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

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.

Seema YadavXianming WeiPriya JoyceFelicity AtkinEmily DeomanoYue SunLoan T. NguyenElizabeth M. RossTony CavallaroKaren S. AitkenBen J. Hayes & Kai P. Voss-Fels

Theoretical and Applied Genetics July 2021; vol. 134: 2235–2252

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.

Abstract

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 was observed for CCS and Fibre. Our results imply that non-additive genetic variance is important for complex traits in sugarcane, although further work is required to better understand the variance component partitioning in a highly polyploid context. Genomics-based breeding will likely benefit from exploiting non-additive genetic effects, especially in designing crossing schemes. These findings can help to improve clonal prediction, enabling a more accurate identification of variety candidates for the sugarcane industry.

 

See: https://link.springer.com/article/10.1007/s00122-021-03822-1

 

Figure 3: Decomposition of genetic variance into additive, dominance, additive–additive epistatic, and residual variance in two forward prediction scenarios. a Proportion of genetic variance in forward prediction scenarios 1 a/1b (1,825 clones from 2013–2015 used as training population) for six different covariance structures (see Table 2). b Proportion of genetic variance in forward prediction scenario 2 (2,397 clones from 2013–2016 used as training population) for six different covariance structures (see Table 2). Va = additive genetic variance; Vd = dominance genetic variance; Vaa = additive–additive epistasis variance; Ve = error variance; Model A = additive model; Model AH additive plus heterozygosity; Model AD additive plus dominance model; Model ADH additive, dominance plus heterozygosity; Model ADE  additive, dominance and epistatic effect; Model ADEH  additive, dominance, epistatic plus heterozygosity; TCH tonnes of cane per hectare; CCS   commercial cane sugar; Fibre = Fibre content.

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