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Brain Catalog: a comprehensive resource for the genetic landscape of brain-related traits
A broad range of complex phenotypes are related to dysfunctions in brain (hereafter referred to as brain-related traits), including various mental and behavioral disorders and diseases of the nervous system. These traits in general share overlapping symptoms, pathogenesis, and genetic components. He...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9825493/ https://www.ncbi.nlm.nih.gov/pubmed/36243988 http://dx.doi.org/10.1093/nar/gkac895 |
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author | Pan, Siyu Kang, Hongen Liu, Xinxuan Lin, Shiqi Yuan, Na Zhang, Zhang Bao, Yiming Jia, Peilin |
author_facet | Pan, Siyu Kang, Hongen Liu, Xinxuan Lin, Shiqi Yuan, Na Zhang, Zhang Bao, Yiming Jia, Peilin |
author_sort | Pan, Siyu |
collection | PubMed |
description | A broad range of complex phenotypes are related to dysfunctions in brain (hereafter referred to as brain-related traits), including various mental and behavioral disorders and diseases of the nervous system. These traits in general share overlapping symptoms, pathogenesis, and genetic components. Here, we present Brain Catalog (https://ngdc.cncb.ac.cn/braincatalog), a comprehensive database aiming to delineate the genetic components of more than 500 GWAS summary statistics datasets for brain-related traits from multiple aspects. First, Brain Catalog provides results of candidate causal variants, causal genes, and functional tissues and cell types for each trait identified by multiple methods using comprehensive annotation datasets (58 QTL datasets spanning 6 types of QTLs). Second, Brain Catalog estimates the SNP-based heritability, the partitioning heritability based on functional annotations, and genetic correlations among traits. Finally, through bidirectional Mendelian randomization analyses, Brain Catalog presents inference of risk factors that are likely causal to each trait. In conclusion, Brain Catalog presents a one-stop shop for the genetic components of brain-related traits, potentially serving as a valuable resource for worldwide researchers to advance the understanding of how GWAS signals may contribute to the biological etiology of brain-related traits. |
format | Online Article Text |
id | pubmed-9825493 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-98254932023-01-10 Brain Catalog: a comprehensive resource for the genetic landscape of brain-related traits Pan, Siyu Kang, Hongen Liu, Xinxuan Lin, Shiqi Yuan, Na Zhang, Zhang Bao, Yiming Jia, Peilin Nucleic Acids Res Database Issue A broad range of complex phenotypes are related to dysfunctions in brain (hereafter referred to as brain-related traits), including various mental and behavioral disorders and diseases of the nervous system. These traits in general share overlapping symptoms, pathogenesis, and genetic components. Here, we present Brain Catalog (https://ngdc.cncb.ac.cn/braincatalog), a comprehensive database aiming to delineate the genetic components of more than 500 GWAS summary statistics datasets for brain-related traits from multiple aspects. First, Brain Catalog provides results of candidate causal variants, causal genes, and functional tissues and cell types for each trait identified by multiple methods using comprehensive annotation datasets (58 QTL datasets spanning 6 types of QTLs). Second, Brain Catalog estimates the SNP-based heritability, the partitioning heritability based on functional annotations, and genetic correlations among traits. Finally, through bidirectional Mendelian randomization analyses, Brain Catalog presents inference of risk factors that are likely causal to each trait. In conclusion, Brain Catalog presents a one-stop shop for the genetic components of brain-related traits, potentially serving as a valuable resource for worldwide researchers to advance the understanding of how GWAS signals may contribute to the biological etiology of brain-related traits. Oxford University Press 2022-10-16 /pmc/articles/PMC9825493/ /pubmed/36243988 http://dx.doi.org/10.1093/nar/gkac895 Text en © The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Database Issue Pan, Siyu Kang, Hongen Liu, Xinxuan Lin, Shiqi Yuan, Na Zhang, Zhang Bao, Yiming Jia, Peilin Brain Catalog: a comprehensive resource for the genetic landscape of brain-related traits |
title | Brain Catalog: a comprehensive resource for the genetic landscape of brain-related traits |
title_full | Brain Catalog: a comprehensive resource for the genetic landscape of brain-related traits |
title_fullStr | Brain Catalog: a comprehensive resource for the genetic landscape of brain-related traits |
title_full_unstemmed | Brain Catalog: a comprehensive resource for the genetic landscape of brain-related traits |
title_short | Brain Catalog: a comprehensive resource for the genetic landscape of brain-related traits |
title_sort | brain catalog: a comprehensive resource for the genetic landscape of brain-related traits |
topic | Database Issue |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9825493/ https://www.ncbi.nlm.nih.gov/pubmed/36243988 http://dx.doi.org/10.1093/nar/gkac895 |
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