Genomics and Rare Diseases

Calibration and refinement of ACMG/AMP criteria for variant classification with BayesQuantify

BayesQuantify refines ACMG/AMP variant classification criteria using Bayesian methods, providing objective thresholds for evidence levels across multiple datasets.

Federated Learning for the pathogenicity annotation of genetic variants in multi-site clinical settings

This study evaluates federated learning for variant pathogenicity annotation in multi-site clinical settings, showing comparable or better performance than centralized models.

NCBoost v2: a classifier for non-coding variants in Mendelian diseases

NCBoost v2 improves pathogenicity scoring for non-coding variants in Mendelian diseases using updated conservation features and splice-altering scores, outperforming existing methods.

Evaluating Genome Sequencing Strategies: Trio, Singleton, and Standard Testing in Rare Disease Diagnosis

This study compares trio, singleton, and standard genome sequencing in rare disease diagnosis, finding trio sequencing has the highest diagnostic yield.

Characterization of snRNA-related neurodevelopmental disorders through the Spanish Undiagnosed Rare Disease Programs

The paper identifies de novo pathogenic variants in snRNA genes causing neurodevelopmental disorders in patients from the Spanish URD Programs.

Framework for combined functional and computational assessment of variant pathogenicity in the sodium leak channel NALCN

The paper establishes a framework to assess NALCN variant pathogenicity using functional and computational methods, distinguishing benign from gain- and loss-of-function variants.

Phenotypic pleiotropy of missense variants in human B cell-confinement receptor P2RY8

The paper uses deep mutational scanning and structural analysis to study the functional effects of missense variants in the P2RY8 receptor and improves variant effect predictions.

Comprehensive Comparison of a Genotype-Forward vs. Phenotype-Forward Approach for Monogenic Cardiovascular Diseases in the UK Biobank Cohort

This study compares genotype-forward and phenotype-forward approaches for detecting monogenic cardiovascular diseases in the UK Biobank, finding the genotype-forward method has a higher diagnostic yield.

acmgscaler: An R package and Colab for standardised gene-level variant effect score calibration within the ACMG/AMP framework

The paper introduces acmgscaler, an R package and Colab notebook for calibrating gene-level variant effect scores within the ACMG/AMP framework using MAVEs and VEPs.

Disease-specific variant pathogenicity prediction using multimodal biomedical language models

This paper introduces a deep learning framework for predicting disease-specific variant pathogenicity, showing improved accuracy over existing methods.

Systematic analysis of naturally occurring missense mutations in human manganese transporters: predictions and structural insights

The paper analyzes naturally occurring missense mutations in human manganese transporters, comparing computational predictions with structural models to identify discrepancies and improve variant prioritization.

Availability of benign missense variant truthsets for validation of functional assays: current status and a novel systematic approach

The paper evaluates the limitations of existing benign missense variant truthsets for MAVEs and proposes a proactive-systematic framework to improve their construction for better functional evidence classification.

Genome sequencing reveals the impact of pseudoexons in rare genetic disease

Genome sequencing identifies pseudoexons as a common cause of Mendelian disease, with some variants missed by prior exome testing.

Quantifying evidence for phenotypic specificity (PP4) for syndromic phenotypes: Large-scale integration of rare germline FH variants from diagnostic laboratory testing for HLRCC (Hereditary Leiomyomatosis and Renal Cell Cancer) and renal cancer

The paper quantifies phenotypic specificity of rare FH variants in HLRCC and renal cancer using likelihood ratios from clinical and population data.

Implementing a consultation service for translating genomic research findings into the clinic: lessons from the SickKids Genome Board

The paper describes a consultation service for evaluating genomic research findings in a paediatric hospital, highlighting its role in assessing variant pathogenicity and managing clinical implications.

RENOVO-NF1 accurately predicts NF1 missense variant pathogenicity

RENOVO-NF1 predicts pathogenicity of NF1 missense variants with high accuracy, reclassifying many variants of unknown significance.

Heterozygous variants in PLCG1 affect hearing, vision, cardiac, and immune function

The paper reports seven individuals with PLCG1 variants causing hearing, vision, cardiac, and immune issues, and shows these variants have gain-of-function effects in Drosophila models.

VDB: The Arab Variation and Disease Burden Database

The paper introduces AVDB, a database of genetic variants from Arab populations, providing pathogenicity classifications and disease burden metrics to improve genomic screening and diagnostics.

Assessing variant effect predictors and disease mechanisms in intrinsically disordered proteins

The paper evaluates variant effect predictors and disease mechanisms in intrinsically disordered proteins, finding reduced sensitivity for pathogenic variants in these regions and highlighting the need for disorder-informed prediction strategies.

Mechanistic Modelling of Recessive Disease through Allelic Integration of Variant Effects

The paper develops a method to estimate enzymatic activities from genetic variants using fitness assays and combines allele effects to predict pathogenicity in recessive diseases.

GenMasterTable: A user-friendly desktop application for filtering, summarising, and visualising large-scale annotated genetic variants

GenMasterTable is a desktop application that filters, summarizes, and visualizes large-scale annotated genetic variants, offering a user-friendly interface for non-programmers.

From Uncertain to Actionable: Significant Reduction in Variants of Uncertain Significance in Hereditary Germline Testing via Multi-Institutional Real-World Evidence

This paper introduces a method to reclassify variants of uncertain significance using real-world evidence from large-scale datasets, significantly improving variant interpretation.

High-resolution mapping of DMD duplications using long-read sequencing enables precise carrier screening for Duchenne muscular dystrophy

This study uses long-read sequencing to precisely map DMD duplications, improving carrier screening and variant classification for Duchenne muscular dystrophy.

MutationAssessor in cBioPortal

MutationAssessor version 4 provides updated functional impact scores for mutations using expanded data and improved analysis methods, now integrated into cBioPortal.

EpiPred: A gene-specific machine learning model for classifying missense variants in the epilepsy-related gene STXBP1

EpiPred improves STXBP1 variant interpretation, enabling precision genetic diagnoses and promoting access to targeted precision therapies.

Predicting Genetic Variant Pathogenicity Using Vector Embeddings

This paper introduces a semantic embedding framework to predict genetic variant pathogenicity using natural language descriptions and pre-trained models, achieving high accuracy across multiple genes.

Community-Driven Copy Number Variant Discovery at Scale: Results from a Rare Disease Genomics Hackathon

The paper presents a scalable CNV analysis workflow applied to rare disease cohorts, identifying 39 candidate pathogenic SVs within 2 days using collaborative tools and databases.

Exome-wide analysis of congenital kidney anomalies reveals new genes and shared architecture with developmental disorders

This study identifies new genes and shared genetic architecture between kidney anomalies and other developmental disorders using exome-wide analysis of clinical cohorts.

Joint, multifaceted genomic analysis enables diagnosis of diverse, ultra-rare monogenic presentations

The paper presents a joint genomic analysis of UDN patients using whole genome sequencing and new statistical methods to prioritize disease genes, revealing new disease associations and releasing a software tool for cohort analysis.

PreMode predicts mode-of-action of missense variants by deep graph representation learning of protein sequence and structural context

PreMode predicts the mode-of-action of missense variants using graph neural networks, achieving state-of-the-art performance in mode-of-action classification.