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Statistical Methods of SNP Data Analysis and Applications

Author(s): Alexander Bulinski | Oleg Butkovsky | Victor Sadovnichy | Alexey Shashkin | Pavel Yaskov | Alexander Balatskiy | Larisa Samokhodskaya | Vsevolod Tkachuk

Journal: Open Journal of Statistics
ISSN 2161-718X

Volume: 02;
Issue: 01;
Start page: 73;
Date: 2012;
Original page

Keywords: Genetic Data Statistical Analysis | Multifactor Dimensionality Reduction | Ternary Logic Regression | Random Forests | Stochastic Gradient Boosting | Independent Rule | Single Nucleotide Polymorphisms | Coronary Heart Disease | Myocardial Infarction

We develop various statistical methods important for multidimensional genetic data analysis. Theorems justifying application of these methods are established. We concentrate on the multifactor dimensionality reduction, logic regression, random forests, stochastic gradient boosting along with their new modifications. We use complementary approaches to study the risk of complex diseases such as cardiovascular ones. The roles of certain combinations of single nucleotide polymorphisms and non-genetic risk factors are examined. To perform the data analysis concerning the coronary heart disease and myocardial infarction the Lomonosov Moscow State University supercomputer “Chebyshev” was employed.
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