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Genomic prediction with incomplete omics data

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In animal breeding, there has been an increasing interest in investigating the added value of intermediate omics traits such as transcriptomes, metabolites and methylation patterns in genomic predictions. Such data are available only for small number of animals. The “singlestep genomic prediction” machinery, which was first proposed to combine pedigree information of a large number of individuals, and genomic information of a fraction of the population, can be useful to handle incomplete omics data. Such an approach, when applied to incomplete omics data scenarios, imply a simple linear relationship from genotypes to different omics traits, which in reality may be very complex. Little is known about the accuracy of genetic evaluations when the omics traits are generated for the whole population. Here, we present two different approaches to handle incomplete omics data, and investigate their impact on genomic predictions, using simulations.
TitelWCGALP 2022 Programme book
ForlagWageningen Academic Publishers
Udgivelsesårjul. 2022
StatusUdgivet - jul. 2022
BegivenhedWorld Congress on Genetics Applied to Livestock Production - Rotterdam, Holland
Varighed: 3 jul. 20228 jul. 2022
Konferencens nummer: 12


KonferenceWorld Congress on Genetics Applied to Livestock Production

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