GARFIELD classifies disease-relevant genomic features through integration of functional annotations with association signals
| Autor: | Ian Dunham, Matthias Geihs, Nicholas J. Timpson, Nicole Soranzo, Josine L. Min, Ewan Birney, Iotchkova, Klaudia Walter, Ritchie Grs., Sandro Morganella |
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| Přispěvatelé: | Consortium, Uk10K |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Association (object-oriented programming)
Quantitative Trait Loci Molecular Sequence Annotation/methods Disease Computational biology Biology Quantitative trait locus Regulatory Sequences Nucleic Acid Polymorphism Single Nucleotide Article 03 medical and health sciences 0302 clinical medicine Genetic variation Genetics Humans Gene 030304 developmental biology Genetic association 0303 health sciences Genome Molecular Sequence Annotation Genomics Regulatory Sequences Nucleic Acid/genetics Polymorphism Single Nucleotide/genetics Phenotype Genomics/methods R package Disease/genetics Genome-Wide Association Study/methods Genome/genetics ICEP Quantitative Trait Loci/genetics 030217 neurology & neurosurgery Software Genome-Wide Association Study |
| Zdroj: | Lotchkova, V, Ritchie, G R S, Geihs, M, Morganella, S, Min, J, Walter, K, Timpson, N, Dunham, I, Birney, E, Soranzo, N 2019, ' GARFIELD classifies disease-relevant genomic features through integration of functional annotations with association signals ', Nature Genetics, vol. 51, no. 2, pp. 343-353 . https://doi.org/10.1038/s41588-018-0322-6 |
| ISSN: | 1546-1718 |
| Popis: | Loci discovered by genome-wide association studies predominantly map outside protein-coding genes. The interpretation of the functional consequences of non-coding variants can be greatly enhanced by catalogs of regulatory genomic regions in cell lines and primary tissues. However, robust and readily applicable methods are still lacking by which to systematically evaluate the contribution of these regions to genetic variation implicated in diseases or quantitative traits. Here we propose a novel approach that leverages genome-wide association studies’ findings with regulatory or functional annotations to classify features relevant to a phenotype of interest. Within our framework, we account for major sources of confounding not offered by current methods. We further assess enrichment of genome-wide association studies for 19 traits within Encyclopedia of DNA Elements- and Roadmap-derived regulatory regions. We characterize unique enrichment patterns for traits and annotations driving novel biological insights. The method is implemented in standalone software and an R package, to facilitate its application by the research community. |
| Databáze: | OpenAIRE |
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