Impact of Mislabeling on Genomic Selection in Cassava Breeding. Issue 4 (21st June 2018)

• Record Type: Journal Article
• Authors: Yabe, Shiori; Iwata, Hiroyoshi; Jannink, Jean-Luc
• Is Part Of: Crop science. Volume 58:Issue 4(2018)
• Page Start: 1470
• Page End: 1480
• Publication Date: 2018-06-21
• Subjects: Crop science -- Periodicals; Cultures -- Périodiques; Cultures de plein champ -- Périodiques; Crop science; Nutzpflanzen; Zeitschrift; Pflanzenbau; Periodicals; 633
• DOI: 10.2135/cropsci2017.07.0442
• Languages: English
• ISSNs: 0011-183X

Abstract

Abstract : In plant breeding, humans occasionally make mistakes. Genomic selection is particularly prone to human error because it involves more steps than conventional phenotypic selection. The impact of human mistakes should be determined to evaluate the cost effectiveness of controlling human error in plant breeding. We used simulation to evaluate the impact of mislabeling, where marker scores from one plant are associated with the performance records of another plant in cassava ( Manihot esculenta Crantz) breeding. Results showed that, although selection with mislabeling reduced genetic gains, scenarios including six levels of mislabeling (from 5 to 50%) persisted in achieving gain because mislabeling decreased the genetic variance lost from the population. Breeding populations with higher rates of mislabeling experienced lower selection intensity, resulting in higher genetic variance, which partially compensated for the mislabeling. For low mislabeling rates (10% or less), the increased genetic variance observed under mislabeling led to improved accuracy of the prediction model in later selection cycles. Large‐scale mislabeling should therefore be prevented, but the value of preventing small‐scale mislabeling depends on the effort already being invested in preventing the loss of genetic variance during the course of selection. In a program, such as the one we simulated, that makes no effort to avoid loss of genetic variance, small‐scale mislabeling has a less negative effect than expected. We assume that negative effects would be greater if best practices to avoid genetic variance loss were already implemented.