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

Pascal Croiseau, Fanny Mollandin, Andrea Rau, Marie-Pierre Sanchez, Didier Boichard

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Pascal Croiseau, Fanny Mollandin, Andrea Rau, Marie-Pierre Sanchez, Didier Boichard. Genomic prediction using functional annotations and QTL features in dairy and beef cattle.breeds. 75. Annual Meeting of the European Federation of Animal Science (EAAP 2024), European Federation of Animal Science (EAAP), Sep 2024, Florence, Italy. pp.292. hal-04694136

HAL Id: hal-04694136

https://hal.inrae.fr/hal-04694136v1

Submitted on 11 Sep 2024

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Distributed under a Creative Commons Attribution 4.0 International License
Session 15

Estimating haplotype and mutation effects in the context of genome sequence via ancestral recombination graphs

G. Mafra Fortuna<fnref n="1" />, J. Obšteter², A. Moškrič², G. Gorjanc<fnref n="1" />

Genomic prediction with whole-genome sequence data is challenging due to high computational cost and limited increase in accuracy but it has the opportunity to capture causal mutations and their effects in the context of the genome sequence for biological discovery. To overcome the challenges, we developed a statistical model that leverages local tree structure in an ancestral recombination graph (ARG). ARGs describe genetic variation in the sampled genomes with past mutation, recombination, and branching/coalescence events. We parameterise the difference between ancestor-descendent haplotype values by the effect of the mutation(s) between the haplotypes (=branch effects). Thus, haplotype values equal the sum of all branch effects from the tree root to the haplotype. This makes the mutation effects sequence-dependent, capturing local epistasis and ancestry-specific effects. The tree structure generates a recursive and sparse model via conditional distributions of ancestor-descendant haplotype values, allowing efficient calculations via generalised Cholesky decomposition of variance and precision matrices between haplotype values. We demonstrate the model with a sample of cattle mtDNA and associated real/simulated phenotypes. Since mtDNA is non-recombinant, we work with a single ARG spanning the entire genome. Results show how our mutation-event-aware model differs from the standard variant-allele-dosage model and generates more accurate estimates of mutation effects and haplotype values in different genetic backgrounds. Extending the model to recombining genomes still imposes a significant computational challenge and is ongoing research.

Theatre 10

Session 15

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

P. Croiseau<fnref n="1" />, F. Mollandin<fnref n="1" />, A. Rau<fnref n="1" />, M. P. Sanchez<fnref n="1" />, D. Boichard<fnref n="1" />

Existing Bayesian genomic evaluation methods such as BayesRC can account for biological annotations but not more 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-annotated SNPs, the BayesRCπ approach assigns a multi-annotated SNP to its optimal annotation and within a specific effect class (null, small, medium or strong effect). These strategies were tested in two dairy breeds (Montbéliarde and 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 and beef breeds, respectively. The traits of interest were milk, protein, and fat yields, protein and fat contents in dairy cattle, and weight at 18 months, thickness of bones, muscular and skeletal development in beef cattle. Although assuming different variance priors in a BayesR model led to a significant improvement in the accuracy of genomic predictions for a number of traits, incorporating annotation classes via the BayesRC and BayesRCπ models did not 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 annotation being more frequently assigned to the medium and strong effect classes with the BayesRCπ model than with the BayesR model. This suggests that the biological information is useful in identifying SNP with strong effects, which may favour more robust prediction equations over time.

<footnote n="1">¹ The University of Edinburgh, The Roslin Institute, Easter Bush, EH259RG Edinburgh, United Kingdom, ² The Agricultural Institute of Slovenia, Animal Science, Hacquetova ulica 17, 1000 Ljubljana, Slovenia</footnote>

<footnote n="1">¹ Université Paris-Saclay, INRAE, AgroParisTech, GABI, Domaine de Vilvert, 78362 Jouy en Josas, France</footnote>