1. Combining genomic and genealogical information in a reproducing kernel hilbert spaces regression model for genome-enabled predictions in dairy cattle
- Author
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L. A. García-Cortés, Oscar González-Recio, Silvia T. Rodríguez-Ramilo, Dept Mejora Genet Anim, Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria, Dept Tecnico Conafe, Biosci Res Div, Dept Environm & Primary Ind, and Dairy Futures Cooperat Res Ctr
- Subjects
Evolutionary Genetics ,[SDV]Life Sciences [q-bio] ,lcsh:Medicine ,population ,genetic evaluation ,full pedigree ,Statistics ,lcsh:Science ,Animal Management ,Genetics ,0303 health sciences ,Multidisciplinary ,trait ,assisted prediction ,selection ,value ,Linear model ,Agriculture ,Regression analysis ,Genomics ,04 agricultural and veterinary sciences ,Quantitative Biology::Genomics ,Markov Chains ,Pedigree ,Kernel method ,Kernel (statistics) ,Physical Sciences ,Monte Carlo Method ,Statistics (Mathematics) ,Algorithms ,Research Article ,Correlation coefficient ,Mean squared error ,Biology ,03 medical and health sciences ,Linear regression ,Animals ,Statistical Methods ,030304 developmental biology ,Evolutionary Biology ,Population Biology ,Models, Genetic ,Markov chain ,lcsh:R ,0402 animal and dairy science ,Biology and Life Sciences ,Computational Biology ,Bayes Theorem ,040201 dairy & animal science ,Linear Models ,Veterinary Science ,Cattle ,lcsh:Q ,Animal Genetics ,Population Genetics ,Mathematics - Abstract
Genome-enhanced genotypic evaluations are becoming popular in several livestock species. For this purpose, the combination of the pedigree-based relationship matrix with a genomic similarities matrix between individuals is a common approach. However, the weight placed on each matrix has been so far established with ad hoc procedures, without formal estimation thereof. In addition, when using marker- and pedigree-based relationship matrices together, the resulting combined relationship matrix needs to be adjusted to the same scale in reference to the base population. This study proposes a semi-parametric Bayesian method for combining marker- and pedigree-based information on genome-enabled predictions. A kernel matrix from a reproducing kernel Hilbert spaces regression model was used to combine genomic and genealogical information in a semi-parametric scenario, avoiding inversion and adjustment complications. In addition, the weights on marker- versus pedigree-based information were inferred from a Bayesian model with Markov chain Monte Carlo. The proposed method was assessed involving a large number of SNPs and a large reference population. Five phenotypes, including production and type traits of dairy cattle were evaluated. The reliability of the genome-based predictions was assessed using the correlation, regression coefficient and mean squared error between the predicted and observed values. The results indicated that when a larger weight was given to the pedigree-based relationship matrix the correlation coefficient was lower than in situations where more weight was given to genomic information. Importantly, the posterior means of the inferred weight were near the maximum of 1. The behavior of the regression coefficient and the mean squared error was similar to the performance of the correlation, that is, more weight to the genomic information provided a regression coefficient closer to one and a smaller mean squared error. Our results also indicated a greater accuracy of genomic predictions when using a large reference population. © 2014 Rodríguez-Ramilo et al.
- Published
- 2014