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Visualising the pattern of long‐term genotype performance by leveraging a genomic prediction model
Author(s) -
Arief Vivi N.,
DeLacy Ian H.,
Payne Thomas,
Basford Kaye E.
Publication year - 2022
Publication title -
australian and new zealand journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.434
H-Index - 41
eISSN - 1467-842X
pISSN - 1369-1473
DOI - 10.1111/anzs.12362
Subject(s) - genotype , table (database) , biology , grain yield , microbiology and biotechnology , statistics , mathematics , data mining , computer science , agronomy , genetics , gene
Historical data from plant breeding programs provide valuable resources to study the response of genotypes to the changing environment (i.e. genotype‐by‐environment interaction). Such data have been used to evaluate the pattern of genotype performance across regions or locations, but its use to evaluate the long‐term pattern of genotype performance across environments (i.e. locations‐by‐years) has been hampered by the lack of common genotypes across years. This lack of common genotypes is due to the structure of the breeding program, especially for annual crops, where only a proportion of selected genotypes are tested in subsequent years. This has resulted in a sparse prediction of the performance of genotypes across years (i.e. a genotype‐by‐year table). A genomic prediction method that fitted both a relationship matrix among genotypes and a relationship matrix among environments (i.e. years) could overcome this limitation and produce a dense genotype‐by‐year table, thereby enabling some evaluation of long‐term genotype performance. In this paper, we applied the genomic prediction model to the yield data from CIMMYT's Elite Spring Wheat Yield Trials (ESWYT) to visualise the pattern of genotype performance over 25 years.