A note on simulating null distributions for G matrix comparisons

Michael B. Morrissey, Sandra Hangartner, Keyne Monro

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


Genetic variances and covariances, summarised in G matrices, are key determinants of the course of adaptive evolution. Consequently, understanding how G matrices vary among populations is critical to answering a variety of questions in evolutionary biology. A method has recently been proposed for generating null distributions of statistics pertaining to differences in G matrices among populations. The general approach facilitated by this method is likely to prove to be very important in studies of the evolution of G. We have identified an issue in the method that will cause it to create null distributions of differences in G matrices that are likely to be far too narrow. The issue arises from the fact that the method as currently used generates null distributions of statistics pertaining to differences in G matrices across populations by simulating breeding value vectors based on G matrices estimated from data, randomising these vectors across populations, and then calculating null values of statistics from G matrices that are calculated directly from the variances and covariances among randomised vectors. This calculation treats breeding values as quantities that are directly measurable, instead of predicted from G matrices that are themselves estimated from patterns of covariance among kin. The existing method thus neglects a major source of uncertainty in Gmatrices, which renders it anticonservative. We first suggest a correction to the method. We then apply the original and modified methods to a very simple instructive scenario. Finally, we demonstrate the use of both methods in the analysis of a real data set.
Original languageEnglish
VolumeEarly View
Early online date22 Oct 2019
Publication statusE-pub ahead of print - 22 Oct 2019


  • Differentiation
  • G matrix
  • Null distribution
  • Quantitiatve genetics
  • Tensor analysis


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