Informing disease modelling with brain-relevant functional genomic annotations

Abstract

The past decade has seen a surge in the number of disease/trait-associated variants, largely because of the union of studies to share genetic data and the availability of electronic health records from large cohorts for research use. Variant discovery for neurological and neuropsychiatric genome-wide association studies, including schizophrenia, Parkinson's disease and Alzheimer's disease, has greatly benefitted; however, the translation of these genetic association results to interpretable biological mechanisms and models is lagging. Interpreting disease-associated variants requires knowledge of gene regulatory mechanisms and computational tools that permit integration of this knowledge with genome-wide association study results. Here, we summarize key conceptual advances in the generation of brain-relevant functional genomic annotations and amongst tools that allow integration of these annotations with association summary statistics, which together provide a new and exciting opportunity to identify disease-relevant genes, pathways and cell types in silico. We discuss the opportunities and challenges associated with these developments and conclude with our perspective on future advances in annotation generation, tool development and the union of the two.

Keywords: neurodegenerative disorders; cellular resolution; functional genomic annotations; genome-wide association; neuropsychiatric disorders.

Figure 1

Identifying annotations and genes of interest using GWAS summary statistics, relevant functional genomic annotations and genetic tools. To follow up on GWAS risk loci experimentally, the wet lab researcher requires disease-relevant model systems, biological pathways and genes, and ideally some indication of how disease affects gene expression or regulation (i.e. directionality of effect). Identifying annotations of interest and genes of interest are complementary approaches that combine to constrain the model systems, gene targets and gene-specific pathways to pursue in functional experiments. For a description of the various functional genomic annotation types and an overview of the tools see Box 1 and Table 2, respectively.

Figure 2

The 2D space of cellular and molecular resolution. Individual functional genomic annotations can be thought to lie somewhere on the axes of cellular resolution (spanning from whole tissue to single cells) and molecular resolution (spanning across epigenetic phenotypes, transcriptomic phenotypes and intermediates between the two). Points on the plot are purely illustrative, roughly depicting the relative number of functional annotations in each discrete population, with the most annotations currently found in the category of tissue-level steady-state mRNA levels and the least in the category of single cell steady-state isoform levels. To illustrate this categorization, examples of functional annotations highlighted in this review have been labelled. We expect that with future developments, the axis of molecular resolution will become increasingly populated with intermediate phenotypes such as steady-state isoform levels and various other RNA processing steps. Thus, in the future, populations within a category of cellular resolution will become less discrete across molecular phenotypes. For a description of above-mentioned molecular phenotypes and how they are assayed, see Box 1, and for further details on labelled functional annotations, see Table 1. Brain and single cell icons made by Eucalyp, Freepik and Smashicons from www.flaticon.com.