Virtual StatsPD@Waite meeting

Every month, the professional development meetings of statisticians and data scientists at Waite, known as StatsPD@Waite, bring together specialists in various aspects of data sciences in agriculture from Waite, Roseworthy and Adelaide.

Please join us for the next Virtual StatsPD@Waite seminar where our collaborators Nicole Dron from NSW Department of Primary Industry Industries and Hari Dadu from Agriculture Victoria will present on the research internship they recently undertook in the Biometry Hub.

Please note that the StatsPD@Waite meetings are recorded. If you have a question to the speaker but would rather not be recorded, please send me your question via chat during the meeting and it will be asked on your behalf. 

Please email Beata Sznajder for details of the Zoom meeting.

QTL mapping using targeted phenomics and environmental proxies to improve understanding of the underlying mechanisms and genetics behind Phytophthora root rot resistance in chickpea  

Nicole Dron - NSW Department of Primary Industry Industries 

Breeding for the Phytophthora root rot (PRR) resistance is notoriously challenging. To date Phytophthora (PRR) resistance has been incorporated from both landrace (C. arietinum) and the genetically diverse wild Cicer species in chickpea. Previous studies on the recombinant inbred line (RIL) population D9024 (resistant wild C. echinospermum backcross 04067-81-2-1-1 // moderately susceptible C. arietinum Yorker) identified quantitative trait loci (QTL) for PRR resistance (Amalraj et al., 2019). Given the importance in breeding PRR resistance derived from C. echinospermum, it is crucial to further explore whether it is possible to genetically uncouple poor agronomic traits that contribute to a yield penalty, from those QTL regions contributing to improved PRR resistance. However, a large component of this exploratory research requires complex statistical and computational approaches to deliver accurate research outcomes.  

To better understand this complexity, I enrolled in the Biometry Hub Research Internship program. The program has allowed me to increase my knowledge of the statistical computing software R and improve my statistical literacy associated with my project specific tasks. Specific skills gained during the internship included: 1) the ability to use R packages qtl and ASMap to improve QTL map consensus marker density of the 9024 RIL population from 314 SNP to 607 by incorporating additional silico-DArT markers, 2) the ability to perform QTL analysis using ASReml-R and whole genome average interval mapping (WGAIM) R packages that uses a mixed model QTL mapping approach to rapidly tests all genetic marker intervals simultaneously, and 3) generate phenotypic predictions from the model. These approaches have allowed me to identify QTL controlling targeted flavonoid expression and waterlogging tolerance root traits in chickpea. This information will inform breeders and provides an important step towards a molecular selection tool to improve both PRR resistance and agronomic fit in new chickpea varieties.   

Reference 

Amalraj, A., Taylor, J., Bithell, S., Li, Y., Moore, K., Hobson, K. & Sutton, T. (2019). Mapping resistance to Phytophthora root rot identifies independent loci from cultivated (Cicer arietinum L.) and wild (Cicer echinospermum PH Davis) chickpea. Theoretical and Applied Genetics, 132, 1017-1033. 

Developing and applying robust statistical tools for crop disease management 

Hari Dadu - Agriculture Victoria, Horsham 

At Agriculture Victoria, Horsham, the Crop protection Team has many responsibilities including the development of disease management strategies, surveillance of diseases and germplasm screening for new resistances for both cereal and pulse crops. Many of the projects require statistical expertise and this expertise becomes extremely valuable for the team to achieve different project objectives. To obtain this expertise I enrolled in the Biometry Hub Research Internship program with the aim to upgrade my statistical skills particularly performing tasks using the statistical computing software R and designing cloud applications with the R based Shiny and peripheral packages.  

The first component of the internship involved the analysis of historic data produced in the National barley project and the investigation of the virulences of Pyrenophora teres maculata (causal organism of Spot form of net blotch disease) within the Australian population. This was achieved using R and the linear mixed modelling package ASReml-R. The results showed variation in the virulences of Pyrenophora t. maculata population and presented isolates of concern for the industry.  

The other component of my internship included development of a cloud based Shiny app to help our disease surveillance activity. The aim of the app was to assist researchers to capture passport data of a disease sample during surveys and produce quick summary through various data visualisation forms such as a map, table and graph for direct incorporation into project reports. Through the internship I’ve successfully developed the app named “Victoria Disease Surveillance” and published it on a Shiny server for use during the season. 

The new statistical and computing skillset obtained through the internship has improved my ability to generate experimental designs, perform complex data analysis tasks using R and develop useful cloud based applications for our team to use. As a consequence, this has improved our research productivity as well the quality of our research outcomes.