Core collection of Taiwanese Phalaenopsis orchids
Cite this dataset
Kao, Chung-Feng; Lai, Ya-Syuan (2024). Core collection of Taiwanese Phalaenopsis orchids [Dataset]. Dryad. https://doi.org/10.5061/dryad.fn2z34v2z
Abstract
This study establishes Taiwan's first Phalaenopsis orchid core collection, crucial for preserving genetic diversity and key traits. It analyzed 207 cultivars using phenotypic and genotypic data, employing novel techniques like multiple imputation and weighted k-means clustering. 22 core accessions were selected using the 'P+G strategy' and MGD algorithms, showcasing effectiveness in preserving diversity. The evaluation revealed significant diversity, surpassing alternatives, supported by pedigree analysis. The study underscores the importance of rigorous evaluation for quality and effectiveness. It contributes to orchid breeding, conservation, and sustainable agriculture, offering insights into genetic landscapes and breeding advancements.
README: Core collection of Taiwanese Phalaenopsis orchids
We have submitted our core collection data (MGD_coreset.csv).
https://doi.org/10.5061/dryad.fn2z34v2z
Descriptions
This study establishes the first core collection for Taiwanese Phalaenopsis orchids, employing a novel approach crucial for preserving genetic diversity and key traits.
- Variety: sample ID
- Flower diameter (cm): flower diameter of orchids (cm)
- Plant height (cm): plant height of orchids (cm)
- SSRN_N: simple sequence repeats (SSR) markers (0 = absent, 1 = exist)
- cluster: clustering of the entire collection by weighted k-means
Methods
We devised a two-step ‘P+G strategy’ to capture key traits and genetic diversity in orchid germplasm. Steps include: (1) Imputing missing data using multiple imputations. (2) Exploring germplasm structure with weighted k-means clustering. (3) Estimating dissimilarity using the MGD algorithm. (4) Constructing core accessions based on MGD metric via 2-step ‘P+G strategy’.
Funding
National Science and Technology Council, Award: NSTC 112-2313-B-005-019
National Science and Technology Council, Award: NSTC 112-2622-B-005-001