Hanwoo is highly prized for its marbling ability and is the most important cattle breed in Korea. In order to maintain the integrity of the breed and for product certification purposes it is important to develop tools to confirm the origin of the products. Breed composition estimates based on a large number of molecular markers (e.g. HD SNP arrays) are highly accurate but expensive for routine usage. The identification of a reliable panel with a small number of markers will reduce costs and can enable broader adoption of the technology by industry. In this work a heuristic optimization method was used to find the most reliable subset of markers, from the Illumina BovineHD array, to estimate breed proportion in Hanwoo. Accuracies of breed proportion estimates above 90% can be achieved using as little as 200 markers. The best balance between accuracy and number of SNP was obtained with 500 markers achieving 94% accuracy. Rapid and cost effective breed composition prediction in Hanwoo cattle based on a SNP panel with at least 200 markers will help to certify the products with an acceptable accuracy and ensure breed purity within the breeding program. The method described herein is directly applicable to other breeds.

Background: Smallholder dairy farming in much of the developing world is based on the use of crossbred cows that combine local adaptation traits of indigenous breeds with high milk yield potential of exotic dairy breeds. Pedigree recording is rare in such systems which means that it is impossible to make informed breeding decisions. High-density single nucleotide polymorphism (SNP) assays allow accurate estimation of breed composition and parentage assignment but are too expensive for routine application. Our aim was to determine the level of accuracy achieved with low-density SNP assays.

Methods: We constructed subsets of 100 to 1500 SNPs from the 735k-SNP Illumina panel by selecting: (a) on high minor allele frequencies (MAF) in a crossbred population; (b) on large differences in allele frequency between ancestral breeds; (c) at random; or (d) with a differential evolution algorithm. These panels were tested on a dataset of 1933 crossbred dairy cattle from Kenya/Uganda and on crossbred populations from Ethiopia (N = 545) and Tanzania (N = 462). Dairy breed proportions were estimated by using the ADMIXTURE program, a regression approach, and SNP-best linear unbiased prediction, and tested against estimates obtained by ADMIXTURE based on the 735k-SNP panel. Performance for parentage assignment was based on opposing homozygotes which were used to calculate the separation value (sv) between true and false assignments.

Results: Panels of SNPs based on the largest differences in allele frequency between European dairy breeds and a combined Nelore/N'Dama population gave the best predictions of dairy breed proportion ( r² = 0.962 to 0.994 for 100 to 1500 SNPs) with an average absolute bias of 0.026. Panels of SNPs based on the highest MAF in the crossbred population (Kenya/Uganda) gave the most accurate parentage assignments (sv = -1 to 15 for 100 to 1500 SNPs).

Conclusions: Due to the different required properties of SNPs, panels that did well for breed composition did poorly for parentage assignment and vice versa. A combined panel of 400 SNPs was not able to assign parentages correctly, thus we recommend the use of 200 SNPs either for breed proportion prediction or parentage assignment, independently.

Background: Body weight (BW) is an important trait for meat production in sheep. Although over the past few years, numerous quantitative trait loci (QTL) have been detected for production traits in cattle, few QTL studies have been reported for sheep, with even fewer on meat production traits. Our objective was to perform a genome-wide association study (GWAS) with the medium-density Illumina Ovine SNP50 BeadChip to identify genomic regions and corresponding haplotypes associated with BW in Australian Merino sheep. Methods: A total of 1781 Australian Merino sheep were genotyped using the medium-density Illumina Ovine SNP50 BeadChip. Among the 53 862 single nucleotide polymorphisms (SNPs) on this array, 48 640 were used to perform a GWAS using a linear mixed model approach. Genotypes were phased with 'hsphase'; to estimate SNP haplotype effects, linkage disequilibrium blocks were identified in the detected QTL region. Results: Thirty-nine SNPs were associated with BW at a Bonferroni-corrected genome-wide significance threshold of 1 %. One region on sheep ('Ovis aries') chromosome 6 (OAR6) between 36.15 and 38.56 Mb, included 13 significant SNPs that were associated with BW; the most significant SNP was OAR6_41936490.1 (P = 2.37x10−16) at 37.69 Mb with an allele substitution effect of 2.12 kg, which corresponds to 0.248 phenotypic standard deviations for BW. The region that surrounds this association signal on OAR6 contains three genes: 'leucine aminopeptidase 3' (LAP3), which is involved in the processing of the oxytocin precursor; 'NCAPG non-SMC condensin I complex, subunit G (NCAPG)', which is associated with foetal growth and carcass size in cattle; and 'ligand dependent nuclear receptor corepressor-like (LCORL)', which is associated with height in humans and cattle. Conclusions: The GWAS analysis detected 39 SNPs associated with BW in sheep and a major QTL region was identified on OAR6. In several other mammalian species, regions that are syntenic with this region have been found to be associated with body size traits, which may reflect that the underlying biological mechanisms share a common ancestry. These findings should facilitate the discovery of causative variants for BW and contribute to marker-assisted selection.

High-throughput sequencing technology is rapidly replacing expression arrays and becoming the standard method for global expression profiling studies. The development of low-cost, rapid sequencing technologies has enabled detailed quantification of gene expression levels, affecting almost every field in the life sciences. In this chapter, we will overview the key points for gene expression analysis using RNA-seq data. First, we will discuss the workflows of RNA-seq data analysis followed by a discussion about the currently available tools for data analysis and a comparison between these tools. The chapter concludes with a discussion about the application of RNA-seq data analysis in livestock. In the appendix, using an example from livestock RNA-seq data, we show a simple script for RNA-seq data analysis.

Recent studies have shown that the bovine X chromosome contains more than a thousand genes, some of which may be economically important. However, since males are heterogametic for the X chromosome, it has, to date, largely been ignored for genomic prediction and its information content has not been fully explored. The genotyping quality of the X chromosome is the first question that must be addressed. In this study, we suggest a simple method to impute the X chromosome of the sire using a half-sib family in order to check genotyping accuracy. The results showed that the suggested method allows for a robust imputation of the X chromosome in ungenotyped sires and is useful for the routine quality control of the genomic data.