Back

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

Penaloza, J. S.; CCVM Consortium, ; Moreland, B.; Gaither, J. B.; Landis, B. J.; Ware, S. M.; McBride, K. L.; White, P.

2024-10-02 genomics
10.1101/2024.09.30.615967 bioRxiv
Show abstract

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.

Matching journals

The top 9 journals account for 50% of the predicted probability mass.

1
Frontiers in Genetics
197 papers in training set
Top 0.3%
10.3%
2
Database
51 papers in training set
Top 0.1%
9.3%
3
Human Genomics
21 papers in training set
Top 0.1%
7.3%
4
Journal of the American Heart Association
119 papers in training set
Top 1%
4.9%
5
BMC Medical Genomics
36 papers in training set
Top 0.1%
4.9%
6
Scientific Reports
3102 papers in training set
Top 31%
3.9%
7
PLOS ONE
4510 papers in training set
Top 38%
3.6%
8
Genetic Epidemiology
46 papers in training set
Top 0.2%
3.1%
9
BMC Genomics
328 papers in training set
Top 1%
3.1%
50% of probability mass above
10
Genomics
60 papers in training set
Top 0.4%
2.9%
11
Human Genetics
25 papers in training set
Top 0.1%
2.8%
12
Frontiers in Cardiovascular Medicine
49 papers in training set
Top 1%
2.4%
13
npj Genomic Medicine
33 papers in training set
Top 0.2%
2.4%
14
Genes
126 papers in training set
Top 0.6%
2.1%
15
Human Molecular Genetics
130 papers in training set
Top 1%
1.9%
16
Genome Medicine
154 papers in training set
Top 4%
1.7%
17
Genomics, Proteomics & Bioinformatics
171 papers in training set
Top 3%
1.7%
18
PLOS Genetics
756 papers in training set
Top 11%
1.2%
19
Journal of Personalized Medicine
28 papers in training set
Top 0.6%
1.2%
20
Journal of Genetics and Genomics
36 papers in training set
Top 2%
1.0%
21
The American Journal of Human Genetics
206 papers in training set
Top 3%
0.9%
22
Nucleic Acids Research
1128 papers in training set
Top 16%
0.8%
23
Journal of Cellular and Molecular Medicine
18 papers in training set
Top 0.8%
0.8%
24
F1000Research
79 papers in training set
Top 4%
0.8%
25
PLOS Computational Biology
1633 papers in training set
Top 23%
0.8%
26
Journal of Clinical Medicine
91 papers in training set
Top 6%
0.8%
27
Cell Proliferation
12 papers in training set
Top 0.4%
0.8%
28
International Journal of Molecular Sciences
453 papers in training set
Top 15%
0.8%
29
eLife
5422 papers in training set
Top 57%
0.8%
30
European Journal of Human Genetics
49 papers in training set
Top 1%
0.7%