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Purpose Internationally adopted variant interpretation guidelines from the American College of Medical Genetics and Genomics (ACMG) are generic and require disease-specific refinement. Here we developed CardioClassifier ( http://www.cardioclassifier.org ), a semiautomated decision-support tool for inherited cardiac conditions (ICCs). Methods CardioClassifier integrates data retrieved from multiple sources with user-input case-specific information, through an interactive interface, to support variant interpretation. Combining disease- and gene-specific knowledge with variant observations in large cohorts of cases and controls, we refined 14 computational ACMG criteria and created three ICC-specific rules. Results We benchmarked CardioClassifier on 57 expertly curated variants and show full retrieval of all computational data, concordantly activating 87.3% of rules. A generic annotation tool identified fewer than half as many clinically actionable variants (64/219 vs. 156/219, Fisher’s P  = 1.1  ×  10 −18 ), with important false positives, illustrating the critical importance of disease and gene-specific annotations. CardioClassifier identified putatively disease-causing variants in 33.7% of 327 cardiomyopathy cases, comparable with leading ICC laboratories. Through addition of manually curated data, variants found in over 40% of cardiomyopathy cases are fully annotated, without requiring additional user-input data. Conclusion CardioClassifier is an ICC-specific decision-support tool that integrates expertly curated computational annotations with case-specific data to generate fast, reproducible, and interactive variant pathogenicity reports, according to best practice guidelines.

Accurate discrimination of benign and pathogenic rare variation remains a priority for clinical genome interpretation. State-of-the-art machine learning variant prioritization tools are imprecise and ignore important parameters defining gene–disease relationships, e.g., distinct consequences of gain-of-function versus loss-of-function variants. We hypothesized that incorporating disease-specific information would improve tool performance. We developed a disease-specific variant classifier, CardioBoost, that estimates the probability of pathogenicity for rare missense variants in inherited cardiomyopathies and arrhythmias. We assessed CardioBoost’s ability to discriminate known pathogenic from benign variants, prioritize disease-associated variants, and stratify patient outcomes. CardioBoost has high global discrimination accuracy (precision recall area under the curve [AUC] 0.91 for cardiomyopathies; 0.96 for arrhythmias), outperforming existing tools (4–24% improvement). CardioBoost obtains excellent accuracy (cardiomyopathies 90.2%; arrhythmias 91.9%) for variants classified with >90% confidence, and increases the proportion of variants classified with high confidence more than twofold compared with existing tools. Variants classified as disease-causing are associated with both disease status and clinical severity, including a 21% increased risk (95% confidence interval [CI] 11–29%) of severe adverse outcomes by age 60 in patients with hypertrophic cardiomyopathy. A disease-specific variant classifier outperforms state-of-the-art genome-wide tools for rare missense variants in inherited cardiac conditions (https://www.cardiodb.org/cardioboost/), highlighting broad opportunities for improved pathogenicity prediction through disease specificity.

The MuTHER Consortium reports an analysis of the genetics of gene expression in three tissues from approximately 850 mono- and dizygotic twins. They systematically dissect cis and trans genetic effects and estimate non-genetic effects on gene expression. Sequence-based variation in gene expression is a key driver of disease risk. Common variants regulating expression in cis have been mapped in many expression quantitative trait locus (eQTL) studies, typically in single tissues from unrelated individuals. Here, we present a comprehensive analysis of gene expression across multiple tissues conducted in a large set of mono- and dizygotic twins that allows systematic dissection of genetic ( cis and trans ) and non-genetic effects on gene expression. Using identity-by-descent estimates, we show that at least 40% of the total heritable cis effect on expression cannot be accounted for by common cis variants, a finding that reveals the contribution of low-frequency and rare regulatory variants with respect to both transcriptional regulation and complex trait susceptibility. We show that a substantial proportion of gene expression heritability is trans to the structural gene, and we identify several replicating trans variants that act predominantly in a tissue-restricted manner and may regulate the transcription of many genes.

Stalked crinoids are uncommon fossils in the Cenozoic. This is particularly due to their continuous decline starting from the Late Cretaceous and gradual restriction to the deep-sea environment, which bears a fossil record bias. On the other hand, in recent times, new data have emerged documenting some relict populations of sea lilies in the shallow marine facies from the Cenozoic. Here, we report shallow-water occurrences of Eocene crinoids from Romania that are classified as Isselicrinus . The representatives of Isselicrinus reached their greatest palaeogeographic distribution during the Eocene in offshore environments, and the only find of these crinoids from shallow-water facies was from the Southern Hemisphere. Thus, our discovery documents the first Eocene shallow-water occurrence of this taxon from the Northern Hemisphere. This finding shows that isocrinids locally might have remained in shallow environments after the initiation of the so-called Mesozoic marine revolution (MMR).

Background International guidelines for variant interpretation in Mendelian disease set stringent criteria to report a variant as (likely) pathogenic, prioritising control of false-positive rate over test sensitivity and diagnostic yield. Genetic testing is also more likely informative in individuals with well-characterised variants from extensively studied European-ancestry populations. Inherited cardiomyopathies are relatively common Mendelian diseases that allow empirical calibration and assessment of this framework. Methods We compared rare variants in large hypertrophic cardiomyopathy (HCM) cohorts (up to 6179 cases) to reference populations to identify variant classes with high prior likelihoods of pathogenicity, as defined by etiological fraction (EF). We analysed the distribution of variants using a bespoke unsupervised clustering algorithm to identify gene regions in which variants are significantly clustered in cases. Results Analysis of variant distribution identified regions in which variants are significantly enriched in cases and variant location was a better discriminator of pathogenicity than generic computational functional prediction algorithms. Non-truncating variant classes with an EF ≥ 0.95 were identified in five established HCM genes. Applying this approach leads to an estimated 14–20% increase in cases with actionable HCM variants, i.e. variants classified as pathogenic/likely pathogenic that might be used for predictive testing in probands’ relatives. Conclusions When found in a patient confirmed to have disease, novel variants in some genes and regions are empirically shown to have a sufficiently high probability of pathogenicity to support a “likely pathogenic” classification, even without additional segregation or functional data. This could increase the yield of high confidence actionable variants, consistent with the framework and recommendations of current guidelines. The techniques outlined offer a consistent and unbiased approach to variant interpretation for Mendelian disease genetic testing. We propose adaptations to ACMG/AMP guidelines to incorporate such evidence in a quantitative and transparent manner.

While there have been studies exploring regulatory variation in one or more tissues, the complexity of tissue-specificity in multiple primary tissues is not yet well understood. We explore in depth the role of cis-regulatory variation in three human tissues: lymphoblastoid cell lines (LCL), skin, and fat. The samples (156 LCL, 160 skin, 166 fat) were derived simultaneously from a subset of well-phenotyped healthy female twins of the MuTHER resource. We discover an abundance of cis-eQTLs in each tissue similar to previous estimates (858 or 4.7% of genes). In addition, we apply factor analysis (FA) to remove effects of latent variables, thus more than doubling the number of our discoveries (1,822 eQTL genes). The unique study design (Matched Co-Twin Analysis--MCTA) permits immediate replication of eQTLs using co-twins (93%-98%) and validation of the considerable gain in eQTL discovery after FA correction. We highlight the challenges of comparing eQTLs between tissues. After verifying previous significance threshold-based estimates of tissue-specificity, we show their limitations given their dependency on statistical power. We propose that continuous estimates of the proportion of tissue-shared signals and direct comparison of the magnitude of effect on the fold change in expression are essential properties that jointly provide a biologically realistic view of tissue-specificity. Under this framework we demonstrate that 30% of eQTLs are shared among the three tissues studied, while another 29% appear exclusively tissue-specific. However, even among the shared eQTLs, a substantial proportion (10%-20%) have significant differences in the magnitude of fold change between genotypic classes across tissues. Our results underline the need to account for the complexity of eQTL tissue-specificity in an effort to assess consequences of such variants for complex traits.

This systematic review aims to evaluate factors influencing fluoride release from dental nanocomposite materials. A comprehensive database search was conducted in February 2025 using PubMed, Web of Science, and Scopus. The search terms “fluoride release AND nanocomposites” were applied following PRISMA guidelines. Out of 336 initially identified articles, 17 studies met the inclusion criteria and were selected for analysis. Seventeen studies confirmed that fluoride-releasing nanocomposites are effective, with fluoride ion release influenced by material composition, nanofiller type, and storage conditions. Studies showed that acidic environments (pH 4–5.5) significantly enhanced fluoride release, particularly in materials containing nano-CaF2 or fluoridated hydroxyapatite, which responded to pH changes. Quantitative comparisons revealed that daily fluoride release values ranged from <0.1 μg/cm2/day in commercial composites to greater than 6500 μg/cm2/day in BT-based nanocomposites and up to 416,667 μg/cm2/day in modified GICs. Additionally, some composites exhibited fluoride recharging capabilities, with materials incorporating pyromellitic glycerol dimethacrylate (PMGDM) and ethoxylated bisphenol A dimethacrylate (EBPADMA) demonstrating prolonged fluoride and calcium ion release after recharge exposure, rather than the highest initial values. Despite releasing lower fluoride levels than conventional GIC and RMGI materials, fluoride-releasing nanocomposites demonstrate significant anti-caries potential and clinical applicability, with some formulations supporting periodontal regeneration and caries prevention around orthodontic brackets. However, the lack of consistency in study protocols—including differences in storage media, sample geometry, and measurement methods—limits direct comparison of outcomes. Therefore, the most critical direction for future research is the development of standardized testing protocols to ensure reliable, comparable results across material groups.

Most genome-wide methylation studies (EWAS) of multifactorial disease traits use targeted arrays or enrichment methodologies preferentially covering CpG-dense regions, to characterize sufficiently large samples. To overcome this limitation, we present here a new customizable, cost-effective approach, methylC-capture sequencing (MCC-Seq), for sequencing functional methylomes, while simultaneously providing genetic variation information. To illustrate MCC-Seq, we use whole-genome bisulfite sequencing on adipose tissue (AT) samples and public databases to design AT-specific panels. We establish its efficiency for high-density interrogation of methylome variability by systematic comparisons with other approaches and demonstrate its applicability by identifying novel methylation variation within enhancers strongly correlated to plasma triglyceride and HDL-cholesterol, including at CD36 . Our more comprehensive AT panel assesses tissue methylation and genotypes in parallel at ∼4 and ∼3 M sites, respectively. Our study demonstrates that MCC-Seq provides comparable accuracy to alternative approaches but enables more efficient cataloguing of functional and disease-relevant epigenetic and genetic variants for large-scale EWAS. Currently, genome-wide methylation studies are limited to using targeted arrays or enrichment to assess large sample sizes. Here, Allum et al . demonstrate MethylC-Capture Sequencing, a cost-effective method for investigating genetic and epigenetic variation.

Chronic pain is a global public health problem, but the underlying molecular mechanisms are not fully understood. Here we examine genome-wide DNA methylation, first in 50 identical twins discordant for heat pain sensitivity and then in 50 further unrelated individuals. Whole-blood DNA methylation was characterized at 5.2 million loci by MeDIP sequencing and assessed longitudinally to identify differentially methylated regions associated with high or low pain sensitivity (pain DMRs). Nine meta-analysis pain DMRs show robust evidence for association (false discovery rate 5%) with the strongest signal in the pain gene TRPA1 ( P =1.2 × 10 −13 ). Several pain DMRs show longitudinal stability consistent with susceptibility effects, have similar methylation levels in the brain and altered expression in the skin. Our approach identifies epigenetic changes in both novel and established candidate genes that provide molecular insights into pain and may generalize to other complex traits. Genetically identical twins provide a valuable resource to identify epigenetic factors associated with complex traits. Here the authors adopt this approach and find that differential methylation of the pain gene TRPA1 is associated with pain sensitivity in humans.

We determined the relationships between DNA sequence variation and DNA methylation using blood samples from 3,799 Europeans and 3,195 South Asians. We identify 11,165,559 SNP–CpG associations (methylation quantitative trait loci (meQTL), P  < 10 −14 ), including 467,915 meQTL that operate in trans . The meQTL are enriched for functionally relevant characteristics, including shared chromatin state, High-throuhgput chromosome conformation interaction, and association with gene expression, metabolic variation and clinical traits. We use molecular interaction and colocalization analyses to identify multiple nuclear regulatory pathways linking meQTL loci to phenotypic variation, including UBASH3B (body mass index), NFKBIE (rheumatoid arthritis) , MGA (blood pressure) and COMMD7 (white cell counts). For rs6511961 , chromatin immunoprecipitation followed by sequencing (ChIP–seq) validates zinc finger protein (ZNF)333 as the likely trans acting effector protein. Finally, we used interaction analyses to identify population- and lineage-specific meQTL, including rs174548 in FADS1 , with the strongest effect in CD8 + T cells, thus linking fatty acid metabolism with immune dysregulation and asthma. Our study advances understanding of the potential pathways linking genetic variation to human phenotype. Genome-wide association analyses of DNA methylation in peripheral blood from 3,799 Europeans and 3,195 South Asians identify unique SNP–CpG associations (meQTL), providing insights into molecular mechanisms and the potential links to phenotypic variation.