Comparing Core Sampling Algorithms Across Crops

We used four datasets of varying size and composition to compare the performance of different core sampling algorithms:

Rice data: 1000 accessions for which 39 phenotypic traits were recorded, including 28 qualitative and 11 quantitative traits. Available from the PowerCore project [15 (link)] and previously used to assess the performance of several other core sampling algorithms, including SimEli [13 (link)].

Coconut data: 1014 accessions characterized using 30 crop-specific SSR markers. Used in multiple previous core selection studies [4 (link), 13 (link), 14 (link)].

Maize data: 1250 accessions characterized with 1117 SNP markers. Distributed as part of the R package synbreedData [27 ].

Pea data: 4428 accessions characterized by 17 RBIP markers [28 (link), 29 (link)]. Previously used to compare the performance of Core Hunter 2 with other core sampling algorithms for large datasets [19 (link)].

All cores sampled in the performed experiments comprised 20% of the entire collection for the rice, coconut and maize datasets, and 10% for the large pea dataset.

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De Beukelaer H., Davenport G.F, & Fack V. (2018). Core Hunter 3: flexible core subset selection. BMC Bioinformatics, 19, 203.

Publication 2018

Coconut Crop Maize Phenotypic Rice

Corresponding Organization :

Other organizations : Ghent University, Plant & Food Research

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Protocol cited in 7 other protocols

Variable analysis

independent variables

Core sampling algorithms

dependent variables

Performance of core sampling algorithms

control variables

Size of datasets (20% of entire collection for rice, coconut and maize datasets, and 10% for the large pea dataset)

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