GenAMap: Visualization strategies for structured association mapping

Curtis, Ross E.; Kinnaird, Peter; Xing, Eric P.

doi:10.1109/biovis.2011.6094052

Cited by 11 publications

(11 citation statements)

References 29 publications

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“…Once we estimated the parameters for association strengths using GFlasso (see Methods), we carried out an in-depth biological analysis. We have automated the full pipeline of our analysis and made it available with our distribution of the GenAMap software platform for genome-wide association (GWAS) and eQTL analysis [15,16]. …”

Section: Resultsmentioning

confidence: 99%

“…( b ) In our GFlasso analysis of the same dataset, we not only found eQTL hotspots, but also discovered multiple contributing eQTL hotspots, where the same module of multiple genes is associated with multiple eQTL hotspots. This figure was created using GenAMap [ [16] ] .…”

Section: Resultsmentioning

confidence: 99%

“…Indeed, it is important to take into account the inherent structures present in the data when looking for associations. Not only are there powerful methods available, but these methods are also available in a unified visualization software framework through GenAMap [16,43], that is easily assessable by analysts and experimentalists.…”

Section: Discussionmentioning

confidence: 99%

“…We developed a software platform called GenAMap [15,16] that implements the full analysis pipeline of structured genome-phenome association analysis and provides tools to visualize the results from the analysis. The GFlasso analysis pipeline that we used in this paper is fully integrated into GenAMap, including creating the trait network and running the GFlasso program on the eQTL data.…”

Section: Methodsmentioning

confidence: 99%

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Structured association analysis leads to insight into Saccharomyces cerevisiaegene regulation by finding multiple contributing eQTL hotspots associated with functional gene modules

et al. 2013

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BackgroundAssociation analysis using genome-wide expression quantitative trait locus (eQTL) data investigates the effect that genetic variation has on cellular pathways and leads to the discovery of candidate regulators. Traditional analysis of eQTL data via pairwise statistical significance tests or linear regression does not leverage the availability of the structural information of the transcriptome, such as presence of gene networks that reveal correlation and potentially regulatory relationships among the study genes. We employ a new eQTL mapping algorithm, GFlasso, which we have previously developed for sparse structured regression, to reanalyze a genome-wide yeast dataset. GFlasso fully takes into account the dependencies among expression traits to suppress false positives and to enhance the signal/noise ratio. Thus, GFlasso leverages the gene-interaction network to discover the pleiotropic effects of genetic loci that perturb the expression level of multiple (rather than individual) genes, which enables us to gain more power in detecting previously neglected signals that are marginally weak but pleiotropically significant.ResultsWhile eQTL hotspots in yeast have been reported previously as genomic regions controlling multiple genes, our analysis reveals additional novel eQTL hotspots and, more interestingly, uncovers groups of multiple contributing eQTL hotspots that affect the expression level of functional gene modules. To our knowledge, our study is the first to report this type of gene regulation stemming from multiple eQTL hotspots. Additionally, we report the results from in-depth bioinformatics analysis for three groups of these eQTL hotspots: ribosome biogenesis, telomere silencing, and retrotransposon biology. We suggest candidate regulators for the functional gene modules that map to each group of hotspots. Not only do we find that many of these candidate regulators contain mutations in the promoter and coding regions of the genes, in the case of the Ribi group, we provide experimental evidence suggesting that the identified candidates do regulate the target genes predicted by GFlasso.ConclusionsThus, this structured association analysis of a yeast eQTL dataset via GFlasso, coupled with extensive bioinformatics analysis, discovers a novel regulation pattern between multiple eQTL hotspots and functional gene modules. Furthermore, this analysis demonstrates the potential of GFlasso as a powerful computational tool for eQTL studies that exploit the rich structural information among expression traits due to correlation, regulation, or other forms of biological dependencies.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Structured association analysis leads to insight into Saccharomyces cerevisiaegene regulation by finding multiple contributing eQTL hotspots associated with functional gene modules

et al. 2013

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“…Em outros casos, a análise é feita através da visualização de dados quantitativos, envolvendo técnicas diversas como diagramas (Murali et al, 1993) e gráficos em duas ou três dimensões (Willson et al, 1992;Curtis et al, 2011;Ellis et al, 2013). A título de exemplo, a Figura 9 apresenta a interface do software GenAMap, software para mapeamento de associações entre dados e mutações genéticas.…”

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