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Gaither, J. B.

Publications and source records attributed to Gaither, J. B..

2 recordsLinked to original sources

Identification of Long Non-coding RNA Candidate Disease Genes Associated with Clinically Reported CNVs in Congenital Heart Disease

AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSBackgroundC_ST_ABSCopy Number Variants (CNVs) contribute to 3-10% of isolated Congenital Heart Disease (CHD) cases, but their roles in disease pathogenesis are often unclear. Traditionally, diagnostics have focused on protein-coding genes, overlooking the pathogenic potential of non-coding regions constituting 99% of the genome. Long non-coding RNAs (lncRNAs) are increasingly recognized for their roles in development and disease. MethodsIn this study, we systematically analyzed candidate lncRNAs overlapping with clinically validated CNVs in 1,363 CHD patients from the Cytogenomics of Cardiovascular Malformations (CCVM) Consortium. We identified heart-expressed lncRNAs, constructed a gene regulatory network using Weighted Gene Co-expression Network Analysis (WGCNA), and identified gene modules significantly associated with heart development. Functional enrichment analyses and network visualizations were conducted to elucidate the roles of these lncRNAs in cardiac development and disease. The code is stably archived at https://doi.org/10.5281/zenodo.13799847. ResultsWe identified 18 lncRNA candidate genes within modules significantly correlated with heart tissue, highlighting their potential involvement in CHD pathogenesis. Notably, lncRNAs such as lnc-STK32C-3, lnc-TBX20-1, and CRMA demonstrated strong associations with known CHD genes. Strikingly, while only 7.6% of known CHD genes were impacted by a CNV, 68.8% of the CNVs contained a lncRNA expressed in the heart. ConclusionsOur findings highlight the critical yet underexplored role of lncRNAs in the genomics of CHD. By investigating CNV-associated lncRNAs, this study paves the way for deeper insights into the genetic basis of CHD by incorporating non-coding genomic regions. The research underscores the need for advanced annotation techniques and broader genetic database inclusion to fully capture the potential of lncRNAs in disease mechanisms. Overall, this work emphasizes the importance of the non-coding genome as a pivotal factor in CHD pathogenesis, potentially uncovering novel contributors to disease risk.

genomics↗

SNVstory: A dockerized algorithm for rapid and accurate inference of sub-continental ancestry

Knowing a patients genetic ancestry is crucial in clinical settings, providing benefits such as tailored genetic testing, targeted health screening based on ancestral disease-predisposition rates, and personalized medication dosages. However, self-reported ancestry can be subjective, making it difficult to apply consistently. Moreover, existing approaches utilize genome sequencing data to infer ancestry at the continental level, creating the need for methods optimized for individual ancestry assignment. We present SNVstory, a method built upon three independent machine learning models for accurately inferring the sub-continental ancestry of individuals. SNVstory includes a feature-importance scheme, unique among open-source ancestral tools, which allows the user to track the ancestral signal broadcast by a given gene or locus. We apply SNVstory to a clinical dataset, comparing self-reported ethnicity and race to our inferred genetic ancestry. SNVstory represents a significant advance in methods to assign genetic ancestry, predicting ancestry across 36 different populations with high accuracy.

genomics↗