Principal component analysis revisited: fast multi-trait genetic evaluations with smooth convergence

Abstract

A cornerstone in breeding and population genetics is the genetic evaluation procedure, needed to make important decisions on population management. Multivariate mixed model analysis, in which many traits is considered jointly, utilizes genetic and environmental correlations between traits to improve the accuracy. However, the number of parameters in the multi-trait model grows exponentially with the number of traits which reduces its scalability. Here, we suggest using principal component analysis (PCA) to reduce the dimensions of the response variables, and then using the computed principal components (PC) as separate responses in the genetic evaluation analysis. As PCs are orthogonal to each other, multivariate analysis is no longer needed and separate univariate analyses can be performed instead. We compared the approach to traditional multivariate analysis in terms of computational requirement and rank lists according to predicted genetic merit on two forest tree datasets with 22 and 27 measured traits respectively. Obtained rank lists of the top 50 individuals were in good agreement. Interestingly, the required computational time of the approach only took a few seconds without convergence issues, unlike the traditional approach which required considerably more time to run (seven and ten hours respectively). Our approach can easily handle missing data and can be used with all available linear mixed models software as it does not require any specific implementation. The approach can help to mitigate difficulties with multi-trait genetic analysis in both breeding and wild populations.