Impact of Mislabeling on Genomic Selection in Cassava Breeding

Crop Science

Yabe, Shiori^1¹  Iwata, Hiroyoshi^2²  Jannink, Jean-Luc^3³ 

[1] Dep. of Agricultural and Environmental Biology, Graduate School of Agricultural and Life Science, The Univ. of Tokyo, Bunkyo, Tokyo 113-8657, Japan USDA-ARS, Robert W. Holley Center for Agriculture and Health, and Cornell Univ. Section of Plant Breeding and Genetics, Ithaca, NY 14853^1;Dep. of Agricultural and Environmental Biology, Graduate School of Agricultural and Life Science, The Univ. of Tokyo, Bunkyo, Tokyo 113-8657, Japan^2;USDA-ARS, Robert W. Holley Center for Agriculture and Health, and Cornell Univ. Section of Plant Breeding and Genetics, Ithaca, NY 14853^3

关键词: CET;    clonal evaluation trial;    GS;    genomic selection;    PYT;    preliminary yield trial;    QTL;    quantitative trait locus/loci;    SNP;    single-nucleotide polymorphism;   

DOI  :  10.2135/cropsci2017.07.0442

学科分类：农业科学（综合）

来源: Crop Science

【 摘 要 】

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.

【 授权许可】

CC BY