Black Sigatoka disease: new technologies to strengthen eradication strategies in Australia

In 2001, an incursion of Mycosphaerella fijiensis, the causal agent of black Sigatoka, was detected in Australia's largest commercial banana growing region, the Tully Banana Production Area in North Queensland. An intensive surveillance and eradication campaign was undertaken which resulted in the reinstatement of the disease-free status for black Sigatoka in 2005. This was the first time black Sigatoka had ever been eradicated from commercial plantations. The success of the eradication campaign was testament to good working relationships between scientists, growers, crop monitors, quarantine regulatory bodies and industry. A key contributing factor to the success was the deployment of a PCR-based molecular diagnostic assay, developed by the Cooperative Research Centre for Tropical Plant Protection (CRCTPP). This assay complemented morphological identification and allowed high throughput diagnosis of samples facilitating rapid decision-making during the eradication campaign. This paper describes the development and successful deployment of molecular diagnostics for black Sigatoka. Shortcomings in the gel-based assay are discussed and the advantages of highly specific real-time PCR assays, capable of differentiating between Mycosphaerella fijiensis, Mycosphaerella musicola and Mycosphaerella eumusae are outlined. Real-time assays may provide a powerful diagnostic tool for applications in surveillance, disease forecasting and resistance testing for Sigatoka leaf spot diseases.

Simple sequence repeat markers associated with three quantitative trait loci for black point resistance can be used to enrich selection populations in bread wheat

Black point in wheat has the potential to cost the Australian industry $A30.4 million a year. It is difficult and expensive to screen for resistance, so the aim of this study was to validate 3 previously identified quantitative trait loci (QTLs) for black point resistance on chromosomes 2B, 4A, and 3D of the wheat variety Sunco. Black point resistance data and simple sequence repeat (SSR) markers, linked to the resistance QTLs and suited to high-throughput assay, were analysed in the doubled haploid population, Batavia (susceptible) × Pelsart (resistant). Sunco and Pelsart both have Cook in their pedigree and both have the Triticum timopheevii translocation on 2B. SSR markers identified for the 3 genetic regions were gwm319 (2B, T. timopheevii translocation), wmc048 (4AS), and gwm341 (3DS). Gwm319 and wmc048 were associated with black point resistance in the validation population. Gwm341 may have an epistatic influence on the trait because when resistance alleles were present at both gwm319 and wmc048, the Batavia-derived allele at gwm341 was associated with a higher proportion of resistant lines. Data are presented showing the level of enrichment achieved for black point resistance, using 1, 2, or 3 of these molecular markers, and the number of associated discarded resistant lines. The level of population enrichment was found to be 1.83-fold with 6 of 17 resistant lines discarded when gwm319 and wmc048 were both used for selection. Interactions among the 3 QTLs appear complex and other genetic and epigenetic factors influence susceptibility to black point. Polymorphism was assessed for these markers within potential breeding material. This indicated that alternative markers to wmc048 may be required for some parental combinations. Based on these results, marker-assisted selection for the major black point resistance QTLs can increase the rate of genetic gain by improving the selection efficiency and may facilitate stacking of black point resistances from different sources.

How accurate are the marker orders in crop linkage maps generated from large marker datasets?

Marker ordering during linkage map construction is a critical component of QTL mapping research. In recent years, high-throughput genotyping methods have become widely used, and these methods may generate hundreds of markers for a single mapping population. This poses problems for linkage analysis software because the number of possible marker orders increases exponentially as the number of markers increases. In this paper, we tested the accuracy of linkage analyses on simulated recombinant inbred line data using the commonly used Map Manager QTX (Manly et al. 2001: Mammalian Genome 12, 930-932) software and RECORD (Van Os et al. 2005: Theoretical and Applied Genetics 112, 30-40). Accuracy was measured by calculating two scores: % correct marker positions, and a novel, weighted rank-based score derived from the sum of absolute values of true minus observed marker ranks divided by the total number of markers. The accuracy of maps generated using Map Manager QTX was considerably lower than those generated using RECORD. Differences in linkage maps were often observed when marker ordering was performed several times using the identical dataset. In order to test the effect of reducing marker numbers on the stability of marker order, we pruned marker datasets focusing on regions consisting of tightly linked clusters of markers, which included redundant markers. Marker pruning improved the accuracy and stability of linkage maps because a single unambiguous marker order was produced that was consistent across replications of analysis. Marker pruning was also applied to a real barley mapping population and QTL analysis was performed using different map versions produced by the different programs. While some QTLs were identified with both map versions, there were large differences in QTL mapping results. Differences included maximum LOD and R-2 values at QTL peaks and map positions, thus highlighting the importance of marker order for QTL mapping

A simple, one-tube assay for the simultaneous detection and diagnosis of ten Australian poultry Eimeria

Coccidiosis is a costly worldwide enteric disease of chickens caused by parasites of the genus Eimeria. At present, there are seven described species that occur globally and a further three undescribed, operational taxonomic units (OTUs X, Y, and Z) that are known to infect chickens from Australia. Species of Eimeria have both overlapping morphology and pathology and frequently occur as mixed-species infections. This makes definitive diagnosis with currently available tests difficult and, to date, there is no test for the detection of the three OTUs. This paper describes the development of a PCR-based assay that is capable of detecting all ten species of Eimeria, including OTUs X, Y, and Z in field samples. The assay is based on a single set of generic primers that amplifies a single diagnostic fragment from the mitochondrial genome of each species. This one-tube assay is simple, low-cost, and has the capacity to be high throughput. It will therefore be of great benefit to the poultry industry for Eimeria detection and control, and the confirmation of identity and purity of vaccine strains.

Genomic regions influencing seminal root traits in barley

Water availability is a major limiting factor for crop production, making drought adaptation and its many component traits a desirable attribute of plant cultivars. Previous studies in cereal crops indicate that root traits expressed at early plant developmental stages, such as seminal root angle and root number, are associated with water extraction at different depths. Here, we conducted the first study to map seminal root traits in barley (Hordeum vulgare L.). Using a recently developed high-throughput phenotyping method, a panel of 30 barley genotypes and a doubled-haploid (DH) population (ND24260 × 'Flagship') comprising 330 lines genotyped with diversity array technology (DArT) markers were evaluated for seminal root angle (deviation from vertical) and root number under controlled environmental conditions. A high degree of phenotypic variation was observed in the panel of 30 genotypes: 13.5 to 82.2 and 3.6 to 6.9° for root angle and root number, respectively. A similar range was observed in the DH population: 16.4 to 70.5 and 3.6 to 6.5° for root angle and number, respectively. Seven quantitative trait loci (QTL) for seminal root traits (root angle, two QTL; root number, five QTL) were detected in the DH population. A major QTL influencing both root angle and root number (RAQ2/RNQ4) was positioned on chromosome 5HL. Across-species analysis identified 10 common genes underlying root trait QTL in barley, wheat (Triticum aestivum L.), and sorghum [Sorghum bicolor (L.) Moench]. Here, we provide insight into seminal root phenotypes and provide a first look at the genetics controlling these traits in barley.