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Key Message: Simulations highlight the potential of genomic selection to substantially increase genetic gain for complex traits in sugarcane. The success rate depends on the trait genetic architecture and the implementation strategy. Genomic selection (GS) has the potential to increase the rate of genetic gain in sugarcane beyond the levels achieved by conventional phenotypic selection (PS). To assess different implementation strategies, we simulated two different GS-based breeding strategies and compared genetic gain and genetic variance over five breeding cycles to standard PS. GS scheme 1 followed similar routines like conventional PS but included three rapid recurrent genomic selection (RRGS) steps. GS scheme 2 also included three RRGS steps but did not include a progeny assessment stage and therefore differed more fundamentally from PS. Under an additive trait model, both simulated GS schemes achieved annual genetic gains of 2.6-2.7% which were 1.9 times higher compared to standard phenotypic selection (1.4%). For a complex non-additive trait model, the expected annual rates of genetic gain were lower for all breeding schemes; however, the rates for the GS schemes (1.5-1.6%) were still greater than PS (1.1%). Investigating cost-benefit ratios with regard to numbers of genotyped clones showed that substantial benefits could be achieved when only 1500 clones were genotyped per 10-year breeding cycle for the additive genetic model. Our results show that under a complex non-additive genetic model, the success rate of GS depends on the implementation strategy, the number of genotyped clones and the stage of the breeding program, likely reflecting how changes in QTL allele frequencies change additive genetic variance and therefore the efficiency of selection. These results are encouraging and motivate further work to facilitate the adoption of GS in sugarcane breeding.

A major focus of sugarcane variety improvement programs is to increase sugar yield, which can be accomplished by either increasing the sugar content of the cane or by increasing cane yield, as the correlation between these traits is low. We used a cross between an Australian sugarcane variety Q165, and a Saccharum officinarum accession, IJ76-514, to dissect the inheritance of yield-related traits in the complex polyploid sugarcane. A population of 227 individuals was grown in a replicated field trial and evaluated over 3 years for stalk weight, stalk diameter, stalk number, stalk length and total biomass. Over 1,000 AFLP and SSR markers were scored across the population and used to identify quantitative trait loci (QTL). In total, 27 regions were found that were significant at the 5% threshold using permutation tests with at least one trait; individually, they explained from 4 to 10% of the phenotypic variation and up to 46% were consistent across years. With the inclusion of digeneic interactions, from 28 to 60% of the variation was explained for these traits. The 27 genomic regions were located on 22 linkage groups (LGs) in six of the eight homology groups (HGs) indicating that a number of alleles or quantitative trait alleles (QTA) at each QTL contribute to the trait; from one to three alleles had an effect on the traits for each QTL identified. Alleles of a candidate gene, TEOSINTE BRANCHED 1 (TB1), the major gene controlling branching in maize, were mapped in this population using either an SSR or SNP markers. Two alleles showed some association with stalk number, but unlike maize, TB1 is not a major gene controlling branching in sugarcane but only has a minor and variable effect.

Anthracnose, caused by Colletotrichum trifolii, is one of the most serious diseases of lucerne worldwide. The disease is managed through deployment of resistant cultivars, but new pathotypes present a challenge to the successful implementation of this strategy. This paper reports the genetic map locations of quantitative trait loci (QTL) for reaction to races 1, 2 and 4 of C. trifolii in a single autotetraploid lucerne clone, designated W126 from the Australian cv. Trifecta. Resistance was mapped in a backcross population of 145 individuals, and reaction was assessed both by spray and injection inoculation of stems. Resistance to injection inoculation with races 1 and 4 was incompletely dominant and closely linked (phenotypic markers 2.2 cM apart); these resistances mapped to a linkage group homologous to Medicago truncatula linkage group 8. When the spray inoculation data were subjected to QTL analysis, the strongest QTL for resistance was located on linkage group 8; six QTL were identified for race 1 and four for race 4. Resistance to race 2 was incompletely recessive; four QTL were identified and these include one QTL on linkage group 4 that was also identified for race 1. Modelling of the interactions between individual QTL and marker effects allowed a total of 52-63% of the phenotypic variation to be described for each of the different races. These markers will have value in breeding lucerne, carrying multiple sources of resistance to the three known races of C. trifolii.

In eastern Australia and California, USA, one of the major lethal fungal diseases of lucerne (Medicago sativa) is Stagonospora root and crown rot, caused by Stagonospora meliloti. Quantitative trait loci (QTL) involved in resistance and susceptibility to S. meliloti were identified in an autotetraploid lucerne backcross population of 145 individuals. Using regression analysis and interval mapping, we detected one region each on linkage groups 2, 6 and 7 that were consistently associated with disease reaction to S. meliloti in two separate experiments. The largest QTL on linkage group 7, which is associated with resistance to S. meliloti, contributed up to 17% of the phenotypic variation. The QTL located on linkage group 2, which is potentially a resistance allele in repulsion to the markers for susceptibility to S. meliloti, contributed up to 8% of the phenotypic variation. The QTL located on linkage group 6, which is associated with susceptibility to S. meliloti, contributed up to 16% of the phenotypic variation. A further two unlinked markers contributed 5 and 8% of the phenotypic variation, and were detected in only one experiment. A total of 517 simple sequence repeat (SSR) markers from Medicago truncatula were screened on the parents of the mapping population. Only 27 (6%) SSR markers were polymorphic and could be incorporated into the autotetraploid map of M. sativa. This allowed alignment of our M. sativa linkage map with published M. truncatula maps. The markers linked to the QTL we have reported will be useful for marker assisted selection for partial resistance to S. meliloti in lucerne.

The identification of markers linked to quantitative trait loci (QTLs) for increased sugar accumulation could improve the effectiveness of current breeding strategies in sugarcane. Progeny from a cross between a high sucrose producing cultivar, (denotes Australian plant breeding rights), and a Saccharum officinarum clone, IJ76-514 were grown in two field experiments in different years, and evaluated in the early and mid-season phases of crop maturity, to identify robust QTLs in affecting sucrose content in cane. Using an extensive genetic map constructed for with over 1,000 AFLP and SSR markers, a total of 37 QTLs were identified for brix and pol of which, 16 were detected in both experiments. Of these 37 QTL, 30 were clustered into 12 genomic regions in six of the eight homo(eo)logous groups. Each QTL explained from 3 to 9% of the phenotypic variation observed. Both positive and negative effects were identified and the location of the QTLs on linkage groups belonging to the same homo(eo)logy group suggested that a number of the QTLs were allelic forms of the same genes. Of the 37 QTLs identified, the majority were significant in both early and mature cane, but 8 were identified as early specific QTLs and 9 as mature cane QTLs. In total, 97 interactions were significant (P<10(-5)) and these were localised to 32 genomic regions of which 6 were detected with both years' data. Models including all the QTLs explained from 37 to 66% of the total phenotypic variation, depending on the trait. The results will be subsequently applied in marker assisted breeding.