scholarly journals Integrative analysis of eQTL and GWAS summary statistics reveals novel genes related to Alzheimer's disease

2021 ◽  
Vol 17 (S5) ◽  
Author(s):  
Pradeep Varathan ◽  
Priyanka Gorijala ◽  
Tanner Y. Jacobson ◽  
Kwangsik Nho ◽  
Shannon L. Risacher ◽  
...  
2020 ◽  
Author(s):  
Emily Greenfest-Allen ◽  
Conor Klamann ◽  
Prabhakaran Gangadharan ◽  
Amanda Kuzma ◽  
Yuk Yee Leung ◽  
...  

AbstractINTRODUCTIONThe NIAGADS Alzheimer’s Genomics Database is an interactive knowledgebase for AD genetics that provides access to GWAS summary statistics datasets deposited at NIAGADS, a national genetics data repository for AD and related dementia (ADRD).METHODSThe website makes available >70 genome-wide summary statistics datasets from GWAS and genome sequencing analysis for AD/ADRD. Variants identified from these datasets are mapped to up-to-date variant and gene annotations from a variety of resources and linked to functional genomics data.The database is powered by a big data optimized relational database and ontologies to consistently annotate study designs and phenotypes, facilitating data harmonization and efficient real-time data analysis and variant or gene report generation.RESULTSDetailed variant reports provide tabular and interactive graphical summaries of known ADRD associations, as well as highlight variants flagged by the Alzheimer’s Disease Sequencing Project (ADSP). Gene reports provide summaries of co-located ADRD risk-associated variants and have been expanded to include meta-analysis results from aggregate association tests performed by the ADSP allowing us to flag genes with genetic-evidence for AD.DISCUSSIONThe GenomicsDB makes available >100 million variant annotations, including ~30 million (5 million novel) variants identified as AD-relevant by ADSP, for browsing and real-time mining via the website or programmatically through a REST API. With a newly redesigned, efficient, search interface and comprehensive record pages linking summary statistics to variant and gene annotations, this resource makes these data both accessible and interpretable, establishing itself as valuable tool for AD research.


2017 ◽  
Vol 136 (10) ◽  
pp. 1341-1351 ◽  
Author(s):  
Yen-Chen Anne Feng ◽  
◽  
Kelly Cho ◽  
Sara Lindstrom ◽  
Peter Kraft ◽  
...  

2019 ◽  
Vol 137 (4) ◽  
pp. 557-569 ◽  
Author(s):  
Stephen A. Semick ◽  
Rahul A. Bharadwaj ◽  
Leonardo Collado-Torres ◽  
Ran Tao ◽  
Joo Heon Shin ◽  
...  

2020 ◽  
Vol 75 (2) ◽  
pp. 531-545 ◽  
Author(s):  
Bo-Hyun Kim ◽  
Yong-Ho Choi ◽  
Jin-Ju Yang ◽  
SangYun Kim ◽  
Kwangsik Nho ◽  
...  

NeuroImage ◽  
2010 ◽  
Vol 51 (2) ◽  
pp. 542-554 ◽  
Author(s):  
Jason L. Stein ◽  
Xue Hua ◽  
Jonathan H. Morra ◽  
Suh Lee ◽  
Derrek P. Hibar ◽  
...  

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