Abstract
Over the past 15 years, genome-wide association studies (GWASs) have enabled the systematic identification of genetic loci associated with traits and diseases. However, due to resolution issues and methodological limitations, the true causal variants and genes associated with traits remain difficult to identify. In this post-GWAS era, many biological and computational fine-mapping approaches now aim to solve these issues. Here, we review fine-mapping and gene prioritization approaches that, when combined, will improve the understanding of the underlying mechanisms of complex traits and diseases. Fine-mapping of genetic variants has become increasingly sophisticated: initially, variants were simply overlapped with functional elements, but now the impact of variants on regulatory activity and direct variant-gene 3D interactions can be identified. Moreover, gene manipulation by CRISPR/Cas9, the identification of expression quantitative trait loci and the use of co-expression networks have all increased our understanding of the genes and pathways affected by GWAS loci. However, despite this progress, limitations including the lack of cell-type- and disease-specific data and the ever-increasing complexity of polygenic models of traits pose serious challenges. Indeed, the combination of fine-mapping and gene prioritization by statistical, functional and population-based strategies will be necessary to truly understand how GWAS loci contribute to complex traits and diseases.
Highlights
Most, if not all, phenotypic traits and diseases have a genetic component that influences their development, susceptibility or characteristics
The massively parallel reporter assay (MPRA; electronic supplementary material, table S2), can test over 30 000 candidate variants by synthetically creating 180 bp DNA fragments containing both alleles of a variant with a unique barcode and integrating these into GFPreporter plasmids that are subsequently transfected into different cell lines [56]
Multiple strategies as described here and collected in databases such as the EnhancerAtlas2.0 [69] should be combined to confidently fine-map causal variants and link them to genes that play a role in genome-wide association studies (GWASs) traits
Summary
If not all, phenotypic traits and diseases have a genetic component that influences their development, susceptibility or characteristics. The cell-type and tissue- or disease-specific micro-environment greatly affect which variants, TFs, genes and pathways are involved (figure 1) These complexities make it difficult to understand how GWAS loci contribute to their associated traits and have significantly hampered the interpretation and application of GWAS results. Important to note is that to reduce fine-mapping complexity, most approaches assume that only a single variant per locus contributes to a trait This is, not a proper reflection of reality as multiple variants within a single GWAS locus can have an effect on a single gene’s expression. We assess fine-mapping and gene prioritization approaches that have been used to translate GWAS loci to a functional understanding of the associated trait, while taking cell-type- and disease-specific context into account. We discuss the impact of the recent paradigm shift towards polygenic models and how these can be used to aid in the identification of gene networks that highlight core disease genes (figure 1c)
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