Linkage Disequilibrium in Genetic Association Studies Improves the Performance of Grammatical Evolution Neural Networks

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{conf/cibcb/MotsingerRFDR07,
  author =       "Alison A. Motsinger and David M. Reif and 
                 Theresa J. Fanelli and Anna C. Davis and Marylyn D. Ritchie",
  title =        "Linkage Disequilibrium in Genetic Association Studies
                 Improves the Performance of Grammatical Evolution
                 Neural Networks",
  booktitle =    "IEEE Symposium on Computational Intelligence and
                 Bioinformatics and Computational Biology, CIBCB '07",
  year =         "2007",
  pages =        "1--8",
  address =      "Honolulu, HI, USA",
  month =        "1-5 " # apr,
  publisher =    "IEEE",
  keywords =     "genetic algorithms, genetic programming, grammatical
                 evolution, gene-gene interactions, genetic association
                 studies, genetic epidemiology, grammatical evolution
                 neural networks, linkage disequilibrium, biology
                 computing, diseases, genetics, neural nets",
  ISBN =         "1-4244-0710-9",
  URL =          "http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=04221197",
  size =         "8 pages",
  abstract =     "One of the most important goals in genetic
                 epidemiology is the identification of genetic
                 factors/features that predict complex diseases. The
                 ubiquitous nature of gene-gene interactions in the
                 underlying etiology of common diseases creates an
                 important analytical challenge, spurring the
                 introduction of novel, computational approaches. One
                 such method is a grammatical evolution neural network
                 (GENN) approach. GENN has been shown to have high power
                 to detect such interactions in simulation studies, but
                 previous studies have ignored an important feature of
                 most genetic data: linkage disequilibrium (LD). LD
                 describes the non-random association of alleles not
                 necessarily on the same chromosome. This results in
                 strong correlation between variables in a dataset,
                 which can complicate analysis. In the current study,
                 data simulations with a range of LD patterns are used
                 to assess the impact of such correlated variables on
                 the performance of GENN. Our results show that not only
                 do patterns of strong LD not decrease the power of GENN
                 to detect genetic associations, they actually increase
                 its power",
  notes =        "SNP",
  bibdate =      "2009-04-29",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/conf/cibcb/cibcb2007.html#MotsingerRFDR07",
}

Genetic Programming entries for Alison A Motsinger David M Reif Theresa J Fanelli Anna C Davis Marylyn D Ritchie

Citations