Computational and qPCR-Based Determination of Gene Copy Number as a Molecular Marker for Transgenic Plant Analysis

The molecular characterization of genetically modified plants is commonly based on the detection of foreign DNA sequences introduced during transformation. While this strategy is effective for conventional transgenic plants, it becomes inadequate for cisgenic and intragenic plants in which the introduced gene originates from the host genome and is regulated by native promoter and terminator elements. Following segregation-based elimination of selectable marker cassettes, such cisgenic plants may become indistinguishable from wild-type plants using standard sequence-dependent PCR assays. This chapter presents a quantitative real-time PCR (qPCR) and computation-based approach for estimating gene copy number as an auxiliary molecular marker for the analysis of cisgenic plant lines. The method relies on quantitative differences in endogenous gene dosage rather than the detection of heterologous DNA. Genomic DNA from putative cisgenic and control plants is analyzed by qPCR using serial template dilutions and primer pairs targeting both a cisgenic candidate gene and an endogenous single-copy reference gene. Linear regression of cycle threshold (Ct) values against log₂-transformed DNA concentrations is performed, and differences in regression intercepts are used to infer relative gene copy number. Because the underlying data processing, regression modeling, and copy number calculations may be technically demanding for routine users, a dedicated standalone computational tool, CisScope, was developed to automate the entire analytical workflow. CisScope integrates data parsing, statistical modeling, and result interpretation into an intuitive graphical interface and is distributed as an executable file, eliminating the need for programming expertise or specialized statistical software. Although not intended for regulatory diagnostics, this integrated experimental, computational framework offers a practical and accessible solution to a major limitation of sequence-based detection strategies in cisgenic plant biotechnology and is well-suited for both research and educational applications.