全基因组关联研究中的统计分析方法

随着人类基因组计划的完成,疾病的全基因组关联研究成为可能.该类研究的数据特点是:高维、小样本.面对浩瀚的数据,传统分析方法 受到严重挑战.文中介绍全基因组关联研究中的数据分析策略和步骤,包括质量控制、分析、结果 表示等,并对全基因组关联研究的局限性和目前统计分析方法 的不足进行讨论.

Abstract：

In lieu of large samples of cases and/or controls with hundreds of markers spreading throughout the human genome, researchers started to notice the dramatic increase of genome-wide association study (GWAS) for complex disorders, in the last 5 years. This paper highlights the statistical challenges in such huge-scale genetic studies, and introduces the analytical strategies and steps for handling GWAS data. Such issues as quality control of data, population stratification, methods available to data analysis and results presentation, replication, as well as the limitations of GWAS studies and the challenges presenting for statistics, are addressed.

Statistical methodologies used in genome-wide association studies

In lieu of large samples of cases and/or controls with hundreds of markers spreading throughout the human genome, researchers started to notice the dramatic increase of genome-wide association study (GWAS) for complex disorders, in the last 5 years. This paper highlights the statistical challenges in such huge-scale genetic studies, and introduces the analytical strategies and steps for handling GWAS data. Such issues as quality control of data, population stratification, methods available to data analysis and results presentation, replication, as well as the limitations of GWAS studies and the challenges presenting for statistics, are addressed.