Computational Molecular Biology 2024, Vol.14, No.5, 220-228 http://bioscipublisher.com/index.php/cmb 221 2 Genomics and Gene Evolution Studies 2.1 Genomic structural variations Genomic structural variations, including insertions, deletions, duplications, and translocations, play a significant role in gene evolution by altering gene function and regulation. These variations can lead to phenotypic diversity and adaptation in populations (Ding, 2024). Integrative approaches that combine various omics data, such as genomics, transcriptomics, and proteomics, are essential for understanding the impact of these structural variations on gene function and evolution. For instance, the integration of high-throughput phenotyping and multi-omics data in canola has revealed prime candidate genes associated with metabolic and vegetative growth variations, highlighting the importance of structural variations in plant trait evolution (Figure 1) (Knoch et al., 2023). Figure 1 Experimental workflow to generate the phenotyping and omics data (Adopted from Knoch et al., 2023) 2.2 Genome-wide association studies (GWAS) Genome-wide association studies (GWAS) have been instrumental in identifying genetic loci associated with complex traits and diseases. However, the interpretation of these loci often remains challenging due to their location in noncoding regions and the complexity of linkage disequilibrium (LD). Integrative approaches that incorporate functional annotations and multi-omics data can enhance the power and resolution of GWAS. For example, a scalable Bayesian method has been developed to integrate functional information into GWAS, improving the identification of causal variants and underlying biological mechanisms (Yang et al., 2017). Multi-omics studies have been used to interpret GWAS findings, providing insights into the pathogenesis of complex diseases and their causative factors (Sun and Hu, 2016; Akiyama, 2020). 2.3 Comparative genomics Comparative genomics involves the analysis of genomic features across different species to understand evolutionary relationships and the conservation of genetic elements. Integrative approaches that combine comparative genomics with other omics data can provide a comprehensive view of gene evolution. For instance, integrative multi-omics analyses have been used to identify candidate genes for metabolic and vegetative growth variations in canola, demonstrating the power of combining genomic, transcriptomic, and metabolomic data. Moreover, integrative methods have been applied to transcriptome-wide association studies (TWAS) to identify
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