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.
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This study was supported by the US National Science Foundation (NSF)–Plant Genome Program (DBI-0321467, 0703908 and 0820619), NSF–Plant Genome Comparative Sequencing Program (DBI-06638566), US National Institutes of Health (1R21AR055228-01A1), National Heart, Lung, and Blood Institute (U 01 HL72524, HL54776 and 5U01HL072524-06), US Department of Agriculture Research Service (53-K06–5-10 and 58–1950-9–001), USDA–Cooperative State Research, Education and Extension Service National Research Initiative (2006-35300-17155), Morris Animal Foundation (D04CA-135), WALTHAM Centre for Pet Nutrition, Cornell Advanced Technology in Biotechnology and the Collaborative Research Program in the Cornell Veterinary College. The authors would like to thank K. Zhao for providing the source code to compute kinship and L. Rigamer Lirette, A.L. Ingham and S. Myles for editing of the manuscript.
The authors declare no competing financial interests.
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Zhang, Z., Ersoz, E., Lai, CQ. et al. Mixed linear model approach adapted for genome-wide association studies. Nat Genet 42, 355–360 (2010). https://doi.org/10.1038/ng.546
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