A practical view of fine-mapping and gene prioritization in the post-genome-wide association era
| Autor: | Broekema, R. V., Bakker, O. B., Jonkers, I. H. |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Multifactorial Inheritance
Genome-wide association study Review Review Article VARIANTS DISEASE TRANSCRIPTIONAL VARIATION 0302 clinical medicine polygenic diseases single-nucleotide polymorphisms GWAS CRISPR lcsh:QH301-705.5 SNPS 0303 health sciences education.field_of_study General Neuroscience Chromosome Mapping Phenotype fine-mapping Identification (biology) EXPRESSION Quantitative Trait Loci Immunology Population Single-nucleotide polymorphism Computational biology Biology Polymorphism Single Nucleotide General Biochemistry Genetics and Molecular Biology complex traits 03 medical and health sciences SINGLE-CELL Quantitative Trait Heritable Humans Genetic Predisposition to Disease education Alleles 030304 developmental biology Genetic association genome-wide association study RISK PREDICTION IDENTIFICATION Cas9 Genetic Variation Epistasis Genetic Genetics Population Gene Expression Regulation Genes lcsh:Biology (General) Expression quantitative trait loci causal variants and genes 030217 neurology & neurosurgery RESPONSES |
| Zdroj: | Open Biology, Vol 10, Iss 1 (2020) Open Biology |
| ISSN: | 2046-2441 |
| DOI: | 10.1098/rsob.190221 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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