MISS: a non-linear methodology based on mutual information for genetic association studies in both population and sib-pairs analysis.
BACKGROUND
Finding association between genetic variants and phenotypes related to disease has become an important vehicle for the study of complex disorders. In this context, multi-loci genetic association might unravel additional information when compared with single loci search. The main goal of this work is to propose a non-linear methodology based on information theory for finding combinatorial association between multi-SNPs and a given phenotype.


RESULTS
The proposed methodology, called MISS (mutual information statistical significance), has been integrated jointly with a feature selection algorithm and has been tested on a synthetic dataset with a controlled phenotype and in the particular case of the F7 gene. The MISS methodology has been contrasted with a multiple linear regression (MLR) method used for genetic association in both, a population-based study and a sib-pairs analysis and with the maximum entropy conditional probability modelling (MECPM) method, which searches for predictive multi-locus interactions. Several sets of SNPs within the F7 gene region have been found to show a significant correlation with the FVII levels in blood. The proposed multi-site approach unveils combinations of SNPs that explain more significant information of the phenotype than their individual polymorphisms. MISS is able to find more correlations between SNPs and the phenotype than MLR and MECPM. Most of the marked SNPs appear in the literature as functional variants with real effect on the protein FVII levels in blood.


BACKGROUND
The code is available at http://sisbio.recerca.upc.edu/R/MISS_0.2.tar.gz
Brunel
Helena
H
Institut de Bioenginyeria de Catalunya, Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial, Universitat Politècnica de Catalunya, Pau Gargallo 5, 08028 Barcelona, Spain. helena.brunel@upc.edu
Gallardo-Chacón
Joan-Josep
JJ
Buil
Alfonso
A
Vallverdú
Montserrat
M
Soria
José Manuel
JM
Caminal
Pere
P
Perera
Alexandre
A
D000465:Algorithms; D002874:Chromosome Mapping; D019295:Computational Biology / Q000379:methods*; D056726:Genetic Association Studies / Q000379:methods*; D014644:Genetic Variation; D006801:Humans; D017711:Nonlinear Dynamics; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide / Q000235:genetics; D044382:Population Groups / Q000235:genetics; D012044:Regression Analysis; D035781:Siblings; D013927:Thrombosis / Q000235:genetics