This work was supported by the National Institutes of Health (AG026916 to PK, U01 HL08471 and P60 DK20595 to NC).
Statistical Applications in Genetics and Molecular Biology
Reconstructability Analysis, Information Theory, Probabilistic graphical modeling, Multivariate analysis discrete multivariate modeling, Data mining
There are a number of common human diseases for which the genetic component may include an epistatic interaction of multiple genes. Detecting these interactions with standard statistical tools is difficult because there may be an interaction effect, but minimal or no main effect. Reconstructability analysis (RA) uses Shannon’s information theory to detect relationships between variables in categorical datasets. We applied RA to simulated data for five different models of gene-gene interaction, and find that even with heritability levels as low as 0.008, and with the inclusion of 50 non-associated genes in the dataset, we can identify the interacting gene pairs with an accuracy of greater than or equal to 80%. We applied RA to a real dataset of type 2 non-insulin-dependent diabetes (NIDDM) cases and controls, and closely approximated the results of more conventional single SNP disease association studies. In addition, we replicated prior evidence for epistatic interactions between SNPs on chromosomes 2 and 15.
Shervais, S., Kramer, P. L., Westaway, S. K., Cox, N. J., & Zwick, M. (2010). Reconstructability Analysis as a Tool for Identifying Gene-Gene Interactions in Studies of Human Diseases. Statistical Applications In Genetics & Molecular Biology, 9(1), 1-25.