Sequential Estimation of Genetic and Phenotypic Parameters in Multitrait Mixed Model Analysis
Open Access
- 1 October 1986
- journal article
- research article
- Published by American Dairy Science Association in Journal of Dairy Science
- Vol. 69 (10) , 2696-2703
- https://doi.org/10.3168/jds.s0022-0302(86)80716-0
Abstract
Single-trait and multitrait (2-, 3-, 4-, and 5-trait) restricted maximum likelihood methods were applied to the same set of data with complete information on all traits. Results suggest that parameter estimates from a data set vary depending upon the type of analysis (single- or multitrait model) and upon the other traits included in multitrait analysis. The choice of parameter estimation method for a breeding design should be based on the breeding goal. In parameter estimation or sire evaluation, traits included in a multitrait analysis should correspond to the traits of interest in the breeding goal. Multitrait analysis explores all intercorrelations simultaneously in parameter estimation and thus provides a complete picture of all interrelationships among traits. In contrast, single-trait analysis produces pairwise (sample) correlations and ignores the possible contribution of other related traits under study to the pairwise correlation. The 5-trait model analysis through canonical transformation was about 300% more efficient in terms of computer time than single-trait model analysis of the same 5 traits. In this study, parameter estimates converged faster under multitrait analysis through canonical transformation than under single-trait analysis.This publication has 24 references indexed in Scilit:
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