Genetic correlations between quantitative traits measured in many breeding programs are pervasive. These correlations indicate that measurements of one trait carry information on other traits. Current single-trait (univariate) genomic selection does not take advantage of this information. Multivariate genomic selection on multiple traits could accomplish this but has been little explored and tested in practical breeding programs. In this study, three multivariate linear models (i.e., GBLUP, BayesA, and BayesCp) were presented and compared to univariate models using simulated and real quantitative traits controlled by different genetic architectures. We also extended BayesA with fixed hyperparameters to a full hierarchical model that estimated hyperparameters and BayesCp to impute missing phenotypes. We found that optimal marker-effect variance priors depended on the genetic architecture of the trait so that estimating them was beneficial. We showed that the prediction accuracy for a low-heritability trait could be significantly increased by multivariate genomic selection when a correlated high-heritability trait was available. Further, multiple-trait genomic selection had higher prediction accuracy than single-trait genomic selection when phenotypes are not available on all individuals and traits. Additional factors affecting the performance of multiple-trait genomic selection were explored.T HE principle of genomic selection is to estimate simultaneously the effect of all markers in a training population consisting of phenotyped and genotyped individuals (Meuwissen et al. 2001). Genomic estimated breeding values (GEBVs) are then calculated as the sum of estimated marker effects for genotyped individuals in a prediction population. Fitting all markers simultaneously ensures that marker-effect estimates are unbiased, small effects are captured, and there is no multiple testing.Current genomic prediction models usually use only a single phenotypic trait. However, new varieties of crops and animals are evaluated for their performance on multiple traits. Crop breeders record phenotypic data for multiple traits in categories such as yield components (e.g., grain weight or biomass), grain quality (e.g., taste, shape, color, nutrient content), and resistance to biotic or abiotic stress. To take advantage of genetic correlation in mapping causal loci, multi-trait QTL mapping methods have been developed using maximum-likelihood (Jiang and Zeng 1995) and Bayesian (Banerjee et al. 2008; Xu et al. 2009) methods. Calus andVeerkamp (2011) recently presented three multiple-trait genomic selection (MT-GS) models: ridge regression (GBLUP), BayesSSVS, and BayesCp. The authors ranked the performances of these MT-GS methods (BayesSSVS . BayesCp . GBLUP) based on simulated traits under a single genetic architecture. Genetic correlation was shown to be a key factor determining the MT-GS advantage over single-trait genomic selection (ST-GS). A few issues for these MT-GS methods still need attention. First, genetic architectu...

Fusarium head blight (FHB) resistance is quantitative and diffi cult to evaluate. Genomic selection (GS) could accelerate FHB resistance breeding. We used U.S. cooperative FHB wheat nursery data to evaluate GS models for several FHB resistance traits including deoxynivalenol (DON) levels. For all traits we compared the models: ridge regression (RR), Bayesian LASSO (BL), reproducing kernel Hilbert spaces (RKHS) regression, random forest (RF) regression, and multiple linear regression (MLR) (fi xed effects). For DON, we evaluated additional prediction methods including bivariate RR models, phenotypes for correlated traits, and RF regression models combining markers and correlated phenotypes as predictors. Additionally, for all traits, we compared different marker sets including genomewide markers, FHB quantitative trait loci (QTL) targeted markers, and both sets combined. Genomic selection accuracies were always higher than MLR accuracies, RF and RKHS regression were often the most accurate methods, and for DON, marker plus trait RF regression was more accurate than all other methods. For all traits except DON, using QTL targeted markers alone led to lower accuracies than using genomewide markers. This study indicates that cooperative FHB nursery data can be useful for GS, and prior information about correlated traits and QTL could be used to improve accuracies in some cases.

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A retrospective study was undertaken to evaluate the long-term results of bilateral alveolar bone grafting carried out at Great Ormond Street Hospital from 1983 to 1993. Fifty-five consecutive complete bilateral cleft lip and palate patients (36 males and 19 females) who had the operation were included in this study. The total number of cleft sites was 110. At the time of alveolar bone grafting, the mean age of the patients was 12.3 years with a range of 8.4-19.9 years. Cancellous bone from the iliac crest was grafted into the alveolar cleft areas. The cleft sites were studied in two groups according to whether the cleft canine had erupted prior to bone grafting or not. The erupted canine group was composed of 43 cleft sites and the unerupted canine group of 67 sites. At the time of this study, the cleft canine had subsequently erupted at 101 sites. Anterior occlusal radiographs were taken before and after bone grafting. The minimum period of observation after alveolar bone grafting was one year. Criteria described previously were utilized to assess the height of the interdental septum. The results show that bone grafting before canine eruption has a higher clinical success rate compared with that carried out after canine eruption. The critical variable affecting the quality of bilateral alveolar bone grafting is the timing of the surgery.

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