Human Genetics最新文献

Developmental anomalies of the hearing organ, the cochlea, are diagnosed in approximately one-fourth of individuals with congenital. The majority of patients with cochlear malformations remain etiologically undiagnosed due to insufficient knowledge about underlying genes or the inability to make conclusive interpretations of identified genetic variants. We used exome sequencing for the genetic evaluation of hearing loss associated with cochlear malformations in three probands from unrelated families deafness. We subsequently generated monoclonal induced pluripotent stem cell (iPSC) lines, bearing patient-specific knockins and knockouts using CRISPR/Cas9 to assess pathogenicity of candidate variants. We detected FGF3 (p.Arg165Gly) and GREB1L (p.Cys186Arg), variants of uncertain significance in two recognized genes for deafness, and PBXIP1(p.Trp574*) in a candidate gene. Upon differentiation of iPSCs towards inner ear organoids, we observed developmental aberrations in knockout lines compared to their isogenic controls. Patient-specific single nucleotide variants (SNVs) showed similar abnormalities as the knockout lines, functionally supporting their causality in the observed phenotype. Therefore, we present human inner ear organoids as a potential tool to validate the pathogenicity of DNA variants associated with cochlear malformations.

The Roma people have a complex demographic history shaped by their recent dispersal from a South Asian origin into Europe, accompanied by continuous population bottlenecks and gene flow. After settling in the Balkans around 1,000 years ago, the Roma gradually dispersed across Europe, and approximately 500 years ago, they established in the Iberian Peninsula what is now one of the largest Roma populations in Western Europe. Focusing specifically on the Iberian Roma, we conducted the most comprehensive genome-wide analysis of European Roma populations to date. Using allele frequency and haplotype-based methods, we analysed 181 individuals to investigate their genetic diversity, social dynamics, and migration histories at both continental and local scales. Our findings demonstrate significant gene flow from populations encountered during the Roma's dispersal and confirm their South Asian origins. We show that, between the 14th and 19th centuries, the Roma spread westward from the Balkans in various waves, with multiple admixture events. Furthermore, our findings refute previous hypotheses of a North African dispersal route into Iberia and genetic connections to Jewish populations. The Iberian Roma exhibit ten times greater genetic differentiation compared to non-Roma Iberians, indicating significant regional substructure. Additionally, we provide the first genetic evidence of assortative mating within Roma groups, highlighting distinct mating patterns and suggesting a gradual shift towards increased integration with non-Roma individuals. This study significantly enhances our understanding of how demographic history and complex genetic structure have shaped the genetic diversity of Roma populations, while also highlighting the influence of their evolving social dynamics.

Myotonic dystrophy type 1 (DM1) and type 2 (DM2) are autosomal dominant neuromuscular disorders associated with expansions of microsatellites, respectively, in DMPK and CNBP. Their pathogenesis is linked to the global aberrant alternative splicing (AAS) of many genes and marks mostly muscular and neuronal tissues, while blood is the least affected. Recent data in DM1 skeletal muscles indicated that abnormalities in RNA metabolism also include global upregulation of circular RNAs (circRNAs). CircRNAs are a heterogeneous group considered splicing errors and by-products of canonical splicing. To elucidate whether circRNA dysregulation is an inherent feature of the myotonic environment, we perform their analysis in the frontal cortex and whole blood of DM1 and DM2 patients. We find a global elevation of circRNAs in both tissues, and its magnitude is neither correlated with the differences in their parental gene expression nor is associated with AAS published earlier. Aberrantly spliced cassette exons of linear transcripts affected in DM1 and DM2 are not among the circularized exons, which unique genomic features prerequisite back-splicing. However, the blueprint of the AAS of linear RNAs is found in a variety of circRNA isoforms. The heterogeneity of circRNAs also originates from the utilization of exonic and intronic cryptic donors/acceptors in back splice junctions, and intron-containing circRNAs are more characteristic of the blood. Overall, this study reveals circRNA dysregulation in various tissues from DM1 and DM2; however, their levels do not correlate with the AAS in linear RNAs, suggesting a potential independent regulatory mechanism underlying circRNA upregulation in myotonic dystrophy.

The genetic relationship between migraine and stroke remains underexplored, particularly in the context of druggable targets. Previous studies have been limited by small sample sizes and a lack of focus on genetic-targeted therapies for these conditions. We analyzed the association and causality between migraine and stroke using multivariable logistic regression in the UK Biobank cohort and Mendelian randomization (MR) analyses based on genome-wide association study (GWAS) data. Integrating expression quantitative trait loci (eQTLs) data from blood and brain regions, we explored the phenotypic and genetic links between migraine medications, drug target, and stroke. Additionally, we explored novel druggable genes for migraine and evaluated their effects on migraine signaling molecules and stroke risk. Migraine was significantly associated with stroke, particularly ischemic stroke (IS) and intracerebral hemorrhage (ICH), with MR analysis confirming a causal link to ICH. HTR1A emerged as a potential link between antidepressants (preventive medications for migraine) and stroke. We identified 17 migraine-related druggable genes, with 5 genes (HMGCR, TGFB1, TGFB3, KCNK5, IMPDH2) associated with nine existing drugs. Further MR analysis identified correlation of CELSR3 and IMPDH2 with cGMP pathway marker PRKG1, and identified KCNK5, PLXNB1, and MDK as novel migraine-associated druggable genes significantly linked to the stroke risks. These findings established the phenotypic and genetic link between migraine, its medication and stroke, identifying potential targets for single and dual-purpose therapies for migraine and stoke, and emphasized the need for further research to validate these associations.

Background: The increasing integration of non-invasive prenatal testing (NIPT) into antenatal practice and public healthcare systems globally raises both significant challenges in standardising service delivery and important ethical questions around routinisation and reproductive autonomy. This systematic review aims to synthesise existing primary empirical research on healthcare professionals' views on and experiences with NIPT.

Methods: A systematic search was conducted across four major databases in September 2023 and repeated in December 2024. Studies that reported findings from primary empirical research, including quantitative, qualitative and mixed methods research were included.

Results: Searches returned 65 eligible articles, spanning 38 countries and 1 special administrative region and at least 12 professions. Views on who NIPT should have access to and which conditions should be screened for were influenced by perceived clinical utility. While healthcare professionals acknowledged NIPT as beneficial for supporting reproductive autonomy, concerns were raised about the amount and complexity of information to be conveyed during prenatal counseling and potential pressure to test. Cost was also identified as a significant barrier. Challenges reported during post-test counseling included communicating test failures and gaining information from laboratories. Views on the implications of NIPT for decision-making around abortion and for people with disabilities varied.

Conclusions: Healthcare professionals play a critical role in facilitating the access to and decisions by pregnant people around prenatal genetic testing. Addressing barriers in clinical practice and increasing consistency across and access to clinical guidelines and education resources may support healthcare professionals in supporting reproductive autonomy.

Next-generation sequencing (NGS) has revolutionized genetic diagnostics, yet its application in precision medicine remains incomplete, despite significant advances in computational tools for variant annotation. Many variants remain unannotated, and existing tools often fail to accurately predict the range of impacts that variants have on protein function. This limitation restricts their utility in relevant applications such as predicting disease severity and onset age. In response to these challenges, a new generation of computational models is emerging, aimed at producing quantitative predictions of genetic variant impacts. However, the field is still in its early stages, and several issues need to be addressed, including improved performance and better interpretability. This study introduces QAFI, a novel methodology that integrates protein-specific regression models within an ensemble learning framework, utilizing conservation-based and structure-related features derived from AlphaFold models. Our findings indicate that QAFI significantly enhances the accuracy of quantitative predictions across various proteins. The approach has been rigorously validated through its application in the CAGI6 contest, focusing on ARSA protein variants, and further tested on a comprehensive set of clinically labeled variants, demonstrating its generalizability and robust predictive power. The straightforward nature of our models may also contribute to better interpretability of the results.

This paper presents an evaluation of predictions submitted for the "HMBS" challenge, a component of the sixth round of the Critical Assessment of Genome Interpretation held in 2021. The challenge required participants to predict the effects of missense variants of the human HMBS gene on yeast growth. The HMBS enzyme, critical for the biosynthesis of heme in eukaryotic cells, is highly conserved among eukaryotes. Despite the application of a variety of algorithms and methods, the performance of predictors was relatively similar, with Kendall's tau correlation coefficients between predictions and experimental scores around 0.3 for a majority of submissions. Notably, the median correlation (≥ 0.34) observed among these predictors, especially the top predictions from different groups, was greater than the correlation observed between their predictions and the actual experimental results. Most predictors were moderately successful in distinguishing between deleterious and benign variants, as evidenced by an area under the receiver operating characteristic (ROC) curve (AUC) of approximately 0.7 respectively. Compared with the recent two rounds of CAGI competitions, we noticed more predictors outperformed the baseline predictor, which is solely based on the amino acid frequencies. Nevertheless, the overall accuracy of predictions is still far short of positive control, which is derived from experimental scores, indicating the necessity for considerable improvements in the field. The most inaccurately predicted variants in this round were associated with the insertion loop, which is absent in many orthologs, suggesting the predictors still heavily rely on the information from multiple sequence alignment.

The Genetics of Neurodevelopmental Disorders Lab in Padua provided a new intellectual disability (ID) Panel challenge for computational methods to predict patient phenotypes and their causal variants in the context of the Critical Assessment of the Genome Interpretation, 6th edition (CAGI6). Eight research teams submitted a total of 30 models to predict phenotypes based on the sequences of 74 genes (VCF format) in 415 pediatric patients affected by Neurodevelopmental Disorders (NDDs). NDDs are clinically and genetically heterogeneous conditions, with onset in infant age. Here, we assess the ability and accuracy of computational methods to predict comorbid phenotypes based on clinical features described in each patient and their causal variants. We also evaluated predictions for possible genetic causes in patients without a clear genetic diagnosis. Like the previous ID Panel challenge in CAGI5, seven clinical features (ID, ASD, ataxia, epilepsy, microcephaly, macrocephaly, hypotonia), and variants (Pathogenic/Likely Pathogenic, Variants of Uncertain Significance and Risk Factors) were provided. The phenotypic traits and variant data of 150 patients from the CAGI5 ID Panel Challenge were provided as training set for predictors. The CAGI6 challenge confirms CAGI5 results that predicting phenotypes from gene panel data is highly challenging, with AUC values close to random, and no method able to predict relevant variants with both high accuracy and precision. However, a significant improvement is noted for the best method, with recall increasing from 66% to 82%. Several groups also successfully predicted difficult-to-detect variants, emphasizing the importance of variants initially excluded by the Padua NDD Lab.

New thermodynamic and functional studies have been recently conducted to evaluate the impact of amino acid substitutions on the Mitogen Activated Protein Kinases 1 and 3 (MAPK1/3). The Critical Assessment of Genome Interpretation (CAGI) data provider, at Sapienza University of Rome, measured the unfolding free energy and the enzymatic activity of a set of variants (MAPK challenge dataset). Thermodynamic measurements for the denaturant-induced equilibrium unfolding of the phosphorylated and unphosphorylated forms of the MAPKs were obtained by monitoring the far-UV circular dichroism and intrinsic fluorescence changes as a function of denaturant concentration. These values have been used to calculate the change in unfolding free energy between the variant and wild-type proteins at zero concentration of denaturant ( $\Delta \Delta G^{{\text{H}}_2\text{O}}$ ). The enzymatic activity of the phosphorylated MAPKs variants was also measured using Chelation-Enhanced Fluorescence to monitor the phosphorylation of a peptide substrate. The MAPK challenge dataset, composed of a total of 23 single amino acid substitutions (11 and 12 for MAPK1 and MAPK3, respectively), was used to assess the effectiveness of the computational methods in predicting the $\Delta \Delta G^{{\text{H}}_2\text{O}}$ values, associated with the variants, and categorize them as destabilizing and not destabilizing. The data on the enzymatic activity of the MAPKs mutants were used to assess the performance of the methods for predicting the functional impact of the variants. For the sixth edition of CAGI, thirteen independent research groups from four continents (Asia, Australia, Europe and North America) submitted > 80 sets of predictions, obtained from different approaches. In this manuscript, we summarized the results of our assessment to highlight the possible limitations of the available algorithms.

Regular, systematic, and independent assessments of computational tools that are used to predict the pathogenicity of missense variants are necessary to evaluate their clinical and research utility and guide future improvements. The Critical Assessment of Genome Interpretation (CAGI) conducts the ongoing Annotate-All-Missense (Missense Marathon) challenge, in which missense variant effect predictors (also called variant impact predictors) are evaluated on missense variants added to disease-relevant databases following the prediction submission deadline. Here we assess predictors submitted to the CAGI 6 Annotate-All-Missense challenge, predictors commonly used in clinical genetics, and recently developed deep learning methods. We examine performance across a range of settings relevant for clinical and research applications, focusing on different subsets of the evaluation data as well as high-specificity and high-sensitivity regimes. Our evaluations reveal notable advances in current methods relative to older, well-cited tools in the field. While meta-predictors tend to outperform their constituent individual predictors, several newer individual predictors perform comparably to commonly used meta-predictors. Predictor performance varies between high-specificity and high-sensitivity regimes, highlighting that different methods may be optimal for different use cases. We also characterize two potential sources of bias. Predictors that incorporate allele frequency as a predictive feature tend to have reduced performance when distinguishing pathogenic variants from very rare benign variants, and predictors trained on pathogenicity labels from curated variant databases often inherit gene-level label imbalances. Our findings help illuminate the clinical and research utility of modern missense variant effect predictors and identify potential areas for future development.