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Kernel-based whole-genome prediction of complex traits: a review
This review centers on whole-genome regression using kernel methods applied to a wide range of quantitative traits of agricultural importance in animals and plants, with the aim of arriving at an enhanced predictive performance in the light of available genome annotation information. Expand
Estimates of genomic heritability and genome-wide association study for fatty acids profile in Santa Inês sheep
Estimates of genomic heritabilities and elucidating important genomic regions can contribute to a better understanding of the genetic control of fatty acid deposition and improve the selection strategies to enhance meat quality and health attributes. Expand
Genome-enabled prediction of quantitative traits in chickens using genomic annotation
The results suggest that the whole-genome approach remains as a promising tool if interest is on prediction of complex traits, and that the predictive performance from using genic regions marked by SNPs was consistently better than that from SNPs in IGR. Expand
Kernel-based variance component estimation and whole-genome prediction of pre-corrected phenotypes and progeny tests for dairy cow health traits
It is concluded that capturing non-additive genetic variation, especially epistatic variation, in a cross-validation framework remains a significant challenge even when it is important, as seems to be the case for health traits in dairy cows. Expand
Machine learning and data mining advance predictive big data analysis in precision animal agriculture
Precision animal agriculture is poised to rise to prominence in the livestock enterprise in the domains of management, production, welfare, sustainability, health surveillance, and environmentalExpand
Predicting bull fertility using genomic data and biological information.
The findings suggest that genomic prediction of bull fertility is feasible in dairy cattle and provides potential for accurate genome-guided decisions, such as early culling of bull calves with low SCR predictions. Expand
An application of MeSH enrichment analysis in livestock
It is demonstrated that MeSH can be regarded as another choice of annotation to draw biological inferences from genes identified via experimental analyses that can enhance the functional interpretations for specific biological conditions or the genetic basis of complex traits in livestock species. Expand
Differential contribution of genomic regions to marked genetic variation and prediction of quantitative traits in broiler chickens
All genic and non-genic regions contributed to phenotypic variation for the three traits studied and the whole-genome approach was reaffirmed as the best tool for genome-enabled prediction of quantitative traits. Expand
Dissection of additive genetic variability for quantitative traits in chickens using SNP markers.
The results suggest that the contribution of SNPs to marked additive genetic variability is dependent on the allele frequency spectrum, and it was found that increasing marker density beyond 100K SNPs did not capture additional additive genetic variance. Expand
Utilizing random regression models for genomic prediction of a longitudinal trait derived from high‐throughput phenotyping
This study provides the first application of RR models for genomic prediction of a longitudinal trait in rice and demonstrates that RR models can be effectively used to improve the accuracy of genomic prediction for complex traits compared to a TP approach. Expand