Accounting for overlapping functional annotations as biological priors in genomic prediction models of complex traits

It is now widespread to build whole-genome regression models using genomic data to predict complex traits in a wide range of fields, including farm animal and plant breeding and human genetics. Functional genomic annotations, such as the accessibility of chromatin or methylation status in relevant tissues, have the potential to provide valuable insight into the position and effect size of causal genetic variants underlying complex traits. In the H2020 GENE-SWitCH project, we aimed to develop and validate Bayesian models able to fully leverage such complex functional annotations for improved accuracy and interpretability of genomic predictions in the pig and poultry breeding sectors. To this end, we defined and implemented a flexible framework for genomic prediction called BayesRCO to simultaneously take advantage of the availability of multiple functional genomic annotations. In this talk, I’ll describe the intuition behind our proposed model and discuss some of our key take-away messages from early use cases.

Long-term effects of high-fiber maternal diets on the functional genome of pig offspring

Accounting for overlapping annotations as biological priors in genomic prediction models of complex traits

It is now widespread in farm animal and plant breeding to use genotyping data to predict phenotypes with genomic prediction models. Functional genomic annotations (e.g., the accessibility of chromatin or methylation status in relevant tissues), have the potential to provide valuable insight into the location and effect size of causal genetic variants underlying complex traits. Developing and validating genomic prediction models able to fully leverage such complex functional annotations for improved accuracy and interpretability was one of the aims of the H2020 GENE-SWitCH project. To this end, we defined and implemented a flexible framework for genomic prediction called BayesRCO to simultaneously take advantage of the availability of multiple functional genomic annotations. In this talk, I’ll describe the intuition behind our proposed model and discuss some of our key take-away messages from early results.

Functional annotations to guide prediction of tissue-specific gene expression from cis-regulatory sequencing variants

Leveraging multi-omic data for integrative exploratory and predictive analyses

The increased availability and affordability of high-throughput sequencing technologies in recent years have facilitated the use of multi-omic studies, expanding and enriching our understanding of complex systems across hierarchical biological levels. Integrative methods for these heterogeneous and multi-faceted omics data have shown promise for enhancing the interpretability of exploratory analyses, improving predictive power, and contributing to a holistic understanding of systems biology. However, such integrative analyses are accompanied by several major obstacles, including the potentially ambiguous relationships among omic levels, high dimensionality coupled with small sample sizes, technical artefacts due to batch effects, potentially incomplete or missing data… and the occasional difficulty in posing well-defined and answerable research questions of such data. In light of these challenges, in this talk I will discuss some of our recent methodological contributions to integrate multi-omic data for exploratory and predictive analyses.