Statistical methods of SNP data analysis with applications

Alexander Bulinski (1, 2) , Oleg Butkovsky (1) , Alexey Shashkin (1) , Pavel Yaskov (1, 3)

1 Department of Mathematics and Mechanics [Moscow]
2 LPMA - Laboratoire de Probabilités et Modèles Aléatoires
3 SMI | RAS - Steklov Mathematical Institute [Moscow]

Various statistical methods important for genetic analysis are considered and developed. Namely, we concentrate on the multifactor dimensionality reduction, logic regression, random forests and stochastic gradient boosting. These methods and their new modifications, e.g., the MDR method with "independent rule", are used to study the risk of complex diseases such as cardiovascular ones. The roles of certain combinations of single nucleotide polymorphisms and external risk factors are examined. To perform the data analysis concerning the ischemic heart disease and myocardial infarction the supercomputer SKIF "Chebyshev" of the Lomonosov Moscow State University was employed.

Mots clés: supercomputer.; Genetic data statistical analysis; multifactor dimensionality reduction; logic regression; random forests; stochastic gradient boosting; independent rule; single nucleotide polymorphisms; external factors; ischemic heart disease; myocardial infarction; supercomputer

Domaines: Probabilités [math.PR]; Applications [stat.AP]

Alexander Bulinski, Oleg Butkovsky, Alexey Shashkin, Pavel Yaskov. Statistical methods of SNP data analysis with applications. 2011. ⟨hal-00600143⟩