Genomewide Rapid Association Using Mixed Model and Regression: A Fast and Simple Method For Genomewide Pedigree-Based Quantitative Trait Loci Association Analysis

By Yurii S. Aulchenko, D. de Koning, C. Haley. Genetics, 2007

458 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/928afe31f95af2c64d87a8224bc75f58ac843266

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In Genetics

Citations in the corpus

Development and Application of High-density SNP Arrays in Genomic Studies of Domestic Animals (2010, unknown): “In recent years, different statistical approaches for genomic selection have been developed, derived from either nonparametric Bayesian models or parametric methods including genomic best linear unbiased prediction (GBLUP) and mixed regression models (Gianola et al., 2006; Aulchenko et al., 2007; Ve”
Many sequence variants affecting diversity of adult human height (2008, methodology): “This procedure has similar motivation as that previously described for the GRAMMAR procedur”
An efficient multi-locus mixed model approach for genome-wide association studies in structured populations (2012, unknown): “This approach has been shown to perform well in plants, animals and humans 6,10,11,12 and methods have been developed to allow the analysis of large GWAS datasets in a reasonable amount of time 11,13,14 .”
Quality Control Procedures for Genome Wide Association Studies (2011, methodology): “…treated as independent samples in the downstream analyses, having many re-lated samples in the dataset would result in increased type I and type II errors; thus, analytical methods such as mixed-model regression (Aulchenko et al., 2007) must be used in place of simple linear or logistic regression.”
Efficiently controlling for case-control imbalance and sample relatedness in large-scale genetic association studies (2017, methodology): “Linear mixed models (LMM) are widely used to account for these issues in GWAS for both binary and quantitative traits 19–25 .”
ProbABEL package for genome-wide association analysis of imputed data (2010, methodology): “…of thus defined model is possible by numerical maximization of the likelihood function, however, the estimation of such model for large data sets is not computationally feasible for hundreds of thousands to millions of SNPs tested in the context of GWAS, as we have demonstrated previously [13].”