Genomic prediction for parasite resistance in sheep using whole-genome sequence data The objective of the study is to compare QTL mapping precision and the accuracy of genomic prediction for parasite resistance in sheep using pre-selected variants identified from the high-density SNP panel (600k) and the imputed whole-genome sequence (WGS) data and to evaluate the prediction accuracy when using selected SNPs. The results of this paper show that the use of WGS variants located within or close to QTL regions can improve the prediction accuracy of parasite resistance compared to using variants selected from the high-density SNP panel.

We report the results of the first large scale milk recording and genetic evaluation for crossbred cows in a smallholder dairy production system in India. Preliminary results represented 8,144 smallholder crossbred cows with a total of 140,214 daily milk records, of which 2,946 animals were genotyped with the GGP Bovine and Illumina SNP assays. Data were adjusted for fixed effects and analysed with a random regression (RR) model with the 1st degree Legendre polynomial and heterogeneous variance. Heritabilities of milk yield ranged from 0.14 to 0.22 throughout the lactation period, with an average value of 0.19. Genomic Estimated breeding values (GEBV) for the genotyped animals including the smallholder crossbred cows and the bulls and dams from the BAIF bull stud ranged from +1.9 to -1.4 kg/day. The moderate heritability of the milk yield found in our results together with the wide range of GEBV, indicate that a good response to genomic selection for milk yield can be expected for smallholder dairy farms in India.

The aim of the present thesis was to identify genomic regions associated with parasite resistance in sheep and to evaluate the potential improvements in genomic prediction accuracies when incorporating genomic information in estimating breeding values. Data were derived from a large reference population of sheep developed in Australia, based on the CRC Information Nucleus Flock (INF). Worm egg counts (WEC) were collected from animals that were naturally infected in the field with mixed gastrointestinal worm species. Egg counts determined the presence of three predominant strongyle species; Teladorsagia circumcincta, Haemonchus contortus, and Trichostrongylus colubriformis. Heritability estimate for WEC based on pedigree relationships (0.20±0.03) was similar to those obtained from genomic relationships calculated from 50k and 600k genotypes. In a genome partitioning analysis, the genetic variance explained by each chromosome was proportional to the chromosomal length, providing strong evidence that parasite resistance is a polygenic trait with a large number of loci underlying the mechanism of resistance.
Genome wide association studies (GWAS) and regional heritability mapping (RHM) identified a significant region on OAR2 associated with parasite resistance. Haplotype analysis confirmed a haplotype block within this region on OAR2, which overlaps with GALNTL6 (Polypeptide N-Acetylgalactosaminyltransferase Like 6) gene, responsible for mucus production. Fine-mapping RHM analysis with smaller window sizes identified more significant regions on OAR6, OAR18, OAR24 as well as OAR20 within the major histocompatibility complex (MHC). Each region explained only a small proportion of WEC heritability, ranging from 2% to 5%. Pathway analyses revealed key genes involved in innate and acquired immune system pathways as well as cytokine signalling pathways. Mucus production and haemostasis are also relevant in protecting the host from parasite infections.
The accuracy of genomic predictions was evaluated for different groups of animals that had varying degree of relationships to their respective training populations. A closer relationship between the training and validation groups led to a higher accuracy of genomic prediction for WEC. GBLUP predicted breeding values more accurately than pedigree-based BLUP, especially when the relationship between training and validation groups was distant. These results highlight the importance of the relationships between animals in training and validation sets as a key factor in determining prediction accuracies.
The increased availability of whole-genome sequence (WGS) data, combined with a larger number of genotyped animals, made it possible to split datasets into QTL discovery and training/validation subsets and evaluate the prediction accuracy across the three marker densities. The performance of genomic prediction was evaluated using cross-validation design across sire families. Prediction accuracy of WEC improved slightly from 0.16±0.02 using 50k genotypes to 0.18±0.01 and 0.19±0.01 when using HD and WGS data, respectively. Variants selected from WGS data using GWAS and RHM methods improved the prediction accuracy substantially, when fitted alongside 50k genotypes, compared to when the 50k genotypes were fitted alone. However, when variant selection was based only on GWAS, the prediction accuracy increased by 5%, whereas when selection was limited to variants with the lowest GWAS p-values in windows identified by RHM, the prediction accuracy increased by 9%. These findings offer potentially important implications for future genomic prediction studies for parasite resistance.

Background: This study aimed at (1) comparing the accuracies of genomic prediction for parasite resistance in sheep based on whole-genome sequence (WGS) data to those based on 50k and high-density (HD) single nucleotide polymorphism (SNP) panels; (2) investigating whether the use of variants within quantitative trait loci (QTL) regions that were selected from regional heritability mapping (RHM) in an independent dataset improved the accuracy more than variants selected from genome-wide association studies (GWAS); and (3) comparing the prediction accuracies between variants selected from WGS data to variants selected from the HD SNP panel.

Results: The accuracy of genomic prediction improved marginally from 0.16 ± 0.02 and 0.18 ± 0.01 when using all the variants from 50k and HD genotypes, respectively, to 0.19 ± 0.01 when using all the variants from WGS data. Fitting a GRM from the selected variants alongside a GRM from the 50k SNP genotypes improved the prediction accuracy substantially compared to fitting the 50k SNP genotypes alone. The gain in prediction accuracy was slightly more pronounced when variants were selected from WGS data compared to when variants were selected from the HD panel. When sequence variants that passed the GWAS -log₁₀(p value) threshold of 3 across the entire genome were selected, the prediction accuracy improved by 5% (up to 0.21 ± 0.01), whereas when selection was limited to sequence variants that passed the same GWAS −log₁₀(p value) threshold of 3 in regions identified by RHM, the accuracy improved by 9% (up to 0.25 ± 0.01).

Conclusions: Our results show that through careful selection of sequence variants from the QTL regions, the accuracy of genomic prediction for parasite resistance in sheep can be improved. These findings have important implications for genomic prediction in sheep.

This study aimed to identify loci underlying variation in parasite resistance, as measured by worm egg count (WEC), in a large multi-breed sheep population using genome-wide association studies (GWAS) and regional heritability mapping (RHM) approaches. A total of 7153 animals with both genotype data and WEC phenotypes were included in this analysis. Strong evidence of association was observed on chromosome 2 by both approaches. However, RHM had a greater power to identify loci than GW AS analysis. RHM identified an additional region at the genomewide significance level on chromosome 6. This region was also previously found to be associated with mastitis resistance and facial eczema susceptibility in sheep, indicating that some pleiotropic effects are possibly affecting a wide range of sheep diseases. Three other regions on chromosome I, 3 and 24 reached the suggestive threshold.

In this study, we estimated the additive genetic variance explained by genomic markers for parasite resistance in a large mixed population of sheep and compared this estimate to the additive genetic variance explained by pedigree. Furthermore, we partitioned the total genetic variance by fitting both of genomic relationship matrix (GRM) and numerator relationship matrix (NRM) simultaneously into a genomic component explained by genomic relationships and a polygenic component explained by pedigree relationships. In this analysis, all the genetic variation explained by pedigree could be captured by the 50K SNP chip markers. When both of GRM and NRM were fitted simultaneously, 73.7% of total genetic variance was explained by genomic effects while the remaining variance (26.3%) was explained by pedigree effects. The proportion of genetic variance explained by genomic effects was further partitioned into 26 chromosomes. A significant relationship was found between chromosome-specific variance and the length of the chromosome (R² = 0.26). This indicates that disease resistance is a largely polygenic trait with a large number of genes involved in the mechanisms of resistance but there are some chromosomal regions that explain a larger proportion of the variation.

Background: This study aimed at identifying genomic regions that underlie genetic variation of worm egg count, as an indicator trait for parasite resistance in a large population of Australian sheep, which was genotyped with the highdensity 600 K Ovine single nucleotide polymorphism array. This study included 7539 sheep from diferent locations across Australia that underwent a feld challenge with mixed gastrointestinal parasite species. Faecal samples were collected and worm egg counts for three strongyle species, i.e. Teladorsagia circumcincta, Haemonchus contortus and Trichostrongylus colubriformis were determined. Data were analysed using genome-wide association studies (GWAS) and regional heritability mapping (RHM).
Results: Both RHM and GWAS detected a region on Ovis aries (OAR) chromosome 2 that was highly signifcantly associated with parasite resistance at a genome-wise false discovery rate of 5%. RHM revealed additional signifcant regions on OAR6, 18, and 24. Pathway analysis revealed 13 genes within these signifcant regions (SH3RF1, HERC2, MAP3K, CYFIP1, PTPN1, BIN1, HERC3, HERC5, HERC6, IBSP, SPP1, ISG20, and DET1), which have various roles in innate and acquired immune response mechanisms, as well as cytokine signalling. Other genes involved in haemostasis regulation and mucosal defence were also detected, which are important for protection of sheep against invading parasites.
Conclusions: This study identifed signifcant genomic regions on OAR2, 6, 18, and 24 that are associated with parasite resistance in sheep. RHM was more powerful in detecting regions that afect parasite resistance than GWAS. Our results support the hypothesis that parasite resistance is a complex trait and is determined by a large number of genes with small efects, rather than by a few major genes with large efects.