DOI: 10.5061/dryad.0rxwdbs8p
title: Data for: Machine learning for genomic and pedigree prediction in sugarcane

description: Sugarcane (Saccharum spp.) plays a crucial role in global sugar
 production; however, the efficiency of breeding programs has been hindered
 by its heterozygous polyploid genomes. Considering non-additive genetic
 effects is essential in genome prediction (GP) models of crops with highly
 heterozygous polyploid genomes. This study incorporates non-additive
 genetic effects and pedigree information using machine learning methods to
 track sugarcane breeding lines and enhance the prediction by assessing the
 degree of association between genotypes. This study measured the stem
 biomass and sugar content of 297 clones from 87 families within a breeding
 population used in the Japanese sugarcane breeding program. Subsequently,
 we conducted analyses based on the marker genotypes of 33,149
 single-nucleotide polymorphisms. To validate the accuracy of GP in the
 population, we first predicted the prediction accuracy of the best linear
 unbiased prediction (BLUP) based on a genomic relationship matrix.
 Prediction accuracy was assessed using two different cross-validation
 methods: repeated 10-fold cross-validation and leave-one-family-out
 cross-validation. The accuracy of GP of the first method ranged from 0.36
 to 0.74 and of the second method from 0.15 to 0.63. Next, we compared the
 prediction accuracy of BLUP and two machine learning methods: random
 forests and simulation annealing ensemble (SAE), a newly developed machine
 learning method that explicitly models the interaction between variables.
 Both pedigree and genomic information were utilized as input in these
 methods. Through repeated 10-fold cross-validation, we found that the
 accuracy of the machine learning methods consistently surpassed that of
 BLUP in most cases. In leave-one-family-out cross-validation, SAE
 demonstrated the highest accuracy among the methods. These results
 underscore the effectiveness of GP in Japanese sugarcane breeding and
 highlight the significant potential of machine learning methods.
description: # Dataset used in the paper "Machine Learning for Genomic and
 Pedigree Prediction in Sugarcane"
 [https://doi.org/10.5061/dryad.0rxwdbs8p](https://doi.org/10.5061/dryad.0rxwdbs8p) This dataset was used in the paper to compare different methods for building genomic prediction models. Specifically, it includes phenotypic data (pheno.csv), marker genotype data (geno.csv), a genomic relationship matrix (grm.csv), a numerator relationship matrix (prm.csv), and IDs representing families for lines evaluated in the sugarcane breeding program in Japan. These data were compared using a new machine learning method called Simulated Annealing Ensemble (SAE), Random Forest, and commonly used methods for genomic prediction such as genomic BLUP (GBLUP) and Pedigree-based BLUP (PBLUP). ## Description of the data and file structure pheno.csv: Estimated genotypic values calculated from data from multiple environments using a mixture model. This is the y value of the genomic prediction. Columns: traits, rows: genotypes geno.csv: Scored marker genotype data for all genotypes. The scores take 0 or 1 values. Columns: markers, rows: genotypes grm.csv: Genomic relationship matrix. A square matrix of the number of genotypes ✕ number of genotypes. prm.csv: Numerator relationship matrix (Relationship matrix computed from the pedigree). A square matrix of the number of genotypes ✕ number of genotypes. fam.csv: family tree ID. Rows are genotypes. Missing values are expressed as NA. ## Code/Software The Python library for the newly developed SAE method is accessible at [https://github.com/m-inamori/SAEnsemble](https://github.com/m-inamori/SAEnsemble).