Genomics and Rare Diseases

The paper shows allostery is a common cause of loss-of-function variants in human genetic diseases and identifies a distance-dependent decay of mutational effects.

This paper uses somatic mutation data to improve the classification of germline variants in developmental disorders by identifying genes where somatic and germline mechanisms are concordant.

The paper identifies ARID1B variants that impair protein interactions, stability, and regulation, linking them to neurodevelopmental disorders and cancers.

The paper functionally profiles 2193 ASS1 missense variants to determine their pathogenicity and epistatic interactions in Citrullinemia Type I.

This study evaluates the impact of updating FoldX from version 5 to 5.1 on MAVISp stability predictions, finding high agreement and minimal shifts in folding free energy.

The paper identifies high mutation rates in histone genes due to AID/APOBEC enzymes and introduces HistMTR to predict pathogenicity, showing histone mutations contribute to diseases.

This study evaluates long-read genome sequencing in rare disease diagnostics, finding high concordance with standard-of-care tests and incremental diagnostic benefits.

The paper uses saturation genome editing to assess the functional impact of RAD51D and XRCC2 variants, showing high accuracy in distinguishing pathogenic from benign variants.

The paper estimates penetrance of disease variants in genes prioritized for genomic newborn screening using adult biobank data, finding higher penetrance than ICD codes suggest.

The paper estimates mutation rates and variant effects using 700k exomes to identify genes with mutational hotspots and improve variant prioritization for genetic diagnostics.

This study develops GLM-Missense, a genomic language model fine-tuned for missense variant impact prediction, showing it captures nucleotide-level effects missed by existing predictors.

The paper introduces GLOF, a large-scale expert-curated dataset of missense variants classified as loss-of-function, gain-of-function, or neutral, to benchmark computational methods for predicting variant functional mechanisms.

The paper classifies G6PD variants using functional assays to identify clinically relevant variants and those unlikely to contribute to G6PD deficiency.

DeltaMut provides a large database of AlphaFold2-predicted missense variant structures to improve variant effect prediction.

The paper reports a clinical implementation of a whole-exome sequencing-based preconception carrier screening program in Uzbekistan, identifying actionable genetic risks in couples and singletons.

MAGI provides mechanistic insights into genetic variants by integrating genomic data and clinical annotations to improve variant interpretation and generate testable hypotheses.

The paper develops a framework to map rare variants onto protein structures and identifies disease-associated 3D neighborhoods in 872 genes for Mendelian disease and others for autism, epilepsy, and schizophrenia.

The paper develops a standardized case-level categorization metric for genomic results across the CSER2 consortium to assess diagnostic yield and identify resolvable factors in inconclusive cases.

This study shows that PacBio HiFi long-read sequencing with Paraphase variant caller detects all clinically relevant variants in paralogous regions, improving variant calling accuracy.

This study assesses the reproducibility of pathogenicity classification using the animal variant classification guidelines across three species, finding 65% reproducibility for pathogenicity and 83% clinically relevant agreement.

The paper surveys genetics professionals on their use of functional evidence in variant classification for hearing and vision loss, highlighting challenges in data quality, accessibility, and integration into clinical practice.

This study evaluates RNA-seq's diagnostic utility in three patient groups with non-diagnostic exome sequencing, finding higher yields in groups with candidate variants.

PFES quantifies the protein context of missense variants, enabling mechanistically transparent interpretation and reclassification of variants of uncertain significance.

The Evidence Aggregator (EvAgg) uses generative AI to streamline rare disease diagnostics by extracting and summarizing genetic variant and clinical information from literature, improving efficiency and accuracy.

This study identifies population-specific variants in Jordanian individuals, revealing admixture patterns and reclassifying variants to improve clinical interpretation.

The paper investigates if structural distance to the tRNA-binding interface improves missense variant pathogenicity prediction, finding that existing models like AlphaMissense and EVE already capture this information.

This paper introduces a vision-based model for predicting CNV pathogenicity using track-based spatial representations, achieving improved performance on benchmarks and clinical datasets.

This study detects low-level mosaic variants in blood DNA from individuals with congenital hyperinsulinism using tNGS and confirms them with ddPCR.

DAVP uses an agentic-LLM pipeline to prioritize genetic variants for diagnosis, achieving high recall in identifying causal variants across multiple datasets.