Plant Gene and Trait 2025, Vol.16, No.5, 215-224 http://genbreedpublisher.com/index.php/pgt 218 5.2 QTL mapping and GWAS for key traits QTL mapping and GWAS are the core methods for studying the complex traits of lotus. By combining phenotypic and genotypic data from large populations, researchers have identified QTL and candidate genes related to petal number, flower shape, plant structure and starch content, etc. For instance, GWAS identified gene and transposon variations related to the number of petals and stamen petalization. Some of these genes also have pleiotropy and can affect the traits of multiple floral organs (Gao et al., 2022; Zhao et al., 2023; Hu et al., 2024). In terms of edible quality, QTL mapping and association analysis also identified key genes controlling seed starch synthesis and rhizome expansion, providing theoretical support for molecular design breeding (Li et al., 2020; Sun et al., 2020). 5.3 Genome editing (CRISPR/Cas) and functional genomics for trait improvement With the continuous improvement of the reference genome, the application prospects of gene editing technologies such as CRISPR/Cas in the lotus research are very promising. At present, the genetic transformation system of lotus is not yet mature, but some progress has been made in the mining of functional genes and the research of transcription factors (Figure 1) (Qi et al., 2022; Song et al., 2022; Sun et al., 2025). In other ornamental plants, CRISPR/Cas has been successfully used to regulate flower color, shape and flowering period. These experiences provide references for achieving precise editing in lotus in the future (Giovannini et al., 2021; Muhammed et al., 2025). Furthermore, the multi-omics combined with functional genomics approach has identified some key genes related to petal morphology, starch synthesis and stress resistance, all of which provide rich target resources for molecular breeding (Sun et al., 2020; Song et al., 2022; He et al., 2025). Figure 1 Flowchart of the molecular breeding process of lotus (Adopted from Qi et al., 2022)
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