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BackgroundGenomic selection has been successfully implemented in plant and animal breeding programs to shorten generation intervals and accelerate genetic progress per unit of time. In practice, genomic selection can be used to improve several correlated traits simultaneously via multiple-trait prediction, which exploits correlations between traits. However, few studies have explored multiple-trait genomic selection. Our aim was to infer genetic correlations between three traits measured in broiler chickens by exploring kinship matrices based on a linear combination of measures of pedigree and marker-based relatedness. A predictive assessment was used to gauge genetic correlations.MethodsA multivariate genomic best linear unbiased prediction model was designed to combine information from pedigree and genome-wide markers in order to assess genetic correlations between three complex traits in chickens, i.e. body weight at 35 days of age (BW), ultrasound area of breast meat (BM) and hen-house egg production (HHP). A dataset with 1351 birds that were genotyped with the 600 K Affymetrix platform was used. A kinship kernel (K) was constructed as K = λ G + (1 − λ)A, where A is the numerator relationship matrix, measuring pedigree-based relatedness, and G is a genomic relationship matrix. The weight (λ) assigned to each source of information varied over the grid λ = (0, 0.2, 0.4, 0.6, 0.8, 1). Maximum likelihood estimates of heritability and genetic correlations were obtained at each λ, and the “optimum” λ was determined using cross-validation.ResultsEstimates of genetic correlations were affected by the weight placed on the source of information used to build K. For example, the genetic correlation between BW–HHP and BM–HHP changed markedly when λ varied from 0 (only A used for measuring relatedness) to 1 (only genomic information used). As λ increased, predictive correlations (correlation between observed phenotypes and predicted breeding values) increased and mean-squared predictive error decreased. However, the improvement in predictive ability was not monotonic, with an optimum found at some 0 < λ < 1, i.e., when both sources of information were used together.ConclusionsOur findings indicate that multiple-trait prediction may benefit from combining pedigree and marker information. Also, it appeared that expected correlated responses to selection computed from standard theory may differ from realized responses. The predictive assessment provided a metric for performance evaluation as well as a means for expressing uncertainty of outcomes of multiple-trait selection.

(Co) variances for greasy fleece weight (GFW), clean fleece weight (CFW), mean fibre diameter (MFD), staple strength (SS), coefficient of variation of fibre diameter (CVFD), birthweight (BW), weaning weight (WW), and yearling weight (YW) were estimated for 5108 Australian Merino sheep from the CSIRO Fine Wool Project, born between 1990 and 1994. Covariances between these traits and number of lambs weaned per ewe joined (NLW) were also estimated. Significant maternal genetic effects were found for GFW, CFW, BW, WW, and YW. Estimates of heritability were biased upwardly when maternal effects were ignored. The maternal heritability estimates for GFW, CFW, BW, WW, and YW were 0.17, 0.15, 0.38, 0.28, and 0.13, respectively. Maternal effects were not important for MFD, CVFD, SS, and NLW. Direct-maternal genetic correlations within each fleece weight and bodyweight trait were estimated to be moderately negative (–0.26 to –0.48). The effect of ignoring maternal genetic effect was explored using selection index theory. Accounting for the maternal effects in both the selection criteria and breeding objective increased the overall response by 14.3%, 4.8%, 2.6%, 1.4%, and 0.0% in 3, 6, 12, 20 and 30% micron premium scenarios, respectively, compared with when the maternal effects were only included in breeding objective. Complete ignorance of the maternal effects led to overestimation in overall response of 2.8–35.7% for different micron premium scenarios in contrast to when the maternal effects were ignored in the selection index weight, but were included in the breeding objective. The results indicate that the maternal genetic effects of fleece weight and bodyweight should be considered in Merino breeding programs.