Impact of Mislabeling on Genomic Selection in Cassava Breeding

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.

Comprehensive genotyping of a Brazilian cassava (Manihot esculenta Crantz) germplasm bank: insights into diversification and domestication

“…The Bitter-Group shows greater genetic diversity and less drift, followed by Admix-Group and NE_LandRaces, while the rest had less than 50, making them predisposed to genetic drift. However, it has been shown that small effective population size of a genetic pool indicates high LD and potential for improved prediction accuracy (Yabe et al 2018a , b ). Applying the 50/500 rule as described by Franklin and colleagues (Franklin and Frankham 1998 ), a genetic group’s effective population size of at least 50 individuals is necessary for conservation of genetic diversity in the short term and avoids inbreeding depression.…”

“…The comparison of average LD decay threshold to the standard threshold of 0.1 ( r 2 ) for all genetic pools shows a similar trend with average genetic pool’s LD decay having higher genetic distances across groups (Supplementary Figure 5b). Given the allogamous nature of cassava, the pattern of LD may reduce prediction accuracy, since LD is crucial for genomic selection (GS) breeding (Jannink 2010 ; Yabe et al 2018a , b ). However, the high LD observed in this study indicates potential for improved accuracy using this population for GS breeding.…”

Large‐scale genome‐wide association study, using historical data, identifies conserved genetic architecture of cyanogenic glucoside content in cassava (Manihot esculenta Crantz) root

“…This is attributed to post‐introduction hybridization between sweet and bitter cassava, and the inconsistent transfer of ethnobotanical knowledge of use‐category management to Africa (Bradbury et al ., 2013). The mislabeling of germplasm in Africa (Yabe et al ., 2018) may also have contributed to the observed difference. These differences were also observed with the distribution plots for the individuals assayed in our analysis for HCN in Latin American (bimodal distribution) and African (almost normally distributed) populations.…”

Comprehensive genotyping of Brazilian Cassava (Manihot esculentaCrantz) Germplasm Bank: insights into diversification and domestication

“…Accession mislabelling has been reported to reduce the genetic gain of cassava by up to 40% in Africa (Yabe, Iwata, and Jannink 2018). In order to address germplasm duplication in the Brazilian collection, we inferred genetic identity between accessions using an identity-by-state (IBS) analysis.…”