Cargando…
Informing disease modelling with brain-relevant functional genomic annotations
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-wid...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6885670/ https://www.ncbi.nlm.nih.gov/pubmed/31603214 http://dx.doi.org/10.1093/brain/awz295 |
_version_ | 1783474767100116992 |
---|---|
author | Reynolds, Regina H Hardy, John Ryten, Mina Gagliano Taliun, Sarah A |
author_facet | Reynolds, Regina H Hardy, John Ryten, Mina Gagliano Taliun, Sarah A |
author_sort | Reynolds, Regina H |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-6885670 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-68856702019-12-05 Informing disease modelling with brain-relevant functional genomic annotations Reynolds, Regina H Hardy, John Ryten, Mina Gagliano Taliun, Sarah A Brain Review Articles 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. Oxford University Press 2019-12 2019-10-11 /pmc/articles/PMC6885670/ /pubmed/31603214 http://dx.doi.org/10.1093/brain/awz295 Text en © The Author(s) (2019). Published by Oxford University Press on behalf of the Guarantors of Brain. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Review Articles Reynolds, Regina H Hardy, John Ryten, Mina Gagliano Taliun, Sarah A Informing disease modelling with brain-relevant functional genomic annotations |
title | Informing disease modelling with brain-relevant functional genomic annotations |
title_full | Informing disease modelling with brain-relevant functional genomic annotations |
title_fullStr | Informing disease modelling with brain-relevant functional genomic annotations |
title_full_unstemmed | Informing disease modelling with brain-relevant functional genomic annotations |
title_short | Informing disease modelling with brain-relevant functional genomic annotations |
title_sort | informing disease modelling with brain-relevant functional genomic annotations |
topic | Review Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6885670/ https://www.ncbi.nlm.nih.gov/pubmed/31603214 http://dx.doi.org/10.1093/brain/awz295 |
work_keys_str_mv | AT reynoldsreginah informingdiseasemodellingwithbrainrelevantfunctionalgenomicannotations AT hardyjohn informingdiseasemodellingwithbrainrelevantfunctionalgenomicannotations AT rytenmina informingdiseasemodellingwithbrainrelevantfunctionalgenomicannotations AT gaglianotaliunsaraha informingdiseasemodellingwithbrainrelevantfunctionalgenomicannotations |