Varietal selection for yield from a series of multi-environment trials can be regarded as a multi-trait selection problem in which the yields in different environments are synonymous with traits. As such an analysis of the data combined across environments should be conducted in order to form an index for selection. Analytical methods that include appropriate models for both the genetic variance structure (that is, the variances and covariances of genotype effects from different environments) and the residual variance structure (which typically comprises spatial covariance models for each trial) have been published previously. In the case of perennial crops, yields are often obtained from multiple harvests which implies that the data comprise short sequences of repeated measurements. Varietal performance in individual harvests is important for selection so that a combined analysis across both trials and harvests is required. The repeated measures nature of the data provides additional modelling challenges. In this paper we propose an approach for the analysis of multi-environment, multi-harvest data that accommodates the major sources of variation and correlation (including temporal). The approach is illustrated using two examples from sugarcane breeding programmes. The proposed models were found to provide a superior fit to the data and thence more accurate selection decisions than the common practice of conducting separate analyses of individual trials and harvests.

A statistical approach for the analysis of multi-environment trials (METs) is presented, in which selection of best performing lines, best parents, and best combination of parents can be determined. The genetic effect of a line is partitioned into additive, dominance and residual non-additive effects. The dominance effects are estimated through the incorporation of the dominance relationship matrix, which is presented under varying levels of inbreeding. A computationally efficient way of fitting dominance effects is presented which partitions dominance effects into between family dominance and within family dominance line effects. The overall approach is applicable to inbred lines, hybrid lines and other general population structures where pedigree information is available.

Association between markers and sugarcane diseases were investigated in a collection of 154 sugarcane clones, consisting of important ancestors or parents, and cultivars. 1,068 polymorphic AFLP and 141 SRR markers were scored across all clones. Data on the four most important diseases in the Australian sugarcane industry were obtained; these diseases being pachymetra root rot (Pachymetra chaunorhiza B.J. Croft & M.W. Dick), leaf scald (Xanthomonas albilineans Dowson), Fiji leaf gall (Fiji disease virus), and smut (Ustilago scitaminea H. & P. Sydow). By a simple regression analysis, association between markers and diseases could be readily detected. However, many of these associations were due to the effects of embedded population structure and random effects. After taking population structure into account, we found that 59% of the phenotypic variation in smut resistance ratings could be accounted for by 11 markers, 32% of variation for leaf scald and pachymetra root rot rating by 4 markers, and 26% of Fiji leaf gall by 5 markers. The results suggest that marker-trait associations can be readily detected in populations generated from modern sugarcane breeding programs. This may be due to special features of past sugarcane breeding programs leading to persistent linkage disequilibrium in modern parental populations.

Key messageNon-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.AbstractIn 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.

Sugarcane is a major industrial crop cultivated in tropical and subtropical regions of the world. It is the primary source of sugar worldwide, accounting for more than 70% of world sugar consumption. Additionally, sugarcane is emerging as a source of sustainable bioenergy. However, the increase in productivity from sugarcane has been small compared to other major crops, and the rate of genetic gains from current breeding programs tends to be plateauing. In this review, some of the main contributors for the relatively slow rates of genetic gain are discussed, including (i) breeding cycle length and (ii) low narrow-sense heritability for major commercial traits, possibly reflecting strong non-additive genetic effects involved in quantitative trait expression. A general overview of genomic selection (GS), a modern breeding tool that has been very successfully applied in animal and plant breeding, is given. This review discusses key elements of GS and its potential to significantly increase the rate of genetic gain in sugarcane, mainly by (i) reducing the breeding cycle length, (ii) increasing the prediction accuracy for clonal performance, and (iii) increasing the accuracy of breeding values for parent selection. GS approaches that can accurately capture non-additive genetic effects and potentially improve the accuracy of genomic estimated breeding values are particularly promising for the adoption of GS in sugarcane breeding. Finally, different strategies for the efficient incorporation of GS in a practical sugarcane breeding context are presented. These proposed strategies hold the potential to substantially increase the rate of genetic gain in future sugarcane breeding.