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

The purpose of the study was to assess the impact of various model structur es on REML estimates of variance components using data on alevin weight fro m two replicate populations from the Genetic Improvement Program for Coho s almon (Chile). Data consisted of 130 d alevin weight from a dams-nested-wit hin-sires mating design over two consecutive generations. Relationship info rmation included direct and collateral relatives but parental individuals l acked records. The construction of a range of animal models considered rand om effects of direct additive genetic, maternal additive genetic and full-s ib family effects as well as the covariance of direct and maternal genetic effects. Fixed effects of year (generation) and spawn date of dams within y ear were considered and also evaluated. The relative effectiveness of vario us models in describing the data set were assessed using likelihood ratio t ests. 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 anima l model applied, markedly influenced the magnitude of variance component es timates. 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 o f dams within year. Simple models based on animal as the only random effect gave upward biased estimates of additive genetic variance. The most approp riate model for the data set was one based on both year and spawn date as f ixed effects, and animal and full-sib family as random effects. The results from models combining maternal genetic and full-sib family effects to expl oit the full covariance structure of the data showed that there was confoun ding between these variance component estimates. The most consistent interp retation of this result was that common environmental effects and non-addit ive genetic effects were more important sources of variability than materna l genetic effects. The study also demonstrated high variability in paramete r estimates for replicate populations. (C) 1999 Elsevier Science B.V. All r ights reserved.