Statistical Methods of SNP Data Analysis and Applications

• 作者: Alexander Balatskiy Alexander Bulinski Alexey Shashkin Larisa Samokhodskaya Oleg Butkovsky Pavel Yaskov Victor Sadovnichy Vsevolod Tkachuk
• 来源期刊: 统计学期刊(英文)
• 学科: 医学
• 关键词: 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
• 年，卷（期）: 2012,（1）
• 页码范围: 73-87
• 页数: 15页
• 分类号: R73

摘要

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.