR se clasificaron en modificables (CAR_M) y no modificables (CAR_NM). Las variables de resultado fueron la compuesta cardiovascular (VC_CV) (revisita u hospitalización por ICA o mortalidad cardiovascular) y días vivos fuera del hospital (DVFH) a 30 días del alta. RESULTADOS: Se incluyeron 380 pacientes con una edad media de 86 (DE 5,5) años, 61,2% mujeres. Un 65,1% tuvo CAR_M, 47,8% CAR_NM y 81,6% ambos. Ochenta y tres pacientes (21,8%) presentaron la VC_CV a 30 días. La media de DVFH a 30 días fue de 27,6 (DE 6,1) días. La presencia de CAR modificable, no modificable o ambos, se asoció más frecuentemente a la VC_CV a 30 días (25,0% vs 17,2%, p = 0,092; 27,6% vs 16,7%, p = 0,010; 24,7% vs 15,2%, p = 0,098) y a menos DVFH a 30 días [26,9 (7,0) vs 28,4 (4,4), p = 0,011; 27,1 (7,0) vs 28,0 (5,0), p = 0,127; 27,1 (6,7) vs 28,8 (3,4), p = 0,005], respectivamente. Tras el análisis multivariante, los CAR_M se asociaron de forma independiente con menos DVFH a 30 días (diferencia absoluta ajustada ­1,3 días; IC 95% ­2,7 a ­0,1) y los CAR_NM con más eventos en la VC_CV a 30 días (diferencia absoluta ajustada 10,4%; IC 95% 2,1% a 18,7%). CONCLUSIONES: Los CAR son frecuentes en los mayores frágiles con ICA dados de alta desde URG_UV y su presencia se asocia a peores resultados a 30 días tras alta.