Genomics and Rare Diseases

This paper reviews parental and caregiver needs during pediatric genome-wide sequencing, identifying informational and emotional support requirements across different stages of the process.

The paper identifies common hypomorphic TNFAIP3 variants associated with immune dysregulation and underdiagnosed haploinsufficiency diseases, suggesting widespread underrecognition of these conditions.

Genome sequencing improves diagnostic yield for developmental and epileptic encephalopathies by identifying structural and non-coding variants missed by exome or gene panel testing.

ProteoCast predicts the functional impact of missense variants using evolutionary information, achieving high accuracy across the Drosophila proteome.

This paper introduces a geometric framework for dynamic protein mutation detection, showing it can distinguish pathogenic variants from neutral ones with high accuracy.

This study uses RNA sequencing to identify pathogenic variants in mitochondrial disease patients who were undiagnosed by DNA sequencing, revealing cryptic splicing and regulatory events.

This study quantifies how patient-level factors and site-level practices affect diagnostic yield in exome and genome sequencing across five clinical sites.

This study calibrates in-frame indel variant effect predictors for clinical classification, showing they reach multiple evidence levels but perform worse than missense predictors.

This study uses long-read sequencing to characterize MUC1 VNTR variation in ADTKD-MUC1, identifying frameshift mutations and their association with disease progression.

The paper develops a lightweight pipeline to process germline VCF files, enabling robust variant extraction and read evidence quality control for DDR loci analysis in colon crypts.

This study introduces pVEP, a framework showing how genetic background affects AI predictions of variant effects, revealing variant effects vary across haplotypes.

MAVISp provides a modular structural framework for analyzing protein variant effects, offering over eight million variants across 1000 proteins with curated data and a web server for accessibility.

The paper benchmarks SNP annotation tools and gene models across the genome, showing discrepancies in annotation and their impact on pathway analysis.

PAVS is a database of phenotype-associated variants from Saudi Arabian rare disease patients, enabling gene prioritization through phenotype-based similarity search.

Berrylyzer is a variant prioritization system for prenatal genetic diagnosis that outperforms existing tools in ranking diagnostic variants and recall rates.

OncoVI is a Python-based tool that automates oncogenicity classification of somatic variants using guidelines, achieving 80% accuracy on test data and 79% agreement with expert assessments.

The paper examines GT>GC 5' splice-site variants, showing they can retain wild-type transcript and challenge variant classification.

The paper estimates genetic prevalence for 22 autosomal recessive conditions using gnomAD data and develops a tool to democratize these calculations, highlighting the dynamic nature of prevalence estimates.

This paper improves variant effect predictors by calibrating them gene- and domain-aware, increasing evidence assignment and accuracy for variant classification.

This study identifies MAU2 variants as a new cause of Cornelia de Lange Syndrome and describes associated molecular and clinical features.

The paper introduces MES, a metric to evaluate variant effect predictors and MAVEs based on clinical evidence yield, showing discrepancies with AUROC.

The paper performs whole exome sequencing on 44,028 British South Asians to identify gene-phenotype associations and knockout genes, finding novel links and implications for drug development.

The paper presents a framework for prioritizing targets and patients for antisense oligonucleotide therapies in rare diseases, focusing on equitable and systematic decision-making.

gnomAD v4 (730,947 exomes) brings LOFTEE-2 loss-of-function annotation (0.90 precision, strict mode), missense-aware constraint, and LLM-mined literature evidence combined with constraint to rank disease genes.

CanVar-UK is a web platform that supports collaborative interpretation of germline variants in cancer susceptibility genes by integrating variant data, clinical information, and expert discussions.

The paper develops a comprehensive list of genetic conditions for genomic newborn screening, including 777 conditions associated with 743 genes, for implementation across multiple U.S. states and territories.

This study implements whole-exome sequencing in Algeria for neurodevelopmental disorders, achieving molecular diagnoses in 8/14 cases and highlighting challenges in variant interpretation and database underrepresentation.

COBT is a gene-based rare variant burden test for case-only studies that outperforms existing methods and identifies novel candidate variants in rare diseases.

The paper uses blood-based RNA-Seq on 5,412 individuals with rare diseases to identify new candidate diagnoses and shows its utility in prioritizing genetic variants.

The paper benchmarks five machine learning-based variant annotation methods for rare variant association testing across 350,377 UK Biobank participants, finding CADD provides best signal separation and GPN-MSA highest enrichment for loss-of-function intolerant genes.