Abstract
Genomic prediction is a useful tool for plant and animal breeding programs and is starting to be used to predict human diseases as well. A shortcoming that slows down the genomic selection deployment is that the accuracy of the prediction is not known a priori. We propose EthAcc (Estimated THeoretical ACCuracy) as a method for estimating the accuracy given a training set that is genotyped and phenotyped. EthAcc is based on a causal quantitative trait loci model estimated by a genome-wide association study. This estimated causal model is crucial; therefore, we compared different methods to find the one yielding the best EthAcc. The multilocus mixed model was found to perform the best. We compared EthAcc to accuracy estimators that can be derived via a mixed marker model. We showed that EthAcc is the only approach to correctly estimate the accuracy. Moreover, in case of a structured population, in accordance with the achieved accuracy, EthAcc showed that the biggest training set is not always better than a smaller and closer training set. We then performed training set optimization with EthAcc and compared it to CDmean. EthAcc outperformed CDmean on real datasets from sugar beet, maize, and wheat. Nonetheless, its performance was mainly due to the use of an optimal but inaccessible set as a start of the optimization algorithm. EthAcc’s precision and algorithm issues prevent it from reaching a good training set with a random start. Despite this drawback, we demonstrated that a substantial gain in accuracy can be obtained by performing training set optimization.
Highlights
Prediction of unobserved individuals using genomic information has gained increasing importance in plant and animal breeding [1, 2]
This difference in selection can explain the extreme difference in the accuracy that was observed with the two training sets
To delve deeper into what happened, we compared the causal QTLs detected by the forward selection approach of multilocus mixed model (MLMM) in the two training sets previously optimized by CDmean and EthAcc
Summary
Prediction of unobserved individuals using genomic information has gained increasing importance in plant and animal breeding [1, 2]. It is an accurate tool for prediction of complex diseases in humans [3, 4] and is included in the precision medicine initiative [5]. A training set of individuals, the so-called training set, that is both phenotyped and genotyped is used to train a model that is applied to predict unobserved individuals, the so-called test set, on the basis of only genotyping data from the latter. The specific roles of EGD are articulated in the ‘author contributions’ section. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript
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