Genomics and Rare Diseases

The paper quantifies variant pathogenicity in hypertrophic cardiomyopathy using etiological fraction, increasing actionable variant detection by 14-20%.

The paper improves pathogenic variant localization by using a hierarchical Bayesian model to estimate sub-regional intolerance, showing better performance in identifying ClinVar pathogenic variants and improving classification of de novo missense variants.

This study uses UK Biobank data to assess the penetrance and pathogenicity of rare variants, finding reduced penetrance and variable expressivity for some clinically relevant variants.

Phenoxome improves clinical exome variant prioritization by integrating phenotypic and genomic data, achieving high accuracy in identifying causative variants.

The paper develops a database of coding variations from the Indian population to aid in understanding genotype-phenotype correlations and disease mechanisms.

The paper examines the need for orthogonal confirmation of NGS-detected variants in clinical testing and develops criteria to identify false positives with high accuracy.

The paper introduces VarCount for variant calling and identifies enrichment of compound heterozygous variants in epilepsy-related genes using Epi4k and 1000 Genomes data.

The paper responds to a critique of their variant filtering method, demonstrating its robustness and highlighting the benefits of using high-resolution variant frequencies for clinical genome interpretation.

VarQ assesses the functional impact of single amino acid substitutions on protein activity, folding, and interactions using curated variants.

The paper benchmarks variant effect predictors and analyzes SNVs in H. sapiens, S. cerevisiae, and E. coli to predict molecular effects and generate protein complex burden scores.

The paper introduces exSTRa, a method to detect repeat expansions in WES and WGS data, showing superior performance for WES compared to existing methods.

The paper creates a detailed map of constrained coding regions in the human genome using variation data from 123,136 individuals, identifying regions under strong purifying selection and enriched for pathogenic variants.

GRIPT is a case-control analysis method for NGS data that improves disease gene discovery, particularly in highly heterogeneous Mendelian diseases.

The paper estimates the contribution of recessive coding variants to developmental disorders using exome data from 7,448 families, finding low prevalence in European ancestry but higher in Pakistani ancestry.

The paper identifies recurrent de novo mutations in sibling pairs and estimates their recurrence probabilities based on various factors.

VarSome provides a search engine and impact analysis tool for human genetic variation, aggregating global expertise on variants.

The paper reassesses the pathogenicity of gene variants associated with focal human epilepsies using evolutionary constraint, ACMG guidelines, and bioinformatic tools.

HmtVar is a database of over 30,000 human mitochondrial variants with pathogenicity predictions, integrating data from HmtDB and third-party annotations.

The paper introduces SimRVPedigree, an R package for simulating pedigrees ascertained for multiple disease-affected relatives to study family-based study design.

The paper introduces the Medical Genome Reference Bank (MGRB), a whole-genome dataset of 4,000 healthy elderly individuals, to serve as a reference for genetic variant analysis and healthy aging research.

The paper specifies ACMG/AMP variant interpretation guidelines for genetic hearing loss, updating and validating rules through expert curation of 51 variants.

This paper introduces a combinatorial framework, Odin, for early prediction of neurodevelopmental disorders using rare coding variation and functional information, achieving 8% additional autism case detection with less than 1% false positive rate.

This study evaluates copy number variant detection in clinical whole genome sequencing, finding it as sensitive as microarrays with additional variants, and identifies clinically significant CNVs in 15% of cases.

The paper evaluates seven comparative genomic methods for predicting deleterious variants in Arabidopsis thaliana and finds their relative performance varies across species.

OligoPVP identifies oligogenic disease variants by integrating patient phenotypes and gene interactions in whole exome or genome sequences, showing improved performance over existing methods.

The paper provides refined recommendations for applying the PVS1 ACMG/AMP variant criteria to loss of function variants, addressing gaps in the original guideline through evaluation with curated variants.

The paper screens for syndromic gene mutations in individuals with hyperinsulinaemic hypoglycaemia (HH) and finds a rare pathogenic variant in KMT2D associated with Kabuki syndrome.

STRetch detects pathogenic short tandem repeat expansions in whole genome sequencing data, revealing novel disease-causing STRs with low false discovery rate.