UNASSIGNED: Clinical validity of genome sequencing (GS) (>30×) has been preliminarily verified in the post-natal setting. This study is to investigate the potential utility of trio-GS as a prenatal test for diagnosis of central nervous system (CNS) anomalies.
UNASSIGNED: We performed trio-based GS on a prospective cohort of 17 foetuses with CNS abnormalities. Single nucleotide variation (SNV), small insertion and deletion (Indel), copy number variation (CNV), structural variant (SV), and regions with absence of heterozygosity (AOH) were analyzed and classified according to ACMG guidelines.
UNASSIGNED: Trio-GS identified diagnostic findings in 29.4% (5/17) of foetuses, with pathogenic variants found in SON, L1CAM, KMT2D, and ASPM. Corpus callosum (CC) and cavum septum pellucidum (CSP) abnormalities were the most frequent CNS abnormalities (47.1%, 8/17) with a diagnostic yield of 50%. A total of 29.4% (5/17) foetuses had variants of uncertain significance (VUS). Particularly, maternal uniparental disomy 16 and a de novo mosaic 4p12p11 duplication were simultaneously detected in one foetus with abnormal sulcus development. In addition, parentally inherited chromosomal inversions were identified in two foetuses.
UNASSIGNED: GS demonstrates its feasibility in providing genetic diagnosis for foetal CNS abnormalities and shows the potential to expand the application to foetuses with other ultrasound anomalies in prenatal diagnosis.

BACKGROUND: Variant interpretation is essential for identifying patients\' disease-causing genetic variants amongst the millions detected in their genomes. Hundreds of Variant Impact Predictors (VIPs), also known as Variant Effect Predictors (VEPs), have been developed for this purpose, with a variety of methodologies and goals. To facilitate the exploration of available VIP options, we have created the Variant Impact Predictor database (VIPdb).
RESULTS: The Variant Impact Predictor database (VIPdb) version 2 presents a collection of VIPs developed over the past three decades, summarizing their characteristics, ClinGen calibrated scores, CAGI assessment results, publication details, access information, and citation patterns. We previously summarized 217 VIPs and their features in VIPdb in 2019. Building upon this foundation, we identified and categorized an additional 190 VIPs, resulting in a total of 407 VIPs in VIPdb version 2. The majority of the VIPs have the capacity to predict the impacts of single nucleotide variants and nonsynonymous variants. More VIPs tailored to predict the impacts of insertions and deletions have been developed since the 2010s. In contrast, relatively few VIPs are dedicated to the prediction of splicing, structural, synonymous, and regulatory variants. The increasing rate of citations to VIPs reflects the ongoing growth in their use, and the evolving trends in citations reveal development in the field and individual methods.
CONCLUSIONS: VIPdb version 2 summarizes 407 VIPs and their features, potentially facilitating VIP exploration for various variant interpretation applications. VIPdb is available at  https://genomeinterpretation.org/vipdb.

Despite recent efforts to issue clinical guidelines outlining strategies to define the pathogenicity of genomic variants, there is currently no standardized framework for which to make these assertions. This review does not present a step-by-step methodology, but rather takes a holistic approach to discuss many aspects which should be taken into consideration when determining variant pathogenicity. Categorization should be curated to reflect relevant findings within the scope of the specific medical context. Functional characterization should evaluate all available information, including results from literature reviews, different classes of genomic data repositories, and applicable computational predictive algorithms. This article further proposes a multidimensional view to infer pathogenic status from many genomic measurements across multiple axes. Notably, tumor suppressors and oncogenes exhibit fundamentally different biology which helps refine the importance of effects on splicing, mutation interactions, copy number thresholds, rearrangement annotations, germline status, and genome-wide signatures. Understanding these relevant datapoints with thoughtful perspective could aid in the reclassification of variants of unknown significance (VUS), which are ambiguously understood and currently have uncertain clinical implications. Ongoing assessments of VUS examining these relevant biological axes could lead to more accurate classification of variant pathogenicity interpretation in diagnostic oncology.

UNASSIGNED: Variant interpretation is essential for identifying patients\' disease-causing genetic variants amongst the millions detected in their genomes. Hundreds of Variant Impact Predictors (VIPs), also known as Variant Effect Predictors (VEPs), have been developed for this purpose, with a variety of methodologies and goals. To facilitate the exploration of available VIP options, we have created the Variant Impact Predictor database (VIPdb).
UNASSIGNED: The Variant Impact Predictor database (VIPdb) version 2 presents a collection of VIPs developed over the past 25 years, summarizing their characteristics, ClinGen calibrated scores, CAGI assessment results, publication details, access information, and citation patterns. We previously summarized 217 VIPs and their features in VIPdb in 2019. Building upon this foundation, we identified and categorized an additional 186 VIPs, resulting in a total of 403 VIPs in VIPdb version 2. The majority of the VIPs have the capacity to predict the impacts of single nucleotide variants and nonsynonymous variants. More VIPs tailored to predict the impacts of insertions and deletions have been developed since the 2010s. In contrast, relatively few VIPs are dedicated to the prediction of splicing, structural, synonymous, and regulatory variants. The increasing rate of citations to VIPs reflects the ongoing growth in their use, and the evolving trends in citations reveal development in the field and individual methods.
UNASSIGNED: VIPdb version 2 summarizes 403 VIPs and their features, potentially facilitating VIP exploration for various variant interpretation applications.
UNASSIGNED: VIPdb version 2 is available at https://genomeinterpretation.org/vipdb.

Though genes are already known to be responsible for type 1 diabetes mellitus (T1DM), the knowledge of missense mutation of that disease gene has still to be under covered. A genomic database and a bioinformatics-based approach are integrated in the present study in order to address this issue. Initially, nine variants associated with T1DM were retrieved from the GWAS catalogue. Different genomic algorithms such as PolyPhen2.0, SNPs and GTEx analyser programs were used to study the structural and functional effects of these mutations. Subsequently, SNPnexus was also employed to understand the effect of these mutations on the function of the expressed protein. Nine missense variants of T1DM were identified using the GWAS catalogue database. Among these nine SNPs, three were predicted to be related to the progression of T1DM disease by affecting the protein level. TYK2 gene variants with SNP rs34536443 were thought to have a probably damaging effect. Meanwhile, both COL4A3 and IFIH1 genes with SNPs rs55703767 and rs35667974, respectively, might alter protein function through a possibly damaging prediction. Among the variants of the three genes, the TYK2 gene with SNP rs34536443 had the strongest contribution in affecting the development of T1DM, with a score of 0.999. We sincerely hope that the results could be of immense importance in understanding the genetic basis of T1DM.

To date, clinical genetic testing for Mendelian disease variants has focused heavily on exonic coding and intronic gene regions. This multi-step study was undertaken to provide an evidence base for selecting and applying computational approaches for use in clinical classification of 5\' cis-regulatory region variants. Curated datasets of clinically reported disease-causing 5\' cis-regulatory region variants and variants from matched genomic regions in population controls were used to calibrate six bioinformatic tools as predictors of variant pathogenicity. Likelihood ratio estimates were aligned to code weights following ClinGen recommendations for application of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG/AMP) classification scheme. Considering code assignment across all reference dataset variants, performance was best for CADD (81.2%) and REMM (81.5%). Optimized thresholds provided moderate evidence toward pathogenicity (CADD, REMM) and moderate (CADD) or supporting (REMM) evidence against pathogenicity. Both sensitivity and specificity of prediction were improved when further categorizing variants based on location in an EPDnew-defined promoter region. Combining predictions (CADD, REMM, and location in a promoter region) increased specificity at the expense of sensitivity. Importantly, the optimal CADD thresholds for assigning ACMG/AMP codes PP3 (≥10) and BP4 (≤8) were vastly different from recommendations for protein-coding variants (PP3 ≥25.3; BP4 ≤22.7); CADD <22.7 would incorrectly assign BP4 for >90% of reported disease-causing cis-regulatory region variants. Our results demonstrate the need to consider a tiered approach and tailored score thresholds to optimize bioinformatic impact prediction for clinical classification of 5\' cis-regulatory region variants.

暂无摘要。

Despite advances in routine prenatal cytogenetic testing, most anomalous fetuses remain without a genetic diagnosis. Exome sequencing (ES) is a molecular technique that identifies sequence variants across protein-coding regions and is now increasingly used in clinical practice. Fetal phenotypes differ from postnatal and, therefore, prenatal ES interpretation requires a large amount of data deriving from prenatal testing. The aim of our study was to present initial results of the implementation of ES to prenatal diagnosis in Polish patients and to discuss its possible clinical impact on genetic counseling.
In this study we performed a retrospective review of all fetal samples referred to our laboratory for ES from cooperating centers between January 2017 and June 2021.
During the study period 122 fetuses were subjected to ES at our institution. There were 52 abnormal ES results: 31 in the group of fetuses with a single organ system anomaly and 21 in the group of fetuses with multisystem anomalies. The difference between groups was not statistically significant. There were 57 different pathogenic or likely pathogenic variants reported in 33 different genes. The most common were missense variants. In 17 cases the molecular diagnosis had an actual clinical impact on subsequent pregnancies or other family members.
Exome sequencing increases the detection rate in fetuses with structural anomalies and improves genetic counseling for both the affected couple and their relatives.

The importance of genomic data for health is rapidly growing but accessing and gathering information about variants from different sources is hindered by highly heterogeneous representations of variants, as outlined by clinical associations (AMP/ASCO/CAP) in their recommendations. To enable a smooth and effective retrieval of variant-containing documents from different resources, we developed a tool (https://goldorak.hesge.ch/synvar/) that generates for any given SNP - including variant not present in existing databases - its corresponding description at the genome, transcript and protein levels. It provides variant descriptions in the HGVS format as well as in many non-standard formats found in the literature along with database identifiers. We present the SynVar service and evaluate its impact on the recall of a genomic variant curation-support service. Using SynVar to search variants in the literature enables to increase the recall by +133.8% without a strong impact on precision (i.e. 93%).

Several professional societies advise against using real-time Reverse-Transcription PCR (rtRT-PCR) cycle threshold (Ct) values to guide clinical decisions. We comparatively assessed the variability of Ct values generated by six diagnostic approaches by testing serial dilutions of well-characterized isolates of 10 clinically most relevant SARS-CoV-2 genomic variants: Alpha, Beta, Gamma, Delta, Eta, Iota, Omicron, A.27, B.1.258.17, and B.1 with D614G mutation. Comparison of three fully automated rtRT-PCR analyzers and a reference manual rtRT-PCR assay using RNA isolated with three different nucleic acid isolation instruments showed substantial inter-variant intra-test and intra-variant inter-test variability. Ct value differences were dependent on both the rtRT-PCR platform and SARS-CoV-2 genomic variant. Differences ranging from 2.0 to 8.4 Ct values were observed when testing equal concentrations of different SARS-CoV-2 variants. Results confirm that Ct values are an unreliable surrogate for viral load and should not be used as a proxy of infectivity and transmissibility, especially when different rtRT-PCR assays are used in parallel and multiple SARS-CoV-2 variants are circulating. A detailed turn-around time (TAT) comparative assessment showed substantially different TATs, but parallel use of different diagnostic approaches was beneficial and complementary, allowing release of results for more than 81% of non-priority samples within 8 h after admission.