Impact of mislabeling on genomic selection in cassava breeding

Authors: YABE, SHIORI - Cornell University; IWATA, HIROYOSHI - University Of Tokyo; Jannink, Jean-Luc

Submitted to: Crop Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/19/2017
Publication Date: 7/11/2018
Citation: Yabe, S., Iwata, H., Jannink, J. 2018. Impact of mislabeling on genomic selection in cassava breeding. Crop Science. 58:1470-1480. doi: 10.2135/cropsci2017.07.0442
DOI: https://doi.org/10.2135/cropsci2017.07.0442

Interpretive Summary: In plant breeding, humans occasionally make mistakes. Genomic selection, the breeding practice of ma king selections on the basis of predictions from genomic markers, 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 a simulated cassava (Manihot esculenta Crantz) breeding program to evaluate the impact of mislabeling, where marker scores from one plant are associated with the performance records of another plant. As expected, we found that mislabeling reduced genetic gains. Nevertheless, genetic gain persisted even under high (50%) levels of mislabeling because genetic variance was greater under 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. There are, of course, better approaches in genomic selection to preserve genetic variation. These results suggest that using such approaches could be important.

Technical 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.