Pratylenchus thornei: The Relationship Between Presowing Nematode Density and Yield Loss in Wheat and Barley.

The root lesion nematode Pratylenchus thornei causes economic losses in wheat and barley internationally through both reduced grain yield and grain quality. This study investigated the relationships between the presowing P. thornei density and grain yield and the postharvest nematode densities. Four field experiments were conducted at the same site between 2010 and 2014. A range of presowing P. thornei densities was established in the first year by growing three cereal cultivars that ranged from resistant to susceptible. In the following year, plots were sown with the five same cereal cultivars. A linear relationship was observed between the natural log of the presowing P. thornei density and grain yield across all seasons. The results showed that grain yield losses varied between cultivars and seasons. The importance of season was significant, with this study conducted over several seasons, and it highlighted the variability in yield losses between seasons, which will need further investigation. The greatest yield losses observed were 25 to 28% when the maximum presowing P. thornei densities ranged between 150 and 250 P. thornei g of soil-1. An analysis of the relationship between the presowing and postharvest nematode densities revealed that increased presowing nematode densities resulted in decreased multiplication rates in all seasons and in all cultivars. Nematode multiplication rates also varied between seasons. These results explain why it is difficult to predict nematode levels based on cropping history, and additionally, they highlight the importance of growing resistant cultivars to maintain low levels of P. thornei to minimize risk of yield losses.

A Novel Approach to the Design and Analysis of Field Experiments to Study Variation in the Tolerance and Resistance of Cultivars to Root Lesion Nematodes (Pratylenchus spp.).

The root lesion nematode (RLN) species Pratylenchus thornei and P. neglectus are widely distributed within cropping regions of Australia and have been shown to limit grain production. Field experiments conducted to compare the performance of cultivars in the presence of RLNs investigate management options for growers by identifying cultivars with resistance, by limiting nematode reproduction, and tolerance, by yielding well in the presence of nematodes. A novel experimental design approach for RLN experiments is proposed where the observed RLN density, measured prior to sowing, is used to condition the randomization of cultivars to field plots. This approach ensured that all cultivars were exposed to consistent ranges of RLN in order to derive valid assessments of relative cultivar tolerance and resistance. Using data from a field experiment designed using the conditioned randomization approach and conducted in Formartin, Australia, the analysis of tolerance and resistance was undertaken in a linear mixed model framework. Yield response curves were derived using a random regression approach and curves modeling change in RLN densities between sowing and harvest were derived using splines to account for nonlinearity. Groups of cultivars sharing similar resistance levels could be identified. A comparison of slopes of yield response curves of cultivars belonging to the same resistance class identified differing tolerance levels for cultivars with equivalent exposures to both presowing and postharvest RLN densities. As such, the proposed design and analysis approach allowed tolerance to be assessed independently of resistance.

Correction for founder effects in host-viral association studies via principal components.

Viruses such as HIV and Hepatitis C (HCV) replicate rapidly and with high transcription error rates, which may facilitate their escape from immune detection through the encoding of mutations at key positions within human leukocyte antigen (HLA)-specific peptides, thus impeding T-cell recognition. Large-scale population-based host-viral association studies are conducted as hypothesis-generating analyses which aim to determine the positions within the viral sequence at which host HLA immune pressure may have led to these viral escape mutations. When transmission of the virus to the host is HLA-associated, however, standard tests of association can be confounded by the viral relatedness of contemporarily circulating viral sequences, as viral sequences descended from a common ancestor may share inherited patterns of polymorphisms, termed 'founder effects'. Recognizing the correspondence between this problem and the confounding of case-control genome-wide association studies by population stratification, we adapt methods taken from that field to the analysis of host-viral associations. In particular, we consider methods based on principal components analysis within a logistic regression framework motivated by alternative formulations in the Frisch-Waugh-Lovell Theorem. We demonstrate via simulation their utility in detecting true host-viral associations whilst minimizing confounding by associations generated by founder effects. The proposed methods incorporate relatively robust, standard statistical procedures which can be easily implemented using widely available software, and provide alternatives to the more complex computer intensive methods often implemented in this area.