Skip to main content

Benchmarking parametric and machine learning models for genomic prediction of complex traits

Cite this dataset

Azodi, Christina B et al. (2019). Benchmarking parametric and machine learning models for genomic prediction of complex traits [Dataset]. Dryad.


The usefulness of genomic prediction in crop and livestock breeding programs has prompted efforts to develop new and improved genomic prediction algorithms, such as artificial neural networks and gradient tree boosting. However, the performance of these algorithms has not been compared in a systematic manner using a wide range of datasets and models. Using data of 18 traits across six plant species with different marker densities and training population sizes, we compared the performance of six linear and six non-linear algorithms. First, we found that hyperparameter selection was necessary for all non-linear algorithms and that feature selection prior to model training was critical for artificial neural networks when the markers greatly outnumbered the number of training lines. Across all species and trait combinations, no one algorithm performed best, however predictions based on a combination of results from multiple algorithms (i.e. ensemble predictions) performed consistently well. While linear and non-linear algorithms performed best for a similar number of traits, the performance of non-linear algorithms vary more between traits. Although artificial neural networks did not perform best for any trait, we identified strategies (i.e. feature selection, seeded starting weights) that boosted their performance to near the level of other algorithms. Our results highlight the importance of algorithm selection for the prediction of trait values.


Data was downloaded from original authors and uniformally formatted to use as input into parametric and machine learning based genomic prediction models. The maize phenotypic (Hansey et al. 2011) and genotypic (Hirsch et al. 2014) data were from the pan-genome population, maize trait values were averaged over replicate plots. The rice data were from elite breeding lines from the International Rice Research Institute irrigated rice breeding program (Spindel et al. 2015), and dry season trait data averaged over four years were used. The sorghum data were generated from sorghum lines from the US National Plant Germplasm System grown in Urbana, IL (Fernandes et al. 2017) and trait values were averaged over two blocks for this study. The soybean data were generated from the SoyNAM population containing recombinant inbred lines (RILs) derived from 40 biparental populations (Xavier et al. 2016). The white spruce data were obtained from the SmartForests project team, using a SNP-chip developed by Quebec Ministry of Forest Wildlife and Parks (Beaulieu et al. 2014). Switchgrass phenotypic (Lipka et al. 2014) and genotypic (Evans et al. 2017) data were generated from the Northern Switchgrass Association Panel (Evans et al. 2015) which contains clones or genotypes from 66 diverse upland switchgrass populations.

The genotype data was obtained in the form of biallelic SNPs with missing marker data already dropped or imputed by the original authors. Marker calls were converted when necessary to [-1,0,1] corresponding to [aa, Aa, AA] where A was either the reference or the most common allele. Genome locations of maize SNPs were converted from assembly AGPv2 to AGPv4, with AGPv2 SNPs that did not map to AGPv4 being removed, leaving 332,178 markers for the maize analysis. Phenotype values were normalized between 0 and 1. Lines with missing phenotypic value for any of the three traits were removed.