Genomic Variation Prediction: A Summary From Different Views

Review of Computational Methods and Database Sources for Predicting the Effects of Coding Frameshift Small Insertion and Deletion Variations

Genetic variations (including substitutions, insertions, and deletions) exert a profound influence on DNA sequences. These variations are systematically classified as synonymous, nonsynonymous, and nonsense, each manifesting distinct effects on proteins. The implementation of high-throughput sequencing has significantly augmented our comprehension of the intricate interplay between gene variations and protein structure and function, as well as their ramifications in the context of diseases. Frameshift variations, particularly small insertions and deletions (indels), disrupt protein coding and are instrumental in disease pathogenesis. This review presents a succinct review of computational methods, databases, current challenges, and future directions in predicting the consequences of coding frameshift small indels variations. We analyzed the predictive efficacy, reliability, and utilization of computational methods and variant account, reliability, and utilization of database. Besides, we also compared the prediction methodologies on GOF/LOF pathogenic variation data. Addressing the challenges pertaining to prediction accuracy and cross-species generalizability, nascent technologies such as AI and deep learning harbor immense potential to enhance predictive capabilities. The importance of interdisciplinary research and collaboration cannot be overstated for devising effective diagnosis, treatment, and prevention strategies concerning diseases associated with coding frameshift indels variations.

Usefulness and Limitations of Multiple Ligation-Dependent Probe Amplification in Antithrombin Deficiency

Multiplex ligation-dependent probe amplification (MLPA) identifies genetic structural variants in SERPINC1 in 5% of cases with antithrombin deficiency (ATD), the most severe congenital thrombophilia. Our aim was to unravel the utility and limitations of MLPA in a large cohort of unrelated patients with ATD (N = 341). MLPA identified 22 structural variants (SVs) causing ATD (6.5%). MLPA did not detect SVs affecting introns (four cases), and the diagnosis was inaccurate in two cases according to long-range PCR or nanopore sequencing. MLPA was used to detect possible hidden SVs in 61 cases with type I deficiency with single nucleotide variations (SNVs) or small insertion/deletion (INDEL). One case had a false deletion of exon 7, as the 29-bp deletion affected an MLPA probe. We evaluated 32 variants affecting MLPA probes: 27 SNVs and 5 small INDELs. In three cases, MLPA gave false-positive results, all diagnosed as deletions of the affected exon: a small INDEL complex, and two SNVs affecting MLPA probes. Our study confirms the utility of MLPA to detect SVs in ATD, but also shows some limitations in detecting intronic SVs. MLPA renders imprecise and false-positive results for genetic defects which affect MLPA probes. Our results encourage the validation of MLPA results.

Modular networks and genomic variation during progression from stable angina pectoris through ischemic cardiomyopathy to chronic heart failure

Background

Analyzing disease–disease relationships plays an important role for understanding etiology, disease classification, and drug repositioning. However, as cardiovascular diseases with causative links, the molecular relationship among stable angina pectoris (SAP), ischemic cardiomyopathy (ICM) and chronic heart failure (CHF) is not clear.

Methods

In this study, by integrating the multi-database data, we constructed paired disease progression modules (PDPMs) to identified relationship among SAP, ICM and CHF based on module reconstruction pairs (MRPs) of K-value calculation (a Euclidean distance optimization by integrating module topology parameters and their weights) methods. Finally, enrichment analysis, literature validation and structural variation (SV) were performed to verify the relationship between the three diseases in PDPMs.

Results

Total 16 PDPMs were found with K > 0.3777 among SAP, ICM and CHF, in which 6 pairs in SAP–ICM, 5 pairs for both ICM–CHF and SAP–CHF. SAP–ICM was the most closely related by having the smallest average K-value (K = 0.3899) while the maximum is SAP–CHF (K = 0.4006). According to the function of the validation gene, inflammatory response were through each stage of SAP–ICM–CHF, while SAP–ICM was uniquely involved in fibrosis, and genes were related in affecting the upstream of PI3K–Akt signaling pathway. 4 of the 11 genes (FLT1, KDR, ANGPT2 and PGF) in SAP–ICM–CHF related to angiogenesis in HIF-1 signaling pathway. Furthermore, we identified 62.96% SVs were protein deletion in SAP–ICM–CHF, and 53.85% SVs were defined as protein replication in SAP–ICM, while ICM–CHF genes were mainly affected by protein deletion.

Conclusion

The PDPMs analysis approach combined with genomic structural variation provides a new avenue for determining target associations contributing to disease progression and reveals that inflammation and angiogenesis may be important links among SAP, ICM and CHF progression.