| Autor: |
Watanabe K; Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, VU University Amsterdam, Amsterdam, 1081 HV, The Netherlands., Taskesen E; Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, VU University Amsterdam, Amsterdam, 1081 HV, The Netherlands.; VU University Medical Center (VUMC), Alzheimercentrum, Amsterdam, 1081 HV, The Netherlands., van Bochoven A; Faculty of Science, VU University Amsterdam, Amsterdam, 1081 HV, The Netherlands., Posthuma D; Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, VU University Amsterdam, Amsterdam, 1081 HV, The Netherlands. danielle.posthuma@vu.nl.; Department of Clinical Genetics, VU University Medical Center, Amsterdam Neuroscience, Amsterdam, 1081 HV, The Netherlands. danielle.posthuma@vu.nl. |
| Abstrakt: |
A main challenge in genome-wide association studies (GWAS) is to pinpoint possible causal variants. Results from GWAS typically do not directly translate into causal variants because the majority of hits are in non-coding or intergenic regions, and the presence of linkage disequilibrium leads to effects being statistically spread out across multiple variants. Post-GWAS annotation facilitates the selection of most likely causal variant(s). Multiple resources are available for post-GWAS annotation, yet these can be time consuming and do not provide integrated visual aids for data interpretation. We, therefore, develop FUMA: an integrative web-based platform using information from multiple biological resources to facilitate functional annotation of GWAS results, gene prioritization and interactive visualization. FUMA accommodates positional, expression quantitative trait loci (eQTL) and chromatin interaction mappings, and provides gene-based, pathway and tissue enrichment results. FUMA results directly aid in generating hypotheses that are testable in functional experiments aimed at proving causal relations. |
