Modeling regulatory network topology improves genome-wide analyses of complex human traits
Gene regulatory networks are a useful means of inferring functional interactions from large-scale genomic data. Here, the authors develop a Bayesian framework integrating GWAS summary statistics with gene regulatory networks to identify genetic enrichments and associations simultaneously.
        Guardado en:
      
    
                  | Autores principales: | , , | 
|---|---|
| Formato: | article | 
| Lenguaje: | EN | 
| Publicado: | 
        
      Nature Portfolio    
    
      2021
     | 
| Materias: | |
| Acceso en línea: | https://doaj.org/article/3f97afd99a364fa6b7fc3dfd5dfbd711 | 
| Etiquetas: | 
       Agregar Etiqueta    
     
      Sin Etiquetas, Sea el primero en etiquetar este registro!
   
 | 
| id | 
                  oai:doaj.org-article:3f97afd99a364fa6b7fc3dfd5dfbd711 | 
    
|---|---|
| record_format | 
                  dspace | 
    
| spelling | 
                  oai:doaj.org-article:3f97afd99a364fa6b7fc3dfd5dfbd7112021-12-02T15:43:06ZModeling regulatory network topology improves genome-wide analyses of complex human traits10.1038/s41467-021-22588-02041-1723https://doaj.org/article/3f97afd99a364fa6b7fc3dfd5dfbd7112021-05-01T00:00:00Zhttps://doi.org/10.1038/s41467-021-22588-0https://doaj.org/toc/2041-1723Gene regulatory networks are a useful means of inferring functional interactions from large-scale genomic data. Here, the authors develop a Bayesian framework integrating GWAS summary statistics with gene regulatory networks to identify genetic enrichments and associations simultaneously.Xiang ZhuZhana DurenWing Hung WongNature PortfolioarticleScienceQENNature Communications, Vol 12, Iss 1, Pp 1-15 (2021) | 
    
| institution | 
                  DOAJ | 
    
| collection | 
                  DOAJ | 
    
| language | 
                  EN | 
    
| topic | 
                  Science Q  | 
    
| spellingShingle | 
                  Science Q Xiang Zhu Zhana Duren Wing Hung Wong Modeling regulatory network topology improves genome-wide analyses of complex human traits  | 
    
| description | 
                  Gene regulatory networks are a useful means of inferring functional interactions from large-scale genomic data. Here, the authors develop a Bayesian framework integrating GWAS summary statistics with gene regulatory networks to identify genetic enrichments and associations simultaneously. | 
    
| format | 
                  article | 
    
| author | 
                  Xiang Zhu Zhana Duren Wing Hung Wong  | 
    
| author_facet | 
                  Xiang Zhu Zhana Duren Wing Hung Wong  | 
    
| author_sort | 
                  Xiang Zhu | 
    
| title | 
                  Modeling regulatory network topology improves genome-wide analyses of complex human traits | 
    
| title_short | 
                  Modeling regulatory network topology improves genome-wide analyses of complex human traits | 
    
| title_full | 
                  Modeling regulatory network topology improves genome-wide analyses of complex human traits | 
    
| title_fullStr | 
                  Modeling regulatory network topology improves genome-wide analyses of complex human traits | 
    
| title_full_unstemmed | 
                  Modeling regulatory network topology improves genome-wide analyses of complex human traits | 
    
| title_sort | 
                  modeling regulatory network topology improves genome-wide analyses of complex human traits | 
    
| publisher | 
                  Nature Portfolio | 
    
| publishDate | 
                  2021 | 
    
| url | 
                  https://doaj.org/article/3f97afd99a364fa6b7fc3dfd5dfbd711 | 
    
| work_keys_str_mv | 
                  AT xiangzhu modelingregulatorynetworktopologyimprovesgenomewideanalysesofcomplexhumantraits AT zhanaduren modelingregulatorynetworktopologyimprovesgenomewideanalysesofcomplexhumantraits AT winghungwong modelingregulatorynetworktopologyimprovesgenomewideanalysesofcomplexhumantraits  | 
    
| _version_ | 
                  1718385803833376768 |