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    Allele-Specific Network Reveals Combinatorial Interaction That Transcends Small Effects in Psoriasis GWAS

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    Allele-specific network reveals ...
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    Genre
    Journal Article
    Date
    2014-01-01
    Author
    Climer, S
    Templeton, AR
    Zhang, W
    Subject
    Alleles
    Computational Biology
    Genetic Markers
    Genome-Wide Association Study
    Humans
    Polymorphism, Single Nucleotide
    Psoriasis
    Permanent link to this record
    http://hdl.handle.net/20.500.12613/5586
    
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    DOI
    10.1371/journal.pcbi.1003766
    Abstract
    © 2014 Climer et al. Hundreds of genetic markers have shown associations with various complex diseases, yet the “missing heritability” remains alarmingly elusive. Combinatorial interactions may account for a substantial portion of this missing heritability, but their discoveries have been impeded by computational complexity and genetic heterogeneity. We present BlocBuster, a novel systems-level approach that efficiently constructs genome-wide, allele-specific networks that accurately segregate homogenous combinations of genetic factors, tests the associations of these combinations with the given phenotype, and rigorously validates the results using a series of unbiased validation methods. BlocBuster employs a correlation measure that is customized for single nucleotide polymorphisms and returns a multi-faceted collection of values that captures genetic heterogeneity. We applied BlocBuster to analyze psoriasis, discovering a combinatorial pattern with an odds ratio of 3.64 and Bonferroni-corrected p-value of 5.01×10−16. This pattern was replicated in independent data, reflecting robustness of the method. In addition to improving prediction of disease susceptibility and broadening our understanding of the pathogenesis underlying psoriasis, these results demonstrate BlocBuster's potential for discovering combinatorial genetic associations within heterogeneous genome-wide data, thereby transcending the limiting “small effects” produced by individual markers examined in isolation.
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    Public Library of Science (PLoS)
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    PLoS Computational Biology
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    http://dx.doi.org/10.34944/dspace/5568
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