EpiVar Browser: advanced exploration of epigenomics data under controlled access.

MOTIVATION: Human epigenomic data has been generated by large consortia for thousands of cell types to be used as a reference map of normal and disease chromatin states. Since epigenetic data contains potentially identifiable information, similarly to genetic data, most raw files generated by these consortia are stored in controlled-access databases. It is important to protect identifiable information, but this should not hinder secure sharing of these valuable datasets. RESULTS: Guided by the Framework for responsible sharing of genomic and health-related data from the Global Alliance for Genomics and Health (GA4GH), we have developed an approach and a tool to facilitate the exploration of epigenomics datasets' aggregate results, while filtering out identifiable information. Specifically, the EpiVar Browser allows a user to navigate an epigenetic dataset from a cohort of individuals and enables direct exploration of genotype-chromatin phenotype relationships. Because individual genotypes and epigenetic signal tracks are not directly accessible, and rather aggregated in the portal output, no identifiable data is released, yet the interface allows for dynamic genotype-epigenome interrogation. This approach has the potential to accelerate analyses that would otherwise require a lengthy multi-step approval process and provides a generalizable strategy to facilitate responsible access to sensitive epigenomics data. AVAILABILITY AND IMPLEMENTATION: Online portal: https://computationalgenomics.ca/tools/epivar; EpiVar Browser source code: https://github.com/c3g/epivar-browser; bw-merge-window tool source code: https://github.com/c3g/bw-merge-window.

Epigenetic variation impacts individual differences in the transcriptional response to influenza infection.

Humans display remarkable interindividual variation in their immune response to identical challenges. Yet, our understanding of the genetic and epigenetic factors contributing to such variation remains limited. Here we performed in-depth genetic, epigenetic and transcriptional profiling on primary macrophages derived from individuals of European and African ancestry before and after infection with influenza A virus. We show that baseline epigenetic profiles are strongly predictive of the transcriptional response to influenza A virus across individuals. Quantitative trait locus (QTL) mapping revealed highly coordinated genetic effects on gene regulation, with many cis-acting genetic variants impacting concomitantly gene expression and multiple epigenetic marks. These data reveal that ancestry-associated differences in the epigenetic landscape can be genetically controlled, even more than gene expression. Lastly, among QTL variants that colocalized with immune-disease loci, only 7% were gene expression QTL, while the remaining genetic variants impact epigenetic marks, stressing the importance of considering molecular phenotypes beyond gene expression in disease-focused studies.

EpiVar Browser: advanced exploration of epigenomics data under controlled access.

Motivation: Human epigenomic data has been generated by large consortia for thousands of cell types to be used as a reference map of normal and disease chromatin states. Since epigenetic data contains potentially identifiable information, similarly to genetic data, most raw files generated by these consortia are stored in controlled-access databases. It is important to protect identifiable information, but this should not hinder secure sharing of these valuable datasets. Results: Guided by the Framework for responsible sharing of genomic and health-related data from the Global Alliance for Genomics and Health (GA4GH), we have developed a tool to facilitate the exploration of epigenomics datasets' aggregate results, while filtering out identifiable information. Specifically, the EpiVar Browser allows a user to navigate an epigenetic dataset from a cohort of individuals and enables direct exploration of genotype-chromatin phenotype relationships. Because the information about individual genotypes is not accessible and aggregated in the output that is made available, no identifiable data is released, yet the interface allows for dynamic genotype - epigenome interrogation. This approach has the potential to accelerate analyses that would otherwise require a lengthy multi-step approval process and provides a generalisable strategy to facilitate responsible access to sensitive epigenomics data. Availability and implementation: Online portal instance: https://computationalgenomics.ca/tools/epivarSource code: https://github.com/c3g/epivar-browser.

Genome-wide microhomologies enable precise template-free editing of biologically relevant deletion mutations.

The functional effect of a gene edit by designer nucleases depends on the DNA repair outcome at the targeted locus. While non-homologous end joining (NHEJ) repair results in various mutations, microhomology-mediated end joining (MMEJ) creates precise deletions based on the alignment of flanking microhomologies (µHs). Recently, the sequence context surrounding nuclease-induced double strand breaks (DSBs) has been shown to predict repair outcomes, for which µH plays an important role. Here, we survey naturally occurring human deletion variants and identify that 11 million or 57% are flanked by µHs, covering 88% of protein-coding genes. These biologically relevant mutations are candidates for precise creation in a template-free manner by MMEJ repair. Using CRISPR-Cas9 in human induced pluripotent stem cells (hiPSCs), we efficiently create pathogenic deletion mutations for demonstrable disease models with both gain- and loss-of-function phenotypes. We anticipate this dataset and gene editing strategy to enable functional genetic studies and drug screening.