Reconstructability analysis as a tool for identifying gene-gene interactions in studies of human diseases.
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 > or =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
Stephen
S
Eastern Washington University, USA. sshervais@ewu.edu
Kramer
Patricia L
PL
Westaway
Shawn K
SK
Cox
Nancy J
NJ
Zwick
Martin
M
D000465:Algorithms; D001499:Bayes Theorem; D056808:Biostatistics; D016022:Case-Control Studies; D002884:Chromosomes, Human, Pair 15 / Q000235:genetics; D002889:Chromosomes, Human, Pair 2 / Q000235:genetics; D003198:Computer Simulation; D030541:Databases, Genetic; D003924:Diabetes Mellitus, Type 2 / Q000235:genetics; D004194:Disease / Q000235:genetics*; D004843:Epistasis, Genetic / Q000235:genetics*; D005796:Genes / Q000235:genetics*; D023281:Genomics / Q000706:statistics & numerical data*; D006801:Humans; D040582:Inheritance Patterns / Q000235:genetics; D016014:Linear Models; D016015:Logistic Models; D008957:Models, Genetic; D015233:Models, Statistical; D019683:Penetrance; D020641:Polymorphism, Single Nucleotide / Q000235:genetics