DOI: 10.5061/dryad.xksn02vb9
title: Benchmarking parametric and machine learning models for genomic prediction of complex traits

description: 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.
description: 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 <i>et al.</i> 2011) and
 genotypic (Hirsch <i>et al.</i> 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
 <i>et al.</i> 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
 <i>et al.</i> 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 <i>et al.</i> 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 <i>et al.</i> 2014)<sub>.</sub>
 Switchgrass phenotypic (Lipka <i>et al.</i> 2014) and
 genotypic (Evans <i>et al.</i> 2017) data were generated from
 the Northern Switchgrass Association Panel (Evans <i>et
 al.</i> 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.