title: GWAS in a box: statistical and visual analytics of structured associations via GenAMap.
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.
authors.affiliation: Machine Learning Department, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.
authors.forename: Eric P
authors.initials: EP
authors.lastname: Xing
authors.affiliation: Joint Carnegie Mellon - University of Pittsburgh PhD Program in Computational Biology, Pittsburgh, Pennsylvania, United States of America; Lane Center for Computational Biology, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.
authors.forename: Ross E
authors.initials: RE
authors.lastname: Curtis
authors.affiliation: Computer Science Department, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.
authors.forename: Georg
authors.initials: G
authors.lastname: Schoenherr
authors.affiliation: Computer Science Department, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.
authors.forename: Seunghak
authors.initials: S
authors.lastname: Lee
authors.affiliation: Lane Center for Computational Biology, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.
authors.forename: Junming
authors.initials: J
authors.lastname: Yin
authors.affiliation: Language Technologies Institute, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.
authors.forename: Kriti
authors.initials: K
authors.lastname: Puniyani
authors.affiliation: Lane Center for Computational Biology, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.
authors.forename: Wei
authors.initials: W
authors.lastname: Wu
authors.affiliation: Human Computer Interaction Institute, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.
authors.forename: Peter
authors.initials: P
authors.lastname: Kinnaird
mesh_terms: D000818:Animals; D001185:Artificial Intelligence; D055106:Genome-Wide Association Study / Q000379:methods*; D051379:Mice; D040641:Quantitative Trait Loci; D012984:Software