Estimation of genetic parameters from pedigreed populations: lessons from analysis of alevin weight in Coho salmon ( Oncorhynchus kisutch)

Abstract

The purpose of the study was to assess the impact of various model structures on REML estimates of variance components using data on alevin weight from two replicate populations from the Genetic Improvement Program for Coho salmon (Chile). Data consisted of 130 d alevin weight from a dams-nested-within-sires mating design over two consecutive generations. Relationship information included direct and collateral relatives but parental individuals lacked records. The construction of a range of animal models considered random effects of direct additive genetic, maternal additive genetic and full-sib family effects as well as the covariance of direct and maternal genetic effects. Fixed effects of year (generation) and spawn date of dams within year were considered and also evaluated. The relative effectiveness of various models in describing the data set were assessed using likelihood ratio tests. The results demonstrated the importance of the correct interpretation of effects in the data set, particularly those effects that can influence the resemblance between relatives. The data structure, as well as the animal model applied, markedly influenced the magnitude of variance component estimates. Models based on year as the only fixed effect did not describe the data nearly as effectively as models containing both year and spawn data of dams within year. Simple models based on animal as the only random effect gave upward biased estimates of additive genetic variance. The most appropriate model for the data set was one based on both year and spawn date as fixed effects, and animal and full-sib family as random effects. The results from models combining maternal genetic and full-sib family effects to exploit the full covariance structure of the data showed that there was confounding between these variance component estimates. The most consistent interpretation of this result was that common environmental effects and non-additive genetic effects were more important sources of variability than maternal genetic effects. The study also demonstrated high variability in parameter estimates for replicate populations.