Resources

"Factors Affecting Response to Recurrent Genomic Selection in Simulated SoyNAM Populations."


Project Description: Improving response to selection is a core challenge for plant breeders. Advances in genotyping technology have resulted in methods such as genomic selection that use genetic marker information across the whole genome to support selection decisions. We have employed full factorial design and non-linear response modelling to evaluate 360 unique combinations of treatment factors including genetic architecture, heritability, selection intensities, statistical model used to train genomic prediction models, and genomic prediction model updating method and the interaction of these factors that impact rate of response and limit of response to selection. In this article, we have reported our findings on significant factors and interactions among these factors that impact short-term and long-term response and discuss the relevance of these factors to the practice of crop improvement programs that implement genomic selection.



Supplementary resources including data and R package for Vishnu's work: "Factors Affecting Response in Recurrent Genomic Selection."


Step-by-Step Instructions to Use 'SoyNAMPredictionMethods' R Package



  • SoyNAMPredictionMethods - Plot Results from Simulation Output Recommended for Quick Demonstration of Results

  • Introduction to SoyNAMPredictionMethods - Long Version: Brief introduction to the study and R package - Long version will take several hours to run simulations


  • Instructions to Implement Factorial Design Provides link to code for performing simulations for the complete set of factors


  • Methods for Non-linear Models -PartI Process for fitting non-linear random intercept model using 'nlme' R package


  • Methods for Non-linear Models -PartII Process for fitting single-level random intercept non-linear models using 'nlme' R package for cycle subsets


  • Methods for Non-linear Models -PartIII M26- M31 fit with correlation structure


  • Evaluation Metrics Description of evaluation metrics used in this study


  • R package and Data


  • Download SoyNAMPredictionMethods R package "SoyNAMPredictionMethods_0.1.0.tar.gz"