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Evaluation of approaches to detect quantitative trait loci for growth, carcass, and meat quality on swine chromosomes 2, 6, 13, and 18. II. Multivariate and principal component analyses¹

Department of Animal Sciences, University of Illinois at Urbana-Champaign, Urbana 61801

² Correspondence: 1207 W. Gregory Dr. (phone: 217-333-8810; fax: 217-333-8286; e-mail: rodrgzzs{at}uiuc.edu).

The merits of complementary multivariate techniques to identify QTL associated with multiple traits were evaluated. Records from 806 F₂ pigs pertaining to a Berkshire x Duroc three-generation population were available. Six multitrait groups on SSC 2, 6, 13, and 18 with information on 30 markers were studied. Multivariate techniques studied included multivariate models and principal components analysis of each multitrait group. All models included, in addition to systematic effects, additive, dominance, and imprinting coefficients corresponding to a one-QTL model and a random family effect. Multivariate analysis identified QTL associated with genomewise significant variation in four of the multitrait groups. The majority of the multivariate analysis provided greater precision of parameter estimates and higher statistical significance in some cases than univariate approaches, because of the greater parameterization of the multivariate models and moderate information content of the data. Principal component analysis results were consistent with univariate and multivariate analyses and recovered the levels of statistical significance observed in univariate analyses on the original data. In addition, principal component analysis was able to provide a location associated with LM area not detected by other analyses. The relative advantage of multivariate over the univariate approaches varied with the level of genetic covariance between traits because of the modeled QTL effect and information contained in the data; however, multivariate approaches have the unique capability to identify pleiotropic effects or multiple linked QTL.

Key Words: Growth • Interval Mapping • Meat Quality • Multivariate • Principal Components