Mixed linear model approach adapted for genome-wide association studies

被引:1475
作者
Zhang, Zhiwu [1 ]
Ersoz, Elhan [1 ]
Lai, Chao-Qiang [2 ]
Todhunter, Rory J. [3 ]
Tiwari, Hemant K. [4 ]
Gore, Michael A. [5 ]
Bradbury, Peter J. [6 ]
Yu, Jianming [7 ]
Arnett, Donna K. [8 ]
Ordovas, Jose M. [2 ,9 ]
Buckler, Edward S. [1 ,6 ]
机构
[1] Cornell Univ, Inst Genom Divers, Ithaca, NY 14850 USA
[2] Tufts Univ, Nutr & Genom Lab, Jean Mayer US Dept Agr, Human Nutr Res Ctr, Boston, MA 02111 USA
[3] Cornell Univ, Coll Vet Med, Dept Clin Sci, Ithaca, NY 14853 USA
[4] Univ Alabama, Dept Biostat, Birmingham, AL 35294 USA
[5] USDA ARS, Arid Land Agr Res Ctr, Maricopa, AZ USA
[6] USDA ARS, Ithaca, NY 14853 USA
[7] Kansas State Univ, Dept Agron, Manhattan, KS 66506 USA
[8] Univ Alabama, Dept Epidemiol, Birmingham, AL USA
[9] CNIC, Dept Cardiovasc Epidemiol & Populat Genet, Madrid, Spain
基金
美国国家科学基金会; 美国国家卫生研究院;
关键词
QUANTITATIVE TRAIT LOCI; SIRE EVALUATION; POPULATION-STRUCTURE; BREEDING PROGRAM; COMPLEX TRAITS; HIP-DYSPLASIA; SELECTION; PEDIGREE; SAMPLES; POWER;
D O I
10.1038/ng.546
中图分类号
Q3 [遗传学];
学科分类号
071007 ; 090102 ;
摘要
Mixed linear model (MLM) methods have proven useful in controlling for population structure and relatedness within genome-wide association studies. However, MLM-based methods can be computationally challenging for large datasets. We report a compression approach, called 'compressed MLM', that decreases the effective sample size of such datasets by clustering individuals into groups. We also present a complementary approach, 'population parameters previously determined' (P3D), that eliminates the need to re-compute variance components. We applied these two methods both independently and combined in selected genetic association datasets from human, dog and maize. The joint implementation of these two methods markedly reduced computing time and either maintained or improved statistical power. We used simulations to demonstrate the usefulness in controlling for substructure in genetic association datasets for a range of species and genetic architectures. We have made these methods available within an implementation of the software program TASSEL.
引用
收藏
页码:355 / U118
页数:8
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