A review of machine learning models applied to genomic prediction in animal breeding

The advent of modern genotyping technologies has revolutionized genomic
selection in animal breeding. Large marker datasets have shown several
drawbacks for traditional genomic prediction methods in terms of flexibility,
accuracy, and computational power. Recently, the application of machine
learning models in animal breeding has gained a lot of interest due to their
tremendous flexibility and their ability to capture patterns in large noisy
datasets. Here, we present a general overview of a handful of machine learning
algorithms and their application in genomic prediction to provide a meta-picture
of their performance in genomic estimated breeding values estimation, genotype
imputation, and feature selection. Finally, we discuss a potential adoption of
machine learning models in genomic prediction in developing countries. The
results of the reviewed studies showed that machine learning models have indeed
performed well in fitting large noisy data sets and modeling minor nonadditive
effects in some of the studies. However, sometimes conventional methods
outperformed machine learning models, which confirms that there’s no
universal method for genomic prediction. In summary, machine learning
models have great potential for extracting patterns from single nucleotide
polymorphism datasets. Nonetheless, the level of their adoption in animal
breeding is still low due to data limitations, complex genetic interactions, a
lack of standardization and reproducibility, and the lack of interpretability of
machine learning models when trained with biological data. Consequently,
there is no remarkable outperformance of machine learning methods
compared to traditional methods in genomic prediction. Therefore, more
research should be conducted to discover new insights that could enhance
livestock breeding programs