Genomics and Rare Diseases

The paper describes the role of the Centers for Mendelian Genomics in advancing gene discovery through data sharing, collaboration, and resource development over the past decade.

The paper identifies potentially harmful genetic variants in euploid miscarriage embryos and highlights specific genes linked to miscarriage risk.

The paper examines how mutations at different protein interfaces correlate with disease, finding significant variation in pathogenicity across interface types and highlighting discrepancies in computational prediction tools.

The paper introduces MRSD, a metric to determine optimal RNA-seq depth and tissue for assessing splicing variants in Mendelian diseases, showing LCLs are best for most gene panels.

The paper introduces the ClinGen Variant Curation Interface, a platform for applying ACMG/AMP guidelines to classify genetic variants, enabling collaboration and standardization in clinical variant interpretation.

StrVCTVRE predicts the pathogenicity of human genome structural variants using a random forest classifier, achieving 90% sensitivity while filtering half of SVs.

HKG is a variant database of 205 Hong Kong Cantonese exomes that fills a gap in southern Chinese genetics, showing unique variants and improving variant imputation.

The paper compares a new WES protocol, RSMU_exome, to an existing MGI Tech kit, showing it detects more variants with higher sensitivity and specificity while being cheaper and allowing more samples to be pooled.

The paper presents a classifier to predict the clinical impact of structural copy number variations with over 98% accuracy.

The paper evaluates human mutation databases to identify treatable variants using antisense oligonucleotides, finding most pathogenic variants could be targeted therapeutically.

HGDiscovery provides functional and phenotypic information on novel HGD missense mutations using machine learning and structural analysis.

This study evaluates how in silico predictors influence clinical variant classification, finding that PP3 and BP4 criteria are frequently applied and often affect variant classifications.

This study reanalyzes 1,496 Taiwanese whole genome sequences to identify novel variants and clinically relevant genetic markers with potential medical implications.

LYRUS predicts the pathogenicity of missense variants using sequence, structure, and dynamics-based features, achieving higher accuracy than existing methods.

The paper evaluates in silico variant predictors for CYP21A2 SNVs, finding high accuracy for severe phenotypes but variability for mild ones.

DeepSVP prioritizes pathogenic structural variants by integrating genotype and phenotype data using deep learning, improving benchmark performance and identifying novel variants in consanguineous families.

This paper introduces DeepLOF, a deep learning model that improves essential gene prediction by integrating population and functional genomic data, identifying 109 previously undetected short essential genes.

ePat extends PROVEAN to predict pathogenicity of indels and splice variants, and process multiple variants in a single batch.

The paper identifies a founder variant in SZT2 associated with neurodevelopmental disorder and develops a method to distinguish pathogenic variants from VUSs in SZT2.

VarGenius-HZD detects rare homozygous and hemizygous deletions in targeted sequencing with higher sensitivity than existing tools.

This study identifies deleterious SNPs in the TBX5 gene using in silico methods and predicts their structural and functional impacts on HOS.

The paper investigates nsSNPs in the Drd2 gene, identifies F389V as the most impactful variant, and shows its altered binding properties with dopamine and risperidone.

The paper introduces MATK, an automated variant ranking tool for Mendelian disease diagnosis, showing it identifies most expert solutions and finds additional variants in unsolved cases.

This paper introduces BayPR, a Bayesian method that assigns pathogenicity probabilities to genetic variants using population data, achieving high accuracy for Mendelian disorders.

The paper provides consensus guidelines for classifying LDLR variants in Familial Hypercholesterolemia using modified ACMG/AMP criteria.

TierUp automates reanalysis of genomic data to identify novel gene-disease associations, improving diagnostic yield for undiagnosed rare disease cases.

This study reports on 25 new KBG syndrome patients, identifies common clinical features, and provides recommendations for diagnosis and treatment.

This study evaluates the feasibility and costs of returning actionable genomic results from a biobank, finding high false positive rates in genotyping and significant under-testing among at-risk individuals.

The paper introduces synVep, a machine learning method for predicting the effects of synonymous variants, achieving high precision and recall while improving differentiation between pathogenic and benign variants.

M-DATA improves statistical power for identifying risk genes by jointly analyzing de novo mutations across multiple traits, demonstrating its effectiveness in congenital heart disease and autism.