植物学报 ›› 2020, Vol. 55 ›› Issue (6): 715-732.DOI: 10.11983/CBB20091
赵宇慧1, 李秀秀1,2, 陈倬1,2, 鲁宏伟1,2, 刘羽诚1,2, 张志方1,2, 梁承志1,2,*()
收稿日期:
2020-05-20
接受日期:
2020-08-26
出版日期:
2020-11-01
发布日期:
2020-11-11
通讯作者:
梁承志
作者简介:
*E-mail: cliang@genetics.ac.cn基金资助:
Yuhui Zhao1, Xiuxiu Li1,2, Zhuo Chen1,2, Hongwei Lu1,2, Yucheng Liu1,2, Zhifang Zhang1,2, Chengzhi Liang1,2,*()
Received:
2020-05-20
Accepted:
2020-08-26
Online:
2020-11-01
Published:
2020-11-11
Contact:
Chengzhi Liang
摘要:
全基因组关联分析(GWAS)是动植物复杂性状相关基因定位的常用手段。高通量基因分型技术的应用极大地推动了GWAS的发展。在植物中, 利用GWAS不仅能够以较高的分辨率在全基因组水平鉴定出各种自然群体特定性状相关的基因或区间, 而且可揭示表型变异的遗传架构全景图。目前, 人们利用GWAS分析方法已在拟南芥(Arabidopsis thaliana)、水稻(Oryza sativa)、小麦(Triticum aestivum)、玉米(Zea mays)和大豆(Glycine max)等模式植物和重要农作物品系中发掘出与各种性状显著相关的数量性状座位(QTL)及其候选基因位点, 阐明了这些性状的遗传基础, 并为揭示这些性状背后的分子机理提供候选基因, 也为作物高产优质品种的选育提供了理论依据。该文对GWAS的方法、影响因素及数据分析流程进行了详细描述, 以期为相关研究提供参考。
赵宇慧, 李秀秀, 陈倬, 鲁宏伟, 刘羽诚, 张志方, 梁承志. 生物信息学分析方法I: 全基因组关联分析概述. 植物学报, 2020, 55(6): 715-732.
Yuhui Zhao, Xiuxiu Li, Zhuo Chen, Hongwei Lu, Yucheng Liu, Zhifang Zhang, Chengzhi Liang. An Overview of Genome-wide Association Studies in Plants. Chinese Bulletin of Botany, 2020, 55(6): 715-732.
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 |
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 |
图4 721份水稻材料抽穗期全基因组关联分析(GWAS)结果展示 (A) 抽穗期性状关联分析结果的曼哈顿图; (B) QQ图; (C) 局部曼哈顿图和6号染色体尖峰附近的LD热图。曼哈顿图中红色虚线标出候选区间, 黑色虚线表示显著性阈值-log10 (P)=7.80。
Figure 4 Genome-wide association study (GWAS) results of 721 rice accessions for heading date (A) Manhattan plots of GWAS results for heading date; (B) QQ plot; (C) Local manhattan plots and LD heatmap around the peak on chromosome 6. Candidate region was labelled by red dotted line while the black dotted line indicated threshold -log10 (P)=7.80.
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