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Front Genet. 2015 Sep 7;6:276. doi: 10.3389/fgene.2015.00276. eCollection 2015.

An introductory review of parallel independent component analysis (p-ICA) and a guide to applying p-ICA to genetic data and imaging phenotypes to identify disease-associated biological pathways and systems in common complex disorders.

Author information

1
The Olin Neuropsychiatry Research Center, Institute of Living, Hartford CT, USA ; Department of Neurobiology, Yale School of Medicine, Yale University, New Haven CT, USA ; Department of Psychiatry, Yale School of Medicine, Yale University, New Haven CT, USA.
2
Department of Electrical and Computer Engineering, and The Mind Research Network, The University of New Mexico, Albuquerque NM, USA.
3
Department of Psychiatry, Yale School of Medicine, Yale University, New Haven CT, USA ; Department of Electrical and Computer Engineering, and The Mind Research Network, The University of New Mexico, Albuquerque NM, USA.

Abstract

Complex inherited phenotypes, including those for many common medical and psychiatric diseases, are most likely underpinned by multiple genes contributing to interlocking molecular biological processes, along with environmental factors (Owen et al., 2010). Despite this, genotyping strategies for complex, inherited, disease-related phenotypes mostly employ univariate analyses, e.g., genome wide association. Such procedures most often identify isolated risk-related SNPs or loci, not the underlying biological pathways necessary to help guide the development of novel treatment approaches. This article focuses on the multivariate analysis strategy of parallel (i.e., simultaneous combination of SNP and neuroimage information) independent component analysis (p-ICA), which typically yields large clusters of functionally related SNPs statistically correlated with phenotype components, whose overall molecular biologic relevance is inferred subsequently using annotation software suites. Because this is a novel approach, whose details are relatively new to the field we summarize its underlying principles and address conceptual questions regarding interpretation of resulting data and provide practical illustrations of the method.

KEYWORDS:

common disease; common variant; genetic risk; imaging genetics; multivariate; parallel independent component analysis

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