Year: 2014
Type: article
Abstract: 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 http://sailing.cs.cmu.edu/genamap.
Source: PLoS ONE
Institutions: Carnegie Mellon University, University of Pittsburgh
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $2,382
Awards: III-COR+RI: Novel Statistical Models and Algorithms for Network Modeling, Mining and Reverse Engineering, Indexing, Mining and Modeling Spatio-Temporal Patterns of Gene Expressions, G3909270261, Time/Space-Varying Networks of Molecular Interactions: A New Paradigm for Studyin, Genome-Transcription-Phenome-Wide Association: a new paradigm for association stu
OpenAlex ID: W2121006010