AUTHOR=Gianola Daniel , Cecchinato Alessio , Naya Hugo , Schön Chris-Carolin 

TITLE=Prediction of Complex Traits: Robust Alternatives to Best Linear Unbiased Prediction

JOURNAL=Frontiers in Genetics

VOLUME=Volume 9 - 2018

YEAR=2018

URL=https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2018.00195

DOI=10.3389/fgene.2018.00195

ISSN=1664-8021

ABSTRACT=A widely used method for prediction of complex traits in animal and plant breeding is "genomic best linear unbiased prediction" (GBLUP). BLUP is a linear regression of phenotypes on a pedigree or on a genomic relationship matrix, depending on the type of input information. Normality of the distributions of random effects and of model residuals is not required for BLUP but a Gaussian assumption is made implicitly. A potential downside is that Gaussian linear regressions are sensitive to outliers, genetic or environmental in origin. We present simple to implement robust alternatives to BLUP using a linear model with residual t or Laplace distributions instead of a Gaussian one, and evaluate the methods with milk yield records on Italian Brown Swiss cattle, grain yield data in inbred wheat lines, and using three traits measured on accessions of Arabidopsis thaliana. The methods do not use Markov chain Monte Carlo and model hyper-parameters are tuned via some cross-validation. Uncertainty of predictions can be evaluated employing bootstrapping or by random reconstruction of training and testing sets. It was found (e.g., test-day milk yield in cows, flowering time and FRIGIDA expression in Arabidopsis) that the best predictions were often those obtained with the robust methods. The results obtained are encouraging and stimulate further investigation.