Detection of Gene x Gene Interactions in Genome-Wide Association Studies of Human Population Data

Created by W.Langdon from gp-bibliography.bib Revision:1.4192

  author =       "Solomon K. Musani and Daniel Shriner and 
                 Nianjun Liu and Rui Feng and Christopher S. Coffey and 
                 Nengjun Yi and Hemant K. Tiwari and David B. Allison",
  title =        "Detection of Gene x Gene Interactions in Genome-Wide
                 Association Studies of Human Population Data",
  journal =      "Human Heredity",
  year =         "2007",
  volume =       "63",
  number =       "2",
  pages =        "67--84",
  month =        feb,
  keywords =     "genetic algorithms, genetic programming, Epistasis,
                 Genome-wide association, Computational burden
                 Overfitting, Data sparsity, Methodological issues",
  DOI =          "DOI:10.1159/000099179",
  abstract =     "Empirical evidence supporting the commonality of gene
                 ! gene interactions, coupled with frequent failure to
                 replicate results from previous association studies,
                 has prompted statisticians to develop methods to handle
                 this important subject. Nonparametric methods have
                 generated intense interest because of their capacity to
                 handle high-dimensional data. Genome-wide association
                 analysis of large-scale SNP data is challenging
                 mathematically and computationally. In this paper, we
                 describe major issues and questions arising from this
                 challenge, along with methodological implications. Data
                 reduction and pattern recognition methods seem to be
                 the new frontiers in efforts to detect gene x gene
                 interactions comprehensively. Currently, there is no
                 single method that is recognised as the best for
                 detecting, characterising, and interpreting gene ! gene
                 interactions. Instead, a combination of approaches with
                 the aim of balancing their specific strengths may be
                 the optimal approach to investigate gene ! gene
                 interactions in human data.",
  notes =        "Sections on a Statistical Genetics and b Research
                 Methods and Clinical Trials, Department of
                 Biostatistics, c Clinical Nutrition Research Center,
                 University of Alabama at Birmingham, Birmingham, Ala.,

Genetic Programming entries for Solomon K Musani Daniel Shriner Nianjun Liu Rui Feng Christopher S Coffey Nengjun Yi Hemant K Tiwari David B Allison