Improved genomic prediction of clonal performance in sugarcane by exploiting non-additive genetic effects

作者：Seema Yadav, Xianming Wei, Priya Joyce, Felicity Atkin, Emily Deomano, Yue Sun, Loan Nguyen, Elizabeth M. Ross, Tony Cavallaro, Karen S. Aitken, Ben J. Hayes, Kai P. Voss‐Fels · 发表于：Theoretical and Applied Genetics · 年份：2021 · DOI：10.1007/s00122-021-03822-1 · 被引用次数：62 · 研究领域：Sugarcane Cultivation and Processing、Natural Products and Biological Research、Genetics and Plant Breeding

KEY MESSAGE: Non-additive genetic effects seem to play a substantial role in the expression of complex traits in sugarcane. Including non-additive effects in genomic prediction models significantly improves the prediction accuracy of clonal performance. In the recent decade, genetic progress has been slow in sugarcane. One reason might be that non-additive genetic effects contribute substantially to complex traits. Dense marker information provides the opportunity to exploit non-additive effects in genomic prediction. In this study, a series of genomic best linear unbiased prediction (GBLUP) models that account for additive and non-additive effects were assessed to improve the accuracy of clonal prediction. The reproducible kernel Hilbert space model, which captures non-additive genetic effects, was also tested. The models were compared using 3,006 genotyped elite clones measured for cane per hectare (TCH), commercial cane sugar (CCS), and Fibre content. Three forward prediction scenarios were considered to investigate the robustness of genomic prediction. By using a pseudo-diploid parameterization, we found significant non-additive effects that accounted for almost two-thirds of the total genetic variance for TCH. Average heterozygosity also had a major impact on TCH, indicating that directional dominance may be an important source of phenotypic variation for this trait. The extended-GBLUP model improved the prediction accuracies by at least 17% for TCH, but no improvement w...