生物信息学分析方法I: 全基因组关联分析概述

赵宇慧, 李秀秀, 陈倬, 鲁宏伟, 刘羽诚, 张志方, 梁承志

An Overview of Genome-wide Association Studies in Plants

Yuhui Zhao, Xiuxiu Li, Zhuo Chen, Hongwei Lu, Yucheng Liu, Zhifang Zhang, Chengzhi Liang

表1 不同混合线性模型(MLM)的性能比较

Table 1 Performance comparison of different methods in mixed linear model (MLM)

Method Population structure Kinship Precision Characteristic Computational speed Statistical power Application Standard MLM P All markers Low High >100 papers GRAMMAR P Approximate method Very fast Intermediate Barley (200) EMMA P Exact method Intermediate Similar to Standard MLM >100 papers EMMAX P All markers Approximate method High marker densities Fast Similar to Standard MLM >100 papers CMLM P Large sample sizes Better than Standard MLM >100 papers FaST-LMM P A subset of genetic markers Exact method Large sample sizes Fast Similar to Standard MLM Rice (200?1500) GEMMA P Exact method Fast Similar to Standard MLM Arabidopsis thaliana (190-500) ECMLM P Intermediate Better than Standard MLM Sorghum (250-350), soybean (200-400), wheat (250-300) GRAMMAR- Gamma P Approximate method High marker densities Fast Similar to Standard MLM Oilseed rape (200) SUPER P Trait-associated markers Large sample size & high marker density Fast Better than Standard MLM Wheat (300-400) Farm-CPU P A subset of genetic markers Approximate method Large sample size & high marker density Fast Better than Standard MLM Wheat (100-1200), maize (100-5000) BLINK P A subset of genetic markers Approximate method Large sample size & high marker density Faster than FarmCPU Better than FarmCPU