Pathway Analyses and Understanding Disease Associations
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GENOMICS (SM WILLIAMS, SECTION EDITOR)
Pathway Analyses and Understanding Disease Associations Yu Liu • Mark R. Chance
Published online: 27 September 2013 Springer Science + Business Media New York 2013
Abstract High throughput technologies have been applied to investigate the underlying mechanisms of complex diseases, identify disease associations, and help to improve treatment. However, it is challenging to derive biological insight from conventional single gene-based analysis of ‘‘omics’’ data from high-throughput experiments due to sample and patient heterogeneity. To address these challenges, many novel pathway- and network-based approaches have been developed to integrate various ‘‘omics’’ data, such as gene expression, copy number alteration, genome-wide association studies, and interaction data. This review will cover recent methodological developments in pathway analysis for the detection of dysregulated interactions and disease-associated subnetworks, prioritization of candidate disease genes, and disease classifications. For each application, we will also discuss the associated challenges and potential future directions.
Y. Liu M. R. Chance (&) Center for Proteomics and Bioinformatics, Case Western Reserve University, 10900 Euclid Ave, Cleveland, OH 44106, USA e-mail: [email protected] Y. Liu e-mail: [email protected] M. R. Chance Case Comprehensive Cancer Center, Case Western Reserve University, 10900 Euclid Ave, Cleveland, OH 44106, USA M. R. Chance Department of Genetics and Genome Sciences, Case Western Reserve University, 10900 Euclid Ave, Cleveland, OH 44106, USA
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Keywords Pathway analysis Dysregulated interaction Disease association Genome-wide association studies (GWAS) Gene prioritization Disease classification
Introduction Biomedical research has been revolutionized by advanced high-throughput (HT) technologies for study of genomic, transcriptomic, proteomic, and metabolomic ‘‘molecular phenotypes’’ provided by technologies such as microarray, next generation sequencing, RNAi library screening, and high-throughput and high-resolution mass spectrometry [1–3]. However, due to the complexity of diseases, background noise in HT experiments, the need for multiple hypothesis testing corrections, and patient heterogeneity, it has been challenging to interpret the direct results from experiments to elucidate biological mechanisms relevant to complex diseases [4, 5••, 6]. Recently, methods targeted on pathway level analyses have been developed and applied to investigate the underlying mechanism of complex diseases [7]. The rationales behind these methods are multiple: genes/proteins do not work alone, but in an intricate network of interactions and pathways. In addition, complex diseases are more likely caused by the dysregulation of multiple targets in connected pathways and/or different genes in the same pathways in different patients. Pathway analysis has statistical advantages in that it can reduce the dimensionality of HT datasets and provide a focused set of targets for biol
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