Two-stage clustering in genotype-by-environment analyses with missing data

Abstract

Cluster analysis has been commonly used in genotype-by-environment (G × E) analyses, but current methods are inadequate when the data matrix is incomplete. This paper proposes a new method, referred to as two-stage clustering, which relies on a partitioning of squared Euclidean distance into two independent components, the G × E interaction and the genotype main effect. These components are used in the first and second stages of clustering respectively. Two-stage clustering forms the basis for imputing missing values in the G × E matrix, so that a more complete data array is available for other G × E analyses. Imputation for a given genotype uses information from genotypes with similar interaction profiles. This imputation method is shown to improve on an existing nearest cluster method that confounds the G × E interaction and the genotype main effect.