The theory of genomic selection is based on the prediction of the effects of genetic markers in linkage disequilibrium with quantitative trait loci. However, genomic selection also relies on relationships between individuals to accurately predict genetic value. This study aimed to examine the importance of information on relatives versus that of unrelated or more distantly related individuals on the estimation of genomic breeding values.

The theory of genomic selection is based on the prediction of the effects of quantitative trait loci (QTL) in linkage disequilibrium (LD) with markers. However, there is increasing evidence that genomic selection also relies on "relationships" between individuals to accurately predict genetic values. Therefore, a better understanding of what genomic selection actually predicts is relevant so that appropriate methods of analysis are used in genomic evaluations.

Efficient, robust, and accurate genotype imputation algorithms make large-scale application of genomic selection cost effective. An algorithm that imputes alleles or allele probabilities for all animals in the pedigree and for all genotyped single nucleotide polymorphisms (SNP) provides a framework to combine all pedigree, genomic, and phenotypic information into a single-stage genomic evaluation.

The Australian sheep Cooperative Research Centre has initiated an information nucleus with the aim to estimate genetic parameters for new traits, to undertake a large-scale whole-genome association study and to enhance the breeding values of breeding animals in commercial studs. This paper presents the rationale behind the current design factors to meet the main objectives. It then discusses the potential design of an information nucleus if it were a sustainable part of commercial sheep-breeding programs in the long term. Advantages of such an information nucleus are summarised and quantified where possible.

The use of whole-genome sequence (WGS) data for genomic prediction and association studies is highly desirable because the causal mutations should be present in the data. The sequencing of 935 sheep from a range of breeds provides the opportunity to impute sheep genotyped with single nucleotide polymorphism (SNP) arrays to WGS. This study evaluated the accuracy of imputation from SNP genotypes to WGS using this reference population of 935 sequenced sheep. The accuracy of imputation from the Ovine Infinium® HD BeadChip SNP (~ 500 k) to WGS was assessed for three target breeds: Merino, Poll Dorset and F1 Border Leicester × Merino. Imputation accuracy was highest for the Poll Dorset breed, although there were more Merino individuals in the sequenced reference population than Poll Dorset individuals. In addition, empirical imputation accuracies were higher (by up to 1.7%) when using larger multi-breed reference populations compared to using a smaller single-breed reference population. The mean accuracy of imputation across target breeds using the Minimac3 or the FImpute software was 0.94. The empirical imputation accuracy varied considerably across the genome; six chromosomes carried regions of one or more Mb with a mean imputation accuracy of < 0.7. Imputation accuracy in five variant annotation classes ranged from 0.87 (missense) up to 0.94 (intronic variants), where lower accuracy corresponded to higher proportions of rare alleles. The imputation quality statistic reported from Minimac3 (R2) had a clear positive relationship with the empirical imputation accuracy. Therefore, by first discarding imputed variants with an R2 below 0.4, the mean empirical accuracy across target breeds increased to 0.97. Although accuracy of genomic prediction was less affected by filtering on R2 in a multi-breed population of sheep with imputed WGS, the genomic heritability clearly tended to be lower when using variants with an R2 ≤ 0.4. The mean imputation accuracy was high for all target breeds and was increased by combining smaller breed sets into a multi-breed reference. We found that the Minimac3 software imputation quality statistic (R2) was a useful indicator of empirical imputation accuracy, enabling removal of very poorly imputed variants before downstream analyses.

Knowing the phase of marker genotype data can be useful in genome-wide association studies, because it makes it possible to use analysis frameworks that account for identity by descent or parent of origin of alleles and it can lead to a large increase in data quantities via genotype or sequence imputation. Long-range phasing and haplotype library imputation constitute a fast and accurate method to impute phase for SNP data.