Multi-view BLUP: a promising solution for post-omics data integrative prediction

Abstract

Phenotypic prediction is a promising strategy for accelerating plant breeding. Data from multiple sources (called multi-view data) can provide complementary information to characterize a biological object from various aspects. By integrating multi-view information into phenotypic prediction, a multi-view best linear unbiased prediction (MVBLUP) method was proposed in this paper. To measure the importance of multiple data views, the differential evolution algorithm with an early stopping mechanism was used, by which we obtained a multi-view kinship matrix and then incorporated it into the BLUP model for phenotypic prediction. To further illustrate the characteristics of MVBLUP, we performed the empirical experiments on four multi-view datasets in different crops. Compared to the single-view method, the prediction accuracy of the MVBLUP method has improved by 0.038 to 0.201 on average. The results demonstrate that the MVBLUP is an effective integrative prediction method for multi-view data.