Visualising the pattern of long‐term genotype performance by leveraging a genomic prediction model. (3rd May 2022)

• Record Type: Journal Article
• Authors: Arief, Vivi N.; DeLacy, Ian H.; Payne, Thomas; Basford, Kaye E.
• Is Part Of: Australian & New Zealand journal of statistics. Volume 64:Number 2(2022)
• Page Start: 297
• Page End: 312
• Publication Date: 2022-05-03
• Subjects: factor analytic -- genotype‐by‐year -- historical data -- plant breeding -- relationship matrix; Statistics -- Periodicals; 519.5
• DOI: 10.1111/anzs.12362
• Languages: English
• ISSNs: 1369-1473

Abstract

Abstract: 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.