Data mining

Cupples, L. Adrienne; Bailey, Julia N.; Cartier, Kevin C.; Falk, Catherine T.; Liu, Kuang-Yu; Ye, Yuanqing; Yu, Robert; Zhang, Heping; Zhao, Hongyu

doi:10.1002/gepi.20117

Cited by 7 publications

(8 citation statements)

References 17 publications

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“…A third approach uses data mining methods to build predictors of disease from a large number of genetic and/or environmental inputs [7][8][9][10] .…”

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confidence: 99%

Exploiting Gene-Environment Interaction to Detect Genetic Associations

Kraft¹,

Yen²,

et al. 2007

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Complex disease by definition results from the interplay of genetic and environmental factors. However, it is currently unclear how gene-environment interaction can best be used to locate complex disease susceptibility loci, particularly in the context of studies where between 1,000 and 1,000,000 markers are scanned for association with disease. We present a joint test of marginal association and gene-environment interaction for case-control data. We compare the power and sample size requirements of this joint test to other analyses: the marginal test of genetic association, the standard test for gene-environment interaction based on logistic regression, and the case-only test for interaction that exploits gene-environment independence. Although for many penetrance models the joint test of genetic marginal effect and interaction is not the most powerful, it is nearly optimal across all penetrance models we considered. In particular, it generally has better power than the marginal test when the genetic effect is restricted to exposed subjects and much better power than the tests of gene-environment interaction when the genetic effect is not restricted to a particular exposure level. This makes the joint test an attractive tool for large-scale association scans where the true gene-environment interaction model is unknown.

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“…A third approach uses data mining methods to build predictors of disease from a large number of genetic and/or environmental inputs [7][8][9][10] .…”

mentioning

confidence: 99%

Exploiting Gene-Environment Interaction to Detect Genetic Associations

Kraft¹,

Yen²,

et al. 2007

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“…Analogously, Foraita, Bammann and Pigeot [10] demonstrate that the statistical concept of conditional independence underlying graphical models is also not able to reflect biological interaction. Other methods have been developed to identify gene-gene interaction, like data mining methods [11,12], extensions of linear models [13,14] or tree-structured learning approaches [15]. However, especially the performance of data mining approaches may be limited in the presence of main effects or genetic heterogeneity [14,16].…”

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confidence: 99%

A conditional synergy index to assess biological interaction

Foraita

2009

Eur J Epidemiol

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In genetic studies of complex diseases, a crucial task is to identify and quantify gene-gene interactions which are often defined as deviance from genetic additive effects. This statistical definition, however, does not need to reflect the biological interactions of genes. We propose a new method to detect gene-gene interactions. This new approach exploits the concept of synergy and antagonism that is appropriate to capture biological relationships. The conditional synergy index (CSI) describes the extent of interaction on the penetrance scale. We develop the CSI for two-locus disease models and cohort data. The index assumes genotypes to be dichotomized into risk-genotypes (exposed) and non-risk-genotypes (unexposed) but it does not assume the loci to be in linkage equilibrium. We investigate the performance of the CSI and compare it to classical epidemiological interaction measures like Rothman's synergy index (S) and the attributable proportion due to interaction (AP). In addition, the performance of an estimator of this new parameter is illustrated in a practical example.

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“…In GAW 13, eight papers were submitted that formed a group of data mining studies [Costello et al, 2003]. In GAW 14 there were nine such papers [Cupples et al, 2005]. For GAW 15 the number increased to 30.…”

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Data mining of RNA expression and DNA genotype data: Presentation Group 5 contributions to Genetic Analysis Workshop 15

et al. 2007

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The complexity of data available in human genetics continues to grow at an explosive rate. With that growth, the challenges to understanding the meaning of the underlying information also grow. A currently popular approach to dissecting such information falls under the broad category of data mining. This can apply to any approach that tries to extract relevant information from large amounts of data, but often refers to methods that deal, in a non-linear fashion, with very large numbers of variables that cannot be simultaneously handled by more conventional statistical methods. To explore the usefulness of some of these approaches, 13 groups applied a variety of strategies to the first dataset provided to GAW 15 participants. With the extensive microarray and SNP data provided for 14 CEPH families, these groups explored multistage analyses, machine learning methods, network construction, and other techniques to try to answer questions about gene-gene interaction, functional similarities, co-regulated gene expression and the mapping of gene expression determinants, among others. In general, the methods offered strategies to provide a better understanding of the complex pathways involved in gene expression and function. These are still "works in progress," often exploratory in nature, but they provide insights into ways in which the data might be interpreted. Despite the still preliminary nature of some of these methods and the diversity of the approaches, some common themes emerged. The collection of papers and methods offer a starting point for further exploration of complex interactions in human genetic data now readily available.

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Data mining

Cited by 7 publications

References 17 publications

Exploiting Gene-Environment Interaction to Detect Genetic Associations

Exploiting Gene-Environment Interaction to Detect Genetic Associations

A conditional synergy index to assess biological interaction

Data mining of RNA expression and DNA genotype data: Presentation Group 5 contributions to Genetic Analysis Workshop 15

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