Genomic prediction using functional annotations and QTL features in dairy and beef cattle.breeds

Session 15. Role of bioinformatics applied to livestock data - Exploitingstructural variation and pangenome-based techniques for livestock

International audience

إنجليزي. Existing Bayesian genomic evaluation methods such as BayesRC can account for biological annotations but notmore than one annotation per SNP. However, when a SNP is associated with multiple sources of functional annotation,it is not straightforward to identify the most relevant annotation for a trait of interest. To handle multi-annotatedSNPs, the BayesRCπ approach assigns a multi-annotated SNP to its optimal annotation and within a specificeffect class (null, small, medium or strong effect). These strategies were tested in two dairy breeds (Montbéliardeand Normande) and one beef breed (Charolaise). Four different annotation classes were considered (50K chip,GWAS, GWAS meta-analysis, genomic features) for a total of around 100,000 and 50,000 SNPs in the dairy andbeef breeds, respectively. The traits of interest were milk, protein, and fat yields, protein and fat contents in dairycattle, and weight at 18 months, thickness of bones, muscular and skeletal development in beef cattle. Althoughassuming different variance priors in a BayesR model led to a significant improvement in the accuracy of genomicpredictions for a number of traits, incorporating annotation classes via the BayesRC and BayesRCπ models didnot result in any additional gain. However, the posterior distribution of SNPs in the different effect classes (null,small, medium or strong) strongly differed between models, with the SNPs having a strong biological annotationbeing more frequently assigned to the medium and strong effect classes with the BayesRCπ model than with theBayesR model. This suggests that the biological information is useful in identifying SNP with strong effects,which may favour more robust prediction equations over time.