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\begin{document}

\title{Genomic prediction with machine learning in sugarcane, a complex highly
polyploid clonally propagated crop with substantial non-additive
variation for key traits}



\author[1]{Chensong Chen}%
\author[2]{Owen Powell}%
\author[2]{Eric Dinglasan}%
\author[2]{Elizabeth Ross}%
\author[2]{Seema Yadav}%
\author[3]{Xianming Wei}%
\author[3]{Felicity Atkin}%
\author[3]{Emily Deomano}%
\author[4]{Ben Hayes}%
\affil[1]{The University of Queensland}%
\affil[2]{Queensland Alliance for Agriculture and Food Innovation}%
\affil[3]{Sugar Research Australia Ltd}%
\affil[4]{University of Queensland}%


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  \date{May 24, 2024}


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\begin{abstract}
Sugarcane has a complex, highly polyploid genome with multi-species
ancestry. Additive models for genomic prediction of clonal performance
might not capture interactions between genes and alleles from different
ploidies and ancestral species. As such genomic prediction in sugarcane
presents an interesting case for machine learning methods, which are
purportedly able to deal with high levels of complexity in prediction.
Here we investigate deep learning networks (DL), including Multilayer
networks (MLP) and convolution neural networks (CNN), and Random Forest
(RF) for genomic prediction in sugarcane. The data set was 2912
sugarcane clones, scored for 26,086 genome wide SNP markers, with final
assessment trial (FAT) data for total cane harvested (TCH), Commercial
cane sugar (CCS) and Fibre content. The clones in the latest trial
(2017) were used as a validation set. We compared performances of these
methods to GBLUP extended to include dominance and epistatic effects.
The prediction accuracies from GBLUPs were 0.37 for TCH, 0.37 for CCS
and 0.48 for Fibre, while the DL models had accuracies of 0.33 for TCH
prediction, 0.38 for CCS prediction and 0.43 for Fibre. Optimised RF
achieved a prediction accuracy of 0.35 for TCH, 0.38 for CCS and 0.48
for Fibre. Both DL and RF predictions were more accurate additive GBLUP
but generally lower than extended GBLUP. Finally, we identified a
partially shared distribution of SNP selections between RF and GBLUP
models. We conclude RF may have some utility for genomic prediction for
crops with highly complex genomes, particularly if non-additive
interactions can be captured with clonal propagation.%
\end{abstract}%



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