Maximizing efficiency of genomic selection in CIMMYT’s tropical maize breeding program
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Date
2021
Journal Title
Journal ISSN
Volume Title
Publisher
Springer
Abstract
The current strategy for large-scale implementation of genomic selection (GS) at the International Maize and
Wheat Improvement Center (CIMMYT) global maize breeding program has been to train models using information from
full-sibs in a “test-half-predict-half approach.” Although effective, this approach has limitations, as it requires large full-sib
populations and limits the ability to shorten variety testing and breeding cycle times. The primary objective of this study
was to identify optimal experimental and training set designs to maximize prediction accuracy of GS in CIMMYT’s maize
breeding programs. Training set (TS) design strategies were evaluated to determine the most efficient use of phenotypic data
collected on relatives for genomic prediction (GP) using datasets containing 849 (DS1) and 1389 (DS2) DH-lines evaluated
as testcrosses in 2017 and 2018, respectively. Our results show there is merit in the use of multiple bi-parental populations as
TS when selected using algorithms to maximize relatedness between the training and prediction sets. In a breeding program
where relevant past breeding information is not readily available, the phenotyping expenditure can be spread across connected bi-parental populations by phenotyping only a small number of lines from each population. This significantly improves
prediction accuracy compared to within-population prediction, especially when the TS for within full-sib prediction is small.
Finally, we demonstrate that prediction accuracy in either sparse testing or “test-half-predict-half” can further be improved by
optimizing which lines are planted for phenotyping and which lines are to be only genotyped for advancement based on GP
Description
Research Article