Using Genomic Information for Genetic Improvements of Gastrointestinal Parasite Resistance in Australian Sheep
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.
Partitioning the genetic variance into genomic and pedigree components for parasite resistance in sheep
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.