Biomedical subjects

Devon E Bonner

Publications and source records attributed to Devon E Bonner.

3 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↗

PubMed2026

Population-scale detection of methylation outliers from long-read genome sequencing.

BACKGROUND: Aberrant DNA methylation can mediate the functional effects of rare genetic variation and contribute to imprinting disorders, repeat expansion diseases, and other pathogenic regulatory mechanisms. Long-read sequencing technologies now enable genome-wide detection of CpG methylation alongside genetic variation from a single assay. However, methods for systematic identification and interpretation of methylation outliers from long-read sequencing data remain limited. METHODS: We developed METAFORA, a computational workflow for detecting methylation outlier regions from PacBio and Oxford Nanopore long-read sequencing data. METAFORA constructs population-level methylation references, segments the genome into correlated CpG blocks, infers technical and biological sources of variation through hidden factor estimation, models uncertainty due to variable depth sequencing, and computes covariate-adjusted methylation outlier scores for individual samples. We applied METAFORA across large long-read sequencing cohorts and integrated methylation outliers with multi-omic data. METAFORA is implemented as a snakemake workflow available at https://github.com/tjense25/METAFORA. RESULTS: METAFORA identified methylation outlier regions associated with rare structural variants, tandem repeat expansions, and imprinting abnormalities. We found outlier regions were enriched for molecular outliers across transcriptomic and chromatin accessibility datasets, supporting their functional relevance in gene regulation. In a representative case, METAFORA identified an imprinting defect affecting the GNAS locus associated with an STX16 deletion. CONCLUSIONS: METAFORA enables scalable detection and interpretation of methylation outliers from long-read sequencing data and provides a framework for integrating epigenetic outliers with genomic and multi-omic analyses. These approaches may improve interpretation of rare regulatory variation and support discovery of clinically relevant epigenetic abnormalities in genomic medicine.

DNA methylation↗

PubMed2025

Biallelic variants in RNU2-2 cause the most prevalent known recessive neurodevelopmental disorder.

We recently showed that mutations in RNU4-2 and RNU2-2, two genes that are transcribed into small nuclear RNA (snRNA) components of the major spliceosome, are prevalent causes of dominant neurodevelopmental disorders (NDDs). By genetic association comparing 12,776 NDD cases with 56,064 controls, we now demonstrate the existence of a recessive form of RNU2-2 syndrome that, in England, is even more common than the dominant form. We inferred log Bayes factors for dominant and recessive models of association of 14.0 and 18.2, respectively, and observed 17 rare variants with a posterior probability of pathogenicity conditional on recessive association >0.8. This conservative threshold identified 18 probands (all with unaffected parents) and five affected siblings, each carrying two alleles in trans at these variants. A relaxed threshold of >0.6 identified a further 13 candidate probands. We estimate that recessive RNU2-2 syndrome accounts for 7-10% of families with a diagnosed recessive NDD, and is 36-62% as prevalent as the dominant RNU4-2-related disorder ReNU syndrome. We identified a further seven cases in five pedigrees in two replication collections. Cases are characterized by intellectual disability, global developmental delay and seizures. The variants are predicted to destabilize stem loops and binding domains of the U2-2 snRNA that contribute to spliceosome quaternary structure, intron recognition and catalytic function. Despite this, whole-blood derived RNA-seq data from three patients did not reveal splicing defects, in line with previous analogous observations for dominant RNU2-2 syndrome.

Journal Article↗