中文摘要：

在过去几年里，染色体宽的协会学习(GWAS ) 在识别位于许多复杂疾病和特点下面的基因危险性 loci 做了大成功。调查结果提供重要基因卓见进理解疾病的致病。在这份报纸，我们在场为 GWAS 的分析的广泛地使用的途径和策略的概述，提供了一般考虑处理 GWAS 数据。关于数据质量控制，人口结构，协会分析，多重比较和 GWAS 结果的视觉表示的问题被讨论；包括失踪的可遗传性，元分析，基于集合的协会分析，拷贝数字变化分析和 GWAS 队分析的问题的另外的先进话题简短也被介绍。

英文摘要：

In the past few years, genome-wide association study （GWAS） has made great successes in identifying genetic susceptibility loci underlying many complex diseases and traits. The findings provide important genetic insights into understanding pathogenesis of diseases. In this paper, we present an overview of widely used approaches and strategies for analysis of GWAS, offered a general consideration to deal with GWAS data. The issues regarding data quality control, population structure, association analysis, multiple comparison and visual presentation of GWAS results are discussed; other advanced topics including the issue of missing heritability, meta-analysis, setbased association analysis, copy number variation analysis and GWAS cohort analysis are also briefly introduced.