Causal inference for the covariance between breeding values under identity disequilibrium.

ABSTRACT:

Background

The covariance matrix of breeding values is at the heart of prediction methods. Prediction of breeding values can be formulated using either an "observed" or a theoretical covariance matrix, and a major argument for choosing one or the other is the reduction of the computational burden for inverting such a matrix. In this regard, covariance matrices that are derived from Markov causal models possess properties that deliver sparse inverses.

Results

By using causal Markov models, we express the breeding value of an individual as a linear regression on ancestral breeding values, plus a residual term, which we call residual breeding value (RBV). The latter is a noise term that accounts for the uncertainty in prediction due to lack of fit of the linear regression. A no

SUBMITTER: Cantet RJC 

PROVIDER: S-EPMC9502921 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature