Biology subjects

Posey, J.

Publications and source records attributed to Posey, J..

3 recordsLinked to original sources

bioRxiv2026

Unveiling the Hidden Rules: Enhancing NMD Prediction for Protein-Truncating Variants

Nonsense-mediated decay (NMD) is a conserved RNA quality-control pathway that degrades transcripts containing premature termination codons. Because roughly a third of pathogenic variants in ClinVar can lead to truncated protein synthesis, predicting whether such transcripts undergo NMD is central to interpreting variant effects, yet the canonical 50-55 nucleotide rule explains only about half of observed outcome variability. Using paired whole-genome and RNA-sequencing from 10,306 individual samples in the Trans-Omics for Precision Medicine (TOPMed) program, we quantified NMD efficiency for 5,749 germline truncating variants via allele-specific expression and trained a gradient-boosting classifier, TrunCat, that distinguished NMD-sensitive from NMD-escape transcripts with [~]78% ROC-AUC (Receiver Operating Characteristic - Area Under the Curve). A reduced model using the ten features with the highest mean SHAP (SHapley Additive exPlanations) value as a measure of each features average contribution to predictions nearly matched this performance. Applied across large variant databases and a rare-disease cohort, the model produced NMD outcome predictions, with variants of uncertain significance showing higher predicted escape than pathogenic ones. This framework confirms the canonical rule, identifies non-canonical determinants, and offers a scalable resource for interpreting protein-truncating variants.

genomics↗

bioRxiv2026

Building an Interoperable Rare Disease Multi-omic Resource: The GREGoR Data Model and Dataset

Rare disease research and diagnosis rely on the integration of genomic and phenotypic data generated across diverse clinical sites; however, the absence of widely adopted standards for representing genomic data and associated metadata has limited data interoperability, reuse, and cross-study analysis. The Genomics Research to Elucidate the Genetics of Rare Diseases (GREGoR) Consortium was established to investigate challenging rare disease cases and evaluate emerging multi-omic technologies for clinical translation. To support coordinated data integration across distributed research sites, we developed a common Consortium Data Model in partnership with domain experts to standardize the capture of participant-, family-, phenotype- and assay-level metadata, with a particular emphasis on using a modular architecture to support linking of multiple data versions from multiple omic technologies to a single individual and attribution of a genetic finding to the specific technology used for its initial discovery. Adoption of the GREGoR Data Model has enabled continued generation and public release of a harmonized, analysis-ready Consortium Dataset. The most recent release includes phenotypic, family and multi-omic data from 12,292 participants in 5,029 families. Other rare disease data sharing efforts are beginning to adopt this data model which will facilitate cross consortium analyses and empower rare disease research. This work demonstrates that a collaborative, flexible, and scalable data model can enable large-scale rare disease research, facilitate cross-center data harmonization, and enable data interoperability.

genomics↗

bioRxiv2020

De novo mutation in ancestral generations evolves haplotypes contributing to disease

PurposeThe variome of the Turkish (TK) population, a population with a considerable history of admixture and consanguinity, has not been deeply investigated deeply for its potential impact on the genomic architecture of disease traits. MethodsWe generated and analyzed a database of variants derived from exome sequencing (ES) data of 773 TK unrelated, clinically affected individuals with various suspected Mendelian disease traits, and 643 unaffected relatives. ResultsUsing uniform manifold approximation and projection (UMAP), we showed that the TK genomes are more similar to those of Europeans and consist of two main subpopulations: clusters 1 and 2 (N=235 and 1,181) that differ in admixture proportion and variome (https://turkishvariomedb.shinyapps.io/tvdb/). Furthermore, the higher inbreeding coefficient (F) values observed in the TK affected compared to unaffected individuals correlated with a larger median span of long-sized (>2.64 Mb) runs of homozygosity (ROH) regions (p-value=2.09e-18). We show that long-sized ROHs are more likely to be formed on recently configured haplotypes enriched for rare homozygous deleterious variants in the TK-affected compared to TK-unaffected individuals (p-value= 3.35e-11). Analysis of genotype-phenotype correlations reveals that genes with rare homozygous deleterious variants in long-sized ROHs provide the most comprehensive set of molecular diagnoses for the observed disease traits with a systematic quantitative analysis of HPO (Human Phenotype Ontology) terms. ConclusionOur findings support the notion that novel rare variants on newly configured haplotypes arising within the recent past generations of a family or clan contribute significantly to recessive disease traits in the TK population.

genomics↗