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Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease
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Genetic Programming Neural Networks: A Powerful Bioinformatics Tool for Human Genetics.

Marylyn D Ritchie1, Alison A Motsinger, William S Bush

  • 1Center for Human Genetics Research, Department of Molecular Physiology and Biophysics, Vanderbilt University, 519 Light Hall, Nashville, TN 37232.

Applied Soft Computing
|September 28, 2011
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Genetic programming neural networks (GPNN) offer a powerful new approach for identifying complex gene-gene and gene-environment interactions in disease risk studies. GPNN outperforms traditional methods like stepwise logistic regression and classification and regression trees.

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Area of Science:

  • Genetic Epidemiology
  • Computational Biology
  • Statistical Genetics

Background:

  • Identifying genes influencing complex diseases is challenging due to gene-gene and gene-environment interactions.
  • Parametric statistical methods have limitations in detecting these complex genetic effects.
  • Genetic programming neural network (GPNN) was previously introduced to optimize neural network architecture for interaction detection.

Purpose of the Study:

  • To compare the power of GPNN against stepwise logistic regression (SLR) and classification and regression trees (CART) for identifying gene-gene and gene-environment interactions.
  • To evaluate GPNN's effectiveness in detecting complex genetic associations with disease risk.

Main Methods:

  • Utilized simulated data to assess the performance of different analytical methods.
  • Compared GPNN against established methods: stepwise logistic regression (SLR) and classification and regression trees (CART).

Main Results:

  • GPNN demonstrated higher statistical power in identifying gene-gene interactions compared to SLR and CART.
  • GPNN also showed superior power in detecting gene-environment interactions than the traditional methods.
  • Simulated data analysis confirmed GPNN's effectiveness.

Conclusions:

  • GPNN is a more powerful method for detecting gene-gene and gene-environment interactions in genetic association studies.
  • GPNN represents a valuable pattern recognition tool for human disease genetic research.
  • This approach can improve the identification of genetic factors contributing to complex diseases.