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      Pathway-based Analysis Tools for Complex Diseases: A Review

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          Abstract

          Genetic studies are traditionally based on single-gene analysis. The use of these analyses can pose tremendous challenges for elucidating complicated genetic interplays involved in complex human diseases. Modern pathway-based analysis provides a technique, which allows a comprehensive understanding of the molecular mechanisms underlying complex diseases. Extensive studies utilizing the methods and applications for pathway-based analysis have significantly advanced our capacity to explore large-scale omics data, which has rapidly accumulated in biomedical fields. This article is a comprehensive review of the pathway-based analysis methods—the powerful methods with the potential to uncover the biological depths of the complex diseases. The general concepts and procedures for the pathway-based analysis methods are introduced and then, a comprehensive review of the major approaches for this analysis is presented. In addition, a list of available pathway-based analysis software and databases is provided. Finally, future directions and challenges for the methodological development and applications of pathway-based analysis techniques are discussed. This review will provide a useful guide to dissect complex diseases.

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          Powerful SNP-set analysis for case-control genome-wide association studies.

          Michael C Wu, Peter Kraft, Michael Epstein … (2010)
          GWAS have emerged as popular tools for identifying genetic variants that are associated with disease risk. Standard analysis of a case-control GWAS involves assessing the association between each individual genotyped SNP and disease risk. However, this approach suffers from limited reproducibility and difficulties in detecting multi-SNP and epistatic effects. As an alternative analytical strategy, we propose grouping SNPs together into SNP sets on the basis of proximity to genomic features such as genes or haplotype blocks, then testing the joint effect of each SNP set. Testing of each SNP set proceeds via the logistic kernel-machine-based test, which is based on a statistical framework that allows for flexible modeling of epistatic and nonlinear SNP effects. This flexibility and the ability to naturally adjust for covariate effects are important features of our test that make it appealing in comparison to individual SNP tests and existing multimarker tests. Using simulated data based on the International HapMap Project, we show that SNP-set testing can have improved power over standard individual-SNP analysis under a wide range of settings. In particular, we find that our approach has higher power than individual-SNP analysis when the median correlation between the disease-susceptibility variant and the genotyped SNPs is moderate to high. When the correlation is low, both individual-SNP analysis and the SNP-set analysis tend to have low power. We apply SNP-set analysis to analyze the Cancer Genetic Markers of Susceptibility (CGEMS) breast cancer GWAS discovery-phase data.
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            Global functional profiling of gene expression.

            Sorin Drăghici, Purvesh Khatri, Rui Martins … (2003)
            The typical result of a microarray experiment is a list of tens or hundreds of genes found to be differentially regulated in the condition under study. Independent of the methods used to select these genes, the common task faced by any researcher is to translate these lists of genes into a better understanding of the biological phenomena involved. Currently, this is done through a tedious combination of searches through the literature and a number of public databases. We developed Onto-Express (OE) as a novel tool able to automatically translate such lists of differentially regulated genes into functional profiles characterizing the impact of the condition studied. OE constructs functional profiles (using Gene Ontology terms) for the following categories: biochemical function, biological process, cellular role, cellular component, molecular function, and chromosome location. Statistical significance values are calculated for each category. We demonstrate the validity and the utility of this comprehensive global analysis of gene function by analyzing two breast cancer datasets from two separate laboratories. OE was able to identify correctly all biological processes postulated by the original authors, as well as discover novel relevant mechanisms.
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              Significance analysis of functional categories in gene expression studies: a structured permutation approach.

              Anthony P.H. Wright, Andrew B Nobel, William Barry (2005)
              In high-throughput genomic and proteomic experiments, investigators monitor expression across a set of experimental conditions. To gain an understanding of broader biological phenomena, researchers have until recently been limited to post hoc analyses of significant gene lists. We describe a general framework, significance analysis of function and expression (SAFE), for conducting valid tests of gene categories ab initio. SAFE is a two-stage, permutation-based method that can be applied to various experimental designs, accounts for the unknown correlation among genes and enables permutation-based estimation of error rates. The utility and flexibility of SAFE is illustrated with a microarray dataset of human lung carcinomas and gene categories based on Gene Ontology and the Protein Family database. Significant gene categories were observed in comparisons of (1) tumor versus normal tissue, (2) multiple tumor subtypes and (3) survival times. Code to implement SAFE in the statistical package R is available from the authors. http://www.bios.unc.edu/~fwright/SAFE.
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                Author and article information

                Contributors
                Shao-Qi Rao
                Journal
                Journal ID (nlm-ta): Genomics Proteomics Bioinformatics
                Journal ID (iso-abbrev): Genomics Proteomics Bioinformatics
                Title: Genomics, Proteomics & Bioinformatics
                Publisher: Elsevier
                ISSN (Print): 1672-0229
                ISSN (Electronic): 2210-3244
                Publication date PMC-release: 28 October 2014
                Publication date (Print): October 2014
                Publication date (Electronic): 28 October 2014
                Volume: 12
                Issue: 5
                Pages: 210-220
                Affiliations
                [1 ]Institute for Medical Systems Biology, and Department of Medical Statistics and Epidemiology, School of Public Health, Guangdong Medical College, Dongguan 523808, China
                [2 ]Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-Sen University, Guangzhou 510080, China
                [3 ]Community Health Service Management Center of Panyu District, Guangzhou 511400, China
                [4 ]Department of Statistical Sciences, School of Mathematics and Computational Science, Sun Yat-Sen University, Guangzhou 510275, China
                Author notes
                [* ]Corresponding author. raoshaoq@ 123456gdmc.edu.cn
                Article
                Publisher ID: S1672-0229(14)00106-5
                DOI: 10.1016/j.gpb.2014.10.002
                PMC ID: 4411419
                PubMed ID: 25462153
                SO-VID: de3626a4-af24-4a6d-8e8a-f9be60d40932
                Copyright © © 2014 The Authors
                License:

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/).

                History
                Date received : 21 June 2014
                Date revision received : 30 August 2014
                Date accepted : 4 September 2014
                Categories
                Subject: Review

                Keywords: complex disease,pathway-based analysis,algorithms,software and databases
                Data availability:
                Keywords: complex disease, pathway-based analysis, algorithms, software and databases

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