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.
Highlights
A better understanding of the genetic architecture of complex diseases/traits has the potential to improve our understanding of their pathophysiology
To identify annotations of interest using genome-wide association studies (GWASs) summary statistics, one can assess whether genome-wide significant or sub-threshold loci of a complex trait are enriched within the annotation of interest, or using recently developed tools, one can assess whether the overall heritability of a trait is enriched within an annotation of interest
The past few years have seen a growth in the number of brain-relevant functional genomic annotations with increasingly high cellular and molecular resolution, a trend that is set to continue across current and new axes of information
Summary
A better understanding of the genetic architecture of complex diseases/traits has the potential to improve our understanding of their pathophysiology. Together with FACS, this was instrumental in comprehensive gene expression studies, wherein major cell types (astrocytes, endothelial cells, microglia, neurons, oligodendrocytes and oligodendrocyte precursor cells) were isolated from adult mouse or human brain and profiled using microarray or RNA-sequencing (Cahoy et al, 2008; Zhang et al, 2014, 2016) These studies have been especially informative in large part due to the user-friendly format in which these data were released (http://www.brainrnaseq.org/) (Zhang et al, 2016). ScTHS-seq together with single-nucleus Drop-seq (snDrop-seq) was applied to 460 000 single cells from the human adult visual cortex, frontal cortex and cerebellum (in nine individuals: three for scTHS-seq and six for snDrop-seq), allowing a combined analysis of the transcriptome and epigenome (Lake et al, 2018) While such approaches will be key to identifying regulatory elements and processes that shape cell-type identity, their strength lies in the ability to use associations between molecular layers to complete missing data (Lake et al, 2018). As with any emerging technology, single-cell and -nucleus resources derived using high-throughput droplet-based technologies come with limitations including: (i) sparser data; (ii) lower molecular resolution; and (iii) differential cell type loss due to cellular isolation protocols, which require consideration prior to use as annotations (for further details see Box 2) (Kelsey et al, 2017; Svensson et al, 2017a; Bakken et al, 2018a; Lake et al, 2018)
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