Estimating a genetic covariance matrix (G-matrix) relies on specific quantitative genetic data and modeling techniques, such as multivariate animal models and Bayesian frameworks. The available literature consistently utilizes specialized quantitative genetic data structures to calculate these parameters.
The claim states that estimating a G-matrix requires specific quantitative genetic data. The retrieved papers describe various quantitative genetic analyses (such as multivariate threshold models and genomic relationship models) that explicitly require pedigree, genomic, or phenotypic records to compute genetic parameters and covariance matrices like the G-matrix. Papers [0] and [7] explicitly discuss the estimation and modeling of genetic covariance (G) matrices using specialized quantitative genetic data and frameworks. No papers refute this premise.