Christina Anne Austin-Tse

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.

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.

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

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 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.

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.

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.

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.

This study uses saturation genome editing to analyze RNU4-2 variants, identifying functional impacts and redefining the ReNU syndrome critical region.

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 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 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 detects and classifies copy number variants in exome data from 6,633 families, identifying causal CNVs in 173 families and improving classification standards.

The paper investigates ACTN2 variants causing distal myopathy and hypertrophic cardiomyopathy, finding that frameshift variants leading to protein extension cause aggregation, while missense variants do not.

The paper identifies a de novo TLK1 mutation linked to neurodevelopmental disorder and immunodeficiency, showing impaired kinase activity and altered cytokine responses.

The paper presents phenotype and genetic analysis of 206 children with neurodevelopmental disorders from Kenya and South Africa, identifying pathogenic variants in 17% of South African and 38% of Kenyan cases.

The paper shows that variant effect sizes on quantitative endophenotypes support pathogenicity in monogenic disease genes.

The paper describes the formation of the Gene Curation Coalition to standardize gene-disease validity terminology and create a unified database for curated gene-disease assertions.

This study investigates how rare and common genetic variations contribute to QT interval duration, finding that both monogenic and polygenic factors influence QTc prolongation, with polygenic risk modulating the effect of rare variants.

seqr is a web-based tool for analyzing rare disease genomic data, enabling variant filtering, annotation, and collaboration, with over 10,000 families analyzed and 300 novel disease genes discovered.

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.

This paper proposes a framework for automated gene selection in genomic screening, creating gene lists with strong disease associations and demonstrating their utility in healthy genome screening.

This study compares genome sequencing with standard-of-care genetic testing, finding similar diagnostic yields but longer turnaround times for genome sequencing.

This study evaluates genome sequencing as a single diagnostic approach for fetal structural anomalies, showing it detects nearly all variants from standard tests and provides higher diagnostic yield than individual tests.