NeuralNetworksGeneticEpidemiology_08biodatamine

NeuralNetworksGeneticEpidemiology_08biodatamine - BioData...

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Bio Med Central Page 1 of 15 (page number not for citation purposes) BioData Mining Open Access Review Neural networks for genetic epidemiology: past, present, and future Alison A Motsinger-Reif* 1 and Marylyn D Ritchie †2 Address: 1 Bioinformatics Research Center, Department of Statistics, North Carolina State University, Raleigh, NC, USA and 2 Center for Human Genetics Research, Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, USA Email: Alison A Motsinger-Reif* - [email protected]; Marylyn D Ritchie - [email protected] * Corresponding author †Equal contributors Abstract During the past two decades, the field of human genetics has experienced an information explosion. The completion of the human genome project and the development of high throughput SNP technologies have created a wealth of data; however, the analysis and interpretation of these data have created a research bottleneck. While technology facilitates the measurement of hundreds or thousands of genes, statistical and computational methodologies are lacking for the analysis of these data. New statistical methods and variable selection strategies must be explored for identifying disease susceptibility genes for common, complex diseases. Neural networks (NN) are a class of pattern recognition methods that have been successfully implemented for data mining and prediction in a variety of fields. The application of NN for statistical genetics studies is an active area of research. Neural networks have been applied in both linkage and association analysis for the identification of disease susceptibility genes. In the current review, we consider how NN have been used for both linkage and association analyses in genetic epidemiology. We discuss both the successes of these initial NN applications, and the questions that arose during the previous studies. Finally, we introduce evolutionary computing strategies, Genetic Programming Neural Networks (GPNN) and Grammatical Evolution Neural Networks (GENN), for using NN in association studies of complex human diseases that address some of the caveats illuminated by previous work. Introduction The identification of disease susceptibility genes for com- plex, multifactorial disease is arguably the most difficult challenge facing human geneticists today [1]. Most com- mon diseases are the result of complex interactions among multiple genetic factors in addition to a collection of environmental exposures [2]. This has been docu- mented by Ming and Muenke who compiled a list of dis- eases with known epistatic interactions [3]. Traditional gene mapping studies utilize one of two possible research strategies: linkage or association. Linkage analysis deter- mines whether a chromosomal region is preferentially inherited by offspring with the trait of interest by using genotype and phenotype data from multiple biologically- related family members. Linkage analysis capitalizes on the fact that, as a causative gene(s) segregates through a
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NeuralNetworksGeneticEpidemiology_08biodatamine - BioData...

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