Tackling data integration challenges – Integrated transcriptomic and epigenetic differential analysis with idiffomix

(Invited keynote) From complexity to clarity – Tackling the challenges of multi-omic integration

The increasing availability and affordability of high-throughput sequencing technologies have enabled the generation of large-scale multi-omic data, greatly enhancing our understanding of complex biological systems across hierarchical molecular levels. A great deal of attention has been devoted to developing integrative methods that can fully leverage these multifaceted data, despite numerous statistical challenges such as small sample sizes, high dimensionality, heterogeneous measures, missing data, and complex interdependencies within and between omic layers. To date, many multi-omic integrative approaches have been proposed, reflecting the diversity of omics combinations, definitions of inter-omic anchors, and analysis objectives. In this talk, I will provide an overview of some commonly used methods for multi-omics integration, and I will introduce one of our own recent contributions in this field – idiffomix, a joint mixture model for integrated differential analyses of paired transcriptomic and methylation data. I will conclude by discussing some future opportunities and challenges in integrative multi-omics research.

(Invited talk) 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 target tissues at relevant developmental stages, 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 more 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.

Using biological priors in genomic prediction models – QQOQCCP?

(Invited talk) Accounting for overlapping functional annotations as biological priors in Bayesian 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.