Predictive ability of genome-assisted statistical models under various forms of gene action

Mehdi Momen, Ahmad Ayatollahi Mehrgardi, Ayyub Sheikhi, Andreas Kranis, Llibertat Tusell, Gota Morota, Guilherme J M Rosa, Daniel Gianola

Research output: Contribution to journalArticlepeer-review


Recent work has suggested that the performance of prediction models for complex traits may depend on the architecture of the target traits. Here we compared several prediction models with respect to their ability of predicting phenotypes under various statistical architectures of gene action: (1) purely additive, (2) additive and dominance, (3) additive, dominance, and two-locus epistasis, and (4) purely epistatic settings. Simulation and a real chicken dataset were used. Fourteen prediction models were compared: BayesA, BayesB, BayesC, Bayesian LASSO, Bayesian ridge regression, elastic net, genomic best linear unbiased prediction, a Gaussian process, LASSO, random forests, reproducing kernel Hilbert spaces regression, ridge regression (best linear unbiased prediction), relevance vector machines, and support vector machines. When the trait was under additive gene action, the parametric prediction models outperformed non-parametric ones. Conversely, when the trait was under epistatic gene action, the non-parametric prediction models provided more accurate predictions. Thus, prediction models must be selected according to the most probably underlying architecture of traits. In the chicken dataset examined, most models had similar prediction performance. Our results corroborate the view that there is no universally best prediction models, and that the development of robust prediction models is an important research objective.

Original languageEnglish
Pages (from-to)12309
JournalScientific Reports
Issue number1
Early online date17 Aug 2018
Publication statusE-pub ahead of print - 17 Aug 2018


Dive into the research topics of 'Predictive ability of genome-assisted statistical models under various forms of gene action'. Together they form a unique fingerprint.

Cite this