Dalia Kasperavičiūtė

This study examines ancestry-related differences in variant prioritisation and diagnostic outcomes in the UK 100,000 Genomes Project, finding disparities in positive predictive value and variant of uncertain significance frequencies across groups.

The paper develops a comprehensive list of genetic conditions for genomic newborn screening, including 777 conditions associated with 743 genes, for implementation across multiple U.S. states and territories.

The paper uses blood-based RNA-Seq on 5,412 individuals with rare diseases to identify new candidate diagnoses and shows its utility in prioritizing genetic variants.

The paper assesses an automated variant prioritisation strategy for genomic newborn screening in the Generation Study, finding that 3-5% of samples require manual review and <1% need confirmatory testing.

The paper develops a machine learning model to prioritize genes for newborn sequencing programs based on factors like evidence of disease natural history and treatment efficacy.

The paper develops Paraphase, a HiFi-based method to resolve highly similar paralogous genes, revealing genetic diversity and de novo events in segmental duplications.

GenePy identifies potential missed biallelic diagnoses in the 100KGP by scoring variants and analyzing phenotypic overlaps, finding 14% of undiagnosed participants may have a missed diagnosis.

This study identifies genetic differences between pediatric and adult cardiomyopathy, finding more constrained variants in childhood cases and female patients.

The paper introduces MuAt, a deep learning model that learns representations of somatic mutations to predict tumour types and subtypes with high accuracy.

This study identifies TMEM63B variants in patients with developmental and epileptic encephalopathy and neurodegeneration, showing altered cation currents and hematological abnormalities.

EyeG2P improves diagnostic genomic testing efficiency for inherited ophthalmic disorders by increasing precision while maintaining high sensitivity.

The paper develops Paraphase, a method using long-read sequencing to accurately determine SMN1 and SMN2 haplotypes and copy numbers, improving silent carrier detection in spinal muscular atrophy.

The paper introduces DeNovoLOEUF, a method to identify diagnostic variants in genome sequencing data, achieving 96% positive predictive value and uncovering 39 missed diagnoses in the 100,000 Genomes Project.

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

The paper identifies an intermediate-effect variant in UMOD associated with chronic kidney disease risk and shows it has reduced disease severity compared to canonical ADTKD mutations.

This study uses whole-genome sequencing to identify regulatory and novel genic variants contributing to childhood cardiomyopathy.

The paper identifies gene-specific mutational signatures caused by replication errors and endogenous DNA damage using CRISPR-Cas9 knockouts and whole-genome sequencing.

This study demonstrates that whole genome sequencing can effectively detect repeat expansions in neurological disorders with high sensitivity and specificity, enabling diagnosis in previously undiagnosed patients.

The paper identifies genetic loci associated with human cortical structure and finds genetic correlations with neuropsychiatric disorders.

The paper integrates gene essentiality data from human and mouse screens to classify genes into FUSIL categories, identifying developmental lethal genes associated with rare diseases.

The paper identifies 25 genetic loci associated with subcortical brain structure volumes in over 40,000 individuals, revealing novel biology and potential drug targets.