magic_niab

Multiparent advanced generation inter-cross (MAGIC) populations are ideal for learning complex models because of their high genetic recombination, diversity and large sample size Models for genome-wide prediction and association studies usually target a single phenotypic trait. However, in animal and plant genetics it is common to record information on multiple phenotypes for each individual that will be genotyped. Modeling traits individually disregards the fact that they are most likely associated due to pleiotropy and shared biological basis, thus providing only a partial, confounded view of genetic effects and phenotypic interactions. In this article we use data from a Multiparent Advanced Generation Inter-Cross (MAGIC) winter wheat population to explore Bayesian networks as a convenient and interpretable framework for the simultaneous modeling of multiple quantitative traits. We show that they are equivalent to multivariate genetic best linear unbiased prediction (GBLUP) and that they are competitive with single-trait elastic net and single-trait GBLUP in predictive performance. Finally, we discuss their relationship with other additive-effects models and their advantages in inference and interpretation. MAGIC populations provide an ideal setting for this kind of investigation because the very low population structure and large sample size result in predictive models with good power and limited confounding due to relatedness From the library bnlearn, we used the next code in R: install.packages("bnlearn"); library(bnlearn) data(package="bnlearn") # Load the RDA file (this creates an object named 'bn' in your environment) load("magic-niab.rda") # Replace with your actual file path # Rename the object for clarity magic_bn <- bn # Load the RDS file directly magic_bn <- readRDS("magic-niab.rds") # Replace with your actual file path # Generate 1000 synthetic observations from the network magic_data <- rbn(magic_bn, n = 2000) # Save as CSV write.csv(magic_data, "magic_niab_synthetic_data.csv", row.names = FALSE) For this dataset we decided to define the target variable as "YR.GLASS". This is an arbitrary choice, and you may want to explore other columns as the target variable. The target variable is the variable that you want to predict in your machine learning model.