TY - JOUR T1 - Benchmarking algorithms for genomic prediction of complex traits JF - bioRxiv DO - 10.1101/614479 SP - 614479 AU - Christina B. Azodi AU - Andrew McCarren AU - Mark Roantree AU - Gustavo de los Campos AU - Shin-Han Shiu Y1 - 2019/01/01 UR - http://biorxiv.org/content/early/2019/04/20/614479.abstract N2 - The usefulness of Genomic Prediction (GP) in crop and livestock breeding programs has led to efforts to develop new and improved GP approaches including non-linear algorithm, such as artificial neural networks (ANN) (i.e. deep learning) and gradient tree boosting. However, the performance of these algorithms has not been compared in a systematic manner using a wide range of GP 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 five non-linear algorithms, including ANNs. First, we found that hyperparameter selection was critical for all non-linear algorithms and that feature selection prior to model training was necessary for ANNs 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 GP 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 than that of linear algorithms. Although ANNs did not perform best for any trait, we identified strategies (i.e. feature selection, seeded starting weights) that boosted their performance near the level of other algorithms. These results, together with the fact that even small improvements in GP performance could accumulate into large genetic gains over the course of a breeding program, highlights the importance of algorithm selection for the prediction of trait values.GPgenomic predictionANNartificial neural networkrrBLUPridge regression best linear unbiased predictionBABayes ABBBayes BLASSOleast absolute angle and selection operatorBLBayesian LASSOSVRsupport vector regressionlinlinearrbfradial basis functionpolypolynomialRFrandom forestGTBgradient tree boostingpnumber of markersnnumber of linesMSEmean squared errorANOVAanalysis of varianceReLUrectified linear unitENelastic netMWUMann Whitney UDBHdiameter at breast height ER -