Mixed linear model approach adapted for genome-wide association studies

By Zhiwu Zhang, Elhan Ersoz, Chao-Qiang Lai, R. Todhunter, Hemant K. Tiwari, Michael A. Gore, Peter J. Bradbury, Jianming Yu, D. Arnett, J. Ordovás and 1 more. Nature Genetics, 2010

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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.

Subfields: Genetics, Plant Science

View Paper: https://www.semanticscholar.org/paper/d38266031970dcb8a336ec4c6f095bd01e1a2d16

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Citations in the corpus

Genome-wide Efficient Mixed Model Analysis for Association Studies (2012, methodology): “…models (LMMs, also known as mixed linear models, or MLMs) to test for association in genome-wide association studies (GWAS), because of their demonstrated effectiveness in accounting for relatedness among samples and in controlling for population stratification and other confounding factors 1–7 .”
Efficient Bayesian mixed model analysis increases association power in large cohorts (2014, methodology): “Linear mixed models are emerging as the method of choice for association testing in genomewide association studies (GWAS) because they account for both population stratification and cryptic relatedness and achieve increased statistical power by jointly modeling all genotyped markers [1–12].”
Computationally efficient whole-genome regression for quantitative and binary traits (2020, neutral): “6, Supplementary Fig.”
Comparing genomic selection and marker-assisted selection for Fusarium head blight resistance in wheat (Triticum aestivum L.) (2016, background): “Zhao et al. (2014) found MAS outperforming GS for heading time in rye, but not for plant height.”
Association Mapping for Enhancing Maize (Zea mays L.) Genetic Improvement (2011, methodology): “, 2006), nonmetric multidimensional scaling (nMDS) (Zhu and Yu, 2009), and the unifi ed mixed-model approach (Flint-Garcia et al., 2005; Yu et al., 2006; Zhang et al., 2010b).”
Genetic mapping of quantitative trait loci in crops (2017, methodology): “Under a MLM framework, MPWGAIM allows for population structure to be modeled and uses a forward selection approach to reduce model complexity.”
Comprehending crystalline β-carotene accumulation by comparing engineered cell models and the natural carotenoid-rich system of citrus (2011, neutral): “Recently, it has been proposed that AtNRAMP1 is a high-afﬁnity Mn transporter and essential for uptake of Mn from the soil in low Mn conditions (Cailliatte et al. , 2010).”
FaST linear mixed models for genome-wide association studies (2011, methodology): “Statistical methods for correcting these types of confounders have progressed and include approaches such as linear mixed models (LMMs) [8, 2, 9, 3, 4, 5, 6, 10, 7], genomic control, family-based association tests, structured association, and Eigenstrat [7].”
GAPIT Version 3: Boosting Power and Accuracy for Genomic Association and Prediction (2020, methodology): “To reduce the 75 confounding between kinship and testing markers, individuals in the MLM are replaced with 76 their corresponding groups in the compressed MLM (CMLM), which also improves computing 77 efficiency[7].”
Improving power and accuracy of genome-wide association studies via a multi-locus mixed linear model methodology (2016, neutral): “This approach has been called GWAS with population parameters previously defined (P3D) 6 .”