Genomics and Rare Diseases

This paper introduces gMVP, a graph attention neural network for predicting the functional effect of missense variants, showing improved performance over existing methods in identifying damaging variants and separating de novo variants in neurodevelopmental disorders.

This paper introduces ConSpliceML, a machine learning model that improves the identification of deleterious splicing variants by integrating genetic constraint and alternative splicing predictions.

The paper systematically analyzes chrX genes to predict disease associations, identifying 235 genes likely involved in disorders and reporting damaging variants in CDK16 and TRPC5 in patients with intellectual disability or autism.

The paper uses prior ClinVar variant classifications to inform ACMG/AMP variant interpretation by identifying SNVs with similar functional consequences to pathogenic variants.

The paper estimates penetrance of monogenic diabetes variants across different ascertainment contexts, finding age-related variability for HNF1A/HNF4A variants but consistent penetrance for GCK variants.

The paper introduces Cancermuts, a Python tool for prioritizing missense cancer variants, and validates its predictions on AMBRA1 mutations in melanoma.

FAVOR provides an online resource and tool for functional annotation of variants from whole genome sequencing studies to aid in disease gene prioritization.

E-SNPs&GO improves pathogenic variant annotation by embedding protein sequences and function, achieving a MCC score of 0.71 on a blind test set.

The paper identifies novel Mendelian genes by analyzing mouse embryo lethality and linking them to human disease phenotypes and genetic variants.

The paper adapts ACMG/AMP variant curation guidelines for the myocilin gene, leading to changes in variant classification for 38% of tested variants.

This paper introduces MetaRNN and MetaRNN-indel, deep learning models that differentiate rare pathogenic from benign variants, outperforming existing methods and providing interpretable scores.

The paper investigates tubulin mutations' molecular mechanisms and their phenotypic effects, finding that current computational methods poorly predict their pathogenicity due to dominant-negative and gain-of-function mechanisms.

The paper identifies a novel syndrome caused by the gain-of-function variant p.Glu1099Lys in NSD2, leading to distinct clinical features compared to loss-of-function variants.

The paper demonstrates that recurrent genomic deletions mediated by nonallelic homologous recombination drive recessive disease burden and identifies new disease-contributing variants through exome reanalysis.

The paper introduces HapICE, a method to improve variant detection in SBDS using NGS, achieving higher diagnostic accuracy than conventional methods.

This paper evaluates exome sequencing as an alternative to chromosomal microarray for detecting copy number variants in rare disorders, showing improved diagnostic yield in prospective cases.

GenOtoScope automates ACMG/AMP variant classification for congenital hearing loss, achieving higher accuracy and precision than existing tools.

The paper estimates the prevalence of putatively pathogenic leptin variants in the gnomAD database, finding low frequencies with higher risk in South Asian populations.

RAVA-FIRST improves rare variant association testing in whole-genome data by using functionally-informed testing units and filtering, identifying a novel intergenic region linked to venous thromboembolism.

This study evaluates the use of clinical exome sequencing in stroke patients, finding a high frequency of Mendelian disorders in a retrospective cohort.

This study phases de novo mutations using a combined long-read and short-read sequencing approach, successfully determining parent-of-origin for 71% of analyzed mutations.

The paper evaluates Mendelian causes of pediatric neurodegenerative disorders in a cohort of 69 patients, finding 42 genetic diagnoses and 32 unique disorders.

The paper shows that variant effect sizes on quantitative endophenotypes support pathogenicity in monogenic disease genes.

The paper identifies biological properties of ion channel residues associated with variant pathogenicity and links them to clinical phenotypes and functional assays.

AnFiSA is an open-source platform for analyzing sequencing data in rare genetic disease, addressing reproducibility, adaptability, and explainability in genomic analysis.

The paper proposes a new variant classification framework for chronic pancreatitis, introducing new categories to better classify variants in disease-causing and predisposing genes.

This study compares Agilent SureSelect All Exon v8 and v7 probes for exome sequencing, finding v8 improves SNV and indel detection with the same read count.

The paper introduces mvPPT, a missense variant pathogenicity predictor that outperforms existing tools in sensitivity and accuracy using gradient boosting and diverse feature sets.

The paper estimates diagnostic noise in genomic analysis using gene panels by analyzing variant prioritization rates in different panel sizes and disease categories.

The paper evaluates the clinical actionability of 63 myopathy-related genes using ClinGen criteria, finding that only 8 of the top 36 genes had prior ClinGen scores.