Biomedical subjects

Lance W Hahn

Publications and source records attributed to Lance W Hahn.

6 recordsLinked to original sources

PubMed2004

Ideal discrimination of discrete clinical endpoints using multilocus genotypes.

Multifactor Dimensionality Reduction (MDR) is a method for the classification and prediction of discrete clinical endpoints using attributes constructed from multilocus genotype data. Empirical studies with both real and simulated data suggest that MDR has good power for detecting gene-gene interactions in the absence of independent main effects. The purpose of this study is to develop an objective, theory-driven approach to evaluate the strengths and limitations of MDR. To accomplish this goal, we borrow concepts from ideal observer analysis used in visual perception to evaluate the theoretical limits of classifying and predicting discrete clinical endpoints using multilocus genotype data. We conclude that MDR ideally discriminates between low risk and high risk subjects using attributes constructed from multilocus genotype data. We also how that the classification approach used once a multilocus attribute is constructed is similar to that of a naive Bayes classifier. This study provides a theoretical foundation for the continued development, evaluation, and application of the MDR as a data mining tool in the domain of statistical genetics and genetic epidemiology.

Animals↗

PubMed2003

Optimization of neural network architecture using genetic programming improves detection and modeling of gene-gene interactions in studies of human diseases.

BACKGROUND: Appropriate definition of neural network architecture prior to data analysis is crucial for successful data mining. This can be challenging when the underlying model of the data is unknown. The goal of this study was to determine whether optimizing neural network architecture using genetic programming as a machine learning strategy would improve the ability of neural networks to model and detect nonlinear interactions among genes in studies of common human diseases. RESULTS: Using simulated data, we show that a genetic programming optimized neural network approach is able to model gene-gene interactions as well as a traditional back propagation neural network. Furthermore, the genetic programming optimized neural network is better than the traditional back propagation neural network approach in terms of predictive ability and power to detect gene-gene interactions when non-functional polymorphisms are present. CONCLUSION: This study suggests that a machine learning strategy for optimizing neural network architecture may be preferable to traditional trial-and-error approaches for the identification and characterization of gene-gene interactions in common, complex human diseases.

Algorithms↗

PubMed2003

Multifactor dimensionality reduction software for detecting gene-gene and gene-environment interactions.

MOTIVATION: Polymorphisms in human genes are being described in remarkable numbers. Determining which polymorphisms and which environmental factors are associated with common, complex diseases has become a daunting task. This is partly because the effect of any single genetic variation will likely be dependent on other genetic variations (gene-gene interaction or epistasis) and environmental factors (gene-environment interaction). Detecting and characterizing interactions among multiple factors is both a statistical and a computational challenge. To address this problem, we have developed a multifactor dimensionality reduction (MDR) method for collapsing high-dimensional genetic data into a single dimension thus permitting interactions to be detected in relatively small sample sizes. In this paper, we describe the MDR approach and an MDR software package. RESULTS: We developed a program that integrates MDR with a cross-validation strategy for estimating the classification and prediction error of multifactor models. The software can be used to analyze interactions among 2-15 genetic and/or environmental factors. The dataset may contain up to 500 total variables and a maximum of 4000 study subjects. AVAILABILITY: Information on obtaining the executable code, example data, example analysis, and documentation is available upon request. SUPPLEMENTARY INFORMATION: All supplementary information can be found at http://phg.mc.vanderbilt.edu/Software/MDR.

Algorithms↗

PubMed2003

Power of multifactor dimensionality reduction for detecting gene-gene interactions in the presence of genotyping error, missing data, phenocopy, and genetic heterogeneity.

The identification and characterization of genes that influence the risk of common, complex multifactorial diseases, primarily through interactions with other genes and other environmental factors, remains a statistical and computational challenge in genetic epidemiology. This challenge is partly due to the limitations of parametric statistical methods for detecting genetic effects that are dependent solely or partially on interactions with other genes and environmental exposures. We previously introduced multifactor dimensionality reduction (MDR) as a method for reducing the dimensionality of multilocus genotype information to improve the identification of polymorphism combinations associated with disease risk. The MDR approach is nonparametric (i.e., no hypothesis about the value of a statistical parameter is made), is model-free (i.e., assumes no particular inheritance model), and is directly applicable to case-control and discordant sib-pair study designs. Both empirical and theoretical studies suggest that MDR has excellent power for identifying high-order gene-gene interactions. However, the power of MDR for identifying gene-gene interactions in the presence of common sources of noise is not currently known. The goal of this study was to evaluate the power of MDR for identifying gene-gene interactions in the presence of noise due to genotyping error, missing data, phenocopy, and genetic or locus heterogeneity. Using simulated data, we show that MDR has high power to identify gene-gene interactions in the presence of 5% genotyping error, 5% missing data, or a combination of both. However, MDR has reduced power for some models in the presence of 50% phenocopy, and very limited power in the presence of 50% genetic heterogeneity. Extending MDR to address genetic heterogeneity should be a priority for the continued methodological development of this new approach.

Algorithms↗

PubMed2003

Petri net modeling of high-order genetic systems using grammatical evolution.

Understanding how DNA sequence variations impact human health through a hierarchy of biochemical and physiological systems is expected to improve the diagnosis, prevention, and treatment of common, complex human diseases. We have previously developed a hierarchical dynamic systems approach based on Petri nets for generating biochemical network models that are consistent with genetic models of disease susceptibility. This modeling approach uses an evolutionary computation approach called grammatical evolution as a search strategy for optimal Petri net models. We have previously demonstrated that this approach routinely identifies biochemical network models that are consistent with a variety of genetic models in which disease susceptibility is determined by nonlinear interactions between two DNA sequence variations. In the present study, we evaluate whether the Petri net approach is capable of identifying biochemical networks that are consistent with disease susceptibility due to higher order nonlinear interactions between three DNA sequence variations. The results indicate that our model-building approach is capable of routinely identifying good, but not perfect, Petri net models. Ideas for improving the algorithm for this high-dimensional problem are presented.

Algorithms↗

PubMed2002

A cellular automata approach to detecting interactions among single-nucleotide polymorphisms in complex multifactorial diseases.

The identification and characterization of susceptibility genes for common complex multifactorial human diseases remains a statistical and computational challenge. Parametric statistical methods such as logistic regression are limited in their ability to identify genes whose effects are dependent solely or partially on interactions with other genes and environmental exposures. We introduce cellular automata (CA) as a novel computational approach for identifying combinations of single-nucleotide polymorphisms (SNPs) associated with clinical endpoints. This alternative approach is nonparametric (i.e. no hypothesis about the value of a statistical parameter is made), is model-free (i.e. assumes no particular inheritance model), and is directly applicable to case-control and discordant sib-pair study designs. We demonstrate using simulated data that the approach has good power for identifying high-order nonlinear interactions (i.e. epistasis) among four SNPs in the absence of independent main effects.

Algorithms↗