Genome‐enabled prediction for sparse testing in multi‐environmental wheat trials. Issue 3 (12th September 2021)

• Record Type: Journal Article
• Authors: Crespo‐Herrera, Leonardo; Howard, Reka; Piepho, Hans‐Peter; Pérez‐Rodríguez, Paulino; Montesinos‐Lopez, Osval; Burgueño, Juan; Singh, Ravi; Mondal, Suchismita; Jarquín, Diego; Crossa, Jose
• Is Part Of: œplant genome. Volume 14:Issue 3(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-09-12
• Subjects: Plant genomes -- Periodicals; Plant genome mapping -- Periodicals; 572.862
• DOI: 10.1002/tpg2.20151
• Languages: English
• ISSNs: 1940-3372

Abstract

Abstract: Sparse testing in genome‐enabled prediction in plant breeding can be emulated throughout different line allocations where some lines are observed in all environments (overlap) and others are observed in only one environment (nonoverlap). We studied three general cases of the composition of the sparse testing allocation design for genome‐enabled prediction of wheat ( Triticum aestivum L.) breeding: (a) completely nonoverlapping wheat lines in environments, (b) completely overlapping wheat lines in all environments, and (c) a proportion of nonoverlapping/overlapping wheat lines allocated in the environments. We also studied several cases in which the size of the testing population was systematically decreased. The study used three extensive wheat data sets (W1, W2, and W3). Three different genome‐enabled prediction models (M1–M3) were used to study the effect of the sparse testing in terms of the genomic prediction accuracy. Model M1 included only main effects of environments and lines; M2 included main effects of environments, lines, and genomic effects; whereas the remaining model (M3) also incorporated the genomic × environment interaction (GE). The results show that the GE component of the genome‐based model M3 captures a larger genetic variability than the main genomic effects term from models M1 and M2. In addition, model M3 provides higher prediction accuracy than models M1 and M2 for the same allocation designs (different combinations of nonoverlapping/overlapping lines in environments and training set sizes). Overlapped sets of 30–50 lines in all the environments provided stable genomic‐enabled prediction accuracy. Reducing the size of the testing populations under all allocation designs decreases the prediction accuracy, which recovers when more lines are tested in all environments. Model M3 offers the possibility of maintaining the prediction accuracy throughout both extreme situations of all nonoverlapping lines and all overlapping lines. Core Ideas: Genome‐enabled prediction for sparse field testing of lines was used. Allocation design of lines was tested in some environments. Breeding lines in multi‐environments trials with genomic prediction were analyzed.