Multivariate modeling of longitudinal phenotypes to increase the prediction accuracy of target single-time traits using random regression models in soybean

This repository contains the R code supporting the manuscript “Multivariate modeling of longitudinal phenotypes to increase the prediction accuracy of target single-time traits using random regression models in soybean.” The code supports genomic analyses combining canopy coverage measured throughout the growing season with grain yield measured at maturity in the SoyNAM population. The workflow uses single-trait and multi-trait random regression models with BLUPF90 to investigate genetic parameters, genomic prediction accuracy, and indirect selection efficiency. It provides resources for reproducing the analyses and applying the modeling framework to longitudinal phenotypes in soybean.