Abstract
BackgroundThe purpose of this research was to develop a novel information theoretic method and an efficient algorithm for analyzing the gene-gene (GGI) and gene-environmental interactions (GEI) associated with quantitative traits (QT). The method is built on two information-theoretic metrics, the k-way interaction information (KWII) and phenotype-associated information (PAI). The PAI is a novel information theoretic metric that is obtained from the total information correlation (TCI) information theoretic metric by removing the contributions for inter-variable dependencies (resulting from factors such as linkage disequilibrium and common sources of environmental pollutants).ResultsThe KWII and the PAI were critically evaluated and incorporated within an algorithm called CHORUS for analyzing QT. The combinations with the highest values of KWII and PAI identified each known GEI associated with the QT in the simulated data sets. The CHORUS algorithm was tested using the simulated GAW15 data set and two real GGI data sets from QTL mapping studies of high-density lipoprotein levels/atherosclerotic lesion size and ultra-violet light-induced immunosuppression. The KWII and PAI were found to have excellent sensitivity for identifying the key GEI simulated to affect the two quantitative trait variables in the GAW15 data set. In addition, both metrics showed strong concordance with the results of the two different QTL mapping data sets.ConclusionThe KWII and PAI are promising metrics for analyzing the GEI of QT.
Highlights
The purpose of this research was to develop a novel information theoretic method and an efficient algorithm for analyzing the gene-gene (GGI) and gene-environmental interactions (GEI) associated with quantitative traits (QT)
Performance of K-Way Interaction Information (KWII) and Phenotype-Associated Information (PAI) on Simulated Data In these experiments, our goal was to compare the effectiveness of K-way interaction information (KWII) and phenotype-associated information (PAI) on simulated data with known patterns of interactions
The combinations are shown on the y-axis; e.g., 1, 2, P indicates that variables SNP 1, SNP 2 and the QT variable, P, are used in calculating k-way (k = 3) interaction
Summary
The purpose of this research was to develop a novel information theoretic method and an efficient algorithm for analyzing the gene-gene (GGI) and gene-environmental interactions (GEI) associated with quantitative traits (QT). The clinical presentation of many common complex diseases causing morbidity and mortality are associated with deviations from the population distributions of important quantitative traits (QT). Genes in pathways regulating appetite, metabolism, hormones and adipokines may interact with environmental factors such as diet and exercise to determine body weight. The successful identification of the critical gene-environment interactions (GEI) involved in QT such as body weight can provide the scientific basis for preventative public health measures to reduce the exposure of individuals to the modifiable environmental variable/s associated with increased risk
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