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...

Descripción completa

Detalles Bibliográficos
Autores principales: Reynolds, Regina H, Hardy, John, Ryten, Mina, Gagliano Taliun, Sarah A
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