Anne O’Donnell‐Luria

This study explores equitable strategies for participant engagement in rare disease genomic research through interviews with underrepresented individuals and clinicians.

The paper evaluates incomplete penetrance in 4,325 rare disease families, finding 1.6% of diagnosed cases involve inherited variants from unaffected parents and showing divergent transcriptional effects in CHD8-related cases.

AlphaGenome Atlas predicts regulatory effects of non-coding variants across the human genome, improving their prioritization and interpretation.

The Rare Genomes Project provides remote genomic sequencing to rare disease families, achieving a 23.3% diagnostic solve rate and highlighting the role of research in overcoming clinical diagnostic barriers.

IsoRanker uses long-read transcriptomics to prioritize non-coding variants in Mendelian conditions by detecting expression outliers, allelic imbalance, and NMD, leading to the identification of pathogenic variants in one individual.

The paper identifies novel spliceosome-related genes and pseudogenes associated with rare diseases using variant analysis and prioritization methods.

The paper provides guidance for classifying variants in snRNA genes and introduces RNUdb, a tool to support variant annotation and classification.

This study evaluates the diagnostic yield of short-read and long-read sequencing in rare diseases, finding limited added value from long-read sequencing despite increased variant discovery.

The paper introduces a data model and dataset for rare disease research, enabling interoperable multi-omic data integration across clinical sites.

The paper creates a genome-wide map of homozygous deletions in 125,730 individuals, identifying novel rare disease genes and variants.

The paper presents genetic and phenotypic data from 521 children with neurodevelopmental disorders in Kenya and South Africa, highlighting high genetic diagnosis rates linked to environmental risk factors.

This study calibrates in-frame indel variant effect predictors for clinical classification, showing they reach multiple evidence levels but perform worse than missense predictors.

The paper estimates genetic prevalence for 22 autosomal recessive conditions using gnomAD data and develops a tool to democratize these calculations, highlighting the dynamic nature of prevalence estimates.

gnomAD v4 (730,947 exomes) brings LOFTEE-2 loss-of-function annotation (0.90 precision, strict mode), missense-aware constraint, and LLM-mined literature evidence combined with constraint to rank disease genes.

The paper evaluates how well variant effect predictors perform across different genetic ancestries, finding comparable performance across major groups after accounting for allele frequency.

The paper identifies individuals with minor spliceopathies by analyzing transcriptome-wide splicing outliers in RNA-seq data from rare disease cohorts.

The paper identifies biallelic variants in RNU4-2 causing a recessive neurodevelopmental syndrome with distinct white matter changes, expanding the known genotypic and phenotypic spectrum of RNU4-2-related disorders.

The paper develops an automated tool for reanalyzing genomic data in rare disease cohorts, showing improved diagnostic yields through iterative reanalysis.

This study identifies ZFHX4 loss-of-function variants as a cause of a neurodevelopmental disorder with distinctive facial features and brain abnormalities, supported by zebrafish models.

This paper introduces ExCALIBR, a method to calibrate variant scores from functional assays, improving pathogenicity prediction and reducing variants of uncertain significance.

The paper identifies mitochondrial DNA variants in undiagnosed rare disease families using exome and genome sequencing data, leading to diagnoses in 0.4% of cases.

The paper calibrates three new computational tools for clinical variant classification, finding they provide evidence strength comparable to previously recommended tools.

This study evaluates the diagnostic yield of long-read nanopore sequencing in rare disease detection, finding it covers more genes and detects additional variants compared to short-read sequencing.

The paper uses long-read sequencing data to analyze STR variability across 2,645 individuals, identifying disease-associated STR loci with elevated allelic variability and introducing the PLVI metric.

The paper curates 111 gene-disease relationships for syndromic disorders, classifying their clinical validity and identifying gaps in knowledge.

The paper develops and validates SMA Finder, a tool to diagnose spinal muscular atrophy in genome, exome, and panel sequencing data, identifying known and novel cases with high accuracy.

The paper identifies three novel candidate genes for ocular congenital cranial dysinnervation disorders using zebrafish CRISPR screening and protein binding microarrays to assess variant functionality.

The paper assesses rare missense variants in genome sequences using PP3/BP4 evidence strength classifications and finds few variants meet strong or moderate criteria.

The paper evaluates variant prioritization methods for rare disease diagnosis using data from the Rare Genomes Project, finding that top models identify causal variants in most solved families and uncover potential candidates in unsolved ones.

The paper quantifies regional missense mutational intolerance using 730,947 exomes to identify constrained gene regions and improve variant interpretation.