Burghardt Liana T, Young Nevin D, Tiffin Peter
Department of Plant and Microbial Biology, University of Minnesota, St. Paul, Minnesota.
Department of Plant Pathology, University of Minnesota, St. Paul, Minnesota.
Curr Protoc Plant Biol. 2017 Mar;2(1):22-38. doi: 10.1002/cppb.20041.
Genome-wide association studies (GWAS) have developed into a valuable approach for identifying the genetic basis of phenotypic variation. In this article, we provide an overview of the design, analysis, and interpretation of GWAS. First, we present results from simulations that explore key elements of experimental design as well as considerations for collecting the relevant genomic and phenotypic data. Next, we outline current statistical methods and tools used for GWA analyses and discuss the inclusion of covariates to account for population structure and the interpretation of results. Given that many false positive associations will occur in any GWA analysis, we highlight strategies for prioritizing GWA candidates for further statistical and empirical validation. While focused on plants, the material we cover is also applicable to other systems. © 2017 by John Wiley & Sons, Inc.
全基因组关联研究(GWAS)已发展成为一种用于识别表型变异遗传基础的重要方法。在本文中,我们概述了GWAS的设计、分析和解读。首先,我们展示了模拟结果,这些结果探索了实验设计的关键要素以及收集相关基因组和表型数据时的注意事项。接下来,我们概述了当前用于全基因组关联分析的统计方法和工具,并讨论了纳入协变量以考虑群体结构以及结果解读的问题。鉴于在任何全基因组关联分析中都会出现许多假阳性关联,我们重点介绍了对全基因组关联候选基因进行优先级排序以进行进一步统计和实证验证的策略。虽然本文聚焦于植物,但我们涵盖的内容也适用于其他系统。© 2017约翰威立国际出版公司