Genetic Association Studies of Cancer: Where Do We Go from Here?

Fred Hutchinson Cancer Research Center, Seattle, Washington, United States
Cancer Epidemiology Biomarkers & Prevention (Impact Factor: 4.13). 06/2007; 16(5):864-5. DOI: 10.1158/1055-9965.EPI-07-0289
Source: PubMed
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    • "Environmental influences are theorized to operate in this interaction by moderating the phenotypic expression of genotypes [Sing et al., 1996]. As a result, many investigators are calling for studies of gene-environment interaction for common diseases such as heart disease, cancer and even alcoholism [Schwartz et al., 1996; Rebbeck et al., 2007; Enoch 2006]. "
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    ABSTRACT: Complex diseases such as cancer and heart disease result from interactions between an individual's genetics and environment, i.e. their human ecology. Rates of complex diseases have consistently demonstrated geographic patterns of incidence, or spatial "clusters" of increased incidence relative to the general population. Likewise, genetic subpopulations and environmental influences are not evenly distributed across space. Merging appropriate methods from genetic epidemiology, ecology and geography will provide a more complete understanding of the spatial interactions between genetics and environment that result in spatial patterning of disease rates. Geographic information systems (GIS), which are tools designed specifically for dealing with geographic data and performing spatial analyses to determine their relationship, are key to this kind of data integration. Here the authors introduce a new interdisciplinary paradigm, ecogeographic genetic epidemiology, which uses GIS and spatial statistical analyses to layer genetic subpopulation and environmental data with disease rates and thereby discern the complex gene-environment interactions which result in spatial patterns of incidence.
    Genetic Epidemiology 05/2009; 33(4):281-9. DOI:10.1002/gepi.20386 · 2.60 Impact Factor
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    • "Recent developments in high-throughput technology allow large-scale measurement of genomic variations such as gene expression and single nucleotide polymorphisms (SNPs) of a population. Associating these genomic and genetic variations with disease-related phenotypes provides good potential for elucidating etiology of diseases (Rebbeck et al., 2007). It has also been shown that the discovered biomarkers can possibly provide better prognosis and diagnosis than the currently available clinical measures for risk assessment of patients with various diseases (Gevaert et al., 2006; van't Veer et al., 2002). "
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    ABSTRACT: A central problem in biomarker discovery from large-scale gene expression or single nucleotide polymorphism (SNP) data is the computational challenge of taking into account the dependence among all the features. Methods that ignore the dependence usually identify non-reproducible biomarkers across independent datasets. We introduce a new graph-based semi-supervised feature classification algorithm to identify discriminative disease markers by learning on bipartite graphs. Our algorithm directly classifies the feature nodes in a bipartite graph as positive, negative or neutral with network propagation to capture the dependence among both samples and features (clinical and genetic variables) by exploring bi-cluster structures in a graph. Two features of our algorithm are: (1) our algorithm can find a global optimal labeling to capture the dependence among all the features and thus, generates highly reproducible results across independent microarray or other high-thoughput datasets, (2) our algorithm is capable of handling hundreds of thousands of features and thus, is particularly useful for biomarker identification from high-throughput gene expression and SNP data. In addition, although designed for classifying features, our algorithm can also simultaneously classify test samples for disease prognosis/diagnosis. We applied the network propagation algorithm to study three large-scale breast cancer datasets. Our algorithm achieved competitive classification performance compared with SVMs and other baseline methods, and identified several markers with clinical or biological relevance with the disease. More importantly, our algorithm also identified highly reproducible marker genes and enriched functions from the independent datasets. Supplementary results and source code are available at Supplementary data are available at Bioinformatics online.
    Bioinformatics 09/2008; 24(18):2023-9. DOI:10.1093/bioinformatics/btn383 · 4.98 Impact Factor
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    ABSTRACT: Identification of clinical and genetic markers of disease can provide crucial information for both disease treatment and etiology. This complex task involves associating high-dimensional patterns such as largescale gene expressions and single nucleotide polymorphisms (SNPs) with disease-related phenotypes using very few samples. We introduce a new graph-based semi-supervised feature classification algorithm to identify discriminative patterns by learning on bipartite graphs built from clinical variables, gene expressions and SNPs. Instead of performing feature selection or unsupervised bi-clustering, our algorithm directly classifies the feature nodes in a bipartite graph as positive, negative or neutral with network propagation, which captures the interactions between both samples and features (clinical and genetic variables) by exploring the global structure of the graph. Although globally optimized for classifying the features, our algorithm can also simultaneously classify the test samples for disease prognosis/diagnosis. We apply our algorithm to studying the Rosetta breast cancer dataset and chronic fatigue syndrome on a CAMDA contest dataset. Our algorithm identifies interesting clinical and genetic markers, some of which are consistent with previous studies in the literature, and achieves better overall classification performance than support vector machines and Bayesian networks. (Supplemental website:
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