Abstract

Genome Wide Association Studies (GWAS) are a type of experiment that aim to detect genetic variation that may be linked to a type of disease. GWAS typically contain many thousands of covariates, which makes variable selection an exceptionally computationally intensive process. In variable selection, one of the biggest challenges is the extremely large potential set of variants but a limited sample size. Hence, there are two problems: huge computational time burdens for analysing each dataset and another is the sparsity in the number of covariates associated to the response.

In this study, we use variable selection via using Bayesian variable selection and LASSO method in logistic regression model. Moreover, the results are expanded for application in real dataset about cardiovascular disease.

中文翻译：

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贝叶斯变量选择与全基因组关联研究

摘要

全基因组关联研究 (GWAS) 是一种实验，旨在检测可能与某种疾病相关的遗传变异。 GWAS 通常包含数千个协变量，这使得变量选择成为一个计算量特别大的过程。在变量选择中，最大的挑战之一是潜在的变量集非常大，但样本量有限。因此，存在两个问题：分析每个数据集的巨大计算时间负担，另一个问题是与响应相关的协变量数量的稀疏性。

在本研究中，我们通过在逻辑回归模型中使用贝叶斯变量选择和LASSO方法来使用变量选择。此外，结果还扩展到心血管疾病的真实数据集中的应用。