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GWAS in a box: Statistical and visual analytics of structured associations via GenAMap

Xing, EP and Curtis, RE and Schoenherr, G and Lee, S and Yin, J and Puniyani, K and Wu, W and Kinnaird, P (2014) GWAS in a box: Statistical and visual analytics of structured associations via GenAMap. PLoS ONE, 9 (6).

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With the continuous improvement in genotyping and molecular phenotyping technology and the decreasing typing cost, it is expected that in a few years, more and more clinical studies of complex diseases will recruit thousands of individuals for pan-omic genetic association analyses. Hence, there is a great need for algorithms and software tools that could scale up to the whole omic level, integrate different omic data, leverage rich structure information, and be easily accessible to non-technical users. We present GenAMap, an interactive analytics software platform that 1) automates the execution of principled machine learning methods that detect genome- and phenome-wide associations among genotypes, gene expression data, and clinical or other macroscopic traits, and 2) provides new visualization tools specifically designed to aid in the exploration of association mapping results. Algorithmically, GenAMap is based on a new paradigm for GWAS and PheWAS analysis, termed structured association mapping, which leverages various structures in the omic data. We demonstrate the function of GenAMap via a case study of the Brem and Kruglyak yeast dataset, and then apply it on a comprehensive eQTL analysis of the NIH heterogeneous stock mice dataset and report some interesting findings. GenAMap is available from © 2014 Xing et al.


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Item Type: Article
Status: Published
CreatorsEmailPitt UsernameORCID
Xing, EP
Curtis, RE
Schoenherr, G
Lee, S
Yin, J
Puniyani, K
Wu, W
Kinnaird, P
ContributionContributors NameEmailPitt UsernameORCID
Date: 6 June 2014
Date Type: Publication
Journal or Publication Title: PLoS ONE
Volume: 9
Number: 6
DOI or Unique Handle: 10.1371/journal.pone.0097524
Schools and Programs: Dietrich School of Arts and Sciences > Computational Biology
Refereed: Yes
Date Deposited: 30 Jun 2014 18:40
Last Modified: 02 Feb 2019 22:55


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