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Open J. Stat., 2012, Volume 2, Issue 1, Pages 73–87 (Mi ojs1)  

Statistical methods of SNP data analysis and applications

A. Bulinskia, O. Butkovskya, V. Sadovnichya, A. Shashkina, P. Yaskova, A. Balatskiyb, L. Samokhodskayab, V. Tkachukb

a Faculty of Mathematics and Mechanics, Moscow State University, Moscow, Russia
b Faculty of Basic Medicine, Moscow State University, Moscow, Russia

Abstract: 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.

Funding Agency Grant Number
Russian Foundation for Basic Research 10-01-00397a
The work is partially supported by RFBR grant (project 10-01-00397a).


DOI: https://doi.org/10.4236/ojs.2012.21008


Bibliographic databases:

Document Type: Article
Received: 09.10.2011
Revised: 16.11.2011
Accepted:20.11.2011
Language: English

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