Virtual StatsPD@Waite meeting
- Date: Tue, 8 Mar 2022, 10:00 am - 11:00 am
- Location: Hybrid: Online Zoom meeting and limited capacity in person at the Biometry Hub
- Contact: Beata Sznajder email@example.com
- Daniel Tolhurst (The Roslin Institute, University of Edinburgh) Presenter
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 Michael Mumford from Agri-Food and Data Science, Queensland Department of Agriculture and Fisheries will present on incorporating environmental covariates in linear mixed models to account for genotype x environment x management interactions.
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.
Genomic selection using random regressions on known and latent environmental covariates
Daniel Tolhurst - The Roslin Institute, University of Edinburgh
This research introduces a single-stage genomic selection approach which integrates known and latent environmental covariates within a special factor analytic framework. The factor analytic approach of Smith et al. (2001) is an effective method for analysing multi-environment trial (MET) datasets, but has limited practicality since the underlying covariates are latent so the modelled genotype by environment interaction (GEI) is observable, rather than predictable. The advantage of using regressions on known environmental covariates, such as soil moisture and daily temperature, is that the modelled GEI becomes predictable. The approach developed in this research exploits the desirable features of both classes of model, and includes a predictive model for GEI using a joint set of known and latent environmental covariates. This enables factor analytic models to be utilised for forward prediction into future growing environments, and thence provides plant breeding programmes with an effective framework to improve genetic gain amid climate change.
The new factor analytic approach is demonstrated on a late-stage cotton breeding MET dataset from Bayer CropScience. This talk will focus on three appealing features.
- The known environmental covariates explain 34.4% of the genetic variance across cotton growing environments in USA, which represents 93.0% of the crossover GEI. The latent covariates then explain 40.5% of the genetic variance, which represents 87.6% of the non-crossover GEI.
- Efficient selection of candidate genotypes is demonstrated using a recent set of factor analytic selection tools which involve measures of mean line performance and stability across current and future environments.
- The new approach improves the prediction accuracy of mean line performance based on future environments by 0.80 units compared to a traditional random regression.