Genomics and Rare Diseases

This study finds that 1.3% of preterm infants have genetic disorders, with higher rates in those who died or had severe morbidity, and identifies specific disorders linked to increased morbidity and mortality.

The paper assesses improvements in genomic variant classification accuracy over time in ClinVar and HGMD by analyzing misclassification rates in inborn errors of metabolism across ancestry groups.

This paper introduces SMuRF, a framework for deep mutational scanning in disease-related genes, generating functional scores for all coding variants and aiding in variant interpretation.

The paper evaluates the impact of GUIA, a digital genetic counseling tool, on understanding of genetic test results in diverse families, finding improved understanding in the intervention group.

This study uses RNA-sequencing to identify novel diagnoses in Mendelian disorders by detecting aberrant splicing and expression abnormalities, particularly in patients without candidate sequence variants.

The paper re-analyzes clinical aCGH data to identify pathogenic copy number losses, finding that 7.2% of class 3 variants were reclassified as pathogenic or likely pathogenic.

The paper assesses the impact of rare canonical splice site variants on splicing using functional and in silico methods, finding that 24% of variants do not cause loss-of-function.

The paper combines expert guideline-based and machine learning approaches to improve automated prediction of the clinical effect of copy number variations.

The paper identifies non-coding variants in trans with coding variants in patients with developmental disorders, finding them to be a rare cause of recessive disorders.

The paper identifies novel VCP variants causing a childhood-onset neurodevelopmental disorder through exome sequencing and functional analysis.

This study evaluates clinical genome sequencing (GS) over three years at a children's hospital, finding a 35% diagnostic yield and frequent misordering, with higher yields in patients without prior exome sequencing.

This study benchmarks 55 variant effect predictors using deep mutational scanning data from 26 human proteins, finding that unsupervised methods like EVE and ESM-1v perform best, while supervised methods like VARITY show improved handling of data circularity.

The paper estimates carrier frequencies of pathogenic variants in 113 ACMG-recommended genes using exome sequencing data from 1,642 Thai individuals, finding high carrier rates for several disorders.

The paper identifies NTE activity as a biomarker for retinopathy and endocrinopathy in PNPLA6 disorders, using clinical and preclinical data to establish a genotype-activity-phenotype relationship.

SPRI predicts the pathogenicity of missense variants and identifies spatially organized mutational clusters linked to cancer and Mendelian diseases.

The paper uses primate genetic data to predict human variant pathogenicity with a deep learning classifier trained on 4.3 million common missense variants.

The paper identifies discordant genotype calls across sequencing methods in gnomAD, provides a blacklist of problematic variants, and introduces a classifier to detect such variants in other datasets.

The paper shows that cotranslational protein complex assembly is less common for subunits with dominant-negative mutations and develops a model to predict such mutations.

The paper uses pangenome graphs to improve detection of rare structural variants in a rare disease cohort, identifying novel diagnostic variants.

SpliPath identifies pathogenic splice-altering variants in ALS by integrating SpliceAI predictions with transcriptomics data, revealing hidden heritability in rare variant disorders.

This study develops a computational workflow for target adaptive sampling long-read sequencing and applies it to hereditary cancer genomes to identify genetic variants and epigenetic changes.

The paper compares protein stability predictors with DMS-derived functional scores, finding FoldX and Rosetta show strong correlations, especially when considering intermolecular interactions.

This paper introduces a tissue-aware machine learning framework to improve understanding and diagnosis of Mendelian and rare diseases by leveraging tissue-specific gene mechanisms.

The paper introduces REGatta, a method to estimate clinical risk in gene regions using population sequencing data, identifying significant risk differences in breast cancer genes for rare missense variants.

The paper investigates why studies fail to find associations between de novo noncoding variants and complex disorders like ASD, identifying annotation quality and variant reliability as key issues.

GROFFFY filters whole genome sequence data to identify non-coding and polyadenylation site variants, improving detection of disease-causing variants in unsolved cases.

This paper presents the largest catalog of human protein-coding variation from 985,830 individuals, identifying rare variants and their functional implications for precision medicine.

This paper introduces Sequence UNET, a deep learning model for variant effect prediction that is highly scalable and computationally efficient, achieving comparable performance to existing methods on ClinVar data.

The paper identifies clinically relevant variants in poison exons (PEs) that improve diagnostic yield for neurodevelopmental disorders by uncovering variants previously overlooked in standard analyses.