Consolidating 23 years of historical data from an irrigated subtropical rice breeding program in Uruguay

• Published in: Crop science Vol. 63; no. 3; pp. 1300 - 1315
• Main Authors: Rebollo, Inés, Scheffel, Sheila, Blanco, Pedro, Molina, Federico, Martínez, Sebastián, Carracelas, Gonzalo, Aguilar, Ignacio, Pérez de Vida, Fernando, Rosas, Juan Eduardo
• Format: Journal Article
• Language: English
• Published: 01-05-2023
• ISSN: 0011-183X; 1435-0653
• DOI: 10.1002/csc2.20955

Breeding programs generate vast amount of data which are often scattered in separate files. This hinders the application of modern breeding tools such as multi‐environment analyses and genomic selection. This research work describes the process of consolidating 23 years of phenotypic, pedigree, and genomic records from the Uruguayan national rice (Oryza sativa L.) breeding program, and the features and structure of the resulting database. Using a custom‐made R code, we gathered all the available data from 1997 to 2020 corresponding to field trials, blast disease evaluation nurseries, laboratory analyses of milling and cooking quality, pedigree information, and genomic information for selected advanced breeding lines, and organized it into a relational database. Records of 996 trials in 12 locations over a span of 23 years, 91,636 field plots with information on 14 phenotypic variables, pedigree for 19,447 genotypes, and genomic information regarding 61,260 single nucleotide polymorphism (SNP) markers for 965 genotypes were recovered. The dataset is structured in trials, phenotypes, lines, genomic information, and SNP tables in an easy‐to‐access relational database, which will be a valuable resource for rice breeding.
Core Ideas
Uruguayan rice breeding database combines information from indica and japonica germplasms.
The database makes it easier to access 115,141 records of phenotypic, pedigree, and genomic information.
Records of 14 agronomic and grain‐quality traits from 919 high‐quality trials are available.
The public availability of the dataset will boost breeding efficiency and scientific collaboration.