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This chapter provides a comprehensive overview of single-step genomic best linear unbiased prediction (ssGBLUP). It begins by providing a review of the theoretical foundations behind ssGBLUP, then goes on to describe the implementation of ssGBLUP in the livestock industry. The chapter also describes dealing with missing pedigree information and how this can affect dairy cattle programs. A section on the validation of single-step genomic predictions is also included, followed by an overview of the use of variance components estimation. The chapter also includes a section on the reliabilities of genomic estimated breeding values for large dairy populations. Single-step genome-wide association for large dairy populations is also discussed, followed by an overview of advances in genomic evaluations with high-throughput or omics phenotypes.
- 1 Introduction
- 2 Theory behind ssGBLUP
- 3 Single-step GBLUP for millions of genotyped animals
- 4 Dealing with missing pedigree information
- 5 Validation of single-step genomic predictions
- 6 Variance components estimation with large dairy genomic data
- 7 Reliabilities of genomic estimated breeding values for large dairy populations
- 8 Single-step genome-wide association for large dairy populations
- 9 Genomic evaluations with high-throughput or omics phenotypes
- 10 Conclusion and future trends in research
- 11 Where to look for further information
- 12 References