Biomedical subjects

Melanie O'Leary

Publications and source records attributed to Melanie O'Leary.

2 recordsLinked to original sources

PubMed2026

RNU4ATAC-opathy: Clinical, molecular, and transcriptomic insights from a large cohort.

PURPOSE: We aim to better define the genotype and phenotype spectrum of RNU4ATAC-opathy, demonstrate the utility of RNA sequencing (RNA-seq) for variant classification, and highlight the challenges in detecting variants in this noncoding gene. METHODS: Sixty individuals with molecularly confirmed RNU4ATAC-opathy were recruited from multiple clinical and research centers internationally. RNA-seq was available for 7 affected individuals. RESULTS: We report the clinical and molecular findings of 60 individuals, including 42 not previously described, and 33 distinct RNU4ATAC variants, 13 of which are novel. Core features in this cohort-present in most individuals assessed and varying in severity-include microcephaly, short stature, skeletal anomalies, developmental delay, cerebral anomalies, skin conditions, and immune deficiency. Additional findings, such as diabetes, holoprosencephaly, and the absence of various core features in some individuals, highlight the broad phenotypic spectrum. All individuals who underwent RNA-seq showed a consistent pattern of minor intron retention. In 6 individuals, RNA-seq enabled the reclassification of variants of uncertain significance as likely pathogenic. Although RNU4ATAC variants are generally covered by clinical exomes, they are often overlooked in analysis because of their noncoding nature. CONCLUSION: This study highlights the variability of phenotypes and genotypes associated with RNU4ATAC-opathy. Laboratories should ensure RNU4ATAC and other noncoding genes are appropriately assessed by their analysis pipelines.

Lowry-Wood syndrome↗

PubMed2024

Diagnosing missed cases of spinal muscular atrophy in genome, exome, and panel sequencing data sets.

PURPOSE: We set out to develop a publicly available tool that could accurately diagnose spinal muscular atrophy (SMA) in exome, genome, or panel sequencing data sets aligned to a GRCh37, GRCh38, or T2T reference genome. METHODS: The SMA Finder algorithm detects the most common genetic causes of SMA by evaluating reads that overlap the c.840 position of the SMN1 and SMN2 paralogs. It uses these reads to determine whether an individual most likely has 0 functional copies of SMN1. RESULTS: We developed SMA Finder and evaluated it on 16,626 exomes and 3911 genomes from the Broad Institute Center for Mendelian Genomics, 1157 exomes and 8762 panel samples from Tartu University Hospital, and 198,868 exomes and 198,868 genomes from the UK Biobank. SMA Finder's false-positive rate was below 1 in 200,000 samples, its positive predictive value was greater than 96%, and its true-positive rate was 29 out of 29. Most of these SMA diagnoses had initially been clinically misdiagnosed as limb-girdle muscular dystrophy. CONCLUSION: Our extensive evaluation of SMA Finder on exome, genome, and panel sequencing samples found it to have nearly 100% accuracy and demonstrated its ability to reduce diagnostic delays, particularly in individuals with milder subtypes of SMA. Given this accuracy, the common misdiagnoses identified here, the widespread availability of clinical confirmatory testing for SMA, and the existence of treatment options, we propose that it is time to add SMN1 to the American College of Medical Genetics list of genes with reportable secondary findings after genome and exome sequencing.

Humans↗