International Journal of Horticulture, 2025, Vol.15, No.3, 105-112 http://hortherbpublisher.com/index.php/ijh 108 Figure 2 Carotenoid accumulation and AcCCD4 expression during fruit ripening in ‘Yugafu’, (Yu) and ‘Yonekura’ (Yo) (Adopted from Nashima et al., 2022) Image caption: (a) Flesh appearance. (b) Carotenoid content. (c) AcCCD4 relative gene expression. Three biological replicates for each sample were examined to determine carotenoid quantities and conduct gene expression analysis. Error bars indicate SE. VIO, violaxanthin; cis-VIO, 9-cis-violaxanthin; LUT, lutein; ZEA, zeaxanthin; BCR, β-cryptoxanthin; ACA, α-carotene; BCA, β-carotene (Adopted from Nashima et al., 2022) 4.3 Flowering and growth traits Flowering and growth characteristics are the key points to optimize the production cycle and increase the yield of pineapple. Some studies have found that there are genetic differences in flowering and growth traits among different materials. The genetic analysis of half-sib lines by Junior et al. (2021) showed that traits such as fruit quality and soluble solid content provided scientific basis for breeding superior parents. The use of molecular markers such as RFLP and SNP can help to identify key genetic variants that affect flowering time and plant structure, and promote growth traits (Duval et al., 2001; Zhou et al., 2015). The study of Wang et al. (2017) showed that genetic cluster analysis of materials will be conducive to analyzing the genetic basis of flowering and growth traits and formulating targeted breeding strategies. 5 Case Study: Meta-Analysis of Brix-Related QTLs 5.1 Data aggregation process The meta-analysis summarized QTL data related to Brix (soluble solid content) by integrating multiple studies using different molecular markers and genetic analysis methods. Studies included in the analysis included studies using SSR, AFLP, and SNP markers (Paz et al., 2012; Zhou et al., 2015; Wang et al., 2017; Zhao and Qin, 2018; Ismail et al., 2020). The subjects of the study were germplasm resources from Malaysia, Cuba, Indonesia and other regions, which ensured the diversity and representativeness of the data (Paz et al., 2012; Ismail et al., 2020; Hayati and Kasiamdari, 2024).
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